# evo_model.py — Defines EvoDecoderModel used in inference and training
import torch
import torch.nn as nn
import math

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=128):
        super().__init__()
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)  # [1, max_len, d_model]
        self.register_buffer('pe', pe)

    def forward(self, x):
        return x + self.pe[:, :x.size(1)]

class EvoDecoderModel(nn.Module):
    def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6, dim_ff=2048, max_len=128):
        super().__init__()
        self.token_embed = nn.Embedding(vocab_size, d_model)
        self.pos_encoder = PositionalEncoding(d_model, max_len)

        decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_ff, batch_first=True)
        self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)

        self.lm_head = nn.Linear(d_model, vocab_size)

    def generate_square_subsequent_mask(self, sz):
        return torch.triu(torch.full((sz, sz), float('-inf')), diagonal=1)

    def forward(self, input_ids):
        x = self.token_embed(input_ids)
        x = self.pos_encoder(x)

        tgt_mask = self.generate_square_subsequent_mask(x.size(1)).to(x.device)
        x = self.decoder(x, x, tgt_mask=tgt_mask)

        return self.lm_head(x)