import torch
from torch import nn
from typing import Optional
from dataclasses import dataclass
from transformers import PreTrainedModel
from .configuration_mlp import MLPConfig
from transformers.utils import ModelOutput
from transformers.activations import ACT2FN


@dataclass
class MLPOutput(ModelOutput):
    loss: Optional[torch.FloatTensor] = None
    logits: Optional[torch.FloatTensor] = None


class MLPPreTrainedModel(PreTrainedModel):
    config_class = MLPConfig

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()


class MLPModel(MLPPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.act_fn = ACT2FN[config.hidden_act]
        iho = [config.input_size, *config.hidden_size, config.output_size]
        self.linears = nn.ModuleList([
            nn.Linear(iho[i], iho[i+1])
            for i in range(config.num_hidden_layers + 1)
        ])
        self.loss_fn = nn.CrossEntropyLoss(reduce="mean")
        # Initialize weights and apply final processing
        self.post_init()
    
    def forward(self, inputs, labels=None):
        for i in range(len(self.linears) - 1):
            inputs = self.act_fn(self.linears[i](inputs))
        logits = self.linears[-1](inputs)

        loss = None
        if labels is None:
            return ModelOutput(loss=loss, logits=logits)
        else:
            loss = self.loss_fn(logits, labels)
        return ModelOutput(loss=loss, logits=logits)