import torch
from torch import nn
from transformers.modeling_utils import PreTrainedModel
from transformers.modeling_outputs import BaseModelOutput

class Phi3ForCausalLM(PreTrainedModel):
    config_class = Phi3Config
    base_model_prefix = "phi3"

    def __init__(self, config):
        super().__init__(config)
        self.hidden_size = config.hidden_size
        self.num_hidden_layers = config.num_hidden_layers
        self.num_attention_heads = config.num_attention_heads
        
        self.embedding = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = nn.ModuleList([nn.TransformerEncoderLayer(config.hidden_size, config.num_attention_heads) for _ in range(config.num_hidden_layers)])
        self.output_layer = nn.Linear(config.hidden_size, config.vocab_size)

    def forward(self, input_ids):
        embeddings = self.embedding(input_ids)
        hidden_states = embeddings
        for layer in self.layers:
            hidden_states = layer(hidden_states)
        logits = self.output_layer(hidden_states)
        return BaseModelOutput(last_hidden_state=logits)