Upload DeprotForMaskedLM

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "oracle_checkpoint/ss_0_2051___True/15_proteingym_NoStability_0.390",
+  "_name_or_path": "oracle_checkpoint/ss_0_2051___True/10_proteingym_0.436_proteingym_Stability_0.484_proteingym_NoStability_0.414",
   "architectures": [
     "DeprotForMaskedLM"
   ],

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f563f354509953afa2db37f82c3e7631a050e1fa3e671d9f723d39716b999b6
+size 355285344

modeling_deprot.py

@@ -0,0 +1,1413 @@
+from collections.abc import Sequence
+from typing import Optional, Tuple, Union
+from transformers import PretrainedConfig
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    MaskedLMOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from .configuration_deprot import DeprotConfig
+from transformers.models.roformer.modeling_roformer import RoFormerModel
+from transformers.models.esm.modeling_esm import EsmModel
+import torch.nn.functional as F
+
+
+def build_relative_position(query_size, key_size, device):
+    """
+    Build relative position according to the query and key
+
+    We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key
+    \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q -
+    P_k\\)
+
+    Args:
+        query_size (int): the length of query
+        key_size (int): the length of key
+
+    Return:
+        `torch.LongTensor`: A tensor with shape [1, query_size, key_size]
+
+    """
+
+    q_ids = torch.arange(query_size, dtype=torch.long, device=device)
+    k_ids = torch.arange(key_size, dtype=torch.long, device=device)
+    rel_pos_ids = q_ids[:, None] - k_ids.view(1, -1).repeat(query_size, 1)
+    rel_pos_ids = rel_pos_ids[:query_size, :]
+    rel_pos_ids = rel_pos_ids.unsqueeze(0)
+    return rel_pos_ids
+
+
+@torch.jit.script
+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
+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
+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))
+    )
+
+
+def rotate_half(x):
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(x, cos, sin):
+    cos = cos[:, :, : x.shape[-2], :]
+    sin = sin[:, :, : x.shape[-2], :]
+
+    return (x * cos) + (rotate_half(x) * sin)
+
+
+class RotaryEmbedding(torch.nn.Module):
+    """
+    Rotary position embeddings based on those in
+    [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
+    matrices which depend on their relative positions.
+    """
+
+    def __init__(self, dim: int):
+        super().__init__()
+        # Generate and save the inverse frequency buffer (non trainable)
+        inv_freq = 1.0 / (
+            10000 ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim)
+        )
+        inv_freq = inv_freq
+        self.register_buffer("inv_freq", inv_freq)
+
+        self._seq_len_cached = None
+        self._cos_cached = None
+        self._sin_cached = None
+
+    def _update_cos_sin_tables(self, x, seq_dimension=2):
+        seq_len = x.shape[seq_dimension]
+
+        # Reset the tables if the sequence length has changed,
+        # or if we're on a new device (possibly due to tracing for instance)
+        if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
+            self._seq_len_cached = seq_len
+            t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(
+                self.inv_freq
+            )
+            freqs = torch.outer(t, self.inv_freq)
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+
+            self._cos_cached = emb.cos()[None, None, :, :]
+            self._sin_cached = emb.sin()[None, None, :, :]
+
+        return self._cos_cached, self._sin_cached
+
+    def forward(
+        self, q: torch.Tensor, k: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        self._cos_cached, self._sin_cached = self._update_cos_sin_tables(
+            k, seq_dimension=-2
+        )
+
+        return (
+            apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
+            apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
+        )
+
+
+class MaskedConv1d(nn.Conv1d):
+    """A masked 1-dimensional convolution layer.
+
+    Takes the same arguments as torch.nn.Conv1D, except that the padding is set automatically.
+
+         Shape:
+            Input: (N, L, in_channels)
+            input_mask: (N, L, 1), optional
+            Output: (N, L, out_channels)
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        dilation: int = 1,
+        groups: int = 1,
+        bias: bool = True,
+    ):
+        """
+        :param in_channels: input channels
+        :param out_channels: output channels
+        :param kernel_size: the kernel width
+        :param stride: filter shift
+        :param dilation: dilation factor
+        :param groups: perform depth-wise convolutions
+        :param bias: adds learnable bias to output
+        """
+        padding = dilation * (kernel_size - 1) // 2
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding=padding,
+        )
+
+    def forward(self, x, input_mask=None):
+        if input_mask is not None:
+            x = x * input_mask
+        return super().forward(x.transpose(1, 2)).transpose(1, 2)
+
+
+class Attention1dPooling(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.layer = MaskedConv1d(config.hidden_size, 1, 1)
+
+    def forward(self, x, input_mask=None):
+        batch_szie = x.shape[0]
+        attn = self.layer(x)
+        attn = attn.view(batch_szie, -1)
+        if input_mask is not None:
+            attn = attn.masked_fill_(
+                ~input_mask.view(batch_szie, -1).bool(), float("-inf")
+            )
+        attn = F.softmax(attn, dim=-1).view(batch_szie, -1, 1)
+        out = (attn * x).sum(dim=1)
+        return out
+
+
+class MeanPooling(nn.Module):
+    """Mean Pooling for sentence-level classification tasks."""
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, features, input_mask=None):
+        if input_mask is not None:
+            # Applying input_mask to zero out masked values
+            masked_features = features * input_mask.unsqueeze(2)
+            sum_features = torch.sum(masked_features, dim=1)
+            mean_pooled_features = sum_features / input_mask.sum(dim=1, keepdim=True)
+        else:
+            mean_pooled_features = torch.mean(features, dim=1)
+        return mean_pooled_features
+
+
+class ContextPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        scale_hidden = getattr(config, "scale_hidden", 1)
+        if config.pooling_head == "mean":
+            self.mean_pooling = MeanPooling()
+        elif config.pooling_head == "attention":
+            self.mean_pooling = Attention1dPooling(config)
+        self.dense = nn.Linear(
+            config.pooler_hidden_size, scale_hidden * config.pooler_hidden_size
+        )
+        self.dropout = nn.Dropout(config.pooler_dropout)
+        self.config = config
+
+    def forward(self, hidden_states, input_mask=None):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+
+        context_token = self.mean_pooling(hidden_states, input_mask)
+        context_token = self.dropout(context_token)
+        pooled_output = self.dense(context_token)
+        pooled_output = torch.tanh(pooled_output)
+        return pooled_output
+
+    @property
+    def output_dim(self):
+        return self.config.hidden_size
+
+
+class DeprotLayerNorm(nn.Module):
+    """LayerNorm module in the TF style (epsilon inside the square root)."""
+
+    def __init__(self, size, eps=1e-12):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(size))
+        self.bias = nn.Parameter(torch.zeros(size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_type = hidden_states.dtype
+        hidden_states = hidden_states.float()
+        mean = hidden_states.mean(-1, keepdim=True)
+        variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
+        hidden_states = (hidden_states - mean) / torch.sqrt(
+            variance + self.variance_epsilon
+        )
+        hidden_states = hidden_states.to(input_type)
+        y = self.weight * hidden_states + self.bias
+        return y
+
+
+class DisentangledSelfAttention(nn.Module):
+
+    def __init__(self, config: DeprotConfig):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        # Q, K, V projection layers
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        # AA->SS, AA->POS, SS->AA, POS->AA and AA->AA attention layers
+        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)
+        self.position_embedding_type = getattr(
+            config, "position_embedding_type", "relative"
+        )
+        if self.position_embedding_type == "rotary":
+            self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size)
+            if self.relative_attention:
+
+                if "aa2ss" in self.pos_att_type:
+                    self.ss_proj = nn.Linear(
+                        config.hidden_size, self.all_head_size, bias=False
+                    )
+
+                if "ss2aa" in self.pos_att_type:
+                    self.ss_q_proj = nn.Linear(config.hidden_size, self.all_head_size)
+
+        elif self.position_embedding_type == "relative":
+            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.pos_dropout = nn.Dropout(config.hidden_dropout_prob)
+
+                # amino acid to position
+                if "aa2pos" in self.pos_att_type:
+                    self.pos_proj = nn.Linear(
+                        config.hidden_size, self.all_head_size, bias=False
+                    )  # Key
+
+                if "pos2aa" in self.pos_att_type:
+                    self.pos_q_proj = nn.Linear(
+                        config.hidden_size, self.all_head_size
+                    )  # Query
+
+                if "aa2ss" in self.pos_att_type:
+                    self.ss_proj = nn.Linear(
+                        config.hidden_size, self.all_head_size, bias=False
+                    )
+
+                if "ss2aa" in self.pos_att_type:
+                    self.ss_q_proj = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, x):
+        # x [batch_size, seq_len, all_head_size]
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, -1)
+        # x [batch_size, seq_len, num_attention_heads, attention_head_size]
+        x = x.view(new_x_shape)
+        # x [batch_size, num_attention_heads, seq_len, attention_head_size]
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        output_attentions=False,
+        query_states=None,
+        relative_pos=None,
+        rel_embeddings=None,
+        ss_hidden_states=None,
+    ):
+        query_layer = self.transpose_for_scores(self.query(hidden_states))
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        if self.position_embedding_type == "rotary":
+            query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
+
+        rel_att = None
+        scale_factor = 1 + len(self.pos_att_type)
+        scale = torch.sqrt(
+            torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor
+        )
+        query_layer = query_layer / scale.to(dtype=query_layer.dtype)
+
+        # [batch_size, num_attention_heads, seq_len, seq_len]
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        if self.relative_attention:
+            if self.position_embedding_type == "relative":
+                rel_embeddings = self.pos_dropout(rel_embeddings)
+            rel_att = self.disentangled_att_bias(
+                query_layer,
+                key_layer,
+                relative_pos,
+                rel_embeddings,
+                scale_factor,
+                ss_hidden_states,
+            )
+
+        if rel_att is not None:
+            attention_scores = attention_scores + rel_att
+
+        rmask = ~(attention_mask.to(torch.bool))
+        attention_probs = attention_scores.masked_fill(rmask, float("-inf"))
+        attention_probs = torch.softmax(attention_probs, -1)
+        attention_probs = attention_probs.masked_fill(rmask, 0.0)
+        # attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1)
+        attention_probs = self.dropout(attention_probs)
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+        context_layer = context_layer.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 output_attentions:
+            return (context_layer, attention_probs)
+        else:
+            return context_layer
+
+    def disentangled_att_bias(
+        self,
+        query_layer,
+        key_layer,
+        relative_pos,
+        rel_embeddings,
+        scale_factor,
+        ss_hidden_states,
+    ):
+        if self.position_embedding_type == "relative":
+            if relative_pos is None:
+                q = query_layer.size(-2)
+                relative_pos = build_relative_position(
+                    q, key_layer.size(-2), query_layer.device
+                )
+            if relative_pos.dim() == 2:
+                relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)
+            elif relative_pos.dim() == 3:
+                relative_pos = relative_pos.unsqueeze(1)
+            # bxhxqxk
+            elif relative_pos.dim() != 4:
+                raise ValueError(
+                    f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}"
+                )
+
+            att_span = min(
+                max(query_layer.size(-2), key_layer.size(-2)),
+                self.max_relative_positions,
+            )
+            relative_pos = relative_pos.long().to(query_layer.device)
+            rel_embeddings = rel_embeddings[
+                self.max_relative_positions
+                - att_span : self.max_relative_positions
+                + att_span,
+                :,
+            ].unsqueeze(0)
+
+            score = 0
+
+            if "aa2pos" in self.pos_att_type:
+                pos_key_layer = self.pos_proj(rel_embeddings)
+                pos_key_layer = self.transpose_for_scores(pos_key_layer)
+                aa2p_att = torch.matmul(query_layer, pos_key_layer.transpose(-1, -2))
+                aa2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1)
+                aa2p_att = torch.gather(
+                    aa2p_att,
+                    dim=-1,
+                    index=c2p_dynamic_expand(aa2p_pos, query_layer, relative_pos),
+                )
+                score += aa2p_att
+
+            if "pos2aa" in self.pos_att_type:
+                pos_query_layer = self.pos_q_proj(rel_embeddings)
+                pos_query_layer = self.transpose_for_scores(pos_query_layer)
+                pos_query_layer /= torch.sqrt(
+                    torch.tensor(pos_query_layer.size(-1), dtype=torch.float)
+                    * scale_factor
+                )
+                if query_layer.size(-2) != key_layer.size(-2):
+                    r_pos = build_relative_position(
+                        key_layer.size(-2), key_layer.size(-2), query_layer.device
+                    )
+                else:
+                    r_pos = relative_pos
+                p2aa_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)
+                p2aa_att = torch.matmul(
+                    key_layer,
+                    pos_query_layer.transpose(-1, -2).to(dtype=key_layer.dtype),
+                )
+                p2aa_att = torch.gather(
+                    p2aa_att,
+                    dim=-1,
+                    index=p2c_dynamic_expand(p2aa_pos, query_layer, key_layer),
+                ).transpose(-1, -2)
+
+                if query_layer.size(-2) != key_layer.size(-2):
+                    pos_index = relative_pos[:, :, :, 0].unsqueeze(-1)
+                    p2aa_att = torch.gather(
+                        p2aa_att,
+                        dim=-2,
+                        index=pos_dynamic_expand(pos_index, p2aa_att, key_layer),
+                    )
+                score += p2aa_att
+
+            # content -> structure
+            if "aa2ss" in self.pos_att_type:
+                assert ss_hidden_states is not None
+                ss_key_layer = self.ss_proj(ss_hidden_states)
+                ss_key_layer = self.transpose_for_scores(ss_key_layer)
+                # [batch_size, num_attention_heads, seq_len, seq_len]
+                aa2ss_att = torch.matmul(query_layer, ss_key_layer.transpose(-1, -2))
+                score += aa2ss_att
+
+            if "ss2aa" in self.pos_att_type:
+                assert ss_hidden_states is not None
+                ss_query_layer = self.ss_q_proj(ss_hidden_states)
+                ss_query_layer = self.transpose_for_scores(ss_query_layer)
+                ss_query_layer /= torch.sqrt(
+                    torch.tensor(ss_query_layer.size(-1), dtype=torch.float)
+                    * scale_factor
+                )
+                ss2aa_att = torch.matmul(
+                    key_layer, query_layer.transpose(-1, -2).to(dtype=key_layer.dtype)
+                )
+                score += ss2aa_att
+            return score
+        elif self.position_embedding_type == "rotary":
+            score = 0
+            if "aa2ss" in self.pos_att_type:
+                assert ss_hidden_states is not None
+                ss_key_layer = self.ss_proj(ss_hidden_states)
+                ss_key_layer = self.transpose_for_scores(ss_key_layer)
+                aa2ss_att = torch.matmul(query_layer, ss_key_layer.transpose(-1, -2))
+                score += aa2ss_att
+
+            if "ss2aa" in self.pos_att_type:
+                assert ss_hidden_states is not None
+                ss_query_layer = self.ss_q_proj(ss_hidden_states)
+                ss_query_layer = self.transpose_for_scores(ss_query_layer)
+                ss_query_layer /= torch.sqrt(
+                    torch.tensor(ss_query_layer.size(-1), dtype=torch.float)
+                    * scale_factor
+                )
+                ss2aa_att = torch.matmul(
+                    key_layer, query_layer.transpose(-1, -2).to(dtype=key_layer.dtype)
+                )
+                score += ss2aa_att
+            return score
+
+
+class DeprotSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = DeprotLayerNorm(config.hidden_size, config.layer_norm_eps)
+        self.dropout = nn.Dropout(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
+
+
+class DeprotAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self = DisentangledSelfAttention(config)
+        self.output = DeprotSelfOutput(config)
+        self.config = config
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        output_attentions=False,
+        query_states=None,
+        relative_pos=None,
+        rel_embeddings=None,
+        ss_hidden_states=None,
+    ):
+        self_output = self.self(
+            hidden_states,
+            attention_mask,
+            output_attentions,
+            query_states=query_states,
+            relative_pos=relative_pos,
+            rel_embeddings=rel_embeddings,
+            ss_hidden_states=ss_hidden_states,
+        )
+        if output_attentions:
+            self_output, att_matrix = self_output
+        if query_states is None:
+            query_states = hidden_states
+        attention_output = self.output(self_output, query_states)
+
+        if output_attentions:
+            return (attention_output, att_matrix)
+        else:
+            return attention_output
+
+
+class DeprotIntermediate(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: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class DeprotOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = DeprotLayerNorm(config.hidden_size, config.layer_norm_eps)
+        self.dropout = nn.Dropout(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
+
+
+class DeprotLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.attention = DeprotAttention(config)
+        self.intermediate = DeprotIntermediate(config)
+        self.output = DeprotOutput(config)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        query_states=None,
+        relative_pos=None,
+        rel_embeddings=None,
+        output_attentions=False,
+        ss_hidden_states=None,
+    ):
+        attention_output = self.attention(
+            hidden_states,
+            attention_mask,
+            output_attentions=output_attentions,
+            query_states=query_states,
+            relative_pos=relative_pos,
+            rel_embeddings=rel_embeddings,
+            ss_hidden_states=ss_hidden_states,
+        )
+        if output_attentions:
+            attention_output, att_matrix = attention_output
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        if output_attentions:
+            return (layer_output, att_matrix)
+        else:
+            return layer_output
+
+
+class DeprotEncoder(nn.Module):
+    """Modified BertEncoder with relative position bias support"""
+
+    def __init__(self, config):
+        super().__init__()
+        self.layer = nn.ModuleList(
+            [DeprotLayer(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.rel_embeddings = nn.Embedding(
+                self.max_relative_positions * 2, config.hidden_size
+            )
+        self.gradient_checkpointing = False
+
+    def get_rel_embedding(self):
+        rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None
+        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)
+        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), hidden_states.device
+            )
+        return relative_pos
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        output_hidden_states=True,
+        output_attentions=False,
+        query_states=None,
+        relative_pos=None,
+        ss_hidden_states=None,
+        return_dict=True,
+    ):
+        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()
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_attentions)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    next_kv,
+                    attention_mask,
+                    query_states,
+                    relative_pos,
+                    rel_embeddings,
+                    ss_hidden_states,
+                )
+            else:
+                hidden_states = layer_module(
+                    next_kv,
+                    attention_mask,
+                    query_states=query_states,
+                    relative_pos=relative_pos,
+                    rel_embeddings=rel_embeddings,
+                    output_attentions=output_attentions,
+                    ss_hidden_states=ss_hidden_states,
+                )
+
+            if output_attentions:
+                hidden_states, att_m = hidden_states
+
+            if query_states is not None:
+                query_states = hidden_states
+                if isinstance(hidden_states, Sequence):
+                    next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None
+            else:
+                next_kv = hidden_states
+
+            if output_attentions:
+                all_attentions = all_attentions + (att_m,)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, all_hidden_states, all_attentions]
+                if v is not None
+            )
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_attentions,
+        )
+
+
+class DeprotEmbeddings(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", False)
+        if not self.position_biased_input:
+            self.position_embeddings = None
+        else:
+            # assert getattr(config, "position_embedding_type", "relative") == "absolute"
+            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 config.ss_vocab_size > 0:
+            self.ss_embeddings = nn.Embedding(config.ss_vocab_size, self.embedding_size)
+            self.ss_layer_norm = DeprotLayerNorm(
+                config.hidden_size, config.layer_norm_eps
+            )
+
+        if self.embedding_size != config.hidden_size:
+            self.embed_proj = nn.Linear(
+                self.embedding_size, config.hidden_size, bias=False
+            )
+        self.LayerNorm = DeprotLayerNorm(config.hidden_size, config.layer_norm_eps)
+
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.config = config
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        if self.position_biased_input:
+            self.register_buffer(
+                "position_ids",
+                torch.arange(config.max_position_embeddings).expand((1, -1)),
+                persistent=False,
+            )
+
+    def forward(
+        self,
+        input_ids=None,
+        ss_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 and self.position_biased_input:
+            position_ids = self.position_ids[:, :seq_length]
+            if seq_length > position_ids.size(1):
+                zero_padding = (
+                    torch.zeros(
+                        (input_shape[0], seq_length - position_ids.size(1)),
+                        dtype=torch.long,
+                        device=position_ids.device,
+                    )
+                    + 2047
+                )
+                position_ids = torch.cat([position_ids, zero_padding], dim=1)
+
+        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:
+            if self.config.token_dropout:
+                inputs_embeds = self.word_embeddings(input_ids)
+                inputs_embeds.masked_fill_(
+                    (input_ids == self.config.mask_token_id).unsqueeze(-1), 0.0
+                )
+                mask_ratio_train = self.config.mlm_probability * 0.8
+                src_lengths = mask.sum(dim=-1)
+                mask_ratio_observed = (input_ids == self.config.mask_token_id).sum(
+                    -1
+                ).to(inputs_embeds.dtype) / src_lengths
+                inputs_embeds = (
+                    inputs_embeds
+                    * (1 - mask_ratio_train)
+                    / (1 - mask_ratio_observed)[:, None, None]
+                )
+            else:
+                inputs_embeds = self.word_embeddings(input_ids)
+
+        if self.position_embeddings is not None and self.position_biased_input:
+            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)
+
+        if self.config.ss_vocab_size > 0:
+            ss_embeddings = self.ss_embeddings(ss_input_ids)
+            ss_embeddings = self.ss_layer_norm(ss_embeddings)
+            if mask is not None:
+                if mask.dim() != ss_embeddings.dim():
+                    if mask.dim() == 4:
+                        mask = mask.squeeze(1).squeeze(1)
+                    mask = mask.unsqueeze(2)
+                mask = mask.to(ss_embeddings.dtype)
+                ss_embeddings = ss_embeddings * mask
+                ss_embeddings = self.dropout(ss_embeddings)
+            return embeddings, ss_embeddings
+
+        return embeddings, None
+
+
+class DeprotPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = DeprotConfig
+    base_model_prefix = "deprot"
+    _keys_to_ignore_on_load_unexpected = ["position_embeddings"]
+    supports_gradient_checkpointing = True
+
+    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_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, DeprotEncoder):
+            module.gradient_checkpointing = value
+
+
+class DeprotModel(DeprotPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.embeddings = DeprotEmbeddings(config)
+        self.encoder = DeprotEncoder(config)
+        self.config = config
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    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."
+        )
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        ss_input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        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, ss_embeddings = self.embeddings(
+            input_ids=input_ids,
+            ss_input_ids=ss_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,
+            ss_hidden_states=ss_embeddings,
+        )
+        encoded_layers = encoder_outputs[1]
+
+        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,
+        )
+
+
+class DeprotPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
+
+        self.dense = nn.Linear(config.hidden_size, self.embedding_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(self.embedding_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
+
+
+class DeprotLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = DeprotPredictionHeadTransform(config)
+
+        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(self.embedding_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
+
+
+class DeprotOnlyMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = DeprotLMPredictionHead(config)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class DeprotPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = DeprotConfig
+    base_model_prefix = "deprot"
+    _keys_to_ignore_on_load_unexpected = ["position_embeddings"]
+    supports_gradient_checkpointing = True
+
+    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_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, DeprotEncoder):
+            module.gradient_checkpointing = value
+
+
+class DeprotForMaskedLM(DeprotPreTrainedModel):
+    _tied_weights_keys = [
+        "cls.predictions.decoder.weight",
+        "cls.predictions.decoder.bias",
+    ]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.deprot = DeprotModel(config)
+        self.cls = DeprotOnlyMLMHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        ss_input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: 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, MaskedLMOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+        """
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.deprot(
+            input_ids,
+            ss_input_ids=ss_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,
+        )
+
+
+class DeprotForSequenceClassification(DeprotPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        num_labels = getattr(config, "num_labels", 2)
+        self.num_labels = num_labels
+        self.scale_hidden = getattr(config, "scale_hidden", 1)
+        self.deprot = DeprotModel(config)
+        self.pooler = ContextPooler(config)
+        output_dim = self.pooler.output_dim * self.scale_hidden
+
+        self.classifier = 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 = nn.Dropout(drop_out)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.deprot.get_input_embeddings()
+
+    def set_input_embeddings(self, new_embeddings):
+        self.deprot.set_input_embeddings(new_embeddings)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        ss_input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: 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"""
+        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
+        )
+
+        outputs = self.deprot(
+            input_ids,
+            ss_input_ids=ss_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, attention_mask)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    # regression task
+                    loss_fn = 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.gather(
+                            logits,
+                            0,
+                            label_index.expand(label_index.size(0), logits.size(1)),
+                        )
+                        labels = torch.gather(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 = nn.LogSoftmax(-1)
+                    loss = -((log_softmax(logits) * labels).sum(-1)).mean()
+            elif 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 == "binary_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits.squeeze(), labels.squeeze().to(logits.dtype))
+            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.to(logits.dtype))
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class DeprotForTokenClassification(DeprotPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.deprot = DeprotModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: 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"""
+        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.deprot(
+            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()
+            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,
+        )
+
+
+DeprotModel.register_for_auto_class("AutoModel")
+DeprotForMaskedLM.register_for_auto_class("AutoModelForMaskedLM")
+DeprotForSequenceClassification.register_for_auto_class(
+    "AutoModelForSequenceClassification"
+)
+DeprotForTokenClassification.register_for_auto_class("AutoModelForTokenClassification")