modeling_deberta_v2.py

# coding=utf-8
# Copyright 2020 Microsoft and the Hugging Face 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 DeBERTa-v2 model. """

import loralib as lora
import math
from collections.abc import Sequence

import numpy as np
import torch
import torch.nn.functional as F
from torch import _softmax_backward_data, nn
from torch.nn import CrossEntropyLoss, LayerNorm

from ...activations import ACT2FN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutput,
    MaskedLMOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_deberta_v2 import DebertaV2Config


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "DebertaV2Config"
_TOKENIZER_FOR_DOC = "DebertaV2Tokenizer"
_CHECKPOINT_FOR_DOC = "microsoft/deberta-v2-xlarge"

DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "microsoft/deberta-v2-xlarge",
    "microsoft/deberta-v2-xxlarge",
    "microsoft/deberta-v2-xlarge-mnli",
    "microsoft/deberta-v2-xxlarge-mnli",
]


# Copied from transformers.models.deberta.modeling_deberta.ContextPooler
class ContextPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)
        self.dropout = StableDropout(config.pooler_dropout)
        self.config = config

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.

        context_token = hidden_states[:, 0]
        context_token = self.dropout(context_token)
        pooled_output = self.dense(context_token)
        pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)
        return pooled_output

    @property
    def output_dim(self):
        return self.config.hidden_size


# Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2
class XSoftmax(torch.autograd.Function):
    """
    Masked Softmax which is optimized for saving memory

    Args:
        input (:obj:`torch.tensor`): The input tensor that will apply softmax.
        mask (:obj:`torch.IntTensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation.
        dim (int): The dimension that will apply softmax

    Example::

          >>> import torch
          >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax

          >>> # Make a tensor
          >>> x = torch.randn([4,20,100])

          >>> # Create a mask
          >>> mask = (x>0).int()

          >>> y = XSoftmax.apply(x, mask, dim=-1)
    """

    @staticmethod
    def forward(self, input, mask, dim):
        self.dim = dim
        rmask = ~(mask.bool())

        output = input.masked_fill(rmask, float("-inf"))
        output = torch.softmax(output, self.dim)
        output.masked_fill_(rmask, 0)
        self.save_for_backward(output)
        return output

    @staticmethod
    def backward(self, grad_output):
        (output,) = self.saved_tensors
        inputGrad = _softmax_backward_data(grad_output, output, self.dim, output)
        return inputGrad, None, None


# Copied from transformers.models.deberta.modeling_deberta.DropoutContext
class DropoutContext(object):
    def __init__(self):
        self.dropout = 0
        self.mask = None
        self.scale = 1
        self.reuse_mask = True


# Copied from transformers.models.deberta.modeling_deberta.get_mask
def get_mask(input, local_context):
    if not isinstance(local_context, DropoutContext):
        dropout = local_context
        mask = None
    else:
        dropout = local_context.dropout
        dropout *= local_context.scale
        mask = local_context.mask if local_context.reuse_mask else None

    if dropout > 0 and mask is None:
        mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).bool()

    if isinstance(local_context, DropoutContext):
        if local_context.mask is None:
            local_context.mask = mask

    return mask, dropout


# Copied from transformers.models.deberta.modeling_deberta.XDropout
class XDropout(torch.autograd.Function):
    """Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""

    @staticmethod
    def forward(ctx, input, local_ctx):
        mask, dropout = get_mask(input, local_ctx)
        ctx.scale = 1.0 / (1 - dropout)
        if dropout > 0:
            ctx.save_for_backward(mask)
            return input.masked_fill(mask, 0) * ctx.scale
        else:
            return input

    @staticmethod
    def backward(ctx, grad_output):
        if ctx.scale > 1:
            (mask,) = ctx.saved_tensors
            return grad_output.masked_fill(mask, 0) * ctx.scale, None
        else:
            return grad_output, None


# Copied from transformers.models.deberta.modeling_deberta.StableDropout
class StableDropout(torch.nn.Module):
    """
    Optimized dropout module for stabilizing the training

    Args:
        drop_prob (float): the dropout probabilities
    """

    def __init__(self, drop_prob):
        super().__init__()
        self.drop_prob = drop_prob
        self.count = 0
        self.context_stack = None

    def forward(self, x):
        """
        Call the module

        Args:
            x (:obj:`torch.tensor`): The input tensor to apply dropout
        """
        if self.training and self.drop_prob > 0:
            return XDropout.apply(x, self.get_context())
        return x

    def clear_context(self):
        self.count = 0
        self.context_stack = None

    def init_context(self, reuse_mask=True, scale=1):
        if self.context_stack is None:
            self.context_stack = []
        self.count = 0
        for c in self.context_stack:
            c.reuse_mask = reuse_mask
            c.scale = scale

    def get_context(self):
        if self.context_stack is not None:
            if self.count >= len(self.context_stack):
                self.context_stack.append(DropoutContext())
            ctx = self.context_stack[self.count]
            ctx.dropout = self.drop_prob
            self.count += 1
            return ctx
        else:
            return self.drop_prob


# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm
class DebertaV2SelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
        self.dropout = StableDropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


# Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2
class DebertaV2Attention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.self = DisentangledSelfAttention(config)
        self.output = DebertaV2SelfOutput(config)
        self.config = config

    def forward(
        self,
        hidden_states,
        attention_mask,
        return_att=False,
        query_states=None,
        relative_pos=None,
        rel_embeddings=None,
    ):
        self_output = self.self(
            hidden_states,
            attention_mask,
            return_att,
            query_states=query_states,
            relative_pos=relative_pos,
            rel_embeddings=rel_embeddings,
        )
        if return_att:
            self_output, att_matrix = self_output
        if query_states is None:
            query_states = hidden_states
        attention_output = self.output(self_output, query_states)

        if return_att:
            return (attention_output, att_matrix)
        else:
            return attention_output


# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2
class DebertaV2Intermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


# Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm
class DebertaV2Output(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
        self.dropout = StableDropout(config.hidden_dropout_prob)
        self.config = config

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


# Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2
class DebertaV2Layer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.attention = DebertaV2Attention(config)
        self.intermediate = DebertaV2Intermediate(config)
        self.output = DebertaV2Output(config)

    def forward(
        self,
        hidden_states,
        attention_mask,
        return_att=False,
        query_states=None,
        relative_pos=None,
        rel_embeddings=None,
    ):
        attention_output = self.attention(
            hidden_states,
            attention_mask,
            return_att=return_att,
            query_states=query_states,
            relative_pos=relative_pos,
            rel_embeddings=rel_embeddings,
        )
        if return_att:
            attention_output, att_matrix = attention_output
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        if return_att:
            return (layer_output, att_matrix)
        else:
            return layer_output


class ConvLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        kernel_size = getattr(config, "conv_kernel_size", 3)
        groups = getattr(config, "conv_groups", 1)
        self.conv_act = getattr(config, "conv_act", "tanh")
        self.conv = torch.nn.Conv1d(
            config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups
        )
        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
        self.dropout = StableDropout(config.hidden_dropout_prob)
        self.config = config

    def forward(self, hidden_states, residual_states, input_mask):
        out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous()
        rmask = (1 - input_mask).bool()
        out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0)
        out = ACT2FN[self.conv_act](self.dropout(out))

        layer_norm_input = residual_states + out
        output = self.LayerNorm(layer_norm_input).to(layer_norm_input)

        if input_mask is None:
            output_states = output
        else:
            if input_mask.dim() != layer_norm_input.dim():
                if input_mask.dim() == 4:
                    input_mask = input_mask.squeeze(1).squeeze(1)
                input_mask = input_mask.unsqueeze(2)

            input_mask = input_mask.to(output.dtype)
            output_states = output * input_mask

        return output_states


class DebertaV2Encoder(nn.Module):
    """Modified BertEncoder with relative position bias support"""

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

        self.layer = nn.ModuleList([DebertaV2Layer(config) for _ in range(config.num_hidden_layers)])
        self.relative_attention = getattr(config, "relative_attention", False)

        if self.relative_attention:
            self.max_relative_positions = getattr(config, "max_relative_positions", -1)
            if self.max_relative_positions < 1:
                self.max_relative_positions = config.max_position_embeddings

            self.position_buckets = getattr(config, "position_buckets", -1)
            pos_ebd_size = self.max_relative_positions * 2

            if self.position_buckets > 0:
                pos_ebd_size = self.position_buckets * 2

            self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size)

        self.norm_rel_ebd = [x.strip() for x in getattr(config, "norm_rel_ebd", "none").lower().split("|")]

        if "layer_norm" in self.norm_rel_ebd:
            self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)

        self.conv = ConvLayer(config) if getattr(config, "conv_kernel_size", 0) > 0 else None

    def get_rel_embedding(self):
        rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None
        if rel_embeddings is not None and ("layer_norm" in self.norm_rel_ebd):
            rel_embeddings = self.LayerNorm(rel_embeddings)
        return rel_embeddings

    def get_attention_mask(self, attention_mask):
        if attention_mask.dim() <= 2:
            extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
            attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)
            attention_mask = attention_mask.byte()
        elif attention_mask.dim() == 3:
            attention_mask = attention_mask.unsqueeze(1)

        return attention_mask

    def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):
        if self.relative_attention and relative_pos is None:
            q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)
            relative_pos = build_relative_position(
                q, hidden_states.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions
            )
        return relative_pos

    def forward(
        self,
        hidden_states,
        attention_mask,
        output_hidden_states=True,
        output_attentions=False,
        query_states=None,
        relative_pos=None,
        return_dict=True,
    ):
        if attention_mask.dim() <= 2:
            input_mask = attention_mask
        else:
            input_mask = (attention_mask.sum(-2) > 0).byte()
        attention_mask = self.get_attention_mask(attention_mask)
        relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)

        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        if isinstance(hidden_states, Sequence):
            next_kv = hidden_states[0]
        else:
            next_kv = hidden_states
        rel_embeddings = self.get_rel_embedding()
        output_states = next_kv
        for i, layer_module in enumerate(self.layer):

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (output_states,)

            output_states = layer_module(
                next_kv,
                attention_mask,
                output_attentions,
                query_states=query_states,
                relative_pos=relative_pos,
                rel_embeddings=rel_embeddings,
            )
            if output_attentions:
                output_states, att_m = output_states

            if i == 0 and self.conv is not None:
                output_states = self.conv(hidden_states, output_states, input_mask)

            if query_states is not None:
                query_states = output_states
                if isinstance(hidden_states, Sequence):
                    next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None
            else:
                next_kv = output_states

            if output_attentions:
                all_attentions = all_attentions + (att_m,)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (output_states,)

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


def make_log_bucket_position(relative_pos, bucket_size, max_position):
    sign = np.sign(relative_pos)
    mid = bucket_size // 2
    abs_pos = np.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, np.abs(relative_pos))
    log_pos = np.ceil(np.log(abs_pos / mid) / np.log((max_position - 1) / mid) * (mid - 1)) + mid
    bucket_pos = np.where(abs_pos <= mid, relative_pos, log_pos * sign).astype(np.int)
    return bucket_pos


def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1):
    """
    Build relative position according to the query and key

    We assume the absolute position of query :math:`P_q` is range from (0, query_size) and the absolute position of key
    :math:`P_k` is range from (0, key_size), The relative positions from query to key is :math:`R_{q \\rightarrow k} =
    P_q - P_k`

    Args:
        query_size (int): the length of query
        key_size (int): the length of key
        bucket_size (int): the size of position bucket
        max_position (int): the maxium allowed absolute positoin

    Return:
        :obj:`torch.LongTensor`: A tensor with shape [1, query_size, key_size]

    """
    q_ids = np.arange(0, query_size)
    k_ids = np.arange(0, key_size)
    rel_pos_ids = q_ids[:, None] - np.tile(k_ids, (q_ids.shape[0], 1))
    if bucket_size > 0 and max_position > 0:
        rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position)
    rel_pos_ids = torch.tensor(rel_pos_ids, dtype=torch.long)
    rel_pos_ids = rel_pos_ids[:query_size, :]
    rel_pos_ids = rel_pos_ids.unsqueeze(0)
    return rel_pos_ids


@torch.jit.script
# Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand
def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):
    return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])


@torch.jit.script
# Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand
def p2c_dynamic_expand(c2p_pos, query_layer, key_layer):
    return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])


@torch.jit.script
# Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand
def pos_dynamic_expand(pos_index, p2c_att, key_layer):
    return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))


class DisentangledSelfAttention(torch.nn.Module):
    """
    Disentangled self-attention module

    Parameters:
        config (:obj:`DebertaV2Config`):
            A model config class instance with the configuration to build a new model. The schema is similar to
            `BertConfig`, for more details, please refer :class:`~transformers.DebertaV2Config`

    """

    def __init__(self, config):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
            )
        self.num_attention_heads = config.num_attention_heads
        _attention_head_size = config.hidden_size // config.num_attention_heads
        self.attention_head_size = getattr(config, "attention_head_size", _attention_head_size)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        if config.apply_lora:
            self.query_proj = lora.Linear(config.hidden_size, self.all_head_size, r=config.lora_r, lora_alpha=config.lora_alpha, merge_weights=False)
        else:
            self.query_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)

        self.key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)

        if config.apply_lora:
            self.value_proj = lora.Linear(config.hidden_size, self.all_head_size, r=config.lora_r, lora_alpha=config.lora_alpha, merge_weights=False)
        else:
            self.value_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)

        self.share_att_key = getattr(config, "share_att_key", False)
        self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []
        self.relative_attention = getattr(config, "relative_attention", False)

        if self.relative_attention:
            self.position_buckets = getattr(config, "position_buckets", -1)
            self.max_relative_positions = getattr(config, "max_relative_positions", -1)
            if self.max_relative_positions < 1:
                self.max_relative_positions = config.max_position_embeddings
            self.pos_ebd_size = self.max_relative_positions
            if self.position_buckets > 0:
                self.pos_ebd_size = self.position_buckets

            self.pos_dropout = StableDropout(config.hidden_dropout_prob)

            if not self.share_att_key:
                if "c2p" in self.pos_att_type or "p2p" in self.pos_att_type:
                    self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
                if "p2c" in self.pos_att_type or "p2p" in self.pos_att_type:
                    self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = StableDropout(config.attention_probs_dropout_prob)

    def transpose_for_scores(self, x, attention_heads):
        new_x_shape = x.size()[:-1] + (attention_heads, -1)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3).contiguous().view(-1, x.size(1), x.size(-1))

    def forward(
        self,
        hidden_states,
        attention_mask,
        return_att=False,
        query_states=None,
        relative_pos=None,
        rel_embeddings=None,
    ):
        """
        Call the module

        Args:
            hidden_states (:obj:`torch.FloatTensor`):
                Input states to the module usually the output from previous layer, it will be the Q,K and V in
                `Attention(Q,K,V)`

            attention_mask (:obj:`torch.ByteTensor`):
                An attention mask matrix of shape [`B`, `N`, `N`] where `B` is the batch size, `N` is the maximum
                sequence length in which element [i,j] = `1` means the `i` th token in the input can attend to the `j`
                th token.

            return_att (:obj:`bool`, optional):
                Whether return the attention matrix.

            query_states (:obj:`torch.FloatTensor`, optional):
                The `Q` state in `Attention(Q,K,V)`.

            relative_pos (:obj:`torch.LongTensor`):
                The relative position encoding between the tokens in the sequence. It's of shape [`B`, `N`, `N`] with
                values ranging in [`-max_relative_positions`, `max_relative_positions`].

            rel_embeddings (:obj:`torch.FloatTensor`):
                The embedding of relative distances. It's a tensor of shape [:math:`2 \\times
                \\text{max_relative_positions}`, `hidden_size`].


        """
        if query_states is None:
            query_states = hidden_states
        q = self.query_proj(query_states)
        k = self.key_proj(hidden_states)
        v = self.value_proj(hidden_states)
        query_layer = self.transpose_for_scores(q, self.num_attention_heads)
        key_layer = self.transpose_for_scores(k, self.num_attention_heads)
        value_layer = self.transpose_for_scores(v, self.num_attention_heads)

        rel_att = None
        # Take the dot product between "query" and "key" to get the raw attention scores.
        scale_factor = 1
        if "c2p" in self.pos_att_type:
            scale_factor += 1
        if "p2c" in self.pos_att_type:
            scale_factor += 1
        if "p2p" in self.pos_att_type:
            scale_factor += 1
        scale = math.sqrt(query_layer.size(-1) * scale_factor)
        attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / scale
        if self.relative_attention:
            rel_embeddings = self.pos_dropout(rel_embeddings)
            rel_att = self.disentangled_attention_bias(
                query_layer, key_layer, relative_pos, rel_embeddings, scale_factor
            )

        if rel_att is not None:
            attention_scores = attention_scores + rel_att
        attention_scores = attention_scores
        attention_scores = attention_scores.view(
            -1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1)
        )

        # bsz x height x length x dimension
        attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1)
        attention_probs = self.dropout(attention_probs)
        context_layer = torch.bmm(
            attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer
        )
        context_layer = (
            context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), context_layer.size(-1))
            .permute(0, 2, 1, 3)
            .contiguous()
        )
        new_context_layer_shape = context_layer.size()[:-2] + (-1,)
        context_layer = context_layer.view(*new_context_layer_shape)
        if return_att:
            return (context_layer, attention_probs)
        else:
            return context_layer

    def manually_gather(self, input, index):
        assert input.dim() == 3
        assert index.dim() == 2
        assert input.size(1) == index.size(0)
        index = index + torch.arange(start=0, end=index.size(0)*input.shape[-1], step=input.shape[-1], device=index.device).view(-1, 1)
        return torch.index_select(input.view(input.shape[0], -1), dim=-1, index=index.view(-1)).view(input.shape[0], index.shape[0], index.shape[1])

    def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
        if relative_pos is None:
            q = query_layer.size(-2)
            relative_pos = build_relative_position(
                q, key_layer.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions
            )
        if relative_pos.dim() == 2:
            relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)
        elif relative_pos.dim() == 3:
            relative_pos = relative_pos.unsqueeze(1)
        # bsz x height x query x key
        elif relative_pos.dim() != 4:
            raise ValueError(f"Relative postion ids must be of dim 2 or 3 or 4. {relative_pos.dim()}")

        att_span = self.pos_ebd_size
        relative_pos = relative_pos.long().to(query_layer.device)

        rel_embeddings = rel_embeddings[self.pos_ebd_size - att_span : self.pos_ebd_size + att_span, :].unsqueeze(0)
        if self.share_att_key:
            pos_query_layer = self.transpose_for_scores(
                nn.Linear.forward(self.query_proj, rel_embeddings), self.num_attention_heads
            ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1)
            pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).repeat(
                query_layer.size(0) // self.num_attention_heads, 1, 1
            )
        else:
            if "c2p" in self.pos_att_type or "p2p" in self.pos_att_type:
                pos_key_layer = self.transpose_for_scores(
                    self.pos_key_proj(rel_embeddings), self.num_attention_heads
                ).repeat(
                    query_layer.size(0) // self.num_attention_heads, 1, 1
                )  # .split(self.all_head_size, dim=-1)
            if "p2c" in self.pos_att_type or "p2p" in self.pos_att_type:
                pos_query_layer = self.transpose_for_scores(
                    self.pos_query_proj(rel_embeddings), self.num_attention_heads
                ).repeat(
                    query_layer.size(0) // self.num_attention_heads, 1, 1
                )  # .split(self.all_head_size, dim=-1)

        score = 0
        # content->position
        if "c2p" in self.pos_att_type:
            scale = math.sqrt(pos_key_layer.size(-1) * scale_factor)
            c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2))
            c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1)
            c2p_att = self.manually_gather(
                c2p_att,
                c2p_pos.squeeze(0).squeeze(0)
            )
            score += c2p_att / scale

        # position->content
        if "p2c" in self.pos_att_type or "p2p" in self.pos_att_type:
            scale = math.sqrt(pos_query_layer.size(-1) * scale_factor)
            if key_layer.size(-2) != query_layer.size(-2):
                r_pos = build_relative_position(
                    key_layer.size(-2),
                    key_layer.size(-2),
                    bucket_size=self.position_buckets,
                    max_position=self.max_relative_positions,
                ).to(query_layer.device)
                r_pos = r_pos.unsqueeze(0)
            else:
                r_pos = relative_pos

            p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)
            if query_layer.size(-2) != key_layer.size(-2):
                pos_index = relative_pos[:, :, :, 0].unsqueeze(-1)

        if "p2c" in self.pos_att_type:
            p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2))
            p2c_att = self.manually_gather(
                p2c_att,
                p2c_pos.squeeze(0).squeeze(0)
            ).transpose(-1, -2)
            if query_layer.size(-2) != key_layer.size(-2):
                p2c_att = torch.gather(
                    p2c_att,
                    dim=-2,
                    index=pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2))),
                )
            score += p2c_att / scale

        # position->position
        if "p2p" in self.pos_att_type:
            pos_query = pos_query_layer[:, :, att_span:, :]
            p2p_att = torch.matmul(pos_query, pos_key_layer.transpose(-1, -2))
            p2p_att = p2p_att.expand(query_layer.size()[:2] + p2p_att.size()[2:])
            if query_layer.size(-2) != key_layer.size(-2):
                p2p_att = torch.gather(
                    p2p_att,
                    dim=-2,
                    index=pos_index.expand(query_layer.size()[:2] + (pos_index.size(-2), p2p_att.size(-1))),
                )
            p2p_att = torch.gather(
                p2p_att,
                dim=-1,
                index=c2p_pos.expand(
                    [query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)]
                ),
            )
            score += p2p_att

        return score


# Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm
class DebertaV2Embeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, config):
        super().__init__()
        pad_token_id = getattr(config, "pad_token_id", 0)
        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
        self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id)

        self.position_biased_input = getattr(config, "position_biased_input", True)
        if not self.position_biased_input:
            self.position_embeddings = None
        else:
            self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size)

        if config.type_vocab_size > 0:
            self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size)

        if self.embedding_size != config.hidden_size:
            self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias=False)
        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
        self.dropout = StableDropout(config.hidden_dropout_prob)
        self.config = config

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))

    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None):
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = self.position_ids[:, :seq_length]

        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        if self.position_embeddings is not None:
            position_embeddings = self.position_embeddings(position_ids.long())
        else:
            position_embeddings = torch.zeros_like(inputs_embeds)

        embeddings = inputs_embeds
        if self.position_biased_input:
            embeddings += position_embeddings
        if self.config.type_vocab_size > 0:
            token_type_embeddings = self.token_type_embeddings(token_type_ids)
            embeddings += token_type_embeddings

        if self.embedding_size != self.config.hidden_size:
            embeddings = self.embed_proj(embeddings)

        embeddings = self.LayerNorm(embeddings)

        if mask is not None:
            if mask.dim() != embeddings.dim():
                if mask.dim() == 4:
                    mask = mask.squeeze(1).squeeze(1)
                mask = mask.unsqueeze(2)
            mask = mask.to(embeddings.dtype)

            embeddings = embeddings * mask

        embeddings = self.dropout(embeddings)
        return embeddings


# Copied from transformers.models.deberta.modeling_deberta.DebertaPreTrainedModel with Deberta->DebertaV2
class DebertaV2PreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = DebertaV2Config
    base_model_prefix = "deberta"
    _keys_to_ignore_on_load_missing = ["position_ids"]
    _keys_to_ignore_on_load_unexpected = ["position_embeddings"]

    def __init__(self, config):
        super().__init__(config)
        self._register_load_state_dict_pre_hook(self._pre_load_hook)

    def _init_weights(self, module):
        """Initialize the weights."""
        if isinstance(module, nn.Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, DisentangledSelfAttention):
            module.query_proj.reset_parameters()
            module.value_proj.reset_parameters()
            if hasattr(module.query_proj, 'lora_A'):
                module.query_proj.lora_A.data.normal_(mean=0.0, std=self.config.initializer_range)
            if hasattr(module.value_proj, 'lora_A'):
                module.value_proj.lora_A.data.normal_(mean=0.0, std=self.config.initializer_range)


    def _pre_load_hook(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
        """
        Removes the classifier if it doesn't have the correct number of labels.
        """
        self_state = self.state_dict()
        if (
            ("classifier.weight" in self_state)
            and ("classifier.weight" in state_dict)
            and self_state["classifier.weight"].size() != state_dict["classifier.weight"].size()
        ):
            logger.warning(
                f"The checkpoint classifier head has a shape {state_dict['classifier.weight'].size()} and this model "
                f"classifier head has a shape {self_state['classifier.weight'].size()}. Ignoring the checkpoint "
                f"weights. You should train your model on new data."
            )
            del state_dict["classifier.weight"]
            if "classifier.bias" in state_dict:
                del state_dict["classifier.bias"]


DEBERTA_START_DOCSTRING = r"""
    The DeBERTa model was proposed in `DeBERTa: Decoding-enhanced BERT with Disentangled Attention
    <https://arxiv.org/abs/2006.03654>`_ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build on top of
    BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
    improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

    This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
    subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
    general usage and behavior.```


    Parameters:
        config (:class:`~transformers.DebertaV2Config`): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
            weights.
"""

DEBERTA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`transformers.DebertaV2Tokenizer`. See
            :func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.__call__` for
            details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`):
            Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:

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

            `What are attention masks? <../glossary.html#attention-mask>`__
        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `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.html#token-type-ids>`_
        position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `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.html#position-ids>`_
        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        output_attentions (:obj:`bool`, `optional`):
            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
            tensors for more detail.
        output_hidden_states (:obj:`bool`, `optional`):
            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
            more detail.
        return_dict (:obj:`bool`, `optional`):
            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.",
    DEBERTA_START_DOCSTRING,
)
# Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2
class DebertaV2Model(DebertaV2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.embeddings = DebertaV2Embeddings(config)
        self.encoder = DebertaV2Encoder(config)
        self.z_steps = 0
        self.config = config
        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, new_embeddings):
        self.embeddings.word_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
        """
        raise NotImplementedError("The prune function is not implemented in DeBERTa model.")

    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

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

        if attention_mask is None:
            attention_mask = torch.ones(input_shape, device=device)
        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            mask=attention_mask,
            inputs_embeds=inputs_embeds,
        )

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask,
            output_hidden_states=True,
            output_attentions=output_attentions,
            return_dict=return_dict,
        )
        encoded_layers = encoder_outputs[1]

        if self.z_steps > 1:
            hidden_states = encoded_layers[-2]
            layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]
            query_states = encoded_layers[-1]
            rel_embeddings = self.encoder.get_rel_embedding()
            attention_mask = self.encoder.get_attention_mask(attention_mask)
            rel_pos = self.encoder.get_rel_pos(embedding_output)
            for layer in layers[1:]:
                query_states = layer(
                    hidden_states,
                    attention_mask,
                    return_att=False,
                    query_states=query_states,
                    relative_pos=rel_pos,
                    rel_embeddings=rel_embeddings,
                )
                encoded_layers.append(query_states)

        sequence_output = encoded_layers[-1]

        if not return_dict:
            return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :]

        return BaseModelOutput(
            last_hidden_state=sequence_output,
            hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,
            attentions=encoder_outputs.attentions,
        )


@add_start_docstrings("""DeBERTa Model with a `language modeling` head on top. """, DEBERTA_START_DOCSTRING)
# Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM with Deberta->DebertaV2
class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):
    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]

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

        self.deberta = DebertaV2Model(config)
        self.cls = DebertaV2OnlyMLMHead(config)

        self.init_weights()

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings

    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        prediction_scores = self.cls(sequence_output)

        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()  # -100 index = padding token
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

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

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


# copied from transformers.models.bert.BertPredictionHeadTransform with bert -> deberta
class DebertaV2PredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


# copied from transformers.models.bert.BertLMPredictionHead with bert -> deberta
class DebertaV2LMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = DebertaV2PredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states


# copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta
class DebertaV2OnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = DebertaV2LMPredictionHead(config)

    def forward(self, sequence_output):
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores


# Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification with Deberta->DebertaV2
class DebertaV2ForCutoffSequenceClassification(DebertaV2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        num_labels = getattr(config, "num_labels", 2)
        self.num_labels = num_labels
        reg_loss_wgt = getattr(config, "reg_loss_wgt", 0.0)
        self.reg_loss_wgt = reg_loss_wgt
        masking_prob = getattr(config, "masking_prob", 0.0)
        self.masking_prob = masking_prob
        cls_token_id = getattr(config, "cls_token_id", 1)
        sep_token_id = getattr(config, "sep_token_id", 2)
        unk_token_id = getattr(config, "unk_token_id", 3)
        self.cls_token_id = cls_token_id
        self.sep_token_id = sep_token_id
        self.unk_token_id = unk_token_id

        self.deberta = DebertaV2Model(config)
        self.pooler = ContextPooler(config)
        output_dim = self.pooler.output_dim

        self.classifier = torch.nn.Linear(output_dim, num_labels)
        drop_out = getattr(config, "cls_dropout", None)
        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
        self.dropout = StableDropout(drop_out)

        self.init_weights()

    def get_input_embeddings(self):
        return self.deberta.get_input_embeddings()

    def set_input_embeddings(self, new_embeddings):
        self.deberta.set_input_embeddings(new_embeddings)

    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
            If :obj:`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

        masked_inputs = input_ids.clone()
        if self.masking_prob > 0:
            random_indices = torch.bernoulli(torch.full(input_ids.shape, self.masking_prob, device=input_ids.device)).bool()
            masking_indces = (input_ids != self.cls_token_id) & (input_ids != self.sep_token_id) & random_indices & attention_mask.bool()
            masked_inputs[masking_indces] = self.unk_token_id
            
        p1_outputs = self.deberta(
            input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        
        p2_outputs = self.deberta(
            masked_inputs,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        p1_logits = self.classifier(self.dropout(self.pooler(p1_outputs[0])))
        p2_logits = self.classifier(self.dropout(self.pooler(p2_outputs[0])))
        m_logits = (p1_logits + p2_logits) / 2.0

        loss = None
        if labels is not None:
            if labels.dim() == 1 or labels.size(-1) == 1:
                label_index = (labels >= 0).nonzero()
                labels = labels.long()
                if label_index.size(0) > 0:
                    p1_logits = torch.index_select(p1_logits, 0, label_index.view(-1))
                    p2_logits = torch.index_select(p2_logits, 0, label_index.view(-1))
                    m_logits = torch.index_select(m_logits, 0, label_index.view(-1))
                    labels = torch.index_select(labels, 0, label_index.view(-1))
                    loss_fct = CrossEntropyLoss()
                    p1_loss = loss_fct(p1_logits.view(-1, self.num_labels).float(), labels.view(-1))
                    p2_loss = loss_fct(p2_logits.view(-1, self.num_labels).float(), labels.view(-1))
                    p1_m_loss = F.kl_div(F.log_softmax(p1_logits, dim=-1), F.softmax(m_logits, dim=-1), reduction='mean')
                    p2_m_loss = F.kl_div(F.log_softmax(p2_logits, dim=-1), F.softmax(m_logits, dim=-1), reduction='mean')
                else:
                    p1_loss = torch.tensor(0).to(p1_logits)
                    p2_loss = torch.tensor(0).to(p2_logits)
                    p1_m_loss = torch.tensor(0).to(p1_logits)
                    p2_m_loss = torch.tensor(0).to(p2_logits)
            else:
                log_softmax = torch.nn.LogSoftmax(-1)
                p1_loss = -((log_softmax(p1_logits) * labels).sum(-1)).mean()
                p2_loss = -((log_softmax(p2_logits) * labels).sum(-1)).mean()
                p1_m_loss = F.kl_div(F.log_softmax(p1_logits, dim=-1), F.softmax(m_logits, dim=-1), reduction='mean')
                p2_m_loss = F.kl_div(F.log_softmax(p2_logits, dim=-1), F.softmax(m_logits, dim=-1), reduction='mean')
            loss = p1_loss + p2_loss + 0.5 * self.reg_loss_wgt * (p1_m_loss + p2_m_loss)
        if not return_dict:
            return (loss,)
        else:
            return SequenceClassifierOutput(
                loss=loss,
                logits=None,
                hidden_states=None,
                attentions=None,
            )


# Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification with Deberta->DebertaV2
class DebertaV2ForRDropSequenceClassification(DebertaV2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        num_labels = getattr(config, "num_labels", 2)
        self.num_labels = num_labels
        reg_loss_wgt = getattr(config, "reg_loss_wgt", 0.0)
        self.reg_loss_wgt = reg_loss_wgt

        self.deberta = DebertaV2Model(config)
        self.pooler = ContextPooler(config)
        output_dim = self.pooler.output_dim

        self.classifier = torch.nn.Linear(output_dim, num_labels)
        drop_out = getattr(config, "cls_dropout", None)
        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
        self.dropout = StableDropout(drop_out)

        self.init_weights()

    def get_input_embeddings(self):
        return self.deberta.get_input_embeddings()

    def set_input_embeddings(self, new_embeddings):
        self.deberta.set_input_embeddings(new_embeddings)

    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
            If :obj:`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

        p1_outputs = self.deberta(
            input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        
        p2_outputs = self.deberta(
            input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        p1_logits = self.classifier(self.dropout(self.pooler(p1_outputs[0])))
        p2_logits = self.classifier(self.dropout(self.pooler(p2_outputs[0])))

        loss = None
        if labels is not None:
            if labels.dim() == 1 or labels.size(-1) == 1:
                label_index = (labels >= 0).nonzero()
                labels = labels.long()
                if label_index.size(0) > 0:
                    p1_logits = torch.index_select(p1_logits, 0, label_index.view(-1))
                    p2_logits = torch.index_select(p2_logits, 0, label_index.view(-1))
                    labels = torch.index_select(labels, 0, label_index.view(-1))
                    loss_fct = CrossEntropyLoss()
                    p1_loss = loss_fct(p1_logits.view(-1, self.num_labels).float(), labels.view(-1))
                    p2_loss = loss_fct(p2_logits.view(-1, self.num_labels).float(), labels.view(-1))
                    p12_loss = F.kl_div(F.log_softmax(p1_logits, dim=-1), F.softmax(p2_logits, dim=-1), reduction='mean')
                    p21_loss = F.kl_div(F.log_softmax(p2_logits, dim=-1), F.softmax(p1_logits, dim=-1), reduction='mean')
                else:
                    p1_loss = torch.tensor(0).to(p1_logits)
                    p2_loss = torch.tensor(0).to(p2_logits)
                    p12_loss = torch.tensor(0).to(p1_logits)
                    p21_loss = torch.tensor(0).to(p2_logits)
            else:
                log_softmax = torch.nn.LogSoftmax(-1)
                p1_loss = -((log_softmax(p1_logits) * labels).sum(-1)).mean()
                p2_loss = -((log_softmax(p2_logits) * labels).sum(-1)).mean()
                p12_loss = F.kl_div(F.log_softmax(p1_logits, dim=-1), F.softmax(p2_logits, dim=-1), reduction='mean')
                p21_loss = F.kl_div(F.log_softmax(p2_logits, dim=-1), F.softmax(p1_logits, dim=-1), reduction='mean')
            loss = p1_loss + p2_loss + 0.5 * self.reg_loss_wgt * (p12_loss + p21_loss)
        if not return_dict:
            return (loss,)
        else:
            return SequenceClassifierOutput(
                loss=loss,
                logits=None,
                hidden_states=None,
                attentions=None,
            )


@add_start_docstrings(
    """
    DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """,
    DEBERTA_START_DOCSTRING,
)
# Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification with Deberta->DebertaV2
class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        num_labels = getattr(config, "num_labels", 2)
        self.num_labels = num_labels
        reg_loss_wgt = getattr(config, "reg_loss_wgt", 0.0)
        self.reg_loss_wgt = reg_loss_wgt
        masking_prob = getattr(config, "masking_prob", 0.0)
        self.masking_prob = masking_prob
        cls_token_id = getattr(config, "cls_token_id", 1)
        sep_token_id = getattr(config, "sep_token_id", 2)
        unk_token_id = getattr(config, "unk_token_id", 3)
        self.cls_token_id = cls_token_id
        self.sep_token_id = sep_token_id
        self.unk_token_id = unk_token_id

        self.deberta = DebertaV2Model(config)
        self.pooler = ContextPooler(config)
        output_dim = self.pooler.output_dim

        self.classifier = torch.nn.Linear(output_dim, num_labels)
        drop_out = getattr(config, "cls_dropout", None)
        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
        self.dropout = StableDropout(drop_out)

        self.init_weights()

    def get_input_embeddings(self):
        return self.deberta.get_input_embeddings()

    def set_input_embeddings(self, new_embeddings):
        self.deberta.set_input_embeddings(new_embeddings)

    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deberta(
            input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        encoder_layer = outputs[0]
        pooled_output = self.pooler(encoder_layer)
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        if self.reg_loss_wgt > 0:
            masked_inputs = input_ids.clone()
            if self.masking_prob > 0:
                random_indices = torch.bernoulli(torch.full(input_ids.shape, self.masking_prob, device=input_ids.device)).bool()
                masking_indces = (input_ids != self.cls_token_id) & (input_ids != self.sep_token_id) & random_indices & attention_mask.bool()
                masked_inputs[masking_indces] = self.unk_token_id
            
            outputs_2 = self.deberta(
                masked_inputs,
                token_type_ids=token_type_ids,
                attention_mask=attention_mask,
                position_ids=position_ids,
                inputs_embeds=inputs_embeds,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )

            logits_2 = self.classifier(self.dropout(self.pooler(outputs_2[0])))
            logits_m = (logits + logits_2) / 2.0

        loss = None
        if labels is not None and self.reg_loss_wgt == 0:
            if self.num_labels == 1:
                # regression task
                loss_fn = torch.nn.MSELoss()
                logits = logits.view(-1).to(labels.dtype)
                loss = loss_fn(logits, labels.view(-1))
            elif labels.dim() == 1 or labels.size(-1) == 1:
                label_index = (labels >= 0).nonzero()
                labels = labels.long()
                if label_index.size(0) > 0:
                    labeled_logits = torch.index_select(logits, 0, label_index.view(-1))
                    labels = torch.index_select(labels, 0, label_index.view(-1))
                    loss_fct = CrossEntropyLoss()
                    loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1))
                else:
                    loss = torch.tensor(0).to(logits)
            else:
                log_softmax = torch.nn.LogSoftmax(-1)
                loss = -((log_softmax(logits) * labels).sum(-1)).mean()
        elif labels is not None and self.reg_loss_wgt > 0:
            if labels.dim() == 1 or labels.size(-1) == 1:
                label_index = (labels >= 0).nonzero()
                labels = labels.long()
                if label_index.size(0) > 0:
                    logits_1 = torch.index_select(logits, 0, label_index.view(-1))
                    logits_2 = torch.index_select(logits_2, 0, label_index.view(-1))
                    logits_m = torch.index_select(logits_m, 0, label_index.view(-1))
                    labels = torch.index_select(labels, 0, label_index.view(-1))
                    loss_fct = CrossEntropyLoss()
                    loss_1 = loss_fct(logits_1.view(-1, self.num_labels).float(), labels.view(-1))
                    loss_2 = loss_fct(logits_2.view(-1, self.num_labels).float(), labels.view(-1))
                    kl_loss_1 = F.kl_div(F.log_softmax(logits_1, dim=-1), F.softmax(logits_m, dim=-1), reduction='mean')
                    kl_loss_2 = F.kl_div(F.log_softmax(logits_2, dim=-1), F.softmax(logits_m, dim=-1), reduction='mean')
                else:
                    loss_1 = torch.tensor(0).to(logits)
                    loss_2 = torch.tensor(0).to(logits_2)
                    kl_loss_1 = torch.tensor(0).to(logits)
                    kl_loss_2 = torch.tensor(0).to(logits_2)
            else:
                log_softmax = torch.nn.LogSoftmax(-1)
                loss_1 = -((log_softmax(logits) * labels).sum(-1)).mean()
                loss_2 = -((log_softmax(logits_2) * labels).sum(-1)).mean()
                kl_loss_1 = F.kl_div(F.log_softmax(logits, dim=-1), F.softmax(logits_m, dim=-1), reduction='mean')
                kl_loss_2 = F.kl_div(F.log_softmax(logits_2, dim=-1), F.softmax(logits_m, dim=-1), reduction='mean')
            loss = loss_1 + loss_2 + 0.5 * self.reg_loss_wgt * (kl_loss_1 + kl_loss_2)
        
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        else:
            return SequenceClassifierOutput(
                loss=loss,
                logits=logits,
                hidden_states=outputs.hidden_states,
                attentions=outputs.attentions,
            )


@add_start_docstrings(
    """
    DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
    Named-Entity-Recognition (NER) tasks.
    """,
    DEBERTA_START_DOCSTRING,
)
# Copied from transformers.models.deberta.modeling_deberta.DebertaForTokenClassification with Deberta->DebertaV2
class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):
    _keys_to_ignore_on_load_unexpected = [r"pooler"]

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.deberta = DebertaV2Model(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        self.init_weights()

    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(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.deberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            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()
            # Only keep active parts of the loss
            if attention_mask is not None:
                active_loss = attention_mask.view(-1) == 1
                active_logits = logits.view(-1, self.num_labels)
                active_labels = torch.where(
                    active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
                )
                loss = loss_fct(active_logits, active_labels)
            else:
                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,
        )


@add_start_docstrings(
    """
    DeBERTa 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`).
    """,
    DEBERTA_START_DOCSTRING,
)
# Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering with Deberta->DebertaV2
class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
    _keys_to_ignore_on_load_unexpected = [r"pooler"]

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.deberta = DebertaV2Model(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

        self.init_weights()

    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=QuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        start_positions=None,
        end_positions=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
            sequence are not taken into account for computing the loss.
        end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
            sequence are not taken into account for computing the loss.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        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)
        end_logits = end_logits.squeeze(-1)

        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.clamp_(0, ignored_index)
            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 QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )