"""
Silero VAD Processor

Lightweight CPU-based Voice Activity Detection for real-time speech detection.
Runs continuously on audio chunks to determine when users are speaking.
"""

import torch
import numpy as np
from typing import Tuple, Optional
import logging

logger = logging.getLogger('vad')


class VADProcessor:
    """
    Voice Activity Detection using Silero VAD model.
    
    Processes audio chunks and returns speech probability.
    Conservative settings to avoid cutting off speech.
    """
    
    def __init__(
        self,
        sample_rate: int = 16000,
        threshold: float = 0.5,
        min_speech_duration_ms: int = 250,
        min_silence_duration_ms: int = 500,
        speech_pad_ms: int = 30
    ):
        """
        Initialize VAD processor.
        
        Args:
            sample_rate: Audio sample rate (must be 8000 or 16000)
            threshold: Speech probability threshold (0.0-1.0)
            min_speech_duration_ms: Minimum speech duration to trigger (conservative)
            min_silence_duration_ms: Minimum silence to end speech (conservative)
            speech_pad_ms: Padding around speech segments
        """
        self.sample_rate = sample_rate
        self.threshold = threshold
        self.min_speech_duration_ms = min_speech_duration_ms
        self.min_silence_duration_ms = min_silence_duration_ms
        self.speech_pad_ms = speech_pad_ms
        
        # Load Silero VAD model (CPU only)
        logger.info("Loading Silero VAD model (CPU)...")
        self.model, utils = torch.hub.load(
            repo_or_dir='snakers4/silero-vad',
            model='silero_vad',
            force_reload=False,
            onnx=False  # Use PyTorch model
        )
        
        # Extract utility functions
        (self.get_speech_timestamps,
         self.save_audio,
         self.read_audio,
         self.VADIterator,
         self.collect_chunks) = utils
        
        # State tracking
        self.speaking = False
        self.speech_start_time = None
        self.silence_start_time = None
        self.audio_buffer = []
        
        # Chunk buffer for VAD (Silero needs at least 512 samples)
        self.vad_buffer = []
        self.min_vad_samples = 512  # Minimum samples for VAD processing
        
        logger.info(f"VAD initialized: threshold={threshold}, "
                   f"min_speech={min_speech_duration_ms}ms, "
                   f"min_silence={min_silence_duration_ms}ms")
    
    def process_chunk(self, audio_chunk: np.ndarray) -> Tuple[float, bool]:
        """
        Process single audio chunk and return speech probability.
        Buffers small chunks to meet VAD minimum size requirement.
        
        Args:
            audio_chunk: Audio data as numpy array (int16 or float32)
        
        Returns:
            (speech_probability, is_speaking): Probability and current speaking state
        """
        # Convert to float32 if needed
        if audio_chunk.dtype == np.int16:
            audio_chunk = audio_chunk.astype(np.float32) / 32768.0
        
        # Add to buffer
        self.vad_buffer.append(audio_chunk)
        
        # Check if we have enough samples
        total_samples = sum(len(chunk) for chunk in self.vad_buffer)
        
        if total_samples < self.min_vad_samples:
            # Not enough samples yet, return neutral probability
            return 0.0, False
        
        # Concatenate buffer
        audio_full = np.concatenate(self.vad_buffer)
        
        # Process with VAD
        audio_tensor = torch.from_numpy(audio_full)
        
        with torch.no_grad():
            speech_prob = self.model(audio_tensor, self.sample_rate).item()
        
        # Clear buffer after processing
        self.vad_buffer = []
        
        # Update speaking state based on probability
        is_speaking = speech_prob > self.threshold
        
        return speech_prob, is_speaking
    
    def detect_speech_segment(
        self,
        audio_chunk: np.ndarray,
        timestamp_ms: float
    ) -> Optional[dict]:
        """
        Process chunk and detect speech start/end events.
        
        Args:
            audio_chunk: Audio data
            timestamp_ms: Current timestamp in milliseconds
        
        Returns:
            Event dict or None:
            - {"event": "speech_start", "timestamp": float, "probability": float}
            - {"event": "speech_end", "timestamp": float, "probability": float}
            - {"event": "speaking", "probability": float}  # Ongoing speech
        """
        speech_prob, is_speaking = self.process_chunk(audio_chunk)
        
        # Speech started
        if is_speaking and not self.speaking:
            if self.speech_start_time is None:
                self.speech_start_time = timestamp_ms
            
            # Check if speech duration exceeds minimum
            speech_duration = timestamp_ms - self.speech_start_time
            if speech_duration >= self.min_speech_duration_ms:
                self.speaking = True
                self.silence_start_time = None
                logger.debug(f"Speech started at {timestamp_ms}ms, prob={speech_prob:.3f}")
                return {
                    "event": "speech_start",
                    "timestamp": timestamp_ms,
                    "probability": speech_prob
                }
        
        # Speech ongoing
        elif is_speaking and self.speaking:
            self.silence_start_time = None  # Reset silence timer
            return {
                "event": "speaking",
                "probability": speech_prob,
                "timestamp": timestamp_ms
            }
        
        # Silence detected during speech
        elif not is_speaking and self.speaking:
            if self.silence_start_time is None:
                self.silence_start_time = timestamp_ms
            
            # Check if silence duration exceeds minimum
            silence_duration = timestamp_ms - self.silence_start_time
            if silence_duration >= self.min_silence_duration_ms:
                self.speaking = False
                self.speech_start_time = None
                logger.debug(f"Speech ended at {timestamp_ms}ms, prob={speech_prob:.3f}")
                return {
                    "event": "speech_end",
                    "timestamp": timestamp_ms,
                    "probability": speech_prob
                }
        
        # No speech or insufficient duration
        else:
            if not is_speaking:
                self.speech_start_time = None
        
        return None
    
    def reset(self):
        """Reset VAD state."""
        self.speaking = False
        self.speech_start_time = None
        self.silence_start_time = None
        self.audio_buffer.clear()
        logger.debug("VAD state reset")
    
    def get_state(self) -> dict:
        """Get current VAD state."""
        return {
            "speaking": self.speaking,
            "speech_start_time": self.speech_start_time,
            "silence_start_time": self.silence_start_time
        }