AutoAndroidController/py/venv/Lib/site-packages/transformers/models/xlm/modeling_xlm.py

# coding=utf-8
# Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
#
# 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 XLM model.
"""

import math
from dataclasses import dataclass
from typing import Callable, Optional, Union

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

from ...activations import gelu, get_activation
from ...cache_utils import DynamicCache, EncoderDecoderCache
from ...generation import GenerationMixin
from ...modeling_outputs import (
    BaseModelOutput,
    MaskedLMOutput,
    MultipleChoiceModelOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import ModelOutput, auto_docstring, logging
from .configuration_xlm import XLMConfig


logger = logging.get_logger(__name__)


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


def get_masks(slen, lengths, causal, padding_mask=None):
    """
    Generate hidden states mask, and optionally an attention mask.
    """
    alen = torch.arange(slen, dtype=torch.long, device=lengths.device)
    if padding_mask is not None:
        mask = padding_mask
    else:
        assert lengths.max().item() <= slen
        mask = alen < lengths[:, None]

    # attention mask is the same as mask, or triangular inferior attention (causal)
    bs = lengths.size(0)
    if causal:
        attn_mask = alen[None, None, :].repeat(bs, slen, 1) <= alen[None, :, None]
    else:
        attn_mask = mask

    # sanity check
    assert mask.size() == (bs, slen)
    assert causal is False or attn_mask.size() == (bs, slen, slen)

    return mask, attn_mask


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for outputs of question answering models using a [`~modeling_utils.XLMSQuADHead`].
    """
)
class XLMSquadHeadOutput(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.
    """

    loss: Optional[torch.FloatTensor] = None
    start_top_log_probs: Optional[torch.FloatTensor] = None
    start_top_index: Optional[torch.LongTensor] = None
    end_top_log_probs: Optional[torch.FloatTensor] = None
    end_top_index: Optional[torch.LongTensor] = None
    cls_logits: Optional[torch.FloatTensor] = None


class XLMPoolerStartLogits(nn.Module):
    """
    Compute SQuAD start logits from sequence hidden states.

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

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

    def forward(
        self, hidden_states: torch.FloatTensor, p_mask: Optional[torch.FloatTensor] = 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


class XLMPoolerEndLogits(nn.Module):
    """
    Compute SQuAD end logits from sequence hidden states.

    Args:
        config ([`XLMConfig`]):
            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: XLMConfig):
        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: Optional[torch.FloatTensor] = None,
        start_positions: Optional[torch.LongTensor] = None,
        p_mask: Optional[torch.FloatTensor] = 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


class XLMPoolerAnswerClass(nn.Module):
    """
    Compute SQuAD 2.0 answer class from classification and start tokens hidden states.

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

    def __init__(self, config: XLMConfig):
        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: Optional[torch.FloatTensor] = None,
        start_positions: Optional[torch.LongTensor] = None,
        cls_index: Optional[torch.LongTensor] = 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


class XLMSQuADHead(nn.Module):
    r"""
    A SQuAD head inspired by XLNet.

    Args:
        config ([`XLMConfig`]):
            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: XLMConfig):
        super().__init__()
        self.start_n_top = config.start_n_top
        self.end_n_top = config.end_n_top

        self.start_logits = XLMPoolerStartLogits(config)
        self.end_logits = XLMPoolerEndLogits(config)
        self.answer_class = XLMPoolerAnswerClass(config)

    @auto_docstring
    def forward(
        self,
        hidden_states: torch.FloatTensor,
        start_positions: Optional[torch.LongTensor] = None,
        end_positions: Optional[torch.LongTensor] = None,
        cls_index: Optional[torch.LongTensor] = None,
        is_impossible: Optional[torch.LongTensor] = None,
        p_mask: Optional[torch.FloatTensor] = None,
        return_dict: bool = False,
    ) -> Union[XLMSquadHeadOutput, tuple[torch.FloatTensor]]:
        r"""
        hidden_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`):
            Final hidden states of the model on the sequence tokens.
        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Positions of the first token for the labeled span.
        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Positions of the last 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.
        is_impossible (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Whether the question has a possible answer in the paragraph or not.
        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.
        """
        start_logits = self.start_logits(hidden_states, p_mask=p_mask)

        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

            return XLMSquadHeadOutput(loss=total_loss) if return_dict else (total_loss,)

        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)
            cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)

            if not return_dict:
                return (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits)
            else:
                return XLMSquadHeadOutput(
                    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,
                )


class XLMSequenceSummary(nn.Module):
    r"""
    Compute a single vector summary of a sequence hidden states.

    Args:
        config ([`XLMConfig`]):
            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: XLMConfig):
        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: Optional[torch.LongTensor] = 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


class MultiHeadAttention(nn.Module):
    def __init__(self, n_heads, dim, config, layer_idx: int = 0):
        super().__init__()
        self.layer_id = layer_idx
        self.dim = dim
        self.n_heads = n_heads
        self.head_dim = dim // n_heads
        self.dropout = config.attention_dropout
        assert self.dim % self.n_heads == 0

        self.q_lin = nn.Linear(dim, dim)
        self.k_lin = nn.Linear(dim, dim)
        self.v_lin = nn.Linear(dim, dim)
        self.out_lin = nn.Linear(dim, dim)
        self.pruned_heads = set()

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

    def forward(
        self,
        input,
        mask,
        kv=None,
        cache=None,
        head_mask=None,
        output_attentions=False,
        cache_position=None,
    ):
        """
        Self-attention (if kv is None) or attention over source sentence (provided by kv).
        """
        # Input is (bs, qlen, dim)
        # Mask is (bs, klen) (non-causal) or (bs, klen, klen)
        bs, qlen, dim = input.size()
        is_cross_attention = kv is not None
        mask_reshape = (bs, 1, qlen, -1) if mask.dim() == 3 else (bs, 1, 1, -1)

        q = self.q_lin(input).view(bs, -1, self.n_heads, self.head_dim).transpose(1, 2)
        if cache is not None:
            if isinstance(cache, EncoderDecoderCache):
                is_updated = cache.is_updated.get(self.layer_id)
                if is_cross_attention:
                    # after the first generated id, we can subsequently re-use all key/value_states from cache
                    curr_past_key_value = cache.cross_attention_cache
                else:
                    curr_past_key_value = cache.self_attention_cache
            else:
                curr_past_key_value = cache

        current_states = kv if is_cross_attention else input
        if is_cross_attention and cache is not None and is_updated:
            # reuse k,v, cross_attentions
            k = curr_past_key_value.key_cache[self.layer_id]
            v = curr_past_key_value.value_cache[self.layer_id]
        else:
            k = self.k_lin(current_states)
            v = self.v_lin(current_states)
            k = k.view(bs, -1, self.n_heads, self.head_dim).transpose(1, 2)
            v = v.view(bs, -1, self.n_heads, self.head_dim).transpose(1, 2)

            if cache is not None:
                # save all key/value_states to cache to be re-used for fast auto-regressive generation
                cache_position = cache_position if not is_cross_attention else None
                k, v = curr_past_key_value.update(k, v, self.layer_id, {"cache_position": cache_position})
                # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
                if is_cross_attention:
                    cache.is_updated[self.layer_id] = True

        q = q / math.sqrt(self.head_dim)  # (bs, n_heads, qlen, head_dim)
        scores = torch.matmul(q, k.transpose(2, 3))  # (bs, n_heads, qlen, klen)
        mask = (mask == 0).view(mask_reshape).expand_as(scores)  # (bs, n_heads, qlen, klen)
        scores.masked_fill_(mask, torch.finfo(scores.dtype).min)  # (bs, n_heads, qlen, klen)

        weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)  # (bs, n_heads, qlen, klen)
        weights = nn.functional.dropout(weights, p=self.dropout, training=self.training)  # (bs, n_heads, qlen, klen)

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

        context = torch.matmul(weights, v)  # (bs, n_heads, qlen, head_dim)
        context = context.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.head_dim)

        outputs = (self.out_lin(context),)
        if output_attentions:
            outputs = outputs + (weights,)
        return outputs


class TransformerFFN(nn.Module):
    def __init__(self, in_dim, dim_hidden, out_dim, config):
        super().__init__()
        self.dropout = config.dropout
        self.lin1 = nn.Linear(in_dim, dim_hidden)
        self.lin2 = nn.Linear(dim_hidden, out_dim)
        self.act = gelu if config.gelu_activation else nn.functional.relu
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1

    def forward(self, input):
        return apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, input)

    def ff_chunk(self, input):
        x = self.lin1(input)
        x = self.act(x)
        x = self.lin2(x)
        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
        return x


@auto_docstring
class XLMPreTrainedModel(PreTrainedModel):
    config: XLMConfig
    load_tf_weights = None
    base_model_prefix = "transformer"

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

    @property
    def dummy_inputs(self):
        inputs_list = torch.tensor([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
        attns_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
        if self.config.use_lang_emb and self.config.n_langs > 1:
            langs_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
        else:
            langs_list = None
        return {"input_ids": inputs_list, "attention_mask": attns_list, "langs": langs_list}

    def _init_weights(self, module):
        """Initialize the weights."""
        if isinstance(module, nn.Embedding):
            if self.config is not None and self.config.embed_init_std is not None:
                nn.init.normal_(module.weight, mean=0, std=self.config.embed_init_std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        if isinstance(module, nn.Linear):
            if self.config is not None and self.config.init_std is not None:
                nn.init.normal_(module.weight, mean=0, std=self.config.init_std)
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0.0)
        if isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, XLMModel) and self.config.sinusoidal_embeddings:
            create_sinusoidal_embeddings(
                self.config.max_position_embeddings, self.config.emb_dim, out=module.position_embeddings.weight
            )


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for outputs of question answering models using a `XLMSQuADHead`.
    """
)
class XLMForQuestionAnsweringOutput(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.
    """

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


@auto_docstring
class XLMModel(XLMPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        # encoder / decoder, output layer
        self.is_encoder = config.is_encoder
        self.is_decoder = not config.is_encoder
        if self.is_decoder:
            raise NotImplementedError("Currently XLM can only be used as an encoder")
        # self.with_output = with_output
        self.causal = config.causal

        # dictionary / languages
        self.n_langs = config.n_langs
        self.use_lang_emb = config.use_lang_emb
        self.n_words = config.n_words
        self.eos_index = config.eos_index
        self.pad_index = config.pad_index
        # self.dico = dico
        # self.id2lang = config.id2lang
        # self.lang2id = config.lang2id
        # assert len(self.dico) == self.n_words
        # assert len(self.id2lang) == len(self.lang2id) == self.n_langs

        # model parameters
        self.dim = config.emb_dim  # 512 by default
        self.hidden_dim = self.dim * 4  # 2048 by default
        self.n_heads = config.n_heads  # 8 by default
        self.n_layers = config.n_layers
        self.dropout = config.dropout
        self.attention_dropout = config.attention_dropout
        assert self.dim % self.n_heads == 0, "transformer dim must be a multiple of n_heads"

        # embeddings
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.dim)
        if config.n_langs > 1 and config.use_lang_emb:
            self.lang_embeddings = nn.Embedding(self.n_langs, self.dim)
        self.embeddings = nn.Embedding(self.n_words, self.dim, padding_idx=self.pad_index)
        self.layer_norm_emb = nn.LayerNorm(self.dim, eps=config.layer_norm_eps)

        # transformer layers
        self.attentions = nn.ModuleList()
        self.layer_norm1 = nn.ModuleList()
        self.ffns = nn.ModuleList()
        self.layer_norm2 = nn.ModuleList()
        # if self.is_decoder:
        #     self.layer_norm15 = nn.ModuleList()
        #     self.encoder_attn = nn.ModuleList()

        for i in range(self.n_layers):
            self.attentions.append(MultiHeadAttention(self.n_heads, self.dim, config=config, layer_idx=i))
            self.layer_norm1.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
            # if self.is_decoder:
            #     self.layer_norm15.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
            #     self.encoder_attn.append(MultiHeadAttention(self.n_heads, self.dim, dropout=self.attention_dropout))
            self.ffns.append(TransformerFFN(self.dim, self.hidden_dim, self.dim, config=config))
            self.layer_norm2.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))

        if hasattr(config, "pruned_heads"):
            pruned_heads = config.pruned_heads.copy().items()
            config.pruned_heads = {}
            for layer, heads in pruned_heads:
                if self.attentions[int(layer)].n_heads == config.n_heads:
                    self.prune_heads({int(layer): list(map(int, heads))})

        # Initialize weights and apply final processing
        self.post_init()
        self.register_buffer(
            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
        )

    def get_input_embeddings(self):
        return self.embeddings

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

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

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        langs: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        lengths: Optional[torch.Tensor] = None,
        cache: Optional[dict[str, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.Tensor] = None,
        **kwargs,  # Dummy kwargs for now
    ) -> Union[tuple, BaseModelOutput]:
        r"""
        langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
            languages ids which can be obtained from the language names by using two conversion mappings provided in
            the configuration of the model (only provided for multilingual models). More precisely, the *language name
            to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
            *language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).

            See usage examples detailed in the [multilingual documentation](../multilingual).
        lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Length of each sentence that can be used to avoid performing attention on padding token indices. You can
            also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
            `[0, ..., input_ids.size(-1)]`.
        cache (`dict[str, torch.FloatTensor]`, *optional*):
            Instance of `EncoderDecoderCache` that contains precomputed KV states. Can be used to speed up sequential
            decoding.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if input_ids is not None:
            bs, slen = input_ids.size()
        else:
            bs, slen = inputs_embeds.size()[:-1]

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

        if cache is None:
            cache = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))

        if isinstance(cache, tuple):
            cache = EncoderDecoderCache.from_legacy_cache(cache)

        if lengths is None:
            if input_ids is not None:
                lengths = (input_ids != self.pad_index).sum(dim=1).long()
            else:
                lengths = torch.tensor([slen] * bs, device=device)

        # check inputs
        assert lengths.size(0) == bs
        assert lengths.max().item() <= slen

        # generate masks
        mask, attn_mask = get_masks(slen, lengths, self.causal, padding_mask=attention_mask)

        # position_ids
        if position_ids is None:
            position_ids = self.position_ids[:, :slen]
        else:
            assert position_ids.size() == (bs, slen)  # (slen, bs)

        # langs
        if langs is not None:
            assert langs.size() == (bs, slen)  # (slen, bs)

        # Prepare head mask if needed
        head_mask = self.get_head_mask(head_mask, self.config.n_layers)

        # do not recompute cached elements
        if cache is not None and input_ids is not None:
            _slen = slen - cache.get_seq_length()
            input_ids = input_ids[:, -_slen:]
            position_ids = position_ids[:, -_slen:]
            if langs is not None:
                langs = langs[:, -_slen:]
            mask = mask[:, -_slen:]
            attn_mask = attn_mask[:, -_slen:]

        # embeddings
        if inputs_embeds is None:
            inputs_embeds = self.embeddings(input_ids)

        tensor = inputs_embeds + self.position_embeddings(position_ids).expand_as(inputs_embeds)
        if langs is not None and self.use_lang_emb and self.n_langs > 1:
            tensor = tensor + self.lang_embeddings(langs)
        if token_type_ids is not None:
            tensor = tensor + self.embeddings(token_type_ids)
        tensor = self.layer_norm_emb(tensor)
        tensor = nn.functional.dropout(tensor, p=self.dropout, training=self.training)
        tensor *= mask.unsqueeze(-1).to(tensor.dtype)

        # transformer layers
        hidden_states = () if output_hidden_states else None
        attentions = () if output_attentions else None
        for i in range(self.n_layers):
            if output_hidden_states:
                hidden_states = hidden_states + (tensor,)

            # self attention
            attn_outputs = self.attentions[i](
                tensor,
                attn_mask,
                cache=cache,
                head_mask=head_mask[i],
                output_attentions=output_attentions,
                cache_position=cache_position,
            )
            attn = attn_outputs[0]
            if output_attentions:
                attentions = attentions + (attn_outputs[1],)
            attn = nn.functional.dropout(attn, p=self.dropout, training=self.training)
            tensor = tensor + attn
            tensor = self.layer_norm1[i](tensor)

            # FFN
            tensor = tensor + self.ffns[i](tensor)
            tensor = self.layer_norm2[i](tensor)
            tensor *= mask.unsqueeze(-1).to(tensor.dtype)

        # Add last hidden state
        if output_hidden_states:
            hidden_states = hidden_states + (tensor,)

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


class XLMPredLayer(nn.Module):
    """
    Prediction layer (cross_entropy or adaptive_softmax).
    """

    def __init__(self, config):
        super().__init__()
        self.asm = config.asm
        self.n_words = config.n_words
        self.pad_index = config.pad_index
        dim = config.emb_dim

        if config.asm is False:
            self.proj = nn.Linear(dim, config.n_words, bias=True)
        else:
            self.proj = nn.AdaptiveLogSoftmaxWithLoss(
                in_features=dim,
                n_classes=config.n_words,
                cutoffs=config.asm_cutoffs,
                div_value=config.asm_div_value,
                head_bias=True,  # default is False
            )

    def forward(self, x, y=None):
        """Compute the loss, and optionally the scores."""
        outputs = ()
        if self.asm is False:
            scores = self.proj(x)
            outputs = (scores,) + outputs
            if y is not None:
                loss = nn.functional.cross_entropy(scores.view(-1, self.n_words), y.view(-1), reduction="mean")
                outputs = (loss,) + outputs
        else:
            scores = self.proj.log_prob(x)
            outputs = (scores,) + outputs
            if y is not None:
                _, loss = self.proj(x, y)
                outputs = (loss,) + outputs

        return outputs


@auto_docstring(
    custom_intro="""
    The XLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
    embeddings).
    """
)
class XLMWithLMHeadModel(XLMPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["pred_layer.proj.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.transformer = XLMModel(config)
        self.pred_layer = XLMPredLayer(config)

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

    def get_output_embeddings(self):
        return self.pred_layer.proj

    def set_output_embeddings(self, new_embeddings):
        self.pred_layer.proj = new_embeddings

    def prepare_inputs_for_generation(self, input_ids, **kwargs):
        # Overwritten -- this model uses config options to prepare inputs

        mask_token_id = self.config.mask_token_id
        lang_id = self.config.lang_id

        effective_batch_size = input_ids.shape[0]
        mask_token = torch.full((effective_batch_size, 1), mask_token_id, dtype=torch.long, device=input_ids.device)
        input_ids = torch.cat([input_ids, mask_token], dim=1)
        if lang_id is not None:
            langs = torch.full_like(input_ids, lang_id)
        else:
            langs = None
        model_inputs = {"input_ids": input_ids, "langs": langs}

        # They are calculated on the fly on XLMModel.forward()
        kwargs.pop("token_type_ids", None)
        kwargs.pop("attention_mask", 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: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        langs: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        lengths: Optional[torch.Tensor] = None,
        cache: Optional[dict[str, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Union[tuple, MaskedLMOutput]:
        r"""
        langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
            languages ids which can be obtained from the language names by using two conversion mappings provided in
            the configuration of the model (only provided for multilingual models). More precisely, the *language name
            to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
            *language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).

            See usage examples detailed in the [multilingual documentation](../multilingual).
        lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Length of each sentence that can be used to avoid performing attention on padding token indices. You can
            also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
            `[0, ..., input_ids.size(-1)]`.
        cache (`dict[str, torch.FloatTensor]`, *optional*):
            Instance of `EncoderDecoderCache` that contains precomputed KV states. Can be used to speed up sequential
            decoding.
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            langs=langs,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            lengths=lengths,
            cache=cache,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
            **kwargs,
        )

        output = transformer_outputs[0]
        outputs = self.pred_layer(output, labels)  # (loss, logits) or (logits,) depending on if labels are provided.

        if not return_dict:
            return outputs + transformer_outputs[1:]

        return MaskedLMOutput(
            loss=outputs[0] if labels is not None else None,
            logits=outputs[0] if labels is None else outputs[1],
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


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

        self.transformer = XLMModel(config)
        self.sequence_summary = XLMSequenceSummary(config)

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

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        langs: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        lengths: Optional[torch.Tensor] = None,
        cache: Optional[dict[str, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, SequenceClassifierOutput]:
        r"""
        langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
            languages ids which can be obtained from the language names by using two conversion mappings provided in
            the configuration of the model (only provided for multilingual models). More precisely, the *language name
            to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
            *language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).

            See usage examples detailed in the [multilingual documentation](../multilingual).
        lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Length of each sentence that can be used to avoid performing attention on padding token indices. You can
            also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
            `[0, ..., input_ids.size(-1)]`.
        cache (`dict[str, torch.FloatTensor]`, *optional*):
            Instance of `EncoderDecoderCache` that contains precomputed KV states. Can be used to speed up sequential
            decoding.
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            langs=langs,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            lengths=lengths,
            cache=cache,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        output = transformer_outputs[0]
        logits = self.sequence_summary(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 SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


@auto_docstring(
    custom_intro="""
    XLM 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 XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.transformer = XLMModel(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: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        langs: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        lengths: Optional[torch.Tensor] = None,
        cache: Optional[dict[str, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        start_positions: Optional[torch.Tensor] = None,
        end_positions: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, QuestionAnsweringModelOutput]:
        r"""
        langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
            languages ids which can be obtained from the language names by using two conversion mappings provided in
            the configuration of the model (only provided for multilingual models). More precisely, the *language name
            to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
            *language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).

            See usage examples detailed in the [multilingual documentation](../multilingual).
        lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Length of each sentence that can be used to avoid performing attention on padding token indices. You can
            also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
            `[0, ..., input_ids.size(-1)]`.
        cache (`dict[str, torch.FloatTensor]`, *optional*):
            Instance of `EncoderDecoderCache` that contains precomputed KV states. Can be used to speed up sequential
            decoding.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            langs=langs,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            lengths=lengths,
            cache=cache,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

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

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


@auto_docstring
class XLMForQuestionAnswering(XLMPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.transformer = XLMModel(config)
        self.qa_outputs = XLMSQuADHead(config)

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

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        langs: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        lengths: Optional[torch.Tensor] = None,
        cache: Optional[dict[str, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        start_positions: Optional[torch.Tensor] = None,
        end_positions: Optional[torch.Tensor] = None,
        is_impossible: Optional[torch.Tensor] = None,
        cls_index: Optional[torch.Tensor] = None,
        p_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, XLMForQuestionAnsweringOutput]:
        r"""
        langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
            languages ids which can be obtained from the language names by using two conversion mappings provided in
            the configuration of the model (only provided for multilingual models). More precisely, the *language name
            to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
            *language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).

            See usage examples detailed in the [multilingual documentation](../multilingual).
        lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Length of each sentence that can be used to avoid performing attention on padding token indices. You can
            also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
            `[0, ..., input_ids.size(-1)]`.
        cache (`dict[str, torch.FloatTensor]`, *optional*):
            Instance of `EncoderDecoderCache` that contains precomputed KV states. Can be used to speed up sequential
            decoding.
        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.

        Example:

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

        >>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-mlm-en-2048")
        >>> model = XLMForQuestionAnswering.from_pretrained("FacebookAI/xlm-mlm-en-2048")

        >>> 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.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            langs=langs,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            lengths=lengths,
            cache=cache,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        output = transformer_outputs[0]

        outputs = self.qa_outputs(
            output,
            start_positions=start_positions,
            end_positions=end_positions,
            cls_index=cls_index,
            is_impossible=is_impossible,
            p_mask=p_mask,
            return_dict=return_dict,
        )

        if not return_dict:
            return outputs + transformer_outputs[1:]

        return XLMForQuestionAnsweringOutput(
            loss=outputs.loss,
            start_top_log_probs=outputs.start_top_log_probs,
            start_top_index=outputs.start_top_index,
            end_top_log_probs=outputs.end_top_log_probs,
            end_top_index=outputs.end_top_index,
            cls_logits=outputs.cls_logits,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


@auto_docstring
class XLMForTokenClassification(XLMPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.transformer = XLMModel(config)
        self.dropout = nn.Dropout(config.dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        langs: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        lengths: Optional[torch.Tensor] = None,
        cache: Optional[dict[str, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, TokenClassifierOutput]:
        r"""
        langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
            languages ids which can be obtained from the language names by using two conversion mappings provided in
            the configuration of the model (only provided for multilingual models). More precisely, the *language name
            to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
            *language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).

            See usage examples detailed in the [multilingual documentation](../multilingual).
        lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Length of each sentence that can be used to avoid performing attention on padding token indices. You can
            also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
            `[0, ..., input_ids.size(-1)]`.
        cache (`dict[str, torch.FloatTensor]`, *optional*):
            Instance of `EncoderDecoderCache` that contains precomputed KV states. Can be used to speed up sequential
            decoding.
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            langs=langs,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            lengths=lengths,
            cache=cache,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

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

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

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

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


@auto_docstring
class XLMForMultipleChoice(XLMPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.transformer = XLMModel(config)
        self.sequence_summary = XLMSequenceSummary(config)
        self.logits_proj = nn.Linear(config.num_labels, 1)

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

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        langs: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        lengths: Optional[torch.Tensor] = None,
        cache: Optional[dict[str, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, MultipleChoiceModelOutput]:
        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)
        langs (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
            A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
            languages ids which can be obtained from the language names by using two conversion mappings provided in
            the configuration of the model (only provided for multilingual models). More precisely, the *language name
            to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
            *language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).

            See usage examples detailed in the [multilingual documentation](../multilingual).
        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)
        position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Length of each sentence that can be used to avoid performing attention on padding token indices. You can
            also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
            `[0, ..., input_ids.size(-1)]`.
        cache (`dict[str, torch.FloatTensor]`, *optional*):
            Instance of `EncoderDecoderCache` that contains precomputed KV states. Can be used to speed up sequential
            decoding.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
            `input_ids` above)
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

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

        if lengths is not None:
            logger.warning(
                "The `lengths` parameter cannot be used with the XLM multiple choice models. Please use the "
                "attention mask instead."
            )
            lengths = None

        transformer_outputs = self.transformer(
            input_ids=input_ids,
            attention_mask=attention_mask,
            langs=langs,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            lengths=lengths,
            cache=cache,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        output = transformer_outputs[0]
        logits = self.sequence_summary(output)
        logits = self.logits_proj(logits)
        reshaped_logits = logits.view(-1, num_choices)

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

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

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


__all__ = [
    "XLMForMultipleChoice",
    "XLMForQuestionAnswering",
    "XLMForQuestionAnsweringSimple",
    "XLMForSequenceClassification",
    "XLMForTokenClassification",
    "XLMModel",
    "XLMPreTrainedModel",
    "XLMWithLMHeadModel",
]