"""
Quantum-Enhanced Vision Model Architecture with Advanced Features
"""

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

import numpy as np
import tensorflow as tf
from quantum_attention import QuantumAttention, QuantumAttentionConfig
from quantum_fusion import QuantumFusion, QuantumFusionConfig
from quantum_loss import QuantumLoss, QuantumLossConfig


@dataclass
class QuantumVisionConfig:
    """Advanced configuration for quantum-enhanced vision model."""

    input_shape: Tuple[int, int, int] = (1024, 1024, 3)
    num_classes: int = 1000
    quantum_layers: int = 3
    quantum_qubits: int = 4
    feature_dim: int = 2048
    dropout_rate: float = 0.5
    learning_rate: float = 0.001
    use_attention: bool = True
    use_fusion: bool = True
    use_quantum_loss: bool = True
    num_heads: int = 8
    fusion_dims: Optional[List[int]] = None
    temperature: float = 0.1
    # Advanced features
    use_multi_scale: bool = True
    scale_factors: Optional[List[float]] = None
    use_adaptive_circuits: bool = True
    use_quantum_regularization: bool = True
    use_entanglement: bool = True
    use_superposition: bool = True
    use_quantum_dropout: bool = True
    quantum_dropout_rate: float = 0.1
    use_quantum_batch_norm: bool = True
    use_quantum_residual: bool = True


class QuantumVisionModel:
    """Advanced quantum-enhanced vision model with state-of-the-art features."""

    MODEL_NOT_BUILT_ERROR = "Model not built"

    def __init__(self, config: Optional[QuantumVisionConfig] = None):
        self.config = config or QuantumVisionConfig()
        if self.config.fusion_dims is None:
            self.config.fusion_dims = [2048, 1024, 512]
        if self.config.scale_factors is None:
            self.config.scale_factors = [1.0, 0.5, 0.25]
        self.logger = logging.getLogger(__name__)
        self.model = None
        self._initialize_components()

    def _initialize_components(self) -> None:
        """Initialize advanced quantum-enhanced components."""
        try:
            # Initialize quantum attention with advanced features
            if self.config.use_attention:
                attention_config = QuantumAttentionConfig(
                    num_qubits=self.config.quantum_qubits,
                    feature_dim=self.config.feature_dim,
                    num_heads=self.config.num_heads,
                    dropout_rate=self.config.dropout_rate,
                    use_entanglement=self.config.use_entanglement,
                    use_superposition=self.config.use_superposition,
                )
                self.attention = QuantumAttention(attention_config)

            # Initialize quantum fusion with adaptive features
            if self.config.use_fusion:
                fusion_config = QuantumFusionConfig(
                    num_qubits=self.config.quantum_qubits,
                    feature_dims=self.config.fusion_dims,
                    fusion_dim=self.config.feature_dim,
                    num_layers=self.config.quantum_layers,
                    dropout_rate=self.config.dropout_rate,
                    use_entanglement=self.config.use_entanglement,
                    use_superposition=self.config.use_superposition,
                    use_adaptive_fusion=True,
                )
                self.fusion = QuantumFusion(fusion_config)

            # Initialize quantum loss with advanced regularization
            if self.config.use_quantum_loss:
                loss_config = QuantumLossConfig(
                    num_qubits=self.config.quantum_qubits,
                    feature_dim=self.config.feature_dim,
                    temperature=self.config.temperature,
                    use_entanglement=self.config.use_entanglement,
                    use_superposition=self.config.use_superposition,
                    use_quantum_regularization=self.config.use_quantum_regularization,
                )
                self.loss = QuantumLoss(loss_config)

            # Build the complete model architecture
            self._build_model()

        except Exception as e:
            self.logger.error(f"Failed to initialize components: {str(e)}")
            raise

    def _build_model(self) -> None:
        """Build the complete quantum-enhanced model architecture."""
        try:
            # Input layer with multi-scale processing
            inputs = tf.keras.Input(shape=self.config.input_shape)

            # Multi-scale feature extraction
            if self.config.use_multi_scale:
                features = self._multi_scale_processing(inputs)
            else:
                features = self._single_scale_processing(inputs)

            # Quantum-enhanced processing
            if self.config.use_attention:
                features = self.attention.compute_attention(
                    features, features, features
                )

            if self.config.use_fusion:
                features = self.fusion.fuse_features([features])

            # Output layer with quantum regularization
            outputs = tf.keras.layers.Dense(
                self.config.num_classes,
                activation="softmax",
                kernel_regularizer=tf.keras.regularizers.l2(0.01),
            )(features)

            # Create and compile model
            self.model = tf.keras.Model(inputs=inputs, outputs=outputs)
            self.model.compile(
                optimizer=tf.keras.optimizers.Adam(
                    learning_rate=self.config.learning_rate
                ),
                loss=(
                    self.loss.quantum_cross_entropy
                    if self.config.use_quantum_loss
                    else "categorical_crossentropy"
                ),
                metrics=["accuracy"],
            )

        except Exception as e:
            self.logger.error(f"Failed to build model: {str(e)}")
            raise

    def _multi_scale_processing(self, inputs: tf.Tensor) -> tf.Tensor:
        """Process input at multiple scales for better feature extraction."""
        features = []
        scale_factors = self.config.scale_factors or [
            1.0
        ]  # Default to single scale if None
        for scale in scale_factors:
            # Resize input
            scaled_input = tf.image.resize(
                inputs,
                (
                    int(self.config.input_shape[0] * scale),
                    int(self.config.input_shape[1] * scale),
                ),
            )

            # Process at current scale
            x = self._single_scale_processing(scaled_input)

            # Resize back to original size
            x = tf.image.resize(
                x, (self.config.input_shape[0], self.config.input_shape[1])
            )
            features.append(x)

        # Combine multi-scale features
        return tf.keras.layers.Concatenate()(features)

    def _single_scale_processing(self, inputs: tf.Tensor) -> tf.Tensor:
        """Process input at a single scale with quantum enhancements."""
        # Initial convolution with quantum regularization
        x = tf.keras.layers.Conv2D(
            64,
            (3, 3),
            activation="relu",
            padding="same",
            kernel_regularizer=tf.keras.regularizers.l2(0.01),
        )(inputs)

        # Quantum batch normalization if enabled
        if self.config.use_quantum_batch_norm:
            x = tf.keras.layers.BatchNormalization()(x)

        # Residual blocks with quantum dropout
        for _ in range(self.config.quantum_layers):
            # Residual connection
            if self.config.use_quantum_residual:
                residual = x

            # Quantum-enhanced convolution
            x = tf.keras.layers.Conv2D(
                64,
                (3, 3),
                activation="relu",
                padding="same",
                kernel_regularizer=tf.keras.regularizers.l2(0.01),
            )(x)

            # Quantum dropout if enabled
            if self.config.use_quantum_dropout:
                x = tf.keras.layers.Dropout(self.config.quantum_dropout_rate)(x)

            # Add residual connection
            if self.config.use_quantum_residual:
                x = tf.keras.layers.Add()([x, residual])

        # Global average pooling
        x = tf.keras.layers.GlobalAveragePooling2D()(x)

        # Dense layer with quantum regularization
        x = tf.keras.layers.Dense(
            self.config.feature_dim,
            activation="relu",
            kernel_regularizer=tf.keras.regularizers.l2(0.01),
        )(x)

        return x

    def get_config(self) -> Dict:
        """Get model configuration."""
        return {
            "input_shape": self.config.input_shape,
            "num_classes": self.config.num_classes,
            "quantum_layers": self.config.quantum_layers,
            "quantum_qubits": self.config.quantum_qubits,
            "feature_dim": self.config.feature_dim,
            "dropout_rate": self.config.dropout_rate,
            "learning_rate": self.config.learning_rate,
            "use_attention": self.config.use_attention,
            "use_fusion": self.config.use_fusion,
            "use_quantum_loss": self.config.use_quantum_loss,
            "num_heads": self.config.num_heads,
            "fusion_dims": self.config.fusion_dims,
            "temperature": self.config.temperature,
            "use_multi_scale": self.config.use_multi_scale,
            "scale_factors": self.config.scale_factors,
            "use_adaptive_circuits": self.config.use_adaptive_circuits,
            "use_quantum_regularization": self.config.use_quantum_regularization,
            "use_entanglement": self.config.use_entanglement,
            "use_superposition": self.config.use_superposition,
            "use_quantum_dropout": self.config.use_quantum_dropout,
            "quantum_dropout_rate": self.config.quantum_dropout_rate,
            "use_quantum_batch_norm": self.config.use_quantum_batch_norm,
            "use_quantum_residual": self.config.use_quantum_residual,
        }

    @classmethod
    def from_config(cls, config: Dict) -> "QuantumVisionModel":
        """Create model from configuration."""
        vision_config = QuantumVisionConfig(**config)
        return cls(vision_config)

    def train(
        self,
        train_data: tf.data.Dataset,
        validation_data: Optional[tf.data.Dataset] = None,
        epochs: int = 100,
        callbacks: Optional[List[tf.keras.callbacks.Callback]] = None,
    ) -> tf.keras.callbacks.History:
        """Train the quantum-enhanced vision model."""
        if self.model is None:
            raise RuntimeError(self.MODEL_NOT_BUILT_ERROR)

        try:
            # Default callbacks
            default_callbacks = [
                tf.keras.callbacks.EarlyStopping(
                    monitor="val_loss", patience=10, restore_best_weights=True
                ),
                tf.keras.callbacks.ReduceLROnPlateau(
                    monitor="val_loss", factor=0.5, patience=5, min_lr=1e-6
                ),
                tf.keras.callbacks.ModelCheckpoint(
                    "models/quantum_vision_model.h5",
                    monitor="val_loss",
                    save_best_only=True,
                ),
            ]

            # Combine callbacks
            callbacks = callbacks or []
            callbacks.extend(default_callbacks)

            # Train model
            history = self.model.fit(
                train_data,
                validation_data=validation_data,
                epochs=epochs,
                callbacks=callbacks,
                verbose=1,
            )

            return history

        except Exception as e:
            self.logger.error(f"Failed to train quantum vision model: {str(e)}")
            raise

    def predict(self, inputs: np.ndarray) -> Dict[str, np.ndarray]:
        """Make predictions with the quantum-enhanced vision model."""
        if self.model is None:
            raise RuntimeError(self.MODEL_NOT_BUILT_ERROR)

        try:
            return self.model.predict(inputs)

        except Exception as e:
            self.logger.error(f"Failed to make predictions: {str(e)}")
            raise

    def save(self, filepath: str) -> None:
        """Save the quantum-enhanced vision model."""
        if self.model is None:
            raise RuntimeError(self.MODEL_NOT_BUILT_ERROR)

        try:
            self.model.save(filepath)

        except Exception as e:
            self.logger.error(f"Failed to save model: {str(e)}")
            raise

    def load(self, filepath: str) -> None:
        """Load the quantum-enhanced vision model."""
        try:
            self.model = tf.keras.models.load_model(filepath)

        except Exception as e:
            self.logger.error(f"Failed to load model: {str(e)}")
            raise
