"""
Production-Ready Fine-tuning for Causal Models
==============================================

Fine-tuning infrastructure for different data types and use cases.
"""

import torch
import torch.nn as nn
from typing import Dict, List, Optional, Union, Any
from pathlib import Path
import logging

from .trainer import CausalTrainer
from ..models import CausalTransformer
from ..rules import CausalRuleSet


class CausalFineTuner:
    """Production-ready fine-tuning for causal AI models.
    
    Supports:
    - Text data (transformers, language models)
    - Tabular data (classification, regression)
    - Time series data (forecasting, anomaly detection)
    - Image data (classification, generation)
    - Multimodal data (text+image, video)
    """
    
    def __init__(
        self,
        base_model: Union[str, nn.Module],
        task_type: str,
        data_type: str,
        causal_rules: Optional[CausalRuleSet] = None,
        config: Optional[Dict[str, Any]] = None
    ):
        self.task_type = task_type
        self.data_type = data_type
        self.config = config or {}
        self.causal_rules = causal_rules
        
        # Load or initialize model
        self.model = self._setup_model(base_model)
        
        # Setup fine-tuning strategy
        self.strategy = self._setup_finetuning_strategy()
        
        # Setup data preprocessing
        self.preprocessor = self._setup_preprocessor()
        
        self.logger = logging.getLogger(f"causaltorch.finetuner.{task_type}")
    
    def _setup_model(self, base_model: Union[str, nn.Module]) -> nn.Module:
        """Setup model for fine-tuning."""
        if isinstance(base_model, str):
            # Load from checkpoint or model hub
            if base_model.endswith('.pt') or base_model.endswith('.pth'):
                checkpoint = torch.load(base_model)
                model = self._create_model_from_checkpoint(checkpoint)
            else:
                # Load from model hub (HuggingFace, etc.)
                model = self._load_pretrained_model(base_model)
        else:
            model = base_model
        
        # Add task-specific heads
        model = self._add_task_head(model)
        
        # Setup causal constraints
        if self.causal_rules:
            model = self._add_causal_constraints(model)
        
        return model
    
    def _setup_finetuning_strategy(self) -> str:
        """Determine fine-tuning strategy based on task and data."""
        strategies = {
            ('text', 'classification'): 'full_finetuning',
            ('text', 'generation'): 'lora',
            ('tabular', 'classification'): 'head_only',
            ('tabular', 'regression'): 'head_only',
            ('timeseries', 'forecasting'): 'partial_finetuning',
            ('image', 'classification'): 'head_only',
            ('image', 'generation'): 'lora',
            ('multimodal', 'any'): 'adapter'
        }
        
        key = (self.data_type, self.task_type)
        return strategies.get(key, 'full_finetuning')
    
    def _setup_preprocessor(self):
        """Setup data preprocessor based on data type."""
        if self.data_type == 'text':
            return TextPreprocessor(self.config)
        elif self.data_type == 'tabular':
            return TabularPreprocessor(self.config)
        elif self.data_type == 'timeseries':
            return TimeSeriesPreprocessor(self.config)
        elif self.data_type == 'image':
            return ImagePreprocessor(self.config)
        elif self.data_type == 'multimodal':
            return MultimodalPreprocessor(self.config)
        else:
            raise ValueError(f"Unsupported data type: {self.data_type}")
    
    def prepare_model_for_finetuning(self):
        """Prepare model for fine-tuning based on strategy."""
        if self.strategy == 'head_only':
            # Freeze all layers except the task head
            for name, param in self.model.named_parameters():
                if 'head' not in name and 'classifier' not in name:
                    param.requires_grad = False
        
        elif self.strategy == 'partial_finetuning':
            # Freeze early layers, fine-tune later layers
            layers_to_finetune = self.config.get('layers_to_finetune', 2)
            total_layers = len(list(self.model.named_parameters()))
            
            for i, (name, param) in enumerate(self.model.named_parameters()):
                if i < total_layers - layers_to_finetune:
                    param.requires_grad = False
        
        elif self.strategy == 'lora':
            # Implement LoRA (Low-Rank Adaptation)
            self._setup_lora()
        
        elif self.strategy == 'adapter':
            # Add adapter layers
            self._setup_adapters()
        
        # Full fine-tuning keeps all parameters trainable
    
    def _setup_lora(self):
        """Setup LoRA (Low-Rank Adaptation) for efficient fine-tuning."""
        rank = self.config.get('lora_rank', 16)
        alpha = self.config.get('lora_alpha', 32)
        
        # Add LoRA layers to attention modules
        for name, module in self.model.named_modules():
            if isinstance(module, nn.Linear) and 'attention' in name:
                # Replace with LoRA layer
                lora_layer = LoRALinear(module, rank, alpha)
                self._replace_module(self.model, name, lora_layer)
    
    def _setup_adapters(self):
        """Setup adapter layers for efficient fine-tuning."""
        adapter_dim = self.config.get('adapter_dim', 64)
        
        # Add adapter layers after each transformer block
        for name, module in self.model.named_modules():
            if 'block' in name or 'layer' in name:
                adapter = AdapterLayer(module.hidden_size, adapter_dim)
                setattr(module, 'adapter', adapter)
    
    def fit(
        self,
        train_data,
        val_data=None,
        num_epochs: int = 10,
        learning_rate: float = 1e-4,
        **kwargs
    ) -> Dict[str, Any]:
        """Fine-tune the model."""
        # Prepare model
        self.prepare_model_for_finetuning()
        
        # Prepare data
        train_loader = self.preprocessor.create_dataloader(train_data, 'train')
        val_loader = self.preprocessor.create_dataloader(val_data, 'val') if val_data else None
        
        # Setup trainer with fine-tuning specific config
        ft_config = {
            **self.config,
            'max_epochs': num_epochs,
            'lr': learning_rate,
            'task': self.task_type,
            'optimizer': 'AdamW',
            'scheduler': 'cosine',
            'early_stopping': True,
            'patience': 5
        }
        
        trainer = CausalTrainer(
            model=self.model,
            causal_rules=self.causal_rules,
            config=ft_config,
            experiment_name=f"finetune_{self.task_type}_{self.data_type}"
        )
        
        # Train
        results = trainer.fit(train_loader, val_loader, num_epochs)
        
        return results
    
    def predict(self, data) -> torch.Tensor:
        """Make predictions with the fine-tuned model."""
        self.model.eval()
        
        # Preprocess data
        processed_data = self.preprocessor.preprocess(data)
        
        with torch.no_grad():
            if torch.cuda.is_available():
                processed_data = processed_data.cuda()
                self.model = self.model.cuda()
            
            outputs = self.model(processed_data)
        
        return outputs
    
    def save_finetuned_model(self, path: str):
        """Save the fine-tuned model."""
        save_path = Path(path)
        save_path.parent.mkdir(parents=True, exist_ok=True)
        
        checkpoint = {
            'model_state_dict': self.model.state_dict(),
            'config': self.config,
            'task_type': self.task_type,
            'data_type': self.data_type,
            'strategy': self.strategy,
            'causal_rules': self.causal_rules.to_dict() if self.causal_rules else None
        }
        
        torch.save(checkpoint, save_path)
        self.logger.info(f"Fine-tuned model saved to {save_path}")
    
    @classmethod
    def load_finetuned_model(cls, path: str) -> 'CausalFineTuner':
        """Load a fine-tuned model."""
        checkpoint = torch.load(path)
        
        # Recreate fine-tuner
        finetuner = cls(
            base_model=checkpoint['model_state_dict'],
            task_type=checkpoint['task_type'],
            data_type=checkpoint['data_type'],
            config=checkpoint['config']
        )
        
        # Load state
        finetuner.model.load_state_dict(checkpoint['model_state_dict'])
        finetuner.strategy = checkpoint['strategy']
        
        if checkpoint.get('causal_rules'):
            finetuner.causal_rules = CausalRuleSet.from_dict(checkpoint['causal_rules'])
        
        return finetuner


class LoRALinear(nn.Module):
    """Low-Rank Adaptation for Linear layers."""
    
    def __init__(self, original_layer: nn.Linear, rank: int, alpha: float):
        super().__init__()
        self.original_layer = original_layer
        self.rank = rank
        self.alpha = alpha
        
        # Freeze original weights
        for param in self.original_layer.parameters():
            param.requires_grad = False
        
        # Add LoRA weights
        self.lora_A = nn.Parameter(torch.randn(rank, original_layer.in_features))
        self.lora_B = nn.Parameter(torch.zeros(original_layer.out_features, rank))
        
        # Initialize
        nn.init.kaiming_uniform_(self.lora_A, a=5**0.5)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        original_output = self.original_layer(x)
        lora_output = (x @ self.lora_A.T @ self.lora_B.T) * (self.alpha / self.rank)
        return original_output + lora_output


class AdapterLayer(nn.Module):
    """Adapter layer for efficient fine-tuning."""
    
    def __init__(self, hidden_size: int, adapter_size: int):
        super().__init__()
        self.down_project = nn.Linear(hidden_size, adapter_size)
        self.up_project = nn.Linear(adapter_size, hidden_size)
        self.activation = nn.ReLU()
        self.layer_norm = nn.LayerNorm(hidden_size)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        residual = x
        x = self.down_project(x)
        x = self.activation(x)
        x = self.up_project(x)
        x = self.layer_norm(x + residual)
        return x


# Data Preprocessors
class BasePreprocessor:
    """Base class for data preprocessors."""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
    
    def preprocess(self, data):
        raise NotImplementedError
    
    def create_dataloader(self, data, split: str):
        raise NotImplementedError


class TextPreprocessor(BasePreprocessor):
    """Preprocessor for text data."""
    
    def preprocess(self, data):
        # Implement tokenization, padding, etc.
        pass
    
    def create_dataloader(self, data, split: str):
        # Create PyTorch DataLoader for text data
        pass


class TabularPreprocessor(BasePreprocessor):
    """Preprocessor for tabular data."""
    
    def preprocess(self, data):
        # Implement normalization, encoding, etc.
        pass
    
    def create_dataloader(self, data, split: str):
        # Create PyTorch DataLoader for tabular data
        pass


class TimeSeriesPreprocessor(BasePreprocessor):
    """Preprocessor for time series data."""
    
    def preprocess(self, data):
        # Implement windowing, normalization, etc.
        pass
    
    def create_dataloader(self, data, split: str):
        # Create PyTorch DataLoader for time series data
        pass


class ImagePreprocessor(BasePreprocessor):
    """Preprocessor for image data."""
    
    def preprocess(self, data):
        # Implement resizing, normalization, augmentation, etc.
        pass
    
    def create_dataloader(self, data, split: str):
        # Create PyTorch DataLoader for image data
        pass


class MultimodalPreprocessor(BasePreprocessor):
    """Preprocessor for multimodal data."""
    
    def preprocess(self, data):
        # Implement preprocessing for multiple modalities
        pass
    
    def create_dataloader(self, data, split: str):
        # Create PyTorch DataLoader for multimodal data
        pass
