"""
BarVox Audio Processing API - Audio Preprocessing Functions
"""

import logging
import numpy as np
import torch
import torchaudio.transforms as T
from scipy.signal import butter, filtfilt
from typing import Optional

logger = logging.getLogger(__name__)

def butter_highpass(cutoff, fs, order=5):
    """Create a Butterworth high-pass filter."""
    nyq = 0.5 * fs
    normal_cutoff = cutoff / nyq
    b, a = butter(order, normal_cutoff, btype='high', analog=False)
    return b, a

def butter_bandpass(lowcut, highcut, fs, order=5):
    """Create a Butterworth band-pass filter."""
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    b, a = butter(order, [low, high], btype='band')
    return b, a

def highpass_filter(data, cutoff=30, fs=16000, order=2):
    """Apply a high-pass filter to remove low-frequency noise."""
    b, a = butter_highpass(cutoff, fs, order=order)
    y = filtfilt(b, a, data).copy()
    return y

def rms_normalize(waveform_np, target_dbfs=-20.0):
    """Normalize waveform to target dBFS level."""
    rms = np.sqrt(np.mean(np.square(waveform_np)))
    if rms == 0:
        return waveform_np
    scalar = (10 ** (target_dbfs / 20)) / rms
    return waveform_np * scalar

def resample_to_16khz_mono(waveform: torch.Tensor, orig_sample_rate: int) -> torch.Tensor:
    """Resample audio to 16kHz mono."""
    if waveform.shape[0] > 1:
        waveform = torch.mean(waveform, dim=0, keepdim=True)
    
    if orig_sample_rate != 16000:
        resampler = T.Resample(orig_freq=orig_sample_rate, new_freq=16000)
        waveform = resampler(waveform)
    
    return waveform

def apply_noise_reduction(waveform_np, sample_rate=16000):
    """Apply noise reduction."""
    waveform_np = waveform_np - np.mean(waveform_np)
    waveform_np = highpass_filter(waveform_np, cutoff=30, fs=sample_rate, order=2)
    return waveform_np

def apply_normalization(waveform_np, target_dbfs=-20.0):
    """Apply RMS normalization."""
    return rms_normalize(waveform_np, target_dbfs=target_dbfs)

def apply_dynamic_compression(waveform_np, threshold_ratio=0.5, ratio=3.0):
    """Apply dynamic range compression."""
    rms = np.sqrt(np.mean(np.square(waveform_np)))
    threshold = rms * threshold_ratio
    
    compressed = np.copy(waveform_np)
    mask = np.abs(waveform_np) > threshold
    
    compressed[mask] = np.sign(waveform_np[mask]) * (
        threshold + (np.abs(waveform_np[mask]) - threshold) / ratio
    )
    
    gain = np.max(np.abs(waveform_np)) / np.max(np.abs(compressed)) if np.max(np.abs(compressed)) > 0 else 1.0
    compressed = compressed * min(gain, 2.0)
    
    logger.info(f"Applied dynamic compression (threshold_ratio={threshold_ratio}, ratio={ratio})")
    return compressed

def apply_transient_enhancement(waveform_np, sample_rate=16000, attack_boost=1.5):
    """Enhance transients."""
    window_size = int(sample_rate * 0.010)
    envelope = np.zeros_like(waveform_np)
    
    for i in range(len(waveform_np)):
        start = max(0, i - window_size // 2)
        end = min(len(waveform_np), i + window_size // 2)
        envelope[i] = np.sqrt(np.mean(np.square(waveform_np[start:end])))
    
    envelope_diff = np.diff(envelope, prepend=envelope[0])
    transient_mask = envelope_diff > (np.std(envelope_diff) * 0.5)
    
    boost = np.ones_like(waveform_np)
    boost[transient_mask] = attack_boost
    
    from scipy.ndimage import gaussian_filter1d
    boost_smooth = gaussian_filter1d(boost, sigma=window_size / 4)
    
    enhanced = waveform_np * boost_smooth
    
    max_val = np.max(np.abs(enhanced))
    if max_val > 0:
        enhanced = enhanced / max_val * np.max(np.abs(waveform_np))
    
    logger.info(f"Applied transient enhancement (boost={attack_boost})")
    return enhanced

def apply_high_frequency_boost(waveform_np, sample_rate=16000, boost_db=6.0):
    """Apply high-frequency boost (EQ)."""
    b, a = butter_bandpass(2000, 6000, sample_rate, order=4)
    presence_band = filtfilt(b, a, waveform_np)
    
    boost_factor = 10 ** (boost_db / 20.0)
    boosted = waveform_np + presence_band * (boost_factor - 1.0)
    
    max_val = np.max(np.abs(boosted))
    if max_val > 1.0:
        boosted = boosted / max_val
    
    logger.info(f"Applied high-frequency boost (+{boost_db} dB in 2-6 kHz)")
    return boosted

def apply_silero_vad(
    waveform: torch.Tensor,
    sample_rate: int = 16000,
    threshold: float = 0.35,
    min_speech_duration_ms: int = 60,
    min_silence_duration_ms: int = 300,
    padding_before_ms: int = 180,
    padding_after_ms: int = 900,
    max_speech_duration_s: float = 0,
    chunk_selection: str = 'longest'
) -> Optional[torch.Tensor]:
    """Apply Silero VAD with support for 'first', 'longest', or 'last' chunk selection."""
    try:
        from model_loader import get_models
        models = get_models()
        model = models['silero_vad']
        utils = models['silero_utils']
        (get_speech_timestamps, _, _, _, _) = utils
        
        if isinstance(waveform, torch.Tensor):
            waveform_np = waveform.squeeze().cpu().numpy()
        else:
            waveform_np = waveform
        
        total_duration_ms = (len(waveform_np) / sample_rate) * 1000
        
        speech_timestamps = get_speech_timestamps(
            waveform_np, # This was the input for 'x'
            model,
            sampling_rate=sample_rate, # This was the input for 'sr' or 'r'
            threshold=threshold,
            min_speech_duration_ms=min_speech_duration_ms,
            min_silence_duration_ms=min_silence_duration_ms,
            speech_pad_ms=0,
            max_speech_duration_s=max_speech_duration_s if max_speech_duration_s > 0 else float('inf'),
            return_seconds=False
        )
        
        if not speech_timestamps:
            logger.warning("No speech detected by Silero VAD")
            return None
        
        # Original logic was:
        # if chunk_selection == 'first':
        #     selected_chunk = speech_timestamps[0]
        # else:
        #     selected_chunk = max(speech_timestamps, key=lambda x: x['end'] - x['start'])
        
        # Updated to include 'last' option
        if chunk_selection == 'first':
            selected_chunk = speech_timestamps[0]
        elif chunk_selection == 'last':
            selected_chunk = speech_timestamps[-1]
        else:  # 'longest' (default)
            selected_chunk = max(speech_timestamps, key=lambda x: x['end'] - x['start'])
        
        vad_start_ms = (selected_chunk['start'] / sample_rate) * 1000
        vad_end_ms = (selected_chunk['end'] / sample_rate) * 1000
        
        pad_before_samples = int((padding_before_ms / 1000) * sample_rate)
        pad_after_samples = int((padding_after_ms / 1000) * sample_rate)
        
        start_idx = max(0, selected_chunk['start'] - pad_before_samples)
        end_idx = min(len(waveform_np), selected_chunk['end'] + pad_after_samples)
        
        trimmed_waveform_np = waveform_np.copy()[start_idx:end_idx]
        trimmed_tensor = torch.from_numpy(trimmed_waveform_np).float()
        
        final_start_ms = (start_idx / sample_rate) * 1000
        final_end_ms = (end_idx / sample_rate) * 1000
        final_duration_ms = final_end_ms - final_start_ms
        
        logger.info(f"VAD: Original={total_duration_ms:.0f}ms | Speech=[{vad_start_ms:.0f}-{vad_end_ms:.0f}]ms | Final=[{final_start_ms:.0f}-{final_end_ms:.0f}]ms ({final_duration_ms:.0f}ms) | Selection={chunk_selection}")
        
        return trimmed_tensor
        
    except Exception as e:
        logger.error(f"Error in Silero VAD: {e}")
        return None