import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig

class SimpleConfig(PretrainedConfig):
    model_type = "simple-model"

    def __init__(self, vocab_size=100, hidden_size=32, num_labels=2, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_labels = num_labels


class SimpleModel(PreTrainedModel):
    config_class = SimpleConfig

    def __init__(self, config):
        super().__init__(config)
        self.embedding = nn.Embedding(config.vocab_size, config.hidden_size)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        self.post_init()  # important for HF weight init

    def forward(self, input_ids):
        x = self.embedding(input_ids)
        x = x.mean(dim=1)  # simple pooling
        logits = self.classifier(x)
        return {"logits": logits}