modeling.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, AutoModel, AutoConfig

class Encoder(nn.Module):
    def __init__(self, base_encoder):
        super().__init__()
        self.encoder = base_encoder

    def forward(self, inputs):
        outputs = self.encoder(**inputs, output_hidden_states=True)
        last_hidden = outputs.hidden_states[-1]
        mask = inputs["attention_mask"].unsqueeze(-1).float()
        pooled = (last_hidden * mask).sum(1) / mask.sum(1).clamp(min=1e-9)
        return F.normalize(pooled, p=2, dim=1)

class Classifier(nn.Module):
    def __init__(self, input_dim=768, num_classes=28):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(input_dim, 512),
            nn.LayerNorm(512),
            nn.GELU(),
            nn.Dropout(0.25),
            nn.Linear(512, num_classes),
        )

    def forward(self, x):
        return self.mlp(x)

class RobertaEmoPillars(PreTrainedModel):
    config_class = AutoConfig

    def __init__(self, config):
        super().__init__(config)
        base_encoder = AutoModel.from_config(config)  # IMPORTANT: use from_config
        self.encoder = Encoder(base_encoder)
        self.classifier = Classifier(input_dim=base_encoder.config.hidden_size,
                                     num_classes=config.num_labels)
        self.post_init()  # ensures HF weights init

    def forward(self, input_ids=None, attention_mask=None, **kwargs):
        inputs = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            **kwargs
        }


# --- Registration for AutoModel ---
try:
    AutoModel.register(AutoConfig, RobertaEmoPillars)
except:
    pass