AutoAndroidController/py/venv/Lib/site-packages/transformers/models/beit/modeling_beit.py

# coding=utf-8
# Copyright 2021 Microsoft Research 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 BEiT model."""

import collections.abc
import math
import warnings
from dataclasses import dataclass
from typing import Optional, Union

import torch
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss

from ...activations import ACT2FN
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutput,
    BaseModelOutputWithPooling,
    ImageClassifierOutput,
    MaskedLMOutput,
    SemanticSegmenterOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import compile_compatible_method_lru_cache, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import auto_docstring, logging, torch_int
from ...utils.backbone_utils import BackboneMixin
from .configuration_beit import BeitConfig


logger = logging.get_logger(__name__)


@dataclass
@auto_docstring(
    custom_intro="""
    Class for outputs of [`BeitModel`].
    """
)
class BeitModelOutputWithPooling(BaseModelOutputWithPooling):
    r"""
    pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
        Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if
        *config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token
        will be returned.
    """


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).

    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
    argument.
    """
    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


class BeitDropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob: Optional[float] = 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}"


# Based on timm implementation, which can be found here:
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
class BeitEmbeddings(nn.Module):
    """
    Construct the CLS token, position and patch embeddings. Optionally, also the mask token.

    """

    def __init__(self, config: BeitConfig) -> None:
        super().__init__()

        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        if config.use_mask_token:
            self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        else:
            self.mask_token = None
        self.patch_embeddings = BeitPatchEmbeddings(config)
        self.patch_size = config.patch_size
        self.image_size = (
            config.image_size
            if isinstance(config.image_size, collections.abc.Iterable)
            else (config.image_size, config.image_size)
        )
        num_patches = self.patch_embeddings.num_patches
        if config.use_absolute_position_embeddings:
            self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
        else:
            self.position_embeddings = None
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    # Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
        """
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
        images. This method is also adapted to support torch.jit tracing.

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

        num_patches = embeddings.shape[1] - 1
        num_positions = self.position_embeddings.shape[1] - 1

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

        class_pos_embed = self.position_embeddings[:, :1]
        patch_pos_embed = self.position_embeddings[:, 1:]

        dim = embeddings.shape[-1]

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

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

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

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

        return torch.cat((class_pos_embed, patch_pos_embed), dim=1)

    def forward(
        self,
        pixel_values: torch.Tensor,
        bool_masked_pos: Optional[torch.BoolTensor] = None,
        interpolate_pos_encoding: Optional[bool] = None,
    ) -> torch.Tensor:
        if self.position_embeddings is not None and interpolate_pos_encoding is not None:
            warnings.warn(
                "`interpolate_pos_encoding` argument has no effect for BEiTEmbeddings, embeddings are always "
                "interpolated to the input image size. The argument will be removed in transformers v4.51.0."
            )

        _, _, height, width = pixel_values.shape
        embeddings, (patch_height, patch_width) = self.patch_embeddings(pixel_values)
        batch_size, seq_len, _ = embeddings.size()

        if bool_masked_pos is not None:
            mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
            # replace the masked visual tokens by mask_tokens
            w = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
            embeddings = embeddings * (1 - w) + mask_tokens * w

        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        embeddings = torch.cat((cls_tokens, embeddings), dim=1)

        if self.position_embeddings is not None:
            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)

        embeddings = self.dropout(embeddings)

        return embeddings, (patch_height, patch_width)


class BeitPatchEmbeddings(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])
        patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches
        self.patch_shape = patch_shape

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

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        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."
            )

        embeddings = self.projection(pixel_values)
        patch_height, patch_width = embeddings.shape[2], embeddings.shape[3]
        embeddings = embeddings.flatten(2).transpose(1, 2)

        return embeddings, (patch_height, patch_width)


class BeitSelfAttention(nn.Module):
    def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
        super().__init__()
        self.config = config
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
                f"heads {config.num_attention_heads}."
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

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

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

        self.has_relative_position_bias = bool(window_size)
        if self.has_relative_position_bias:
            self.relative_position_bias = BeitRelativePositionBias(config, window_size=window_size)

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        relative_position_bias: Optional[torch.Tensor] = None,
        interpolate_pos_encoding: bool = False,
        resolution: Optional[tuple[int]] = None,
    ) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, 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)
        )

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)

        # Add relative position bias if present.
        if self.has_relative_position_bias:
            height, width = resolution
            window_size = (height // self.config.patch_size, width // self.config.patch_size)
            attention_scores = attention_scores + self.relative_position_bias(
                window_size, interpolate_pos_encoding, dim_size=hidden_states.shape[1]
            )

        # Add shared relative position bias if provided.
        if relative_position_bias is not None:
            attention_scores = attention_scores + relative_position_bias

        # 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)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = torch.matmul(attention_probs, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).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 BeitSdpaSelfAttention(BeitSelfAttention):
    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        relative_position_bias: Optional[torch.Tensor] = None,
        interpolate_pos_encoding: bool = False,
        resolution: Optional[tuple[int]] = None,
    ) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
        if output_attentions or head_mask is not None:
            logger.warning_once(
                "`BeitSdpaSelfAttention` is used but `torch.nn.functional.scaled_dot_product_attention` does not "
                "support `output_attentions=True` or `head_mask`. Falling back to the manual attention implementation, "
                "but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. "
                'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
            )
            return super().forward(
                hidden_states=hidden_states,
                head_mask=head_mask,
                output_attentions=output_attentions,
                relative_position_bias=relative_position_bias,
                interpolate_pos_encoding=interpolate_pos_encoding,
                resolution=resolution,
            )

        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)
        )

        attn_bias = None
        if self.has_relative_position_bias:
            height, width = resolution
            window_size = (height // self.config.patch_size, width // self.config.patch_size)
            attn_bias = self.relative_position_bias(
                window_size, interpolate_pos_encoding, dim_size=hidden_states.shape[1]
            )

        # Add shared relative position bias if provided.
        if relative_position_bias is not None:
            if attn_bias is None:
                attn_bias = relative_position_bias
            else:
                attn_bias += relative_position_bias

        scaling = 1 / math.sqrt(self.attention_head_size)
        context_layer = torch.nn.functional.scaled_dot_product_attention(
            query_layer,
            key_layer,
            value_layer,
            attn_mask=attn_bias,
            dropout_p=self.config.attention_probs_dropout_prob if self.training else 0.0,
            is_causal=False,
            scale=scaling,
        )
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        return context_layer, None


class BeitSelfOutput(nn.Module):
    """
    The residual connection is defined in BeitLayer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, config: BeitConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor, gamma=None) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)

        return hidden_states


BEIT_SELF_ATTENTION_CLASSES = {
    "eager": BeitSelfAttention,
    "sdpa": BeitSdpaSelfAttention,
}


class BeitAttention(nn.Module):
    def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
        super().__init__()
        self.attention = BEIT_SELF_ATTENTION_CLASSES[config._attn_implementation](config, window_size=window_size)
        self.output = BeitSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
        )

        # Prune linear layers
        self.attention.query = prune_linear_layer(self.attention.query, index)
        self.attention.key = prune_linear_layer(self.attention.key, index)
        self.attention.value = prune_linear_layer(self.attention.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        relative_position_bias: Optional[torch.Tensor] = None,
        interpolate_pos_encoding: bool = False,
        resolution: Optional[tuple[int]] = None,
    ) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
        self_outputs = self.attention(
            hidden_states, head_mask, output_attentions, relative_position_bias, interpolate_pos_encoding, resolution
        )

        attention_output = self.output(self_outputs[0], hidden_states)

        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


class BeitIntermediate(nn.Module):
    def __init__(self, config: BeitConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        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 BeitOutput(nn.Module):
    def __init__(self, config: BeitConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        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 BeitLayer(GradientCheckpointingLayer):
    """This corresponds to the Block class in the timm implementation."""

    def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None, drop_path_rate: float = 0.0) -> None:
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = BeitAttention(config, window_size=window_size)
        self.intermediate = BeitIntermediate(config)
        self.output = BeitOutput(config)
        self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.drop_path = BeitDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
        self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

        init_values = config.layer_scale_init_value
        if init_values > 0:
            self.lambda_1 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
            self.lambda_2 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
        else:
            self.lambda_1, self.lambda_2 = None, None

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        relative_position_bias: Optional[torch.Tensor] = None,
        interpolate_pos_encoding: bool = False,
        resolution: Optional[tuple[int, int]] = None,
    ) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
        self_attention_outputs = self.attention(
            self.layernorm_before(hidden_states),  # in BEiT, layernorm is applied before self-attention
            head_mask,
            output_attentions=output_attentions,
            relative_position_bias=relative_position_bias,
            interpolate_pos_encoding=interpolate_pos_encoding,
            resolution=resolution,
        )
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        # apply lambda_1 if present
        if self.lambda_1 is not None:
            attention_output = self.lambda_1 * attention_output

        # first residual connection
        hidden_states = self.drop_path(attention_output) + hidden_states

        # in BEiT, layernorm is also applied after self-attention
        layer_output = self.layernorm_after(hidden_states)

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

        if self.lambda_2 is not None:
            layer_output = self.lambda_2 * layer_output

        # second residual connection
        layer_output = self.drop_path(layer_output) + hidden_states

        outputs = (layer_output,) + outputs

        return outputs


class BeitRelativePositionBias(nn.Module):
    def __init__(self, config: BeitConfig, window_size: tuple) -> None:
        super().__init__()
        self.window_size = window_size
        self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros(self.num_relative_distance, config.num_attention_heads)
        )  # 2*Wh-1 * 2*Ww-1, nH
        # cls to token & token 2 cls & cls to cls

    @compile_compatible_method_lru_cache(maxsize=10)
    def generate_relative_position_index(self, window_size: tuple[int, int]) -> torch.Tensor:
        """
        This method creates the relative position index, modified to support arbitrary window sizes,
        as introduced in [MiDaS v3.1](https://huggingface.co/papers/2307.14460).
        """
        num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
        # cls to token & token 2 cls & cls to cls
        # get pair-wise relative position index for each token inside the window
        window_area = window_size[0] * window_size[1]
        grid = torch.meshgrid(torch.arange(window_size[0]), torch.arange(window_size[1]), indexing="ij")
        coords = torch.stack(grid)  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * window_size[1] - 1
        relative_position_index = torch.zeros(size=(window_area + 1,) * 2, dtype=relative_coords.dtype)
        relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        relative_position_index[0, 0:] = num_relative_distance - 3
        relative_position_index[0:, 0] = num_relative_distance - 2
        relative_position_index[0, 0] = num_relative_distance - 1
        return relative_position_index

    def forward(self, window_size, interpolate_pos_encoding: bool = False, dim_size=None) -> torch.Tensor:
        """
        Modification of timm.models.beit.py: Attention._get_rel_pos_bias to support arbitrary window sizes.
        """
        old_height = 2 * self.window_size[0] - 1
        old_width = 2 * self.window_size[1] - 1

        new_height = 2 * window_size[0] - 1
        new_width = 2 * window_size[1] - 1

        old_relative_position_bias_table = self.relative_position_bias_table

        old_num_relative_distance = self.num_relative_distance
        new_num_relative_distance = new_height * new_width + 3

        old_sub_table = old_relative_position_bias_table[: old_num_relative_distance - 3]

        old_sub_table = old_sub_table.reshape(1, old_width, old_height, -1).permute(0, 3, 1, 2)
        new_sub_table = nn.functional.interpolate(
            old_sub_table, size=(torch_int(new_height), torch_int(new_width)), mode="bilinear"
        )
        new_sub_table = new_sub_table.permute(0, 2, 3, 1).reshape(new_num_relative_distance - 3, -1)

        new_relative_position_bias_table = torch.cat(
            [new_sub_table, old_relative_position_bias_table[old_num_relative_distance - 3 :]]
        )

        relative_position_index = self.generate_relative_position_index(window_size)
        relative_position_bias = new_relative_position_bias_table[relative_position_index.view(-1)]

        # patch_size*num_patches_height, patch_size*num_patches_width, num_attention_heads
        relative_position_bias = relative_position_bias.view(
            window_size[0] * window_size[1] + 1, window_size[0] * window_size[1] + 1, -1
        )
        # num_attention_heads, patch_size*num_patches_width, patch_size*num_patches_height
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()

        if interpolate_pos_encoding:
            relative_position_bias = nn.functional.interpolate(
                relative_position_bias.unsqueeze(1),
                size=(dim_size, dim_size),
                mode="bilinear",
                align_corners=False,
            ).squeeze(1)

        return relative_position_bias.unsqueeze(0)


class BeitEncoder(nn.Module):
    def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
        super().__init__()
        self.config = config
        self.has_relative_position_bias = config.use_shared_relative_position_bias
        if self.has_relative_position_bias:
            self.relative_position_bias = BeitRelativePositionBias(config, window_size=window_size)

        # stochastic depth decay rule
        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers, device="cpu")]
        self.layer = nn.ModuleList(
            [
                BeitLayer(
                    config,
                    window_size=window_size if config.use_relative_position_bias else None,
                    drop_path_rate=dpr[i],
                )
                for i in range(config.num_hidden_layers)
            ]
        )
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        interpolate_pos_encoding: bool = False,
        resolution: Optional[tuple[int, int]] = None,
        return_dict: bool = True,
    ) -> Union[tuple, BaseModelOutput]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.has_relative_position_bias:
                height, width = resolution
                window_size = (height // self.config.patch_size, width // self.config.patch_size)
                relative_position_bias = self.relative_position_bias(
                    window_size, interpolate_pos_encoding=interpolate_pos_encoding, dim_size=hidden_states.shape[1]
                )
            else:
                relative_position_bias = None

            layer_head_mask = head_mask[i] if head_mask is not None else None

            layer_outputs = layer_module(
                hidden_states,
                head_mask=layer_head_mask,
                output_attentions=output_attentions,
                relative_position_bias=relative_position_bias,
                interpolate_pos_encoding=interpolate_pos_encoding,
                resolution=resolution,
            )

            hidden_states = layer_outputs[0]

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

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

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


@auto_docstring
class BeitPreTrainedModel(PreTrainedModel):
    config: BeitConfig
    base_model_prefix = "beit"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _no_split_modules = ["BeitLayer"]
    _keys_to_ignore_on_load_unexpected = [r".*relative_position_index.*"]
    _supports_sdpa = True

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, BeitEmbeddings):
            module.cls_token.data.zero_()
            if module.mask_token is not None:
                module.mask_token.data.zero_()
            if module.position_embeddings is not None:
                module.position_embeddings.data.zero_()
        elif isinstance(module, BeitRelativePositionBias):
            module.relative_position_bias_table.data.zero_()
        elif isinstance(module, BeitLayer):
            if module.lambda_1 is not None:
                module.lambda_1.data.fill_(self.config.layer_scale_init_value)
                module.lambda_2.data.fill_(self.config.layer_scale_init_value)


@auto_docstring
class BeitModel(BeitPreTrainedModel):
    def __init__(self, config: BeitConfig, add_pooling_layer: bool = True) -> None:
        r"""
        add_pooling_layer (bool, *optional*, defaults to `True`):
            Whether to add a pooling layer
        """
        super().__init__(config)
        self.config = config

        self.embeddings = BeitEmbeddings(config)
        self.encoder = BeitEncoder(config, window_size=self.embeddings.patch_embeddings.patch_shape)

        self.layernorm = (
            nn.Identity() if config.use_mean_pooling else nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        )
        self.pooler = BeitPooler(config) 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

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.Tensor,
        bool_masked_pos: Optional[torch.BoolTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: bool = False,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BeitModelOutputWithPooling]:
        r"""
        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
        """
        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.use_return_dict

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        embedding_output, _ = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
        resolution = pixel_values.shape[2:]

        encoder_outputs = self.encoder(
            embedding_output,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            resolution=resolution,
            return_dict=return_dict,
            interpolate_pos_encoding=interpolate_pos_encoding,
        )
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        if not return_dict:
            head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
            return head_outputs + encoder_outputs[1:]

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


class BeitPooler(nn.Module):
    def __init__(self, config: BeitConfig) -> None:
        super().__init__()
        self.layernorm = (
            nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) if config.use_mean_pooling else None
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        if self.layernorm is not None:
            # Mean pool the final hidden states of the patch tokens
            patch_tokens = hidden_states[:, 1:, :]
            pooled_output = self.layernorm(patch_tokens.mean(1))
        else:
            # Pool by simply taking the final hidden state of the [CLS] token
            pooled_output = hidden_states[:, 0]

        return pooled_output


@auto_docstring(
    custom_intro="""
    Beit Model transformer with a 'language' modeling head on top. BEiT does masked image modeling by predicting
    visual tokens of a Vector-Quantize Variational Autoencoder (VQ-VAE), whereas other vision models like ViT and DeiT
    predict RGB pixel values. As a result, this class is incompatible with [`AutoModelForMaskedImageModeling`], so you
    will need to use [`BeitForMaskedImageModeling`] directly if you wish to do masked image modeling with BEiT.
    """
)
class BeitForMaskedImageModeling(BeitPreTrainedModel):
    def __init__(self, config: BeitConfig) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.beit = BeitModel(config, add_pooling_layer=False)

        # Classifier head
        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)

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

    def get_output_embeddings(self):
        return None

    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        bool_masked_pos: Optional[torch.BoolTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: bool = False,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, MaskedLMOutput]:
        r"""
        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
        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).

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, BeitForMaskedImageModeling
        >>> import torch
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
        >>> model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k")

        >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
        >>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
        >>> # create random boolean mask of shape (batch_size, num_patches)
        >>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()

        >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
        >>> loss, logits = outputs.loss, outputs.logits
        >>> list(logits.shape)
        [1, 196, 8192]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.beit(
            pixel_values,
            bool_masked_pos=bool_masked_pos,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            interpolate_pos_encoding=interpolate_pos_encoding,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        sequence_output = self.layernorm(sequence_output)
        prediction_scores = self.lm_head(sequence_output[:, 1:])

        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()  # -100 index = padding token
            masked_lm_loss = loss_fct(prediction_scores[bool_masked_pos], labels)

        if not return_dict:
            output = (prediction_scores,) + outputs[1:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

        return MaskedLMOutput(
            loss=masked_lm_loss,
            logits=prediction_scores,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@auto_docstring(
    custom_intro="""
    Beit Model transformer with an image classification head on top (a linear layer on top of the average of the final
    hidden states of the patch tokens) e.g. for ImageNet.
    """
)
class BeitForImageClassification(BeitPreTrainedModel):
    def __init__(self, config: BeitConfig) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.beit = BeitModel(config, add_pooling_layer=True)

        # Classifier head
        self.classifier = nn.Linear(config.hidden_size, 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: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: bool = False,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, ImageClassifierOutput]:
        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.use_return_dict
        outputs = self.beit(
            pixel_values,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            interpolate_pos_encoding=interpolate_pos_encoding,
            return_dict=return_dict,
        )

        pooled_output = outputs.pooler_output if return_dict else 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 ImageClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


class BeitConvModule(nn.Module):
    """
    A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution
    layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).

    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: Union[int, tuple[int, int]],
        padding: Union[int, tuple[int, int], str] = 0,
        bias: bool = False,
        dilation: Union[int, tuple[int, int]] = 1,
    ) -> None:
        super().__init__()
        self.conv = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            padding=padding,
            bias=bias,
            dilation=dilation,
        )
        self.bn = nn.BatchNorm2d(out_channels)
        self.activation = nn.ReLU()

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        output = self.conv(input)
        output = self.bn(output)
        output = self.activation(output)

        return output


class BeitPyramidPoolingBlock(nn.Module):
    def __init__(self, pool_scale: int, in_channels: int, channels: int) -> None:
        super().__init__()
        self.layers = [
            nn.AdaptiveAvgPool2d(pool_scale),
            BeitConvModule(in_channels, channels, kernel_size=1),
        ]
        for i, layer in enumerate(self.layers):
            self.add_module(str(i), layer)

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        hidden_state = input
        for layer in self.layers:
            hidden_state = layer(hidden_state)
        return hidden_state


class BeitPyramidPoolingModule(nn.Module):
    """
    Pyramid Pooling Module (PPM) used in PSPNet.

    Args:
        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
            Module.
        in_channels (int): Input channels.
        channels (int): Channels after modules, before conv_seg.
        align_corners (bool): align_corners argument of F.interpolate.

    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
    """

    def __init__(self, pool_scales: tuple[int, ...], in_channels: int, channels: int, align_corners: bool) -> None:
        super().__init__()
        self.pool_scales = pool_scales
        self.align_corners = align_corners
        self.in_channels = in_channels
        self.channels = channels
        self.blocks = []
        for i, pool_scale in enumerate(pool_scales):
            block = BeitPyramidPoolingBlock(pool_scale=pool_scale, in_channels=in_channels, channels=channels)
            self.blocks.append(block)
            self.add_module(str(i), block)

    def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
        ppm_outs = []
        for ppm in self.blocks:
            ppm_out = ppm(x)
            upsampled_ppm_out = nn.functional.interpolate(
                ppm_out, size=x.size()[2:], mode="bilinear", align_corners=self.align_corners
            )
            ppm_outs.append(upsampled_ppm_out)
        return ppm_outs


class BeitUperHead(nn.Module):
    """
    Unified Perceptual Parsing for Scene Understanding. This head is the implementation of
    [UPerNet](https://huggingface.co/papers/1807.10221).

    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
    """

    def __init__(self, config: BeitConfig) -> None:
        super().__init__()

        self.pool_scales = config.pool_scales  # e.g. (1, 2, 3, 6)
        self.in_channels = [config.hidden_size] * 4  # e.g. [768, 768, 768, 768]
        self.channels = config.hidden_size
        self.align_corners = False
        self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1)

        # PSP Module
        self.psp_modules = BeitPyramidPoolingModule(
            self.pool_scales,
            self.in_channels[-1],
            self.channels,
            align_corners=self.align_corners,
        )
        self.bottleneck = BeitConvModule(
            self.in_channels[-1] + len(self.pool_scales) * self.channels,
            self.channels,
            kernel_size=3,
            padding=1,
        )
        # FPN Module
        self.lateral_convs = nn.ModuleList()
        self.fpn_convs = nn.ModuleList()
        for in_channels in self.in_channels[:-1]:  # skip the top layer
            l_conv = BeitConvModule(in_channels, self.channels, kernel_size=1)
            fpn_conv = BeitConvModule(self.channels, self.channels, kernel_size=3, padding=1)
            self.lateral_convs.append(l_conv)
            self.fpn_convs.append(fpn_conv)

        self.fpn_bottleneck = BeitConvModule(
            len(self.in_channels) * self.channels,
            self.channels,
            kernel_size=3,
            padding=1,
        )

    def psp_forward(self, inputs):
        x = inputs[-1]
        psp_outs = [x]
        psp_outs.extend(self.psp_modules(x))
        psp_outs = torch.cat(psp_outs, dim=1)
        output = self.bottleneck(psp_outs)

        return output

    def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
        # build laterals
        laterals = [lateral_conv(encoder_hidden_states[i]) for i, lateral_conv in enumerate(self.lateral_convs)]

        laterals.append(self.psp_forward(encoder_hidden_states))

        # build top-down path
        used_backbone_levels = len(laterals)
        for i in range(used_backbone_levels - 1, 0, -1):
            prev_shape = laterals[i - 1].shape[2:]
            laterals[i - 1] = laterals[i - 1] + nn.functional.interpolate(
                laterals[i], size=prev_shape, mode="bilinear", align_corners=self.align_corners
            )

        # build outputs
        fpn_outs = [self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels - 1)]
        # append psp feature
        fpn_outs.append(laterals[-1])

        for i in range(used_backbone_levels - 1, 0, -1):
            fpn_outs[i] = nn.functional.interpolate(
                fpn_outs[i], size=fpn_outs[0].shape[2:], mode="bilinear", align_corners=self.align_corners
            )
        fpn_outs = torch.cat(fpn_outs, dim=1)
        output = self.fpn_bottleneck(fpn_outs)
        output = self.classifier(output)

        return output


class BeitFCNHead(nn.Module):
    """
    Fully Convolution Networks for Semantic Segmentation. This head is implemented of
    [FCNNet](https://huggingface.co/papers/1411.4038>).

    Args:
        config (BeitConfig): Configuration.
        in_channels
        kernel_size (int): The kernel size for convs in the head. Default: 3.
        dilation (int): The dilation rate for convs in the head. Default: 1.


    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
    """

    def __init__(
        self, config: BeitConfig, in_index: int = 2, kernel_size: int = 3, dilation: Union[int, tuple[int, int]] = 1
    ) -> None:
        super().__init__()
        self.in_channels = config.hidden_size
        self.channels = config.auxiliary_channels
        self.num_convs = config.auxiliary_num_convs
        self.concat_input = config.auxiliary_concat_input
        self.in_index = in_index

        conv_padding = (kernel_size // 2) * dilation
        convs = []
        convs.append(
            BeitConvModule(
                self.in_channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation
            )
        )
        for i in range(self.num_convs - 1):
            convs.append(
                BeitConvModule(
                    self.channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation
                )
            )
        if self.num_convs == 0:
            self.convs = nn.Identity()
        else:
            self.convs = nn.Sequential(*convs)
        if self.concat_input:
            self.conv_cat = BeitConvModule(
                self.in_channels + self.channels, self.channels, kernel_size=kernel_size, padding=kernel_size // 2
            )

        self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1)

    def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
        # just take the relevant feature maps
        hidden_states = encoder_hidden_states[self.in_index]
        output = self.convs(hidden_states)
        if self.concat_input:
            output = self.conv_cat(torch.cat([hidden_states, output], dim=1))
        output = self.classifier(output)
        return output


@auto_docstring
class BeitForSemanticSegmentation(BeitPreTrainedModel):
    def __init__(self, config: BeitConfig) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.beit = BeitModel(config, add_pooling_layer=False)

        # FPNs
        if len(self.config.out_indices) != 4:
            raise ValueError(
                "BeitForSemanticSegmentation requires config.out_indices to be a list of 4 integers, "
                "specifying which features to use from the backbone. One can use [3, 5, 7, 11] in case of "
                "a base-sized architecture."
            )
        self.fpn1 = nn.Sequential(
            nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
            nn.BatchNorm2d(config.hidden_size),
            nn.GELU(),
            nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
        )
        self.fpn2 = nn.Sequential(
            nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
        )
        self.fpn3 = nn.Identity()
        self.fpn4 = nn.MaxPool2d(kernel_size=2, stride=2)

        # Semantic segmentation head(s)
        self.decode_head = BeitUperHead(config)
        self.auxiliary_head = BeitFCNHead(config) if config.use_auxiliary_head else None

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

    def compute_loss(self, logits, auxiliary_logits, labels):
        # upsample logits to the images' original size
        upsampled_logits = nn.functional.interpolate(
            logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
        )
        if auxiliary_logits is not None:
            upsampled_auxiliary_logits = nn.functional.interpolate(
                auxiliary_logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
            )
        # compute weighted loss
        loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
        main_loss = loss_fct(upsampled_logits, labels)
        loss = main_loss
        if auxiliary_logits is not None:
            auxiliary_loss = loss_fct(upsampled_auxiliary_logits, labels)
            loss += self.config.auxiliary_loss_weight * auxiliary_loss

        return loss

    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: bool = False,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, SemanticSegmenterOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
            Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, BeitForSemanticSegmentation
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
        >>> model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")

        >>> inputs = image_processor(images=image, return_tensors="pt")
        >>> outputs = model(**inputs)
        >>> # logits are of shape (batch_size, num_labels, height, width)
        >>> logits = outputs.logits
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        if labels is not None and self.config.num_labels == 1:
            raise ValueError("The number of labels should be greater than one")

        outputs = self.beit(
            pixel_values,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=True,  # we need the intermediate hidden states
            interpolate_pos_encoding=interpolate_pos_encoding,
            return_dict=return_dict,
        )

        encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]

        # only keep certain features, and reshape
        # note that we do +1 as the encoder_hidden_states also includes the initial embeddings
        features = [feature for idx, feature in enumerate(encoder_hidden_states) if idx + 1 in self.config.out_indices]
        batch_size = pixel_values.shape[0]
        patch_resolution = self.config.image_size // self.config.patch_size
        features = [
            x[:, 1:, :].permute(0, 2, 1).reshape(batch_size, -1, patch_resolution, patch_resolution) for x in features
        ]

        # apply FPNs
        ops = [self.fpn1, self.fpn2, self.fpn3, self.fpn4]
        for i in range(len(features)):
            features[i] = ops[i](features[i])

        logits = self.decode_head(features)

        auxiliary_logits = None
        if self.auxiliary_head is not None:
            auxiliary_logits = self.auxiliary_head(features)

        loss = None
        if labels is not None:
            loss = self.compute_loss(logits, auxiliary_logits, labels)

        if not return_dict:
            if output_hidden_states:
                output = (logits,) + outputs[1:]
            else:
                output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SemanticSegmenterOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states if output_hidden_states else None,
            attentions=outputs.attentions,
        )


@auto_docstring(
    custom_intro="""
    BEiT backbone, to be used with frameworks like DETR and MaskFormer.
    """
)
class BeitBackbone(BeitPreTrainedModel, BackboneMixin):
    def __init__(self, config):
        super().__init__(config)
        super()._init_backbone(config)

        self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
        self.embeddings = BeitEmbeddings(config)
        self.encoder = BeitEncoder(config, window_size=self.embeddings.patch_embeddings.patch_shape)

        if config.add_fpn:
            if len(self.config.out_indices) != 4:
                raise ValueError(
                    "BeitBackbone requires config.out_indices to be a list of 4 integers, "
                    "specifying which features to use from the backbone. One can use [3, 5, 7, 11] in case of "
                    "a base-sized architecture."
                )
            hidden_size = config.hidden_size
            self.fpn1 = nn.Sequential(
                nn.ConvTranspose2d(hidden_size, hidden_size, kernel_size=2, stride=2),
                nn.BatchNorm2d(hidden_size, eps=config.batch_norm_eps),
                nn.GELU(),
                nn.ConvTranspose2d(hidden_size, hidden_size, kernel_size=2, stride=2),
            )

            self.fpn2 = nn.Sequential(nn.ConvTranspose2d(hidden_size, hidden_size, kernel_size=2, stride=2))
            self.fpn3 = nn.Identity()
            self.fpn4 = nn.MaxPool2d(kernel_size=2, stride=2)

        # 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: Tensor,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> BackboneOutput:
        r"""
        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, AutoBackbone
        >>> import torch
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224")
        >>> model = AutoBackbone.from_pretrained(
        ...     "microsoft/beit-base-patch16-224", 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, 768, 14, 14]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_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

        batch_size = pixel_values.shape[0]
        embedding_output, (patch_height, patch_width) = self.embeddings(pixel_values)
        resolution = pixel_values.shape[2:]

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

        hidden_states = outputs.hidden_states if return_dict else outputs[1]

        feature_maps = ()
        for stage, hidden_state in zip(self.stage_names, hidden_states):
            if stage in self.out_features:
                if self.config.reshape_hidden_states:
                    hidden_state = hidden_state[:, 1:, :]
                    hidden_state = hidden_state.permute(0, 2, 1)
                    hidden_state = hidden_state.reshape(batch_size, -1, patch_height, patch_width)

                feature_maps += (hidden_state,)

        if self.config.add_fpn:
            feature_maps = [
                self.fpn1(feature_maps[0]),
                self.fpn2(feature_maps[1]),
                self.fpn3(feature_maps[2]),
                self.fpn4(feature_maps[3]),
            ]
            feature_maps = tuple(feature_maps)

        if not return_dict:
            if output_hidden_states:
                output = (feature_maps,) + outputs[1:]
            else:
                output = (feature_maps,) + outputs[2:]
            return output

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


__all__ = [
    "BeitForImageClassification",
    "BeitForMaskedImageModeling",
    "BeitForSemanticSegmentation",
    "BeitModel",
    "BeitPreTrainedModel",
    "BeitBackbone",
]