team-10/env/Lib/site-packages/transformers/models/deit/modeling_deit.py

# coding=utf-8
# Copyright 2021 Facebook AI Research (FAIR), Ross Wightman, 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 DeiT model."""

import collections.abc
from dataclasses import dataclass
from typing import Callable, Optional, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACT2FN
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPooling,
    ImageClassifierOutput,
    MaskedImageModelingOutput,
)
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import ModelOutput, auto_docstring, logging, torch_int
from .configuration_deit import DeiTConfig


logger = logging.get_logger(__name__)


class DeiTEmbeddings(nn.Module):
    """
    Construct the CLS token, distillation token, position and patch embeddings. Optionally, also the mask token.
    """

    def __init__(self, config: DeiTConfig, use_mask_token: bool = False) -> None:
        super().__init__()

        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.distillation_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
        self.patch_embeddings = DeiTPatchEmbeddings(config)
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 2, config.hidden_size))
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.patch_size = config.patch_size

    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
        """
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
        images. This method is also adapted to support torch.jit tracing and 2 class embeddings.

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

        num_patches = embeddings.shape[1] - 2
        num_positions = self.position_embeddings.shape[1] - 2

        # 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_and_dist_pos_embed = self.position_embeddings[:, :2]
        patch_pos_embed = self.position_embeddings[:, 2:]

        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_and_dist_pos_embed, patch_pos_embed), dim=1)

    def forward(
        self,
        pixel_values: torch.Tensor,
        bool_masked_pos: Optional[torch.BoolTensor] = None,
        interpolate_pos_encoding: bool = False,
    ) -> torch.Tensor:
        _, _, height, width = pixel_values.shape
        embeddings = self.patch_embeddings(pixel_values)

        batch_size, seq_length, _ = embeddings.size()

        if bool_masked_pos is not None:
            mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
            # replace the masked visual tokens by mask_tokens
            mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask

        cls_tokens = self.cls_token.expand(batch_size, -1, -1)

        distillation_tokens = self.distillation_token.expand(batch_size, -1, -1)

        embeddings = torch.cat((cls_tokens, distillation_tokens, embeddings), dim=1)
        position_embedding = self.position_embeddings

        if interpolate_pos_encoding:
            position_embedding = self.interpolate_pos_encoding(embeddings, height, width)

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


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

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

        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches

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

    def forward(self, pixel_values: 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."
            )
        x = self.projection(pixel_values).flatten(2).transpose(1, 2)
        return x


# Copied from transformers.models.vit.modeling_vit.eager_attention_forward
def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    # Take the dot product between "query" and "key" to get the raw attention scores.
    attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling

    # Normalize the attention scores to probabilities.
    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)

    # This is actually dropping out entire tokens to attend to, which might
    # seem a bit unusual, but is taken from the original Transformer paper.
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)

    # Mask heads if we want to
    if attention_mask is not None:
        attn_weights = attn_weights * attention_mask

    attn_output = torch.matmul(attn_weights, value)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->DeiT
class DeiTSelfAttention(nn.Module):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__()
        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.config = config
        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.dropout_prob = config.attention_probs_dropout_prob
        self.scaling = self.attention_head_size**-0.5
        self.is_causal = False

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

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

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

        context_layer, attention_probs = attention_interface(
            self,
            query_layer,
            key_layer,
            value_layer,
            head_mask,
            is_causal=self.is_causal,
            scaling=self.scaling,
            dropout=0.0 if not self.training else self.dropout_prob,
        )

        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.reshape(new_context_layer_shape)

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

        return outputs


# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->DeiT
class DeiTSelfOutput(nn.Module):
    """
    The residual connection is defined in DeiTLayer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, config: DeiTConfig) -> 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) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)

        return hidden_states


# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->DeiT
class DeiTAttention(nn.Module):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__()
        self.attention = DeiTSelfAttention(config)
        self.output = DeiTSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads: set[int]) -> None:
        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,
    ) -> Union[tuple[torch.Tensor, torch.Tensor], tuple[torch.Tensor]]:
        self_outputs = self.attention(hidden_states, head_mask, 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


# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->DeiT
class DeiTIntermediate(nn.Module):
    def __init__(self, config: DeiTConfig) -> 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


# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->DeiT
class DeiTOutput(nn.Module):
    def __init__(self, config: DeiTConfig) -> 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, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)

        hidden_states = hidden_states + input_tensor

        return hidden_states


# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->DeiT,VIT->DEIT
class DeiTLayer(GradientCheckpointingLayer):
    """This corresponds to the Block class in the timm implementation."""

    def __init__(self, config: DeiTConfig) -> None:
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = DeiTAttention(config)
        self.intermediate = DeiTIntermediate(config)
        self.output = DeiTOutput(config)
        self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

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

        # first residual connection
        hidden_states = attention_output + hidden_states

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

        # second residual connection is done here
        layer_output = self.output(layer_output, hidden_states)

        outputs = (layer_output,) + outputs

        return outputs


# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->DeiT
class DeiTEncoder(nn.Module):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([DeiTLayer(config) for _ 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,
        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,)

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

            layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)

            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 DeiTPreTrainedModel(PreTrainedModel):
    config: DeiTConfig
    base_model_prefix = "deit"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _no_split_modules = ["DeiTLayer"]
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True
    _supports_attention_backend = True

    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
            # `trunc_normal_cpu` not implemented in `half` issues
            module.weight.data = nn.init.trunc_normal_(
                module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
            ).to(module.weight.dtype)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, DeiTEmbeddings):
            module.cls_token.data.zero_()
            module.position_embeddings.data.zero_()
            module.distillation_token.data.zero_()
            if module.mask_token is not None:
                module.mask_token.data.zero_()


@auto_docstring
class DeiTModel(DeiTPreTrainedModel):
    def __init__(self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False) -> None:
        r"""
        add_pooling_layer (bool, *optional*, defaults to `True`):
            Whether to add a pooling layer
        use_mask_token (`bool`, *optional*, defaults to `False`):
            Whether to use a mask token for masked image modeling.
        """
        super().__init__(config)
        self.config = config

        self.embeddings = DeiTEmbeddings(config, use_mask_token=use_mask_token)
        self.encoder = DeiTEncoder(config)

        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.pooler = DeiTPooler(config) if add_pooling_layer else None

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

    def get_input_embeddings(self) -> DeiTPatchEmbeddings:
        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: Optional[torch.Tensor] = None,
        bool_masked_pos: Optional[torch.BoolTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        interpolate_pos_encoding: bool = False,
    ) -> Union[tuple, BaseModelOutputWithPooling]:
        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

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

        # 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)

        # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?)
        expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype
        if pixel_values.dtype != expected_dtype:
            pixel_values = pixel_values.to(expected_dtype)

        embedding_output = self.embeddings(
            pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
        )

        encoder_outputs = self.encoder(
            embedding_output,
            head_mask=head_mask,
            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 = 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 BaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


# Copied from transformers.models.vit.modeling_vit.ViTPooler with ViT->DeiT
class DeiTPooler(nn.Module):
    def __init__(self, config: DeiTConfig):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.pooler_output_size)
        self.activation = ACT2FN[config.pooler_act]

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


@auto_docstring(
    custom_intro="""
    DeiT Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://huggingface.co/papers/2111.09886).

    <Tip>

    Note that we provide a script to pre-train this model on custom data in our [examples
    directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).

    </Tip>
    """
)
class DeiTForMaskedImageModeling(DeiTPreTrainedModel):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__(config)

        self.deit = DeiTModel(config, add_pooling_layer=False, use_mask_token=True)

        self.decoder = nn.Sequential(
            nn.Conv2d(
                in_channels=config.hidden_size,
                out_channels=config.encoder_stride**2 * config.num_channels,
                kernel_size=1,
            ),
            nn.PixelShuffle(config.encoder_stride),
        )

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

    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        bool_masked_pos: Optional[torch.BoolTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        interpolate_pos_encoding: bool = False,
    ) -> Union[tuple, MaskedImageModelingOutput]:
        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).

        Examples:
        ```python
        >>> from transformers import AutoImageProcessor, DeiTForMaskedImageModeling
        >>> 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("facebook/deit-base-distilled-patch16-224")
        >>> model = DeiTForMaskedImageModeling.from_pretrained("facebook/deit-base-distilled-patch16-224")

        >>> 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, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
        >>> list(reconstructed_pixel_values.shape)
        [1, 3, 224, 224]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

        sequence_output = outputs[0]

        # Reshape to (batch_size, num_channels, height, width)
        sequence_output = sequence_output[:, 1:-1]
        batch_size, sequence_length, num_channels = sequence_output.shape
        height = width = int(sequence_length**0.5)
        sequence_output = sequence_output.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)

        # Reconstruct pixel values
        reconstructed_pixel_values = self.decoder(sequence_output)

        masked_im_loss = None
        if bool_masked_pos is not None:
            size = self.config.image_size // self.config.patch_size
            bool_masked_pos = bool_masked_pos.reshape(-1, size, size)
            mask = (
                bool_masked_pos.repeat_interleave(self.config.patch_size, 1)
                .repeat_interleave(self.config.patch_size, 2)
                .unsqueeze(1)
                .contiguous()
            )
            reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none")
            masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels

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

        return MaskedImageModelingOutput(
            loss=masked_im_loss,
            reconstruction=reconstructed_pixel_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@auto_docstring(
    custom_intro="""
    DeiT 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 DeiTForImageClassification(DeiTPreTrainedModel):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.deit = DeiTModel(config, add_pooling_layer=False)

        # 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,
        return_dict: Optional[bool] = None,
        interpolate_pos_encoding: bool = False,
    ) -> 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).

        Examples:

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

        >>> torch.manual_seed(3)  # doctest: +IGNORE_RESULT
        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> # note: we are loading a DeiTForImageClassificationWithTeacher from the hub here,
        >>> # so the head will be randomly initialized, hence the predictions will be random
        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
        >>> model = DeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224")

        >>> inputs = image_processor(images=image, return_tensors="pt")
        >>> outputs = model(**inputs)
        >>> logits = outputs.logits
        >>> # model predicts one of the 1000 ImageNet classes
        >>> predicted_class_idx = logits.argmax(-1).item()
        >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
        Predicted class: Polaroid camera, Polaroid Land camera
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

        sequence_output = outputs[0]

        logits = self.classifier(sequence_output[:, 0, :])
        # we don't use the distillation token

        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@dataclass
@auto_docstring(
    custom_intro="""
    Output type of [`DeiTForImageClassificationWithTeacher`].
    """
)
class DeiTForImageClassificationWithTeacherOutput(ModelOutput):
    r"""
    logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
        Prediction scores as the average of the cls_logits and distillation logits.
    cls_logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
        Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the
        class token).
    distillation_logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
        Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the
        distillation token).
    """

    logits: Optional[torch.FloatTensor] = None
    cls_logits: Optional[torch.FloatTensor] = None
    distillation_logits: Optional[torch.FloatTensor] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None


@auto_docstring(
    custom_intro="""
    DeiT Model transformer with image classification heads on top (a linear layer on top of the final hidden state of
    the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.

    .. warning::

           This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet
           supported.
    """
)
class DeiTForImageClassificationWithTeacher(DeiTPreTrainedModel):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.deit = DeiTModel(config, add_pooling_layer=False)

        # Classifier heads
        self.cls_classifier = (
            nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
        )
        self.distillation_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,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        interpolate_pos_encoding: bool = False,
    ) -> Union[tuple, DeiTForImageClassificationWithTeacherOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

        sequence_output = outputs[0]

        cls_logits = self.cls_classifier(sequence_output[:, 0, :])
        distillation_logits = self.distillation_classifier(sequence_output[:, 1, :])

        # during inference, return the average of both classifier predictions
        logits = (cls_logits + distillation_logits) / 2

        if not return_dict:
            output = (logits, cls_logits, distillation_logits) + outputs[1:]
            return output

        return DeiTForImageClassificationWithTeacherOutput(
            logits=logits,
            cls_logits=cls_logits,
            distillation_logits=distillation_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


__all__ = [
    "DeiTForImageClassification",
    "DeiTForImageClassificationWithTeacher",
    "DeiTForMaskedImageModeling",
    "DeiTModel",
    "DeiTPreTrainedModel",
]