transformers/src/transformers/models/distilbert/modeling_distilbert.py

# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# 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 DistilBERT model adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM) and in
part from HuggingFace PyTorch version of Google AI Bert model (https://github.com/google-research/bert)
"""

import math
from typing import Optional, Union

import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import get_activation
from ...configuration_utils import PretrainedConfig
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask_for_sdpa
from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
    BaseModelOutput,
    MaskedLMOutput,
    MultipleChoiceModelOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import (
    apply_chunking_to_forward,
    find_pruneable_heads_and_indices,
    is_torch_greater_or_equal_than_2_2,
    prune_linear_layer,
)
from ...utils import (
    auto_docstring,
    logging,
)
from .configuration_distilbert import DistilBertConfig


if is_flash_attn_available():
    from ...modeling_flash_attention_utils import _flash_attention_forward


logger = logging.get_logger(__name__)


# UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE #


def create_sinusoidal_embeddings(n_pos: int, dim: int, out: torch.Tensor):
    if is_deepspeed_zero3_enabled():
        import deepspeed

        with deepspeed.zero.GatheredParameters(out, modifier_rank=0):
            if torch.distributed.get_rank() == 0:
                _create_sinusoidal_embeddings(n_pos=n_pos, dim=dim, out=out)
    else:
        _create_sinusoidal_embeddings(n_pos=n_pos, dim=dim, out=out)


def _create_sinusoidal_embeddings(n_pos: int, dim: int, out: torch.Tensor):
    position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
    out.requires_grad = False
    out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
    out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
    out.detach_()


class Embeddings(nn.Module):
    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.dim, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.dim)

        self.LayerNorm = nn.LayerNorm(config.dim, eps=1e-12)
        self.dropout = nn.Dropout(config.dropout)
        self.register_buffer(
            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
        )

    def forward(self, input_ids: torch.Tensor, input_embeds: Optional[torch.Tensor] = None) -> torch.Tensor:
        """
        Parameters:
            input_ids (torch.Tensor):
                torch.tensor(bs, max_seq_length) The token ids to embed.
            input_embeds (*optional*, torch.Tensor):
                The pre-computed word embeddings. Can only be passed if the input ids are `None`.


        Returns: torch.tensor(bs, max_seq_length, dim) The embedded tokens (plus position embeddings, no token_type
        embeddings)
        """
        if input_ids is not None:
            input_embeds = self.word_embeddings(input_ids)  # (bs, max_seq_length, dim)

        seq_length = input_embeds.size(1)

        # Setting the position-ids to the registered buffer in constructor, it helps
        # when tracing the model without passing position-ids, solves
        # issues similar to issue #5664
        if hasattr(self, "position_ids"):
            position_ids = self.position_ids[:, :seq_length]
        else:
            position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)  # (max_seq_length)
            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)  # (bs, max_seq_length)

        position_embeddings = self.position_embeddings(position_ids)  # (bs, max_seq_length, dim)

        embeddings = input_embeds + position_embeddings  # (bs, max_seq_length, dim)
        embeddings = self.LayerNorm(embeddings)  # (bs, max_seq_length, dim)
        embeddings = self.dropout(embeddings)  # (bs, max_seq_length, dim)
        return embeddings


class MultiHeadSelfAttention(nn.Module):
    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.config = config

        self.n_heads = config.n_heads
        self.dim = config.dim
        self.dropout = nn.Dropout(p=config.attention_dropout)
        self.is_causal = False

        # Have an even number of multi heads that divide the dimensions
        if self.dim % self.n_heads != 0:
            # Raise value errors for even multi-head attention nodes
            raise ValueError(f"self.n_heads: {self.n_heads} must divide self.dim: {self.dim} evenly")

        self.q_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
        self.k_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
        self.v_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
        self.out_lin = nn.Linear(in_features=config.dim, out_features=config.dim)

        self.pruned_heads: set[int] = set()
        self.attention_head_size = self.dim // self.n_heads

    def prune_heads(self, heads: list[int]):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.n_heads, self.attention_head_size, self.pruned_heads
        )
        # Prune linear layers
        self.q_lin = prune_linear_layer(self.q_lin, index)
        self.k_lin = prune_linear_layer(self.k_lin, index)
        self.v_lin = prune_linear_layer(self.v_lin, index)
        self.out_lin = prune_linear_layer(self.out_lin, index, dim=1)
        # Update hyper params
        self.n_heads = self.n_heads - len(heads)
        self.dim = self.attention_head_size * self.n_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        mask: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> tuple[torch.Tensor, ...]:
        """
        Parameters:
            query: torch.tensor(bs, seq_length, dim)
            key: torch.tensor(bs, seq_length, dim)
            value: torch.tensor(bs, seq_length, dim)
            mask: torch.tensor(bs, seq_length)

        Returns:
            weights: torch.tensor(bs, n_heads, seq_length, seq_length) Attention weights context: torch.tensor(bs,
            seq_length, dim) Contextualized layer. Optional: only if `output_attentions=True`
        """
        bs, q_length, dim = query.size()
        k_length = key.size(1)
        # assert dim == self.dim, f'Dimensions do not match: {dim} input vs {self.dim} configured'
        # assert key.size() == value.size()

        dim_per_head = self.dim // self.n_heads

        mask_reshp = (bs, 1, 1, k_length)

        def shape(x: torch.Tensor) -> torch.Tensor:
            """separate heads"""
            return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)

        def unshape(x: torch.Tensor) -> torch.Tensor:
            """group heads"""
            return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head)

        q = shape(self.q_lin(query))  # (bs, n_heads, q_length, dim_per_head)
        k = shape(self.k_lin(key))  # (bs, n_heads, k_length, dim_per_head)
        v = shape(self.v_lin(value))  # (bs, n_heads, k_length, dim_per_head)

        q = q / math.sqrt(dim_per_head)  # (bs, n_heads, q_length, dim_per_head)
        scores = torch.matmul(q, k.transpose(2, 3))  # (bs, n_heads, q_length, k_length)
        mask = (mask == 0).view(mask_reshp).expand_as(scores)  # (bs, n_heads, q_length, k_length)
        scores = scores.masked_fill(
            mask, torch.tensor(torch.finfo(scores.dtype).min)
        )  # (bs, n_heads, q_length, k_length)

        weights = nn.functional.softmax(scores, dim=-1)  # (bs, n_heads, q_length, k_length)
        weights = self.dropout(weights)  # (bs, n_heads, q_length, k_length)

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

        context = torch.matmul(weights, v)  # (bs, n_heads, q_length, dim_per_head)
        context = unshape(context)  # (bs, q_length, dim)
        context = self.out_lin(context)  # (bs, q_length, dim)

        if output_attentions:
            return (context, weights)
        else:
            return (context,)


class DistilBertFlashAttention2(MultiHeadSelfAttention):
    """
    DistilBert flash attention module. This module inherits from `MultiHeadSelfAttention` as the weights of the module
    stays untouched. The only required change would be on the forward pass where it needs to correctly call the public
    API of flash attention and deal with padding tokens in case the input contains any of them.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
        self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        mask: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> tuple[torch.Tensor, ...]:
        """
        Parameters:
            query: torch.tensor(bs, seq_length, dim)
            key: torch.tensor(bs, seq_length, dim)
            value: torch.tensor(bs, seq_length, dim)
            mask: torch.tensor(bs, seq_length)

        Returns:
            weights: torch.tensor(bs, n_heads, seq_length, seq_length) Attention weights context: torch.tensor(bs,
            seq_length, dim) Contextualized layer. Optional: only if `output_attentions=True`
        """
        batch_size, q_length, dim = query.size()

        dim_per_head = self.dim // self.n_heads

        def reshape(x: torch.Tensor) -> torch.Tensor:
            """separate heads"""
            return x.view(batch_size, -1, self.n_heads, dim_per_head)

        # Flash attention requires the input to have the shape
        # batch_size x seq_length x head_dim x hidden_dim
        query_states = reshape(self.q_lin(query))
        key_states = reshape(self.k_lin(key))
        value_states = reshape(self.v_lin(value))

        attn_dropout = self.config.attention_dropout if self.training else 0.0

        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
        # therefore the input hidden states gets silently casted in float32. Hence, we need
        # cast them back in the correct dtype just to be sure everything works as expected.
        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
        # in fp32. (LlamaRMSNorm handles it correctly)

        device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
        if query_states.dtype == torch.float32:
            if torch.is_autocast_enabled():
                target_dtype = (
                    torch.get_autocast_dtype(device_type)
                    if hasattr(torch, "get_autocast_dtype")
                    else torch.get_autocast_gpu_dtype()
                )
            # Handle the case where the model is quantized
            elif hasattr(self.config, "_pre_quantization_dtype"):
                target_dtype = self.config._pre_quantization_dtype
            else:
                target_dtype = self.q_lin.weight.dtype

            logger.warning_once(
                f"The input hidden states seems to be silently casted in float32, this might be related to"
                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
                f" {target_dtype}."
            )

            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)

        attn_weights = _flash_attention_forward(
            query_states,
            key_states,
            value_states,
            mask,
            q_length,
            dropout=attn_dropout,
            use_top_left_mask=self._flash_attn_uses_top_left_mask,
            is_causal=self.is_causal,
        )

        attn_weights_reshaped = attn_weights.reshape(batch_size, q_length, self.n_heads * dim_per_head)
        attn_output = self.out_lin(attn_weights_reshaped)

        if output_attentions:
            return (attn_output, attn_weights)
        else:
            return (attn_output,)


class DistilBertSdpaAttention(MultiHeadSelfAttention):
    def __init__(self, config: PretrainedConfig):
        super().__init__(config=config)
        self.dropout_prob = config.attention_dropout
        self.require_contiguous_qkv = not is_torch_greater_or_equal_than_2_2

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        mask: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> tuple[torch.Tensor, ...]:
        """
        Parameters:
            query: torch.tensor(bs, seq_length, dim)
            key: torch.tensor(bs, seq_length, dim)
            value: torch.tensor(bs, seq_length, dim)
            mask: torch.tensor(bs, seq_length)

        Returns:
            weights: torch.tensor(bs, n_heads, seq_length, seq_length) Attention weights context: torch.tensor(bs,
            seq_length, dim) Contextualized layer. Optional: only if `output_attentions=True`
        """
        if output_attentions or head_mask is not None:
            logger.warning_once(
                "DistilBertSdpaAttention 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(
                query,
                key,
                value,
                mask,
                head_mask,
                output_attentions,
            )

        batch_size, _, _ = query.size()
        dim_per_head = self.dim // self.n_heads

        def shape(x: torch.Tensor) -> torch.Tensor:
            """separate heads"""
            return x.view(batch_size, -1, self.n_heads, dim_per_head).transpose(1, 2)

        def unshape(x: torch.Tensor) -> torch.Tensor:
            """group heads"""
            return x.transpose(1, 2).contiguous().view(batch_size, -1, self.n_heads * dim_per_head)

        q = shape(self.q_lin(query))  # (bs, n_heads, q_length, dim_per_head)
        k = shape(self.k_lin(key))  # (bs, n_heads, k_length, dim_per_head)
        v = shape(self.v_lin(value))  # (bs, n_heads, k_length, dim_per_head)

        # SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom
        # attn_mask, so we need to call `.contiguous()` here. This was fixed in torch==2.2.0.
        # Reference: https://github.com/pytorch/pytorch/issues/112577
        if self.require_contiguous_qkv and q.device.type == "cuda" and mask is not None:
            q = q.contiguous()
            k = k.contiguous()
            v = v.contiguous()

        attn_output = torch.nn.functional.scaled_dot_product_attention(
            q,
            k,
            v,
            attn_mask=mask,
            dropout_p=self.dropout_prob if self.training else 0.0,
            is_causal=False,
        )

        attn_output = unshape(attn_output)
        attn_output = self.out_lin(attn_output)

        return (attn_output,)


class FFN(nn.Module):
    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.dropout = nn.Dropout(p=config.dropout)
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.lin1 = nn.Linear(in_features=config.dim, out_features=config.hidden_dim)
        self.lin2 = nn.Linear(in_features=config.hidden_dim, out_features=config.dim)
        self.activation = get_activation(config.activation)

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, input)

    def ff_chunk(self, input: torch.Tensor) -> torch.Tensor:
        x = self.lin1(input)
        x = self.activation(x)
        x = self.lin2(x)
        x = self.dropout(x)
        return x


DISTILBERT_ATTENTION_CLASSES = {
    "eager": MultiHeadSelfAttention,
    "flash_attention_2": DistilBertFlashAttention2,
    "sdpa": DistilBertSdpaAttention,
}


class TransformerBlock(GradientCheckpointingLayer):
    def __init__(self, config: PretrainedConfig):
        super().__init__()

        # Have an even number of Configure multi-heads
        if config.dim % config.n_heads != 0:
            raise ValueError(f"config.n_heads {config.n_heads} must divide config.dim {config.dim} evenly")

        self.attention = DISTILBERT_ATTENTION_CLASSES[config._attn_implementation](config)
        self.sa_layer_norm = nn.LayerNorm(normalized_shape=config.dim, eps=1e-12)

        self.ffn = FFN(config)
        self.output_layer_norm = nn.LayerNorm(normalized_shape=config.dim, eps=1e-12)

    def forward(
        self,
        x: torch.Tensor,
        attn_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> tuple[torch.Tensor, ...]:
        """
        Parameters:
            x: torch.tensor(bs, seq_length, dim)
            attn_mask: torch.tensor(bs, seq_length)

        Returns:
            sa_weights: torch.tensor(bs, n_heads, seq_length, seq_length) The attention weights ffn_output:
            torch.tensor(bs, seq_length, dim) The output of the transformer block contextualization.
        """
        # Self-Attention
        sa_output = self.attention(
            query=x,
            key=x,
            value=x,
            mask=attn_mask,
            head_mask=head_mask,
            output_attentions=output_attentions,
        )
        if output_attentions:
            sa_output, sa_weights = sa_output  # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length)
        else:  # To handle these `output_attentions` or `output_hidden_states` cases returning tuples
            if type(sa_output) is not tuple:
                raise TypeError(f"sa_output must be a tuple but it is {type(sa_output)} type")

            sa_output = sa_output[0]
        sa_output = self.sa_layer_norm(sa_output + x)  # (bs, seq_length, dim)

        # Feed Forward Network
        ffn_output = self.ffn(sa_output)  # (bs, seq_length, dim)
        ffn_output: torch.Tensor = self.output_layer_norm(ffn_output + sa_output)  # (bs, seq_length, dim)

        output = (ffn_output,)
        if output_attentions:
            output = (sa_weights,) + output
        return output


class Transformer(nn.Module):
    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.n_layers = config.n_layers
        self.layer = nn.ModuleList([TransformerBlock(config) for _ in range(config.n_layers)])
        self.gradient_checkpointing = False

    def forward(
        self,
        x: torch.Tensor,
        attn_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: Optional[bool] = None,
    ) -> Union[BaseModelOutput, tuple[torch.Tensor, ...]]:  # docstyle-ignore
        """
        Parameters:
            x: torch.tensor(bs, seq_length, dim) Input sequence embedded.
            attn_mask: torch.tensor(bs, seq_length) Attention mask on the sequence.

        Returns:
            hidden_state: torch.tensor(bs, seq_length, dim) Sequence of hidden states in the last (top)
            layer all_hidden_states: tuple[torch.tensor(bs, seq_length, dim)]
                Tuple of length n_layers with the hidden states from each layer.
                Optional: only if output_hidden_states=True
            all_attentions: tuple[torch.tensor(bs, n_heads, seq_length, seq_length)]
                Tuple of length n_layers with the attention weights from each layer
                Optional: only if output_attentions=True
        """
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

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

            layer_outputs = layer_module(
                hidden_state,
                attn_mask,
                head_mask[i],
                output_attentions,
            )

            hidden_state = layer_outputs[-1]

            if output_attentions:
                if len(layer_outputs) != 2:
                    raise ValueError(f"The length of the layer_outputs should be 2, but it is {len(layer_outputs)}")

                attentions = layer_outputs[0]
                all_attentions = all_attentions + (attentions,)
            else:
                if len(layer_outputs) != 1:
                    raise ValueError(f"The length of the layer_outputs should be 1, but it is {len(layer_outputs)}")

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_state,)

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


# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
@auto_docstring
class DistilBertPreTrainedModel(PreTrainedModel):
    config_class = DistilBertConfig
    load_tf_weights = None
    base_model_prefix = "distilbert"
    supports_gradient_checkpointing = True
    _supports_flash_attn_2 = True
    _supports_sdpa = True

    def _init_weights(self, module: nn.Module):
        """Initialize the weights."""
        if isinstance(module, nn.Linear):
            # 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, Embeddings) and self.config.sinusoidal_pos_embds:
            create_sinusoidal_embeddings(
                self.config.max_position_embeddings, self.config.dim, module.position_embeddings.weight
            )


@auto_docstring
class DistilBertModel(DistilBertPreTrainedModel):
    def __init__(self, config: PretrainedConfig):
        super().__init__(config)

        self.embeddings = Embeddings(config)  # Embeddings
        self.transformer = Transformer(config)  # Encoder
        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
        self._use_sdpa = config._attn_implementation == "sdpa"

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

    def get_position_embeddings(self) -> nn.Embedding:
        """
        Returns the position embeddings
        """
        return self.embeddings.position_embeddings

    def resize_position_embeddings(self, new_num_position_embeddings: int):
        """
        Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.

        Arguments:
            new_num_position_embeddings (`int`):
                The number of new position embedding matrix. If position embeddings are learned, increasing the size
                will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                the size will remove vectors from the end.
        """
        num_position_embeds_diff = new_num_position_embeddings - self.config.max_position_embeddings

        # no resizing needs to be done if the length stays the same
        if num_position_embeds_diff == 0:
            return

        logger.info(f"Setting `config.max_position_embeddings={new_num_position_embeddings}`...")
        self.config.max_position_embeddings = new_num_position_embeddings

        old_position_embeddings_weight = self.embeddings.position_embeddings.weight.clone()

        self.embeddings.position_embeddings = nn.Embedding(self.config.max_position_embeddings, self.config.dim)

        if self.config.sinusoidal_pos_embds:
            create_sinusoidal_embeddings(
                n_pos=self.config.max_position_embeddings, dim=self.config.dim, out=self.position_embeddings.weight
            )
        else:
            with torch.no_grad():
                if num_position_embeds_diff > 0:
                    self.embeddings.position_embeddings.weight[:-num_position_embeds_diff] = nn.Parameter(
                        old_position_embeddings_weight
                    )
                else:
                    self.embeddings.position_embeddings.weight = nn.Parameter(
                        old_position_embeddings_weight[:num_position_embeds_diff]
                    )
        # move position_embeddings to correct device
        self.embeddings.position_embeddings.to(self.device)

    def get_input_embeddings(self) -> nn.Embedding:
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, new_embeddings: nn.Embedding):
        self.embeddings.word_embeddings = new_embeddings

    def _prune_heads(self, heads_to_prune: dict[int, list[list[int]]]):
        """
        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.transformer.layer[layer].attention.prune_heads(heads)

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[BaseModelOutput, tuple[torch.Tensor, ...]]:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        """
        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 input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        head_mask_is_none = head_mask is None
        # Prepare head mask if needed
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        embeddings = self.embeddings(input_ids, inputs_embeds)  # (bs, seq_length, dim)

        if self._use_flash_attention_2:
            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
        else:
            if attention_mask is None:
                attention_mask = torch.ones(input_shape, device=device)  # (bs, seq_length)

            if self._use_sdpa and head_mask_is_none and not output_attentions:
                attention_mask = _prepare_4d_attention_mask_for_sdpa(
                    attention_mask, embeddings.dtype, tgt_len=input_shape[1]
                )

        return self.transformer(
            x=embeddings,
            attn_mask=attention_mask,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )


@auto_docstring(
    custom_intro="""
    DistilBert Model with a `masked language modeling` head on top.
    """
)
class DistilBertForMaskedLM(DistilBertPreTrainedModel):
    _tied_weights_keys = ["vocab_projector.weight"]

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

        self.activation = get_activation(config.activation)

        self.distilbert = DistilBertModel(config)
        self.vocab_transform = nn.Linear(config.dim, config.dim)
        self.vocab_layer_norm = nn.LayerNorm(config.dim, eps=1e-12)
        self.vocab_projector = nn.Linear(config.dim, config.vocab_size)

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

        self.mlm_loss_fct = nn.CrossEntropyLoss()

    def get_position_embeddings(self) -> nn.Embedding:
        """
        Returns the position embeddings
        """
        return self.distilbert.get_position_embeddings()

    def resize_position_embeddings(self, new_num_position_embeddings: int):
        """
        Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.

        Arguments:
            new_num_position_embeddings (`int`):
                The number of new position embedding matrix. If position embeddings are learned, increasing the size
                will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                the size will remove vectors from the end.
        """
        self.distilbert.resize_position_embeddings(new_num_position_embeddings)

    def get_output_embeddings(self) -> nn.Module:
        return self.vocab_projector

    def set_output_embeddings(self, new_embeddings: nn.Module):
        self.vocab_projector = new_embeddings

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[MaskedLMOutput, tuple[torch.Tensor, ...]]:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        dlbrt_output = self.distilbert(
            input_ids=input_ids,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = dlbrt_output[0]  # (bs, seq_length, dim)
        prediction_logits = self.vocab_transform(hidden_states)  # (bs, seq_length, dim)
        prediction_logits = self.activation(prediction_logits)  # (bs, seq_length, dim)
        prediction_logits = self.vocab_layer_norm(prediction_logits)  # (bs, seq_length, dim)
        prediction_logits = self.vocab_projector(prediction_logits)  # (bs, seq_length, vocab_size)

        mlm_loss = None
        if labels is not None:
            mlm_loss = self.mlm_loss_fct(prediction_logits.view(-1, prediction_logits.size(-1)), labels.view(-1))

        if not return_dict:
            output = (prediction_logits,) + dlbrt_output[1:]
            return ((mlm_loss,) + output) if mlm_loss is not None else output

        return MaskedLMOutput(
            loss=mlm_loss,
            logits=prediction_logits,
            hidden_states=dlbrt_output.hidden_states,
            attentions=dlbrt_output.attentions,
        )


@auto_docstring(
    custom_intro="""
    DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """
)
class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
    def __init__(self, config: PretrainedConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config

        self.distilbert = DistilBertModel(config)
        self.pre_classifier = nn.Linear(config.dim, config.dim)
        self.classifier = nn.Linear(config.dim, config.num_labels)
        self.dropout = nn.Dropout(config.seq_classif_dropout)

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

    def get_position_embeddings(self) -> nn.Embedding:
        """
        Returns the position embeddings
        """
        return self.distilbert.get_position_embeddings()

    def resize_position_embeddings(self, new_num_position_embeddings: int):
        """
        Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.

        Arguments:
            new_num_position_embeddings (`int`):
                The number of new position embedding matrix. If position embeddings are learned, increasing the size
                will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                the size will remove vectors from the end.
        """
        self.distilbert.resize_position_embeddings(new_num_position_embeddings)

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[SequenceClassifierOutput, tuple[torch.Tensor, ...]]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence 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

        distilbert_output = self.distilbert(
            input_ids=input_ids,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_state = distilbert_output[0]  # (bs, seq_len, dim)
        pooled_output = hidden_state[:, 0]  # (bs, dim)
        pooled_output = self.pre_classifier(pooled_output)  # (bs, dim)
        pooled_output = nn.ReLU()(pooled_output)  # (bs, dim)
        pooled_output = self.dropout(pooled_output)  # (bs, dim)
        logits = self.classifier(pooled_output)  # (bs, num_labels)

        loss = None
        if labels is not None:
            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,) + distilbert_output[1:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=distilbert_output.hidden_states,
            attentions=distilbert_output.attentions,
        )


@auto_docstring
class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
    def __init__(self, config: PretrainedConfig):
        super().__init__(config)

        self.distilbert = DistilBertModel(config)
        self.qa_outputs = nn.Linear(config.dim, config.num_labels)
        if config.num_labels != 2:
            raise ValueError(f"config.num_labels should be 2, but it is {config.num_labels}")

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

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

    def get_position_embeddings(self) -> nn.Embedding:
        """
        Returns the position embeddings
        """
        return self.distilbert.get_position_embeddings()

    def resize_position_embeddings(self, new_num_position_embeddings: int):
        """
        Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.

        Arguments:
            new_num_position_embeddings (`int`):
                The number of new position embedding matrix. If position embeddings are learned, increasing the size
                will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                the size will remove vectors from the end.
        """
        self.distilbert.resize_position_embeddings(new_num_position_embeddings)

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        start_positions: Optional[torch.Tensor] = None,
        end_positions: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[QuestionAnsweringModelOutput, tuple[torch.Tensor, ...]]:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        distilbert_output = self.distilbert(
            input_ids=input_ids,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = distilbert_output[0]  # (bs, max_query_len, dim)

        hidden_states = self.dropout(hidden_states)  # (bs, max_query_len, dim)
        logits = self.qa_outputs(hidden_states)  # (bs, max_query_len, 2)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()  # (bs, max_query_len)
        end_logits = end_logits.squeeze(-1).contiguous()  # (bs, max_query_len)

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.size()) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (start_logits, end_logits) + distilbert_output[1:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=distilbert_output.hidden_states,
            attentions=distilbert_output.attentions,
        )


@auto_docstring
class DistilBertForTokenClassification(DistilBertPreTrainedModel):
    def __init__(self, config: PretrainedConfig):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.distilbert = DistilBertModel(config)
        self.dropout = nn.Dropout(config.dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

    def get_position_embeddings(self) -> nn.Embedding:
        """
        Returns the position embeddings
        """
        return self.distilbert.get_position_embeddings()

    def resize_position_embeddings(self, new_num_position_embeddings: int):
        """
        Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.

        Arguments:
            new_num_position_embeddings (`int`):
                The number of new position embedding matrix. If position embeddings are learned, increasing the size
                will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                the size will remove vectors from the end.
        """
        self.distilbert.resize_position_embeddings(new_num_position_embeddings)

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[TokenClassifierOutput, tuple[torch.Tensor, ...]]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.distilbert(
            input_ids,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

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

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


@auto_docstring
class DistilBertForMultipleChoice(DistilBertPreTrainedModel):
    def __init__(self, config: PretrainedConfig):
        super().__init__(config)

        self.distilbert = DistilBertModel(config)
        self.pre_classifier = nn.Linear(config.dim, config.dim)
        self.classifier = nn.Linear(config.dim, 1)
        self.dropout = nn.Dropout(config.seq_classif_dropout)

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

    def get_position_embeddings(self) -> nn.Embedding:
        """
        Returns the position embeddings
        """
        return self.distilbert.get_position_embeddings()

    def resize_position_embeddings(self, new_num_position_embeddings: int):
        """
        Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.

        Arguments:
            new_num_position_embeddings (`int`)
                The number of new position embeddings. If position embeddings are learned, increasing the size will add
                newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If
                position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will
                add correct vectors at the end following the position encoding algorithm, whereas reducing the size
                will remove vectors from the end.
        """
        self.distilbert.resize_position_embeddings(new_num_position_embeddings)

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[MultipleChoiceModelOutput, tuple[torch.Tensor, ...]]:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
            `input_ids` above)

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, DistilBertForMultipleChoice
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")
        >>> model = DistilBertForMultipleChoice.from_pretrained("distilbert-base-cased")

        >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
        >>> choice0 = "It is eaten with a fork and a knife."
        >>> choice1 = "It is eaten while held in the hand."
        >>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1

        >>> encoding = tokenizer([[prompt, choice0], [prompt, choice1]], return_tensors="pt", padding=True)
        >>> outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)  # batch size is 1

        >>> # the linear classifier still needs to be trained
        >>> loss = outputs.loss
        >>> logits = outputs.logits
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        inputs_embeds = (
            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
            if inputs_embeds is not None
            else None
        )

        outputs = self.distilbert(
            input_ids,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_state = outputs[0]  # (bs * num_choices, seq_len, dim)
        pooled_output = hidden_state[:, 0]  # (bs * num_choices, dim)
        pooled_output = self.pre_classifier(pooled_output)  # (bs * num_choices, dim)
        pooled_output = nn.ReLU()(pooled_output)  # (bs * num_choices, dim)
        pooled_output = self.dropout(pooled_output)  # (bs * num_choices, dim)
        logits = self.classifier(pooled_output)  # (bs * num_choices, 1)

        reshaped_logits = logits.view(-1, num_choices)  # (bs, num_choices)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)

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

        return MultipleChoiceModelOutput(
            loss=loss,
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


__all__ = [
    "DistilBertForMaskedLM",
    "DistilBertForMultipleChoice",
    "DistilBertForQuestionAnswering",
    "DistilBertForSequenceClassification",
    "DistilBertForTokenClassification",
    "DistilBertModel",
    "DistilBertPreTrainedModel",
]