from typing import Optional, Tuple, Union

import torch
import torch.nn as nn
from transformers import PreTrainedModel, PreTrainedEncoder, PreTrainedDecoder
from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
from transformers.utils import logging

logger = logging.get_logger(__name__)

class CSUMLMEncoder(PreTrainedEncoder):
    def __init__(self, config):
        super().__init__(config)
        # Define the text encoder, image encoder, and audio encoder architectures
        # ...

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        # Implement the forward pass for the encoder
        # ...
        return encoder_outputs

class CSUMLMDecoder(PreTrainedDecoder):
    def __init__(self, config):
        super().__init__(config)
        # Define the decoder architecture
        # ...

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        head_mask=None,
        cross_attn_head_mask=None,
        past_key_values=None,
        inputs_embeds=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        # Implement the forward pass for the decoder
        # ...
        return decoder_outputs

class CSUMLMModel(PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.encoder = CSUMLMEncoder(config)
        self.decoder = CSUMLMDecoder(config)
        self.multimodal_fusion = MultimodalFusion(config)
        # Initialize other components (e.g., attention mechanism, belief desire intent tree)
        # ...

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        encoder_outputs=None,
        past_key_values=None,
        inputs_embeds=None,
        decoder_inputs_embeds=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        # Implement the forward pass for the CSUMLM model
        # ...
        return output

# Register the custom model with Hugging Face Transformers
CSUMLMModel.register_for_auto_class()