"""
Enhanced Multimodal Processor for Bleu.js
Integrates vision, text, and audio processing with quantum capabilities
"""

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

import numpy as np

from ..computer_vision.vision_processor import VisionConfig, VisionProcessor
from ..quantum.quantum_enhancer import QuantumEnhancer


@dataclass
class MultimodalConfig:
    """Configuration for multimodal processing."""

    vision_config: Optional[VisionConfig] = None
    max_text_length: int = 512
    max_audio_length: int = 30  # seconds
    batch_size: int = 32
    use_quantum: bool = True
    fusion_method: str = "attention"  # attention, concat, or quantum
    model_path: str = "models/multimodal"
    cache_results: bool = True
    version: str = "1.1.4"


@dataclass
class MultimodalInput:
    """Input data for multimodal processing."""

    text: Optional[str] = None
    image: Optional[np.ndarray] = None
    audio: Optional[np.ndarray] = None
    metadata: Optional[Dict[str, Any]] = None


@dataclass
class MultimodalOutput:
    """Output data from multimodal processing."""

    features: np.ndarray
    confidence: float
    modalities: Dict[str, float]  # Confidence scores for each modality
    fusion_weights: Dict[str, float]  # Weights used in fusion
    metadata: Dict[str, Any]


class MultimodalProcessor:
    """Enhanced multimodal processor with quantum capabilities."""

    def __init__(self, config: Optional[MultimodalConfig] = None):
        self.config = config or MultimodalConfig()
        self.logger = logging.getLogger(__name__)
        self.vision_processor = VisionProcessor(self.config.vision_config)
        self.quantum_enhancer = None
        self.models = {}
        self.initialized = False

    async def initialize(self) -> None:
        """Initialize all components of the multimodal processor."""
        try:
            # Initialize vision processor
            await self.vision_processor.initialize()

            # Initialize quantum backend if enabled
            if self.config.use_quantum:
                self.quantum_enhancer = QuantumEnhancer()
                await self.quantum_enhancer.initialize()

            # Load multimodal models
            self.models["text_encoder"] = await self._load_text_encoder()
            self.models["audio_encoder"] = await self._load_audio_encoder()
            self.models["fusion_model"] = await self._load_fusion_model()

            self.initialized = True
            self.logger.info("Multimodal processor initialized successfully")

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

    async def process(self, input_data: MultimodalInput) -> MultimodalOutput:
        """Process multimodal input data."""
        if not self.initialized:
            raise RuntimeError("Multimodal processor not initialized")

        try:
            # Process each modality in parallel
            tasks = []
            if input_data.image is not None:
                tasks.append(self._process_vision(input_data.image))
            if input_data.text is not None:
                tasks.append(self._process_text(input_data.text))
            if input_data.audio is not None:
                tasks.append(self._process_audio(input_data.audio))

            results = await asyncio.gather(*tasks)

            # Combine results
            features, confidences = self._combine_results(results)

            # Apply quantum enhancement if enabled
            if self.config.use_quantum and self.quantum_enhancer:
                features = await self.quantum_enhancer.enhance(features)

            # Calculate fusion weights
            fusion_weights = self._calculate_fusion_weights(confidences)

            return MultimodalOutput(
                features=features,
                confidence=np.mean(list(confidences.values())),
                modalities=confidences,
                fusion_weights=fusion_weights,
                metadata=input_data.metadata or {},
            )

        except Exception as e:
            self.logger.error(f"Error processing multimodal data: {str(e)}")
            raise

    async def _process_vision(self, image: np.ndarray) -> Tuple[np.ndarray, float]:
        """Process vision input."""
        vision_output = await self.vision_processor.process_image(image)
        return vision_output.features, vision_output.confidence

    async def _process_text(self, text: str) -> Tuple[np.ndarray, float]:
        """Process text input."""
        # Implement text processing logic here
        # This would use the text_encoder model
        raise NotImplementedError("Text processing not implemented yet")

    async def _process_audio(self, audio: np.ndarray) -> Tuple[np.ndarray, float]:
        """Process audio input."""
        # Implement audio processing logic here
        # This would use the audio_encoder model
        raise NotImplementedError("Audio processing not implemented yet")

    def _combine_results(
        self, results: List[Tuple[np.ndarray, float]]
    ) -> Tuple[np.ndarray, Dict[str, float]]:
        """Combine results from different modalities."""
        features = []
        confidences = {}

        for i, (feature, confidence) in enumerate(results):
            features.append(feature)
            confidences[f"modality_{i}"] = confidence

        # Combine features based on fusion method
        if self.config.fusion_method == "concat":
            combined_features = np.concatenate(features, axis=-1)
        elif self.config.fusion_method == "attention":
            combined_features = self._apply_attention_fusion(features)
        else:  # quantum fusion
            combined_features = self._apply_quantum_fusion(features)

        return combined_features, confidences

    def _apply_attention_fusion(self, features: List[np.ndarray]) -> np.ndarray:
        """Apply attention-based fusion to features."""
        # Implement attention fusion logic here
        raise NotImplementedError("Attention fusion not implemented yet")

    def _apply_quantum_fusion(self, features: List[np.ndarray]) -> np.ndarray:
        """Apply quantum-based fusion to features."""
        # Implement quantum fusion logic here
        raise NotImplementedError("Quantum fusion not implemented yet")

    def _calculate_fusion_weights(
        self, confidences: Dict[str, float]
    ) -> Dict[str, float]:
        """Calculate fusion weights based on modality confidences."""
        total_confidence = sum(confidences.values())
        if total_confidence == 0:
            return {k: 1.0 / len(confidences) for k in confidences}
        return {k: v / total_confidence for k, v in confidences.items()}

    async def _load_text_encoder(self):
        """Load text encoder model."""
        # Implement text encoder loading logic here
        raise NotImplementedError("Text encoder loading not implemented yet")

    async def _load_audio_encoder(self):
        """Load audio encoder model."""
        # Implement audio encoder loading logic here
        raise NotImplementedError("Audio encoder loading not implemented yet")

    async def _load_fusion_model(self):
        """Load fusion model."""
        # Implement fusion model loading logic here
        raise NotImplementedError("Fusion model loading not implemented yet")
