"""
UNIDQ Model Implementation
"""

import torch
import torch.nn as nn
from typing import Dict, Optional, Tuple


class UNIDQConfig:
    """Configuration class for UNIDQ model."""
    
    def __init__(
        self,
        d_model: int = 256,
        n_heads: int = 8,
        n_layers: int = 6,
        d_ff: int = 1024,
        dropout: float = 0.1,
        max_seq_length: int = 512,
        vocab_size: int = 10000,
        num_tasks: int = 5,
    ):
        self.d_model = d_model
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.d_ff = d_ff
        self.dropout = dropout
        self.max_seq_length = max_seq_length
        self.vocab_size = vocab_size
        self.num_tasks = num_tasks


class UNIDQ(nn.Module):
    """
    Unified Transformer for Multi-Task Data Quality.
    
    A transformer-based model for handling multiple data quality tasks including:
    - Error detection
    - Data imputation
    - Schema matching
    - Duplicate detection
    - Outlier detection
    """
    
    def __init__(self, config: UNIDQConfig):
        super().__init__()
        self.config = config
        
        # Embedding layers
        self.token_embedding = nn.Embedding(config.vocab_size, config.d_model)
        self.position_embedding = nn.Embedding(config.max_seq_length, config.d_model)
        
        # Transformer encoder
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=config.d_model,
            nhead=config.n_heads,
            dim_feedforward=config.d_ff,
            dropout=config.dropout,
            batch_first=True,
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=config.n_layers)
        
        # Task-specific heads
        self.task_heads = nn.ModuleDict({
            'error_detection': nn.Linear(config.d_model, 2),
            'imputation': nn.Linear(config.d_model, config.vocab_size),
            'schema_matching': nn.Linear(config.d_model, config.d_model),
            'duplicate_detection': nn.Linear(config.d_model, 2),
            'outlier_detection': nn.Linear(config.d_model, 2),
        })
        
        self.dropout = nn.Dropout(config.dropout)
        
    def forward(
        self,
        input_ids: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        task: Optional[str] = None,
    ) -> Dict[str, torch.Tensor]:
        """
        Forward pass through the model.
        
        Args:
            input_ids: Input token IDs [batch_size, seq_length]
            attention_mask: Attention mask [batch_size, seq_length]
            task: Specific task to run (if None, runs all tasks)
            
        Returns:
            Dictionary of task outputs
        """
        batch_size, seq_length = input_ids.shape
        
        # Generate embeddings
        token_embeds = self.token_embedding(input_ids)
        positions = torch.arange(seq_length, device=input_ids.device).unsqueeze(0)
        position_embeds = self.position_embedding(positions)
        
        embeddings = self.dropout(token_embeds + position_embeds)
        
        # Transform attention mask for transformer
        if attention_mask is not None:
            attention_mask = attention_mask == 0
        
        # Pass through transformer
        hidden_states = self.transformer(embeddings, src_key_padding_mask=attention_mask)
        
        # Task-specific outputs
        outputs = {}
        
        if task is None:
            # Run all tasks
            for task_name, head in self.task_heads.items():
                outputs[task_name] = head(hidden_states)
        else:
            # Run specific task
            if task in self.task_heads:
                outputs[task] = self.task_heads[task](hidden_states)
            else:
                raise ValueError(f"Unknown task: {task}")
        
        return outputs
    
    def save_pretrained(self, save_directory: str):
        """Save model weights and configuration."""
        import os
        import json
        
        os.makedirs(save_directory, exist_ok=True)
        
        # Save model weights
        torch.save(self.state_dict(), os.path.join(save_directory, "pytorch_model.bin"))
        
        # Save config
        config_dict = vars(self.config)
        with open(os.path.join(save_directory, "config.json"), "w") as f:
            json.dump(config_dict, f, indent=2)
    
    @classmethod
    def from_pretrained(cls, load_directory: str):
        """Load model from saved weights and configuration."""
        import os
        import json
        
        # Load config
        with open(os.path.join(load_directory, "config.json"), "r") as f:
            config_dict = json.load(f)
        
        config = UNIDQConfig(**config_dict)
        model = cls(config)
        
        # Load weights
        state_dict = torch.load(
            os.path.join(load_directory, "pytorch_model.bin"),
            map_location="cpu"
        )
        model.load_state_dict(state_dict)
        
        return model
