"""
Enhanced optimization module with advanced features for deep learning models.
"""

import logging
from dataclasses import dataclass
from typing import Dict, Tuple

import numpy as np
import torch
import torch.nn as nn
from ray import tune
from torch.cuda.amp import autocast
from torch.nn.utils import prune
from torch.quantization import QConfig, convert, prepare, quantize_dynamic


@dataclass
class OptimizationConfig:
    """Configuration for model optimization."""

    quantization_method: str = "dynamic"  # dynamic, static, or qat
    pruning_method: str = "l1"  # l1, random, or structured
    pruning_amount: float = 0.3
    distillation_temperature: float = 2.0
    use_ray_tune: bool = False
    num_samples: int = 10
    max_num_epochs: int = 10
    target_metric: str = "val_loss"
    optimization_metric: str = "latency"  # latency, memory, or accuracy


class EnhancedOptimizer:
    """Enhanced optimizer with advanced optimization techniques."""

    def __init__(self, model: nn.Module, config: OptimizationConfig):
        self.model = model
        self.config = config
        self.logger = logging.getLogger(__name__)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    def optimize_model(self, train_loader, val_loader) -> nn.Module:
        """Apply comprehensive model optimization."""
        # Quantization
        quantized_model = self.apply_quantization()

        # Pruning
        pruned_model = self.apply_pruning(quantized_model)

        # Architecture search if enabled
        if self.config.use_ray_tune:
            optimized_model = self.perform_architecture_search(
                pruned_model, train_loader, val_loader
            )
        else:
            optimized_model = pruned_model

        return self.validate_optimization(optimized_model, val_loader)

    def apply_quantization(self) -> nn.Module:
        """Apply quantization based on configuration."""
        if self.config.quantization_method == "dynamic":
            return self._apply_dynamic_quantization()
        elif self.config.quantization_method == "static":
            return self._apply_static_quantization()
        elif self.config.quantization_method == "qat":
            return self._apply_quantization_aware_training()
        else:
            raise ValueError(
                f"Unknown quantization method: {self.config.quantization_method}"
            )

    def _apply_dynamic_quantization(self) -> nn.Module:
        """Apply dynamic quantization to the model."""
        quantized_model = quantize_dynamic(
            self.model, {nn.Linear, nn.LSTM, nn.GRU}, dtype=torch.qint8
        )
        return quantized_model

    def _apply_static_quantization(self) -> nn.Module:
        """Apply static quantization to the model."""
        qconfig = QConfig(
            activation=torch.quantization.default_observer,
            weight=torch.quantization.default_weight_observer,
        )

        model_prepared = prepare(self.model, qconfig)
        # Calibration would go here
        model_quantized = convert(model_prepared)
        return model_quantized

    def _apply_quantization_aware_training(self) -> nn.Module:
        """Apply quantization-aware training."""
        model_qat = prepare(self.model)
        # QAT training would go here
        model_quantized = convert(model_qat)
        return model_quantized

    def apply_pruning(self, model: nn.Module) -> nn.Module:
        """Apply model pruning based on configuration."""
        if self.config.pruning_method == "l1":
            return self._apply_l1_pruning(model)
        elif self.config.pruning_method == "random":
            return self._apply_random_pruning(model)
        elif self.config.pruning_method == "structured":
            return self._apply_structured_pruning(model)
        else:
            raise ValueError(f"Unknown pruning method: {self.config.pruning_method}")

    def _apply_l1_pruning(self, model: nn.Module) -> nn.Module:
        """Apply L1 unstructured pruning."""
        for name, module in model.named_modules():
            if isinstance(module, nn.Linear):
                prune.l1_unstructured(
                    module, name="weight", amount=self.config.pruning_amount
                )
                prune.remove(module, "weight")
        return model

    def _apply_random_pruning(self, model: nn.Module) -> nn.Module:
        """Apply random unstructured pruning."""
        for name, module in model.named_modules():
            if isinstance(module, nn.Linear):
                prune.random_unstructured(
                    module, name="weight", amount=self.config.pruning_amount
                )
                prune.remove(module, "weight")
        return model

    def _apply_structured_pruning(self, model: nn.Module) -> nn.Module:
        """Apply structured pruning."""
        for name, module in model.named_modules():
            if isinstance(module, nn.Linear):
                prune.ln_structured(
                    module, name="weight", amount=self.config.pruning_amount, n=2, dim=0
                )
                prune.remove(module, "weight")
        return model

    def perform_architecture_search(
        self, model: nn.Module, train_loader, val_loader
    ) -> nn.Module:
        """Perform neural architecture search using Ray Tune."""

        def training_function(config):
            # Modify model architecture based on config
            model_copy = self._modify_architecture(model, config)

            # Train and evaluate
            trainer = self._setup_trainer(model_copy, config)
            result = trainer.train()

            # Report metrics
            tune.report(**result)

        # Define search space
        search_space = {
            "lr": tune.loguniform(1e-4, 1e-1),
            "batch_size": tune.choice([16, 32, 64, 128]),
            "hidden_size": tune.choice([64, 128, 256, 512]),
            "num_layers": tune.choice([2, 3, 4, 5]),
        }

        # Run hyperparameter search
        analysis = tune.run(
            training_function,
            config=search_space,
            num_samples=self.config.num_samples,
            scheduler=tune.schedulers.ASHAScheduler(
                max_t=self.config.max_num_epochs, grace_period=1, reduction_factor=2
            ),
        )

        # Get best configuration
        best_config = analysis.get_best_config(
            metric=self.config.target_metric, mode="min"
        )

        # Return model with best architecture
        return self._modify_architecture(model, best_config)

    def _modify_architecture(self, model: nn.Module, config: Dict) -> nn.Module:
        """Modify model architecture based on configuration."""
        # Implementation depends on model architecture
        return model

    def validate_optimization(
        self, model: nn.Module, val_loader
    ) -> Tuple[nn.Module, Dict[str, float]]:
        """Validate optimized model performance."""
        metrics = {}

        # Measure latency
        if self.config.optimization_metric == "latency":
            latency = self._measure_latency(model, val_loader)
            metrics["latency"] = latency

        # Measure memory usage
        if self.config.optimization_metric == "memory":
            memory = self._measure_memory_usage(model)
            metrics["memory"] = memory

        # Measure accuracy
        if self.config.optimization_metric == "accuracy":
            accuracy = self._measure_accuracy(model, val_loader)
            metrics["accuracy"] = accuracy

        self.logger.info(f"Optimization metrics: {metrics}")
        return model, metrics

    def _measure_latency(self, model: nn.Module, val_loader) -> float:
        """Measure model inference latency."""
        model.eval()
        latencies = []

        with torch.no_grad():
            for inputs, _ in val_loader:
                start_time = torch.cuda.Event(enable_timing=True)
                end_time = torch.cuda.Event(enable_timing=True)

                start_time.record()
                with autocast():
                    _ = model(inputs)
                end_time.record()

                torch.cuda.synchronize()
                latencies.append(start_time.elapsed_time(end_time))

        return np.mean(latencies)

    def _measure_memory_usage(self, model: nn.Module) -> float:
        """Measure model memory usage."""
        torch.cuda.reset_peak_memory_stats()
        torch.cuda.empty_cache()

        # Perform a forward pass to measure memory
        dummy_input = torch.randn(1, *model.input_shape).to(self.device)
        with autocast():
            _ = model(dummy_input)

        memory_usage = torch.cuda.max_memory_allocated() / 1024 / 1024  # MB
        return memory_usage

    def _measure_accuracy(self, model: nn.Module, val_loader) -> float:
        """Measure model accuracy."""
        model.eval()
        correct = 0
        total = 0

        with torch.no_grad():
            for inputs, targets in val_loader:
                inputs = inputs.to(self.device)
                targets = targets.to(self.device)

                with autocast():
                    outputs = model(inputs)
                    _, predicted = torch.max(outputs.data, 1)

                total += targets.size(0)
                correct += (predicted == targets).sum().item()

        return correct / total
