image-match/python/py/Lib/site-packages/transformers/models/xlnet/modeling_xlnet.py

# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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 XLNet model.
"""

from collections.abc import Callable
from dataclasses import dataclass

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

from ... import initialization as init
from ...activations import ACT2FN, get_activation
from ...generation import GenerationMixin
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward
from ...utils import ModelOutput, auto_docstring, logging
from .configuration_xlnet import XLNetConfig


logger = logging.get_logger(__name__)


class XLNetRelativeAttention(nn.Module):
    def __init__(self, config):
        super().__init__()

        if config.d_model % config.n_head != 0:
            raise ValueError(
                f"The hidden size ({config.d_model}) is not a multiple of the number of attention "
                f"heads ({config.n_head}"
            )

        self.n_head = config.n_head
        self.d_head = config.d_head
        self.d_model = config.d_model
        self.scale = 1 / (config.d_head**0.5)

        self.q = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
        self.k = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
        self.v = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
        self.o = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
        self.r = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))

        self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
        self.r_s_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
        self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
        self.seg_embed = nn.Parameter(torch.FloatTensor(2, self.n_head, self.d_head))

        self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.dropout)

    @staticmethod
    def rel_shift(x, klen=-1):
        """perform relative shift to form the relative attention score."""
        x_size = x.shape

        x = x.reshape(x_size[1], x_size[0], x_size[2], x_size[3])
        x = x[1:, ...]
        x = x.reshape(x_size[0], x_size[1] - 1, x_size[2], x_size[3])
        # x = x[:, 0:klen, :, :]
        x = torch.index_select(x, 1, torch.arange(klen, device=x.device, dtype=torch.long))

        return x

    @staticmethod
    def rel_shift_bnij(x, klen=-1):
        x_size = x.shape

        x = x.reshape(x_size[0], x_size[1], x_size[3], x_size[2])
        x = x[:, :, 1:, :]
        x = x.reshape(x_size[0], x_size[1], x_size[2], x_size[3] - 1)
        # Note: the tensor-slice form was faster in my testing than torch.index_select
        #       However, tracing doesn't like the nature of the slice, and if klen changes
        #       during the run then it'll fail, whereas index_select will be fine.
        x = torch.index_select(x, 3, torch.arange(klen, device=x.device, dtype=torch.long))
        # x = x[:, :, :, :klen]

        return x

    def rel_attn_core(
        self,
        q_head,
        k_head_h,
        v_head_h,
        k_head_r,
        seg_mat=None,
        attn_mask=None,
        output_attentions=False,
    ):
        """Core relative positional attention operations."""

        # content based attention score
        ac = torch.einsum("ibnd,jbnd->bnij", q_head + self.r_w_bias, k_head_h)

        # position based attention score
        bd = torch.einsum("ibnd,jbnd->bnij", q_head + self.r_r_bias, k_head_r)
        bd = self.rel_shift_bnij(bd, klen=ac.shape[3])

        # segment based attention score
        if seg_mat is None:
            ef = 0
        else:
            ef = torch.einsum("ibnd,snd->ibns", q_head + self.r_s_bias, self.seg_embed)
            ef = torch.einsum("ijbs,ibns->bnij", seg_mat, ef)

        # merge attention scores and perform masking
        attn_score = (ac + bd + ef) * self.scale
        if attn_mask is not None:
            # attn_score = attn_score * (1 - attn_mask) - 1e30 * attn_mask
            if attn_mask.dtype == torch.float16:
                attn_score = attn_score - 65500 * torch.einsum("ijbn->bnij", attn_mask)
            else:
                attn_score = attn_score - 1e30 * torch.einsum("ijbn->bnij", attn_mask)

        # attention probability
        attn_prob = nn.functional.softmax(attn_score, dim=3)
        attn_prob = self.dropout(attn_prob)

        # attention output
        attn_vec = torch.einsum("bnij,jbnd->ibnd", attn_prob, v_head_h)

        if output_attentions:
            return attn_vec, torch.einsum("bnij->ijbn", attn_prob)

        return attn_vec

    def post_attention(self, h, attn_vec, residual=True):
        """Post-attention processing."""
        # post-attention projection (back to `d_model`)
        attn_out = torch.einsum("ibnd,hnd->ibh", attn_vec, self.o)

        attn_out = self.dropout(attn_out)
        if residual:
            attn_out = attn_out + h
        output = self.layer_norm(attn_out)

        return output

    def forward(
        self,
        h,
        g,
        attn_mask_h,
        attn_mask_g,
        r,
        seg_mat,
        mems=None,
        target_mapping=None,
        output_attentions=False,
    ):
        if g is not None:
            # Two-stream attention with relative positional encoding.
            # content based attention score
            if mems is not None and mems.dim() > 1:
                cat = torch.cat([mems, h], dim=0)
            else:
                cat = h

            # content-based key head
            k_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.k)

            # content-based value head
            v_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.v)

            # position-based key head
            k_head_r = torch.einsum("ibh,hnd->ibnd", r, self.r)

            # h-stream
            # content-stream query head
            q_head_h = torch.einsum("ibh,hnd->ibnd", h, self.q)

            # core attention ops
            attn_vec_h = self.rel_attn_core(
                q_head_h,
                k_head_h,
                v_head_h,
                k_head_r,
                seg_mat=seg_mat,
                attn_mask=attn_mask_h,
                output_attentions=output_attentions,
            )

            if output_attentions:
                attn_vec_h, attn_prob_h = attn_vec_h

            # post processing
            output_h = self.post_attention(h, attn_vec_h)

            # g-stream
            # query-stream query head
            q_head_g = torch.einsum("ibh,hnd->ibnd", g, self.q)

            # core attention ops
            if target_mapping is not None:
                q_head_g = torch.einsum("mbnd,mlb->lbnd", q_head_g, target_mapping)
                attn_vec_g = self.rel_attn_core(
                    q_head_g,
                    k_head_h,
                    v_head_h,
                    k_head_r,
                    seg_mat=seg_mat,
                    attn_mask=attn_mask_g,
                    output_attentions=output_attentions,
                )

                if output_attentions:
                    attn_vec_g, attn_prob_g = attn_vec_g

                attn_vec_g = torch.einsum("lbnd,mlb->mbnd", attn_vec_g, target_mapping)
            else:
                attn_vec_g = self.rel_attn_core(
                    q_head_g,
                    k_head_h,
                    v_head_h,
                    k_head_r,
                    seg_mat=seg_mat,
                    attn_mask=attn_mask_g,
                    output_attentions=output_attentions,
                )

                if output_attentions:
                    attn_vec_g, attn_prob_g = attn_vec_g

            # post processing
            output_g = self.post_attention(g, attn_vec_g)

            if output_attentions:
                attn_prob = attn_prob_h, attn_prob_g

        else:
            # Multi-head attention with relative positional encoding
            if mems is not None and mems.dim() > 1:
                cat = torch.cat([mems, h], dim=0)
            else:
                cat = h

            # content heads
            q_head_h = torch.einsum("ibh,hnd->ibnd", h, self.q)
            k_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.k)
            v_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.v)

            # positional heads
            # type casting for fp16 support
            k_head_r = torch.einsum("ibh,hnd->ibnd", r.type(self.r.dtype), self.r)

            # core attention ops
            attn_vec = self.rel_attn_core(
                q_head_h,
                k_head_h,
                v_head_h,
                k_head_r,
                seg_mat=seg_mat,
                attn_mask=attn_mask_h,
                output_attentions=output_attentions,
            )

            if output_attentions:
                attn_vec, attn_prob = attn_vec

            # post processing
            output_h = self.post_attention(h, attn_vec)
            output_g = None

        outputs = (output_h, output_g)
        if output_attentions:
            outputs = outputs + (attn_prob,)
        return outputs


class XLNetFeedForward(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
        self.layer_1 = nn.Linear(config.d_model, config.d_inner)
        self.layer_2 = nn.Linear(config.d_inner, config.d_model)
        self.dropout = nn.Dropout(config.dropout)
        if isinstance(config.ff_activation, str):
            self.activation_function = ACT2FN[config.ff_activation]
        else:
            self.activation_function = config.ff_activation

    def forward(self, inp):
        output = inp
        output = self.layer_1(output)
        output = self.activation_function(output)
        output = self.dropout(output)
        output = self.layer_2(output)
        output = self.dropout(output)
        output = self.layer_norm(output + inp)
        return output


class XLNetLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.rel_attn = XLNetRelativeAttention(config)
        self.ff = XLNetFeedForward(config)
        self.dropout = nn.Dropout(config.dropout)
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1

    def forward(
        self,
        output_h,
        output_g,
        attn_mask_h,
        attn_mask_g,
        r,
        seg_mat,
        mems=None,
        target_mapping=None,
        output_attentions=False,
    ):
        outputs = self.rel_attn(
            output_h,
            output_g,
            attn_mask_h,
            attn_mask_g,
            r,
            seg_mat,
            mems=mems,
            target_mapping=target_mapping,
            output_attentions=output_attentions,
        )
        output_h, output_g = outputs[:2]

        if output_g is not None:
            output_g = apply_chunking_to_forward(
                self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, output_g
            )
        output_h = apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, output_h)

        outputs = (output_h, output_g) + outputs[2:]  # Add again attentions if there are there
        return outputs

    def ff_chunk(self, output_x):
        output_x = self.ff(output_x)
        return output_x


# Copied from transformers.models.xlm.modeling_xlm.XLMPoolerStartLogits with XLM->XLNet
class XLNetPoolerStartLogits(nn.Module):
    """
    Compute SQuAD start logits from sequence hidden states.

    Args:
        config ([`XLNetConfig`]):
            The config used by the model, will be used to grab the `hidden_size` of the model.
    """

    def __init__(self, config: XLNetConfig):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, 1)

    def forward(self, hidden_states: torch.FloatTensor, p_mask: torch.FloatTensor | None = None) -> torch.FloatTensor:
        """
        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`):
                The final hidden states of the model.
            p_mask (`torch.FloatTensor` of shape `(batch_size, seq_len)`, *optional*):
                Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
                should be masked.

        Returns:
            `torch.FloatTensor`: The start logits for SQuAD.
        """
        x = self.dense(hidden_states).squeeze(-1)

        if p_mask is not None:
            if p_mask.dtype == torch.float16:
                x = x * (1 - p_mask) - 65500 * p_mask
            else:
                x = x * (1 - p_mask) - 1e30 * p_mask

        return x


# Copied from transformers.models.xlm.modeling_xlm.XLMPoolerEndLogits with XLM->XLNet
class XLNetPoolerEndLogits(nn.Module):
    """
    Compute SQuAD end logits from sequence hidden states.

    Args:
        config ([`XLNetConfig`]):
            The config used by the model, will be used to grab the `hidden_size` of the model and the `layer_norm_eps`
            to use.
    """

    def __init__(self, config: XLNetConfig):
        super().__init__()
        self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
        self.activation = nn.Tanh()
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dense_1 = nn.Linear(config.hidden_size, 1)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        start_states: torch.FloatTensor | None = None,
        start_positions: torch.LongTensor | None = None,
        p_mask: torch.FloatTensor | None = None,
    ) -> torch.FloatTensor:
        """
        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`):
                The final hidden states of the model.
            start_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`, *optional*):
                The hidden states of the first tokens for the labeled span.
            start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
                The position of the first token for the labeled span.
            p_mask (`torch.FloatTensor` of shape `(batch_size, seq_len)`, *optional*):
                Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
                should be masked.

        <Tip>

        One of `start_states` or `start_positions` should be not `None`. If both are set, `start_positions` overrides
        `start_states`.

        </Tip>

        Returns:
            `torch.FloatTensor`: The end logits for SQuAD.
        """
        assert start_states is not None or start_positions is not None, (
            "One of start_states, start_positions should be not None"
        )
        if start_positions is not None:
            slen, hsz = hidden_states.shape[-2:]
            start_positions = start_positions[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
            start_states = hidden_states.gather(-2, start_positions)  # shape (bsz, 1, hsz)
            start_states = start_states.expand(-1, slen, -1)  # shape (bsz, slen, hsz)

        x = self.dense_0(torch.cat([hidden_states, start_states], dim=-1))
        x = self.activation(x)
        x = self.LayerNorm(x)
        x = self.dense_1(x).squeeze(-1)

        if p_mask is not None:
            if p_mask.dtype == torch.float16:
                x = x * (1 - p_mask) - 65500 * p_mask
            else:
                x = x * (1 - p_mask) - 1e30 * p_mask

        return x


# Copied from transformers.models.xlm.modeling_xlm.XLMPoolerAnswerClass with XLM->XLNet
class XLNetPoolerAnswerClass(nn.Module):
    """
    Compute SQuAD 2.0 answer class from classification and start tokens hidden states.

    Args:
        config ([`XLNetConfig`]):
            The config used by the model, will be used to grab the `hidden_size` of the model.
    """

    def __init__(self, config: XLNetConfig):
        super().__init__()
        self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
        self.activation = nn.Tanh()
        self.dense_1 = nn.Linear(config.hidden_size, 1, bias=False)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        start_states: torch.FloatTensor | None = None,
        start_positions: torch.LongTensor | None = None,
        cls_index: torch.LongTensor | None = None,
    ) -> torch.FloatTensor:
        """
        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`):
                The final hidden states of the model.
            start_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`, *optional*):
                The hidden states of the first tokens for the labeled span.
            start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
                The position of the first token for the labeled span.
            cls_index (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
                Position of the CLS token for each sentence in the batch. If `None`, takes the last token.

        <Tip>

        One of `start_states` or `start_positions` should be not `None`. If both are set, `start_positions` overrides
        `start_states`.

        </Tip>

        Returns:
            `torch.FloatTensor`: The SQuAD 2.0 answer class.
        """
        # No dependency on end_feature so that we can obtain one single `cls_logits` for each sample.
        hsz = hidden_states.shape[-1]
        assert start_states is not None or start_positions is not None, (
            "One of start_states, start_positions should be not None"
        )
        if start_positions is not None:
            start_positions = start_positions[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
            start_states = hidden_states.gather(-2, start_positions).squeeze(-2)  # shape (bsz, hsz)

        if cls_index is not None:
            cls_index = cls_index[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
            cls_token_state = hidden_states.gather(-2, cls_index).squeeze(-2)  # shape (bsz, hsz)
        else:
            cls_token_state = hidden_states[:, -1, :]  # shape (bsz, hsz)

        x = self.dense_0(torch.cat([start_states, cls_token_state], dim=-1))
        x = self.activation(x)
        x = self.dense_1(x).squeeze(-1)

        return x


# Copied from transformers.models.xlm.modeling_xlm.XLMSequenceSummary with XLM->XLNet
class XLNetSequenceSummary(nn.Module):
    r"""
    Compute a single vector summary of a sequence hidden states.

    Args:
        config ([`XLNetConfig`]):
            The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
            config class of your model for the default values it uses):

            - **summary_type** (`str`) -- The method to use to make this summary. Accepted values are:

                - `"last"` -- Take the last token hidden state (like XLNet)
                - `"first"` -- Take the first token hidden state (like Bert)
                - `"mean"` -- Take the mean of all tokens hidden states
                - `"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2)
                - `"attn"` -- Not implemented now, use multi-head attention

            - **summary_use_proj** (`bool`) -- Add a projection after the vector extraction.
            - **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes
              (otherwise to `config.hidden_size`).
            - **summary_activation** (`Optional[str]`) -- Set to `"tanh"` to add a tanh activation to the output,
              another string or `None` will add no activation.
            - **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation.
            - **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation.
    """

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

        self.summary_type = getattr(config, "summary_type", "last")
        if self.summary_type == "attn":
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.summary = nn.Identity()
        if hasattr(config, "summary_use_proj") and config.summary_use_proj:
            if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = nn.Linear(config.hidden_size, num_classes)

        activation_string = getattr(config, "summary_activation", None)
        self.activation: Callable = get_activation(activation_string) if activation_string else nn.Identity()

        self.first_dropout = nn.Identity()
        if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(config.summary_first_dropout)

        self.last_dropout = nn.Identity()
        if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(config.summary_last_dropout)

    def forward(
        self, hidden_states: torch.FloatTensor, cls_index: torch.LongTensor | None = None
    ) -> torch.FloatTensor:
        """
        Compute a single vector summary of a sequence hidden states.

        Args:
            hidden_states (`torch.FloatTensor` of shape `[batch_size, seq_len, hidden_size]`):
                The hidden states of the last layer.
            cls_index (`torch.LongTensor` of shape `[batch_size]` or `[batch_size, ...]` where ... are optional leading dimensions of `hidden_states`, *optional*):
                Used if `summary_type == "cls_index"` and takes the last token of the sequence as classification token.

        Returns:
            `torch.FloatTensor`: The summary of the sequence hidden states.
        """
        if self.summary_type == "last":
            output = hidden_states[:, -1]
        elif self.summary_type == "first":
            output = hidden_states[:, 0]
        elif self.summary_type == "mean":
            output = hidden_states.mean(dim=1)
        elif self.summary_type == "cls_index":
            if cls_index is None:
                cls_index = torch.full_like(
                    hidden_states[..., :1, :],
                    hidden_states.shape[-2] - 1,
                    dtype=torch.long,
                )
            else:
                cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
                cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
            # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
            output = hidden_states.gather(-2, cls_index).squeeze(-2)  # shape (bsz, XX, hidden_size)
        elif self.summary_type == "attn":
            raise NotImplementedError

        output = self.first_dropout(output)
        output = self.summary(output)
        output = self.activation(output)
        output = self.last_dropout(output)

        return output


@auto_docstring
class XLNetPreTrainedModel(PreTrainedModel):
    config: XLNetConfig
    base_model_prefix = "transformer"

    @torch.no_grad()
    def _init_weights(self, module):
        """Initialize the weights."""
        super()._init_weights(module)
        if isinstance(module, XLNetRelativeAttention):
            for param in [
                module.q,
                module.k,
                module.v,
                module.o,
                module.r,
                module.r_r_bias,
                module.r_s_bias,
                module.r_w_bias,
                module.seg_embed,
            ]:
                init.normal_(param, mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, XLNetModel):
            init.normal_(module.mask_emb, mean=0.0, std=self.config.initializer_range)


@dataclass
@auto_docstring(
    custom_intro="""
    Output type of [`XLNetModel`].
    """
)
class XLNetModelOutput(ModelOutput):
    r"""
    last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_predict, hidden_size)`):
        Sequence of hidden-states at the last layer of the model.

        `num_predict` corresponds to `target_mapping.shape[1]`. If `target_mapping` is `None`, then `num_predict`
        corresponds to `sequence_length`.
    mems (`list[torch.FloatTensor]` of length `config.n_layers`):
        Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
        token ids which have their past given to this model should not be passed as `input_ids` as they have
        already been computed.
    """

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


@dataclass
@auto_docstring(
    custom_intro="""
    Output type of [`XLNetLMHeadModel`].
    """
)
class XLNetLMHeadModelOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
        Language modeling loss (for next-token prediction).
    logits (`torch.FloatTensor` of shape `(batch_size, num_predict, config.vocab_size)`):
        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

        `num_predict` corresponds to `target_mapping.shape[1]`. If `target_mapping` is `None`, then `num_predict`
        corresponds to `sequence_length`.
    mems (`list[torch.FloatTensor]` of length `config.n_layers`):
        Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
        token ids which have their past given to this model should not be passed as `input_ids` as they have
        already been computed.
    """

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


@dataclass
@auto_docstring(
    custom_intro="""
    Output type of [`XLNetForSequenceClassification`].
    """
)
class XLNetForSequenceClassificationOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided):
        Classification (or regression if config.num_labels==1) loss.
    logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
        Classification (or regression if config.num_labels==1) scores (before SoftMax).
    mems (`list[torch.FloatTensor]` of length `config.n_layers`):
        Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
        token ids which have their past given to this model should not be passed as `input_ids` as they have
        already been computed.
    """

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


@dataclass
@auto_docstring(
    custom_intro="""
    Output type of [`XLNetForTokenClassificationOutput`].
    """
)
class XLNetForTokenClassificationOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Classification loss.
    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
        Classification scores (before SoftMax).
    mems (`list[torch.FloatTensor]` of length `config.n_layers`):
        Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
        token ids which have their past given to this model should not be passed as `input_ids` as they have
        already been computed.
    """

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


@dataclass
@auto_docstring(
    custom_intro="""
    Output type of [`XLNetForMultipleChoice`].
    """
)
class XLNetForMultipleChoiceOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
        Classification loss.
    logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
        *num_choices* is the second dimension of the input tensors. (see *input_ids* above).

        Classification scores (before SoftMax).
    mems (`list[torch.FloatTensor]` of length `config.n_layers`):
        Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
        token ids which have their past given to this model should not be passed as `input_ids` as they have
        already been computed.
    """

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


@dataclass
@auto_docstring(
    custom_intro="""
    Output type of [`XLNetForQuestionAnsweringSimple`].
    """
)
class XLNetForQuestionAnsweringSimpleOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
    start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length,)`):
        Span-start scores (before SoftMax).
    end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length,)`):
        Span-end scores (before SoftMax).
    mems (`list[torch.FloatTensor]` of length `config.n_layers`):
        Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
        token ids which have their past given to this model should not be passed as `input_ids` as they have
        already been computed.
    """

    loss: torch.FloatTensor | None = None
    start_logits: torch.FloatTensor | None = None
    end_logits: torch.FloatTensor | None = None
    mems: list[torch.FloatTensor] | None = None
    hidden_states: tuple[torch.FloatTensor, ...] | None = None
    attentions: tuple[torch.FloatTensor, ...] | None = None


@dataclass
@auto_docstring(
    custom_intro="""
    Output type of [`XLNetForQuestionAnswering`].
    """
)
class XLNetForQuestionAnsweringOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
        Classification loss as the sum of start token, end token (and is_impossible if provided) classification
        losses.
    start_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
        Log probabilities for the top config.start_n_top start token possibilities (beam-search).
    start_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
        Indices for the top config.start_n_top start token possibilities (beam-search).
    end_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
        Log probabilities for the top `config.start_n_top * config.end_n_top` end token possibilities
        (beam-search).
    end_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
        Indices for the top `config.start_n_top * config.end_n_top` end token possibilities (beam-search).
    cls_logits (`torch.FloatTensor` of shape `(batch_size,)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
        Log probabilities for the `is_impossible` label of the answers.
    mems (`list[torch.FloatTensor]` of length `config.n_layers`):
        Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
        token ids which have their past given to this model should not be passed as `input_ids` as they have
        already been computed.
    """

    loss: torch.FloatTensor | None = None
    start_top_log_probs: torch.FloatTensor | None = None
    start_top_index: torch.LongTensor | None = None
    end_top_log_probs: torch.FloatTensor | None = None
    end_top_index: torch.LongTensor | None = None
    cls_logits: torch.FloatTensor | None = None
    mems: list[torch.FloatTensor] | None = None
    hidden_states: tuple[torch.FloatTensor, ...] | None = None
    attentions: tuple[torch.FloatTensor, ...] | None = None


@auto_docstring
class XLNetModel(XLNetPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.mem_len = config.mem_len
        self.reuse_len = config.reuse_len
        self.d_model = config.d_model
        self.same_length = config.same_length
        self.attn_type = config.attn_type
        self.bi_data = config.bi_data
        self.clamp_len = config.clamp_len
        self.n_layer = config.n_layer

        self.word_embedding = nn.Embedding(config.vocab_size, config.d_model)
        self.mask_emb = nn.Parameter(torch.FloatTensor(1, 1, config.d_model))
        self.layer = nn.ModuleList([XLNetLayer(config) for _ in range(config.n_layer)])
        self.dropout = nn.Dropout(config.dropout)

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

    def get_input_embeddings(self):
        return self.word_embedding

    def set_input_embeddings(self, new_embeddings):
        self.word_embedding = new_embeddings

    def create_mask(self, qlen, mlen):
        """
        Creates causal attention mask. Float mask where 1.0 indicates masked, 0.0 indicates not-masked.

        Args:
            qlen: Sequence length
            mlen: Mask length

        ::

                  same_length=False: same_length=True: <mlen > < qlen > <mlen > < qlen >
               ^ [0 0 0 0 0 1 1 1 1] [0 0 0 0 0 1 1 1 1]
                 [0 0 0 0 0 0 1 1 1] [1 0 0 0 0 0 1 1 1]
            qlen [0 0 0 0 0 0 0 1 1] [1 1 0 0 0 0 0 1 1]
                 [0 0 0 0 0 0 0 0 1] [1 1 1 0 0 0 0 0 1]
               v [0 0 0 0 0 0 0 0 0] [1 1 1 1 0 0 0 0 0]

        """
        mask = torch.ones((qlen, qlen + mlen), device=self.device)
        if self.same_length:
            mask_lo = mask[:, :qlen].tril(-1)
            mask.triu_(mlen + 1)
            mask[:, :qlen] += mask_lo
        else:
            mask.triu_(mlen + 1)

        return mask

    def cache_mem(self, curr_out, prev_mem):
        # cache hidden states into memory.
        if self.reuse_len is not None and self.reuse_len > 0:
            curr_out = curr_out[: self.reuse_len]

        if self.mem_len is None or self.mem_len == 0:
            # If `use_mems` is active but no `mem_len` is defined, the model behaves like GPT-2 at inference time
            # and returns all of the past and current hidden states.
            cutoff = 0
        else:
            # If `use_mems` is active and `mem_len` is defined, the model returns the last `mem_len` hidden
            # states. This is the preferred setting for training and long-form generation.
            cutoff = -self.mem_len
        if prev_mem is None:
            # if `use_mems` is active and `mem_len` is defined, the model
            new_mem = curr_out[cutoff:]
        else:
            new_mem = torch.cat([prev_mem, curr_out], dim=0)[cutoff:]

        return new_mem.detach()

    @staticmethod
    def positional_embedding(pos_seq, inv_freq, bsz=None):
        sinusoid_inp = torch.einsum("i,d->id", pos_seq, inv_freq)
        pos_emb = torch.cat([torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)], dim=-1)
        pos_emb = pos_emb[:, None, :]

        if bsz is not None:
            pos_emb = pos_emb.expand(-1, bsz, -1)

        return pos_emb

    def relative_positional_encoding(self, qlen, klen, bsz=None, device=None):
        # create relative positional encoding.
        freq_seq = torch.arange(0, self.d_model, 2.0, dtype=torch.int64, device=device).float()
        inv_freq = 1 / torch.pow(10000, (freq_seq / self.d_model))

        if self.attn_type == "bi":
            # beg, end = klen - 1, -qlen
            beg, end = klen, -qlen
        elif self.attn_type == "uni":
            # beg, end = klen - 1, -1
            beg, end = klen, -1
        else:
            raise ValueError(f"Unknown `attn_type` {self.attn_type}.")

        if self.bi_data:
            fwd_pos_seq = torch.arange(beg, end, -1.0, dtype=torch.int64, device=device).float()
            bwd_pos_seq = torch.arange(-beg, -end, 1.0, dtype=torch.int64, device=device).float()

            if self.clamp_len > 0:
                fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
                bwd_pos_seq = bwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)

            if bsz is not None:
                fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz // 2)
                bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq, bsz // 2)
            else:
                fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq)
                bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq)

            pos_emb = torch.cat([fwd_pos_emb, bwd_pos_emb], dim=1)
        else:
            fwd_pos_seq = torch.arange(beg, end, -1.0, dtype=torch.int64, device=device).float()
            if self.clamp_len > 0:
                fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
            pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz)

        return pos_emb

    @auto_docstring
    def forward(
        self,
        input_ids: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        mems: torch.Tensor | None = None,
        perm_mask: torch.Tensor | None = None,
        target_mapping: torch.Tensor | None = None,
        token_type_ids: torch.Tensor | None = None,
        input_mask: torch.Tensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        use_mems: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,  # delete after depreciation warning is removed
    ) -> tuple | XLNetModelOutput:
        r"""
        mems (`list[torch.FloatTensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (see `mems` output below) . Can be used to speed up sequential
            decoding. The token ids which have their past given to this model should not be passed as `input_ids` as
            they have already been computed.

            `use_mems` has to be set to `True` to make use of `mems`.
        perm_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length)`, *optional*):
            Mask to indicate the attention pattern for each input token with values selected in `[0, 1]`:

            - if `perm_mask[k, i, j] = 0`, i attend to j in batch k;
            - if `perm_mask[k, i, j] = 1`, i does not attend to j in batch k.

            If not set, each token attends to all the others (full bidirectional attention). Only used during
            pretraining (to define factorization order) or for sequential decoding (generation).
        target_mapping (`torch.FloatTensor` of shape `(batch_size, num_predict, sequence_length)`, *optional*):
            Mask to indicate the output tokens to use. If `target_mapping[k, i, j] = 1`, the i-th predict in batch k is
            on the j-th token. Only used during pretraining for partial prediction or for sequential decoding
            (generation).
        input_mask (`torch.FloatTensor` of shape `batch_size, sequence_length`, *optional*):
            Mask to avoid performing attention on padding token indices. Negative of `attention_mask`, i.e. with 0 for
            real tokens and 1 for padding which is kept for compatibility with the original code base.

            Mask values selected in `[0, 1]`:

            - 1 for tokens that are **masked**,
            - 0 for tokens that are **not masked**.

            You can only uses one of `input_mask` and `attention_mask`.
        use_mems (`bool`, *optional*):
            Whether to use memory states to speed up sequential decoding. If set to `True`, the model will use the hidden
            states from previous forward passes to compute attention, which can significantly improve performance for
            sequential decoding tasks.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        if self.training:
            use_mems = use_mems if use_mems is not None else self.config.use_mems_train
        else:
            use_mems = use_mems if use_mems is not None else self.config.use_mems_eval

        # the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end
        # but we want a unified interface in the library with the batch size on the first dimension
        # so we move here the first dimension (batch) to the end
        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:
            input_ids = input_ids.transpose(0, 1).contiguous()
            qlen, bsz = input_ids.shape[0], input_ids.shape[1]
        elif inputs_embeds is not None:
            inputs_embeds = inputs_embeds.transpose(0, 1).contiguous()
            qlen, bsz = inputs_embeds.shape[0], inputs_embeds.shape[1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        token_type_ids = token_type_ids.transpose(0, 1).contiguous() if token_type_ids is not None else None
        input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None
        attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None
        perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None
        target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None

        mlen = mems[0].shape[0] if mems is not None and mems[0] is not None else 0
        klen = mlen + qlen

        dtype_float = self.dtype
        device = self.device

        # Attention mask
        # causal attention mask
        if self.attn_type == "uni":
            attn_mask = self.create_mask(qlen, mlen)
            attn_mask = attn_mask[:, :, None, None]
        elif self.attn_type == "bi":
            attn_mask = None
        else:
            raise ValueError(f"Unsupported attention type: {self.attn_type}")

        # data mask: input mask & perm mask
        assert input_mask is None or attention_mask is None, "You can only use one of input_mask (uses 1 for padding) "
        "or attention_mask (uses 0 for padding, added for compatibility with BERT). Please choose one."
        if input_mask is None and attention_mask is not None:
            input_mask = 1.0 - attention_mask
        if input_mask is not None and perm_mask is not None:
            data_mask = input_mask[None] + perm_mask
        elif input_mask is not None and perm_mask is None:
            data_mask = input_mask[None]
        elif input_mask is None and perm_mask is not None:
            data_mask = perm_mask
        else:
            data_mask = None

        if data_mask is not None:
            # all mems can be attended to
            if mlen > 0:
                mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask)
                data_mask = torch.cat([mems_mask, data_mask], dim=1)
            if attn_mask is None:
                attn_mask = data_mask[:, :, :, None]
            else:
                attn_mask += data_mask[:, :, :, None]

        if attn_mask is not None:
            attn_mask = (attn_mask > 0).to(dtype_float)

        if attn_mask is not None:
            non_tgt_mask = -torch.eye(qlen).to(attn_mask)
            if mlen > 0:
                non_tgt_mask = torch.cat([torch.zeros([qlen, mlen]).to(attn_mask), non_tgt_mask], dim=-1)
            non_tgt_mask = ((attn_mask + non_tgt_mask[:, :, None, None]) > 0).to(attn_mask)
        else:
            non_tgt_mask = None

        # Word embeddings and prepare h & g hidden states
        if inputs_embeds is not None:
            word_emb_k = inputs_embeds
        else:
            word_emb_k = self.word_embedding(input_ids)
        output_h = self.dropout(word_emb_k)
        if target_mapping is not None:
            word_emb_q = self.mask_emb.expand(target_mapping.shape[0], bsz, -1)
            # else:  # We removed the inp_q input which was same as target mapping
            #     inp_q_ext = inp_q[:, :, None]
            #     word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k
            output_g = self.dropout(word_emb_q)
        else:
            output_g = None

        # Segment embedding
        if token_type_ids is not None:
            # Convert `token_type_ids` to one-hot `seg_mat`
            if mlen > 0:
                mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)
                cat_ids = torch.cat([mem_pad, token_type_ids], dim=0)
            else:
                cat_ids = token_type_ids

            # `1` indicates not in the same segment [qlen x klen x bsz]
            seg_mat = (token_type_ids[:, None] != cat_ids[None, :]).long()
            seg_mat = nn.functional.one_hot(seg_mat, num_classes=2).to(dtype_float)
        else:
            seg_mat = None

        # Positional encoding
        pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz, device=output_h.device)
        pos_emb = self.dropout(pos_emb)

        new_mems = ()
        if mems is None:
            mems = [None] * len(self.layer)

        attentions = [] if output_attentions else None
        hidden_states = [] if output_hidden_states else None
        for i, layer_module in enumerate(self.layer):
            if use_mems:
                # cache new mems
                new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)
            if output_hidden_states:
                hidden_states.append((output_h, output_g) if output_g is not None else output_h)

            outputs = layer_module(
                output_h,
                output_g,
                attn_mask_h=non_tgt_mask,
                attn_mask_g=attn_mask,
                r=pos_emb,
                seg_mat=seg_mat,
                mems=mems[i],
                target_mapping=target_mapping,
                output_attentions=output_attentions,
            )
            output_h, output_g = outputs[:2]
            if output_attentions:
                attentions.append(outputs[2])

        # Add last hidden state
        if output_hidden_states:
            hidden_states.append((output_h, output_g) if output_g is not None else output_h)

        output = self.dropout(output_g if output_g is not None else output_h)

        # Prepare outputs, we transpose back here to shape [bsz, len, hidden_dim] (cf. beginning of forward() method)
        output = output.permute(1, 0, 2).contiguous()

        if not use_mems:
            new_mems = None

        if output_hidden_states:
            if output_g is not None:
                hidden_states = tuple(h.permute(1, 0, 2).contiguous() for hs in hidden_states for h in hs)
            else:
                hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states)

        if output_attentions:
            if target_mapping is not None:
                # when target_mapping is provided, there are 2-tuple of attentions
                attentions = tuple(
                    tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attentions
                )
            else:
                attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)

        if not return_dict:
            return tuple(v for v in [output, new_mems, hidden_states, attentions] if v is not None)

        return XLNetModelOutput(
            last_hidden_state=output, mems=new_mems, hidden_states=hidden_states, attentions=attentions
        )


@auto_docstring(
    custom_intro="""
    XLNet Model with a language modeling head on top (linear layer with weights tied to the input embeddings).
    """
)
class XLNetLMHeadModel(XLNetPreTrainedModel, GenerationMixin):
    _tied_weights_keys = {"lm_loss.weight": "transformer.word_embedding.weight"}

    def __init__(self, config):
        super().__init__(config)
        self.attn_type = config.attn_type
        self.same_length = config.same_length

        self.transformer = XLNetModel(config)
        self.lm_loss = nn.Linear(config.d_model, config.vocab_size, bias=True)

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

    def get_output_embeddings(self):
        return self.lm_loss

    def set_output_embeddings(self, new_embeddings):
        self.lm_loss = new_embeddings

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, use_mems=None, is_first_iteration=False, **kwargs
    ):
        # Overwritten -- this model has unique input preparation

        # Add dummy token at the end (no attention on this one)

        effective_batch_size = input_ids.shape[0]
        dummy_token = torch.zeros((effective_batch_size, 1), dtype=torch.long, device=input_ids.device)

        # At every pass, the attention values for the new token and the two last generated tokens
        # are computed, the rest is reloaded from the `past` cache. A purely auto-regressive model would have
        # offset = 1; offset = 2 seems to have slightly better computation.
        offset = 2

        if past_key_values:
            input_ids = torch.cat([input_ids[:, -offset:], dummy_token], dim=1)
        else:
            input_ids = torch.cat([input_ids, dummy_token], dim=1)

        # Build permutation mask so that previous tokens don't see last token
        sequence_length = input_ids.shape[1]
        perm_mask = torch.zeros(
            (effective_batch_size, sequence_length, sequence_length), dtype=torch.float, device=input_ids.device
        )
        perm_mask[:, :, -1] = 1.0

        # We'll only predict the last token
        target_mapping = torch.zeros(
            (effective_batch_size, 1, sequence_length), dtype=torch.float, device=input_ids.device
        )
        target_mapping[:, 0, -1] = 1.0

        model_inputs = {
            "input_ids": input_ids,
            "perm_mask": perm_mask,
            "target_mapping": target_mapping,
            "use_mems": use_mems,
        }

        # if past is defined in model kwargs then use it for faster decoding
        if past_key_values:
            model_inputs["mems"] = tuple(layer_past[:-offset, :, :] for layer_past in past_key_values)

        # Attention mask is computed on the fly on XLNetModel.forward()
        kwargs.pop("attention_mask", None)
        # TODO: Ignoring use_cache should not happen, fixme.
        kwargs.pop("use_cache", None)
        # Forward ALL kwargs that are uninitialized (e.g. `use_cache`).
        for key, value in kwargs.items():
            if key not in model_inputs:
                model_inputs[key] = value

        return model_inputs

    @auto_docstring
    def forward(
        self,
        input_ids: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        mems: torch.Tensor | None = None,
        perm_mask: torch.Tensor | None = None,
        target_mapping: torch.Tensor | None = None,
        token_type_ids: torch.Tensor | None = None,
        input_mask: torch.Tensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        use_mems: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        **kwargs,  # delete when `use_cache` is removed in XLNetModel
    ) -> tuple | XLNetLMHeadModelOutput:
        r"""
        mems (`list[torch.FloatTensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (see `mems` output below) . Can be used to speed up sequential
            decoding. The token ids which have their past given to this model should not be passed as `input_ids` as
            they have already been computed.

            `use_mems` has to be set to `True` to make use of `mems`.
        perm_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length)`, *optional*):
            Mask to indicate the attention pattern for each input token with values selected in `[0, 1]`:

            - if `perm_mask[k, i, j] = 0`, i attend to j in batch k;
            - if `perm_mask[k, i, j] = 1`, i does not attend to j in batch k.

            If not set, each token attends to all the others (full bidirectional attention). Only used during
            pretraining (to define factorization order) or for sequential decoding (generation).
        target_mapping (`torch.FloatTensor` of shape `(batch_size, num_predict, sequence_length)`, *optional*):
            Mask to indicate the output tokens to use. If `target_mapping[k, i, j] = 1`, the i-th predict in batch k is
            on the j-th token. Only used during pretraining for partial prediction or for sequential decoding
            (generation).
        input_mask (`torch.FloatTensor` of shape `batch_size, sequence_length`, *optional*):
            Mask to avoid performing attention on padding token indices. Negative of `attention_mask`, i.e. with 0 for
            real tokens and 1 for padding which is kept for compatibility with the original code base.

            Mask values selected in `[0, 1]`:

            - 1 for tokens that are **masked**,
            - 0 for tokens that are **not masked**.

            You can only uses one of `input_mask` and `attention_mask`.
        labels (`torch.LongTensor` of shape `(batch_size, num_predict)`, *optional*):
            Labels for masked language modeling. `num_predict` corresponds to `target_mapping.shape[1]`. If
            `target_mapping` is `None`, then `num_predict` corresponds to `sequence_length`.

            The labels should correspond to the masked input words that should be predicted and depends on
            `target_mapping`. Note in order to perform standard auto-regressive language modeling a *<mask>* token has
            to be added to the `input_ids` (see the `prepare_inputs_for_generation` function and examples below)

            Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored, the loss
            is only computed for labels in `[0, ..., config.vocab_size]`
        use_mems (`bool`, *optional*):
            Whether to use memory states to speed up sequential decoding. If set to `True`, the model will use the hidden
            states from previous forward passes to compute attention, which can significantly improve performance for
            sequential decoding tasks.

        Examples:

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

        >>> tokenizer = AutoTokenizer.from_pretrained("xlnet/xlnet-large-cased")
        >>> model = XLNetLMHeadModel.from_pretrained("xlnet/xlnet-large-cased")

        >>> # We show how to setup inputs to predict a next token using a bi-directional context.
        >>> input_ids = torch.tensor(
        ...     tokenizer.encode("Hello, my dog is very <mask>", add_special_tokens=False)
        ... ).unsqueeze(
        ...     0
        ... )  # We will predict the masked token
        >>> perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float)
        >>> perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
        >>> target_mapping = torch.zeros(
        ...     (1, 1, input_ids.shape[1]), dtype=torch.float
        ... )  # Shape [1, 1, seq_length] => let's predict one token
        >>> target_mapping[
        ...     0, 0, -1
        ... ] = 1.0  # Our first (and only) prediction will be the last token of the sequence (the masked token)

        >>> outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping)
        >>> next_token_logits = outputs[
        ...     0
        ... ]  # Output has shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size]

        >>> # The same way can the XLNetLMHeadModel be used to be trained by standard auto-regressive language modeling.
        >>> input_ids = torch.tensor(
        ...     tokenizer.encode("Hello, my dog is very <mask>", add_special_tokens=False)
        ... ).unsqueeze(
        ...     0
        ... )  # We will predict the masked token
        >>> labels = torch.tensor(tokenizer.encode("cute", add_special_tokens=False)).unsqueeze(0)
        >>> assert labels.shape[0] == 1, "only one word will be predicted"
        >>> perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float)
        >>> perm_mask[
        ...     :, :, -1
        ... ] = 1.0  # Previous tokens don't see last token as is done in standard auto-regressive lm training
        >>> target_mapping = torch.zeros(
        ...     (1, 1, input_ids.shape[1]), dtype=torch.float
        ... )  # Shape [1, 1, seq_length] => let's predict one token
        >>> target_mapping[
        ...     0, 0, -1
        ... ] = 1.0  # Our first (and only) prediction will be the last token of the sequence (the masked token)

        >>> outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping, labels=labels)
        >>> loss = outputs.loss
        >>> next_token_logits = (
        ...     outputs.logits
        ... )  # Logits have shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        transformer_outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            mems=mems,
            perm_mask=perm_mask,
            target_mapping=target_mapping,
            token_type_ids=token_type_ids,
            input_mask=input_mask,
            inputs_embeds=inputs_embeds,
            use_mems=use_mems,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )

        hidden_states = transformer_outputs[0]
        # Only compute necessary logits
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_loss(hidden_states[:, slice_indices, :])

        loss = None
        if labels is not None:
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))

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

        return XLNetLMHeadModelOutput(
            loss=loss,
            logits=logits,
            mems=transformer_outputs.mems,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

    @staticmethod
    def _reorder_cache(mems: list[torch.Tensor], beam_idx: torch.Tensor) -> list[torch.Tensor]:
        """
        This function is used to re-order the `mems` cache if [`~PreTrainedModel.beam_search`] or
        [`~PreTrainedModel.beam_sample`] is called. This is required to match `mems` with the correct beam_idx at every
        generation step.
        """
        return [layer_past.index_select(1, beam_idx.to(layer_past.device)) for layer_past in mems]


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

        self.transformer = XLNetModel(config)
        self.sequence_summary = XLNetSequenceSummary(config)
        self.logits_proj = nn.Linear(config.d_model, config.num_labels)

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

    @auto_docstring
    def forward(
        self,
        input_ids: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        mems: torch.Tensor | None = None,
        perm_mask: torch.Tensor | None = None,
        target_mapping: torch.Tensor | None = None,
        token_type_ids: torch.Tensor | None = None,
        input_mask: torch.Tensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        use_mems: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,  # delete when `use_cache` is removed in XLNetModel
    ) -> tuple | XLNetForSequenceClassificationOutput:
        r"""
        mems (`list[torch.FloatTensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (see `mems` output below) . Can be used to speed up sequential
            decoding. The token ids which have their past given to this model should not be passed as `input_ids` as
            they have already been computed.

            `use_mems` has to be set to `True` to make use of `mems`.
        perm_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length)`, *optional*):
            Mask to indicate the attention pattern for each input token with values selected in `[0, 1]`:

            - if `perm_mask[k, i, j] = 0`, i attend to j in batch k;
            - if `perm_mask[k, i, j] = 1`, i does not attend to j in batch k.

            If not set, each token attends to all the others (full bidirectional attention). Only used during
            pretraining (to define factorization order) or for sequential decoding (generation).
        target_mapping (`torch.FloatTensor` of shape `(batch_size, num_predict, sequence_length)`, *optional*):
            Mask to indicate the output tokens to use. If `target_mapping[k, i, j] = 1`, the i-th predict in batch k is
            on the j-th token. Only used during pretraining for partial prediction or for sequential decoding
            (generation).
        input_mask (`torch.FloatTensor` of shape `batch_size, sequence_length`, *optional*):
            Mask to avoid performing attention on padding token indices. Negative of `attention_mask`, i.e. with 0 for
            real tokens and 1 for padding which is kept for compatibility with the original code base.

            Mask values selected in `[0, 1]`:

            - 1 for tokens that are **masked**,
            - 0 for tokens that are **not masked**.

            You can only uses one of `input_mask` and `attention_mask`.
        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).
        use_mems (`bool`, *optional*):
            Whether to use memory states to speed up sequential decoding. If set to `True`, the model will use the hidden
            states from previous forward passes to compute attention, which can significantly improve performance for
            sequential decoding tasks.
        """
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        transformer_outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            mems=mems,
            perm_mask=perm_mask,
            target_mapping=target_mapping,
            token_type_ids=token_type_ids,
            input_mask=input_mask,
            inputs_embeds=inputs_embeds,
            use_mems=use_mems,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )
        output = transformer_outputs[0]

        output = self.sequence_summary(output)
        logits = self.logits_proj(output)

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

        return XLNetForSequenceClassificationOutput(
            loss=loss,
            logits=logits,
            mems=transformer_outputs.mems,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


@auto_docstring
class XLNetForTokenClassification(XLNetPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.transformer = XLNetModel(config)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

    @auto_docstring
    def forward(
        self,
        input_ids: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        mems: torch.Tensor | None = None,
        perm_mask: torch.Tensor | None = None,
        target_mapping: torch.Tensor | None = None,
        token_type_ids: torch.Tensor | None = None,
        input_mask: torch.Tensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        use_mems: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,  # delete when `use_cache` is removed in XLNetModel
    ) -> tuple | XLNetForTokenClassificationOutput:
        r"""
        mems (`list[torch.FloatTensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (see `mems` output below) . Can be used to speed up sequential
            decoding. The token ids which have their past given to this model should not be passed as `input_ids` as
            they have already been computed.

            `use_mems` has to be set to `True` to make use of `mems`.
        perm_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length)`, *optional*):
            Mask to indicate the attention pattern for each input token with values selected in `[0, 1]`:

            - if `perm_mask[k, i, j] = 0`, i attend to j in batch k;
            - if `perm_mask[k, i, j] = 1`, i does not attend to j in batch k.

            If not set, each token attends to all the others (full bidirectional attention). Only used during
            pretraining (to define factorization order) or for sequential decoding (generation).
        target_mapping (`torch.FloatTensor` of shape `(batch_size, num_predict, sequence_length)`, *optional*):
            Mask to indicate the output tokens to use. If `target_mapping[k, i, j] = 1`, the i-th predict in batch k is
            on the j-th token. Only used during pretraining for partial prediction or for sequential decoding
            (generation).
        input_mask (`torch.FloatTensor` of shape `batch_size, sequence_length`, *optional*):
            Mask to avoid performing attention on padding token indices. Negative of `attention_mask`, i.e. with 0 for
            real tokens and 1 for padding which is kept for compatibility with the original code base.

            Mask values selected in `[0, 1]`:

            - 1 for tokens that are **masked**,
            - 0 for tokens that are **not masked**.

            You can only uses one of `input_mask` and `attention_mask`.
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
            where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above)
        use_mems (`bool`, *optional*):
            Whether to use memory states to speed up sequential decoding. If set to `True`, the model will use the hidden
            states from previous forward passes to compute attention, which can significantly improve performance for
            sequential decoding tasks.emory states to speed up sequential decoding. If set to `True`, the model will use the hidden
            states from previous forward passes to compute attention, which can significantly improve performance for
            sequential decoding tasks.
        """
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            mems=mems,
            perm_mask=perm_mask,
            target_mapping=target_mapping,
            token_type_ids=token_type_ids,
            input_mask=input_mask,
            inputs_embeds=inputs_embeds,
            use_mems=use_mems,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        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 XLNetForTokenClassificationOutput(
            loss=loss,
            logits=logits,
            mems=outputs.mems,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@auto_docstring
class XLNetForMultipleChoice(XLNetPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.transformer = XLNetModel(config)
        self.sequence_summary = XLNetSequenceSummary(config)
        self.logits_proj = nn.Linear(config.d_model, 1)

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

    @auto_docstring
    def forward(
        self,
        input_ids: torch.Tensor | None = None,
        token_type_ids: torch.Tensor | None = None,
        input_mask: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        mems: torch.Tensor | None = None,
        perm_mask: torch.Tensor | None = None,
        target_mapping: torch.Tensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        use_mems: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,  # delete when `use_cache` is removed in XLNetModel
    ) -> tuple | XLNetForMultipleChoiceOutput:
        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)
        token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        input_mask (`torch.FloatTensor` of shape `batch_size, num_choices, sequence_length`, *optional*):
            Mask to avoid performing attention on padding token indices. Negative of `attention_mask`, i.e. with 0 for
            real tokens and 1 for padding which is kept for compatibility with the original code base.

            Mask values selected in `[0, 1]`:

            - 1 for tokens that are **masked**,
            - 0 for tokens that are **not masked**.

            You can only uses one of `input_mask` and `attention_mask`.
        mems (`list[torch.FloatTensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (see `mems` output below) . Can be used to speed up sequential
            decoding. The token ids which have their past given to this model should not be passed as `input_ids` as
            they have already been computed.

            `use_mems` has to be set to `True` to make use of `mems`.
        perm_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length)`, *optional*):
            Mask to indicate the attention pattern for each input token with values selected in `[0, 1]`:

            - if `perm_mask[k, i, j] = 0`, i attend to j in batch k;
            - if `perm_mask[k, i, j] = 1`, i does not attend to j in batch k.

            If not set, each token attends to all the others (full bidirectional attention). Only used during
            pretraining (to define factorization order) or for sequential decoding (generation).
        target_mapping (`torch.FloatTensor` of shape `(batch_size, num_predict, sequence_length)`, *optional*):
            Mask to indicate the output tokens to use. If `target_mapping[k, i, j] = 1`, the i-th predict in batch k is
            on the j-th token. Only used during pretraining for partial prediction or for sequential decoding
            (generation).
        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, ...,
        use_mems (`bool`, *optional*):
            Whether to use memory states to speed up sequential decoding. If set to `True`, the model will use the hidden
            states from previous forward passes to compute attention, which can significantly improve performance for
            sequential decoding tasks.
        """
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

        flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        flat_input_mask = input_mask.view(-1, input_mask.size(-1)) if input_mask is not None else None
        flat_inputs_embeds = (
            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
            if inputs_embeds is not None
            else None
        )

        transformer_outputs = self.transformer(
            flat_input_ids,
            token_type_ids=flat_token_type_ids,
            input_mask=flat_input_mask,
            attention_mask=flat_attention_mask,
            mems=mems,
            perm_mask=perm_mask,
            target_mapping=target_mapping,
            inputs_embeds=flat_inputs_embeds,
            use_mems=use_mems,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )

        output = transformer_outputs[0]

        output = self.sequence_summary(output)
        logits = self.logits_proj(output)
        reshaped_logits = logits.view(-1, num_choices)

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

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

        return XLNetForMultipleChoiceOutput(
            loss=loss,
            logits=reshaped_logits,
            mems=transformer_outputs.mems,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


@auto_docstring(
    custom_intro="""
    XLNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """
)
class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.transformer = XLNetModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

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

    @auto_docstring
    def forward(
        self,
        input_ids: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        mems: torch.Tensor | None = None,
        perm_mask: torch.Tensor | None = None,
        target_mapping: torch.Tensor | None = None,
        token_type_ids: torch.Tensor | None = None,
        input_mask: torch.Tensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        start_positions: torch.Tensor | None = None,
        end_positions: torch.Tensor | None = None,
        use_mems: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,  # delete when `use_cache` is removed in XLNetModel
    ) -> tuple | XLNetForQuestionAnsweringSimpleOutput:
        r"""
        mems (`list[torch.FloatTensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (see `mems` output below) . Can be used to speed up sequential
            decoding. The token ids which have their past given to this model should not be passed as `input_ids` as
            they have already been computed.

            `use_mems` has to be set to `True` to make use of `mems`.
        perm_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length)`, *optional*):
            Mask to indicate the attention pattern for each input token with values selected in `[0, 1]`:

            - if `perm_mask[k, i, j] = 0`, i attend to j in batch k;
            - if `perm_mask[k, i, j] = 1`, i does not attend to j in batch k.

            If not set, each token attends to all the others (full bidirectional attention). Only used during
            pretraining (to define factorization order) or for sequential decoding (generation).
        target_mapping (`torch.FloatTensor` of shape `(batch_size, num_predict, sequence_length)`, *optional*):
            Mask to indicate the output tokens to use. If `target_mapping[k, i, j] = 1`, the i-th predict in batch k is
            on the j-th token. Only used during pretraining for partial prediction or for sequential decoding
            (generation).
        input_mask (`torch.FloatTensor` of shape `batch_size, sequence_length`, *optional*):
            Mask to avoid performing attention on padding token indices. Negative of `attention_mask`, i.e. with 0 for
            real tokens and 1 for padding which is kept for compatibility with the original code base.

            Mask values selected in `[0, 1]`:

            - 1 for tokens that are **masked**,
            - 0 for tokens that are **not masked**.

            You can only uses one of `input_mask` and `attention_mask`.
        use_mems (`bool`, *optional*):
            Whether to use memory states to speed up sequential decoding. If set to `True`, the model will use the hidden
            states from previous forward passes to compute attention, which can significantly improve performance for
            sequential decoding tasks.
        """
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            mems=mems,
            perm_mask=perm_mask,
            target_mapping=target_mapping,
            token_type_ids=token_type_ids,
            input_mask=input_mask,
            inputs_embeds=inputs_embeds,
            use_mems=use_mems,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )

        sequence_output = outputs[0]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()

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

        return XLNetForQuestionAnsweringSimpleOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            mems=outputs.mems,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@auto_docstring
class XLNetForQuestionAnswering(XLNetPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.start_n_top = config.start_n_top
        self.end_n_top = config.end_n_top

        self.transformer = XLNetModel(config)
        self.start_logits = XLNetPoolerStartLogits(config)
        self.end_logits = XLNetPoolerEndLogits(config)
        self.answer_class = XLNetPoolerAnswerClass(config)

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

    @auto_docstring
    def forward(
        self,
        input_ids: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        mems: torch.Tensor | None = None,
        perm_mask: torch.Tensor | None = None,
        target_mapping: torch.Tensor | None = None,
        token_type_ids: torch.Tensor | None = None,
        input_mask: torch.Tensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        start_positions: torch.Tensor | None = None,
        end_positions: torch.Tensor | None = None,
        is_impossible: torch.Tensor | None = None,
        cls_index: torch.Tensor | None = None,
        p_mask: torch.Tensor | None = None,
        use_mems: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,  # delete when `use_cache` is removed in XLNetModel
    ) -> tuple | XLNetForQuestionAnsweringOutput:
        r"""
        mems (`list[torch.FloatTensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (see `mems` output below) . Can be used to speed up sequential
            decoding. The token ids which have their past given to this model should not be passed as `input_ids` as
            they have already been computed.

            `use_mems` has to be set to `True` to make use of `mems`.
        perm_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length)`, *optional*):
            Mask to indicate the attention pattern for each input token with values selected in `[0, 1]`:

            - if `perm_mask[k, i, j] = 0`, i attend to j in batch k;
            - if `perm_mask[k, i, j] = 1`, i does not attend to j in batch k.

            If not set, each token attends to all the others (full bidirectional attention). Only used during
            pretraining (to define factorization order) or for sequential decoding (generation).
        target_mapping (`torch.FloatTensor` of shape `(batch_size, num_predict, sequence_length)`, *optional*):
            Mask to indicate the output tokens to use. If `target_mapping[k, i, j] = 1`, the i-th predict in batch k is
            on the j-th token. Only used during pretraining for partial prediction or for sequential decoding
            (generation).
        input_mask (`torch.FloatTensor` of shape `batch_size, sequence_length`, *optional*):
            Mask to avoid performing attention on padding token indices. Negative of `attention_mask`, i.e. with 0 for
            real tokens and 1 for padding which is kept for compatibility with the original code base.

            Mask values selected in `[0, 1]`:

            - 1 for tokens that are **masked**,
            - 0 for tokens that are **not masked**.

            You can only uses one of `input_mask` and `attention_mask`.
        is_impossible (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels whether a question has an answer or no answer (SQuAD 2.0)
        cls_index (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the classification token to use as input for computing plausibility of the
            answer.
        p_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...). 1.0 means token should be
            masked. 0.0 mean token is not masked.
        use_mems (`bool`, *optional*):
            Whether to use memory states to speed up sequential decoding. If set to `True`, the model will use the hidden
            states from previous forward passes to compute attention, which can significantly improve performance for
            sequential decoding tasks.

        Example:

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

        >>> tokenizer = AutoTokenizer.from_pretrained("xlnet/xlnet-base-cased")
        >>> model = XLNetForQuestionAnswering.from_pretrained("xlnet/xlnet-base-cased")

        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(
        ...     0
        ... )  # Batch size 1
        >>> start_positions = torch.tensor([1])
        >>> end_positions = torch.tensor([3])
        >>> outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)

        >>> loss = outputs.loss
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        transformer_outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            mems=mems,
            perm_mask=perm_mask,
            target_mapping=target_mapping,
            token_type_ids=token_type_ids,
            input_mask=input_mask,
            inputs_embeds=inputs_embeds,
            use_mems=use_mems,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )
        hidden_states = transformer_outputs[0]
        start_logits = self.start_logits(hidden_states, p_mask=p_mask)

        outputs = transformer_outputs[1:]  # Keep mems, hidden states, attentions if there are in it

        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, let's remove the dimension added by batch splitting
            for x in (start_positions, end_positions, cls_index, is_impossible):
                if x is not None and x.dim() > 1:
                    x.squeeze_(-1)

            # during training, compute the end logits based on the ground truth of the start position
            end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)

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

            if cls_index is not None and is_impossible is not None:
                # Predict answerability from the representation of CLS and START
                cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
                loss_fct_cls = nn.BCEWithLogitsLoss()
                cls_loss = loss_fct_cls(cls_logits, is_impossible)

                # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
                total_loss += cls_loss * 0.5

            if not return_dict:
                return (total_loss,) + transformer_outputs[1:]
            else:
                return XLNetForQuestionAnsweringOutput(
                    loss=total_loss,
                    mems=transformer_outputs.mems,
                    hidden_states=transformer_outputs.hidden_states,
                    attentions=transformer_outputs.attentions,
                )

        else:
            # during inference, compute the end logits based on beam search
            bsz, slen, hsz = hidden_states.size()
            start_log_probs = nn.functional.softmax(start_logits, dim=-1)  # shape (bsz, slen)

            start_top_log_probs, start_top_index = torch.topk(
                start_log_probs, self.start_n_top, dim=-1
            )  # shape (bsz, start_n_top)
            start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz)  # shape (bsz, start_n_top, hsz)
            start_states = torch.gather(hidden_states, -2, start_top_index_exp)  # shape (bsz, start_n_top, hsz)
            start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1)  # shape (bsz, slen, start_n_top, hsz)

            hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(
                start_states
            )  # shape (bsz, slen, start_n_top, hsz)
            p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
            end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
            end_log_probs = nn.functional.softmax(end_logits, dim=1)  # shape (bsz, slen, start_n_top)

            end_top_log_probs, end_top_index = torch.topk(
                end_log_probs, self.end_n_top, dim=1
            )  # shape (bsz, end_n_top, start_n_top)
            end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
            end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)

            start_states = torch.einsum(
                "blh,bl->bh", hidden_states, start_log_probs
            )  # get the representation of START as weighted sum of hidden states
            cls_logits = self.answer_class(
                hidden_states, start_states=start_states, cls_index=cls_index
            )  # Shape (batch size,): one single `cls_logits` for each sample

            if not return_dict:
                outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits)
                return outputs + transformer_outputs[1:]
            else:
                return XLNetForQuestionAnsweringOutput(
                    start_top_log_probs=start_top_log_probs,
                    start_top_index=start_top_index,
                    end_top_log_probs=end_top_log_probs,
                    end_top_index=end_top_index,
                    cls_logits=cls_logits,
                    mems=transformer_outputs.mems,
                    hidden_states=transformer_outputs.hidden_states,
                    attentions=transformer_outputs.attentions,
                )


__all__ = [
    "XLNetForMultipleChoice",
    "XLNetForQuestionAnswering",
    "XLNetForQuestionAnsweringSimple",
    "XLNetForSequenceClassification",
    "XLNetForTokenClassification",
    "XLNetLMHeadModel",
    "XLNetModel",
    "XLNetPreTrainedModel",
]