"""
Text generation models with causal constraints.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F

try:
    from transformers import GPT2LMHeadModel
except ImportError:
    # Optional dependencies
    pass

from ..layers import CausalAttentionLayer


class CNSG_GPT2(nn.Module):
    """Causal Neuro-Symbolic GPT-2 model for text generation.
    
    This model extends GPT-2 with causal attention to enforce logical
    relationships in generated text.
    
    Args:
        pretrained_model_name (str): Name of pretrained GPT-2 model
        causal_rules (dict): Dictionary of causal rules to enforce
    """
    def __init__(self, pretrained_model_name="gpt2", causal_rules=None):
        super().__init__()
        
        # Load pretrained GPT-2
        self.gpt2 = GPT2LMHeadModel.from_pretrained(pretrained_model_name)
        
        # Add causal attention layer
        self.causal_attn = CausalAttentionLayer(causal_rules or {})
        
        # Set tokenizer (will be initialized by user)
        self.tokenizer = None
    
    def forward(self, input_ids, attention_mask=None, labels=None):
        """Forward pass with causal constraints.
        
        Args:
            input_ids (torch.Tensor): Token IDs
            attention_mask (torch.Tensor, optional): Attention mask
            labels (torch.Tensor, optional): Target token IDs
            
        Returns:
            transformers.modeling_outputs.CausalLMOutputWithCrossAttentions:
                Model outputs with modified attention based on causal rules
        """
        # Run GPT-2 with output_attentions=True to get attention matrices
        outputs = self.gpt2(
            input_ids=input_ids,
            attention_mask=attention_mask,
            labels=labels,
            output_attentions=True
        )
        
        # Get input text
        if self.tokenizer is not None and input_ids is not None:
            input_text = self.tokenizer.decode(input_ids[0])
            
            # Apply causal attention modifications
            if outputs.attentions is not None:
                # For simplicity, we only modify the last layer's attention
                self.causal_attn.tokenizer = self.tokenizer
                modified_attention = self.causal_attn(outputs.attentions[-1], input_text)
                
                # In a full implementation, we would use this modified attention
                # to recompute the final layer's outputs
                
        return outputs
    
    def generate(self, input_ids=None, max_length=None, **kwargs):
        """Generate text with causal constraints.
        
        Args:
            input_ids (torch.Tensor): Input token IDs
            max_length (int, optional): Maximum output length
            **kwargs: Additional generation parameters
            
        Returns:
            torch.Tensor: Generated token IDs
        """
        # For now, we use GPT-2's generation method directly
        # In a full implementation, we would modify the generation
        # algorithm to incorporate causal constraints at each step
        return self.gpt2.generate(
            input_ids=input_ids,
            max_length=max_length,
            **kwargs
        )