"""Audio conversion service"""

from io import BytesIO
import struct

import numpy as np
import scipy.io.wavfile as wavfile
import soundfile as sf
from loguru import logger
from pydub import AudioSegment

from ..core.config import settings


class AudioNormalizer:
    """Handles audio normalization state for a single stream"""

    def __init__(self):
        self.int16_max = np.iinfo(np.int16).max
        self.chunk_trim_ms = settings.gap_trim_ms
        self.sample_rate = 24000  # Sample rate of the audio
        self.samples_to_trim = int(self.chunk_trim_ms * self.sample_rate / 1000)

    async def normalize(self, audio_data: np.ndarray) -> np.ndarray:
        """Convert audio data to int16 range and trim silence from start and end
        
        Args:
            audio_data: Input audio data as numpy array
            
        Returns:
            Normalized and trimmed audio data
        """
        if len(audio_data) == 0:
            raise ValueError("Audio data cannot be empty")
            
        # Convert to float32 for processing
        audio_float = audio_data.astype(np.float32)
        
        # Trim start and end if enough samples
        if len(audio_float) > (2 * self.samples_to_trim):
            audio_float = audio_float[self.samples_to_trim:-self.samples_to_trim]
        
        # Scale to int16 range
        return (audio_float * 32767).astype(np.int16)


class AudioService:
    """Service for audio format conversions"""

    # Default audio format settings balanced for speed and compression
    DEFAULT_SETTINGS = {
        "mp3": {
            "bitrate_mode": "CONSTANT",  # Faster than variable bitrate
            "compression_level": 0.0,  # Balanced compression
        },
        "opus": {
            "compression_level": 0.0,  # Good balance for speech
        },
        "flac": {
            "compression_level": 0.0,  # Light compression, still fast
        },
        "aac": {
            "bitrate": "192k",  # Default AAC bitrate
        },
    }

    @staticmethod
    async def convert_audio(
        audio_data: np.ndarray,
        sample_rate: int,
        output_format: str,
        is_first_chunk: bool = True,
        is_last_chunk: bool = False,
        normalizer: AudioNormalizer = None,
        format_settings: dict = None,
        stream: bool = True,
    ) -> bytes:
        """Convert audio data to specified format

        Args:
            audio_data: Numpy array of audio samples
            sample_rate: Sample rate of the audio
            output_format: Target format (wav, mp3, opus, flac, pcm)
            is_first_chunk: Whether this is the first chunk of a stream
            normalizer: Optional AudioNormalizer instance for consistent normalization across chunks
            format_settings: Optional dict of format-specific settings to override defaults
                Example: {
                    "mp3": {
                        "bitrate_mode": "VARIABLE",
                        "compression_level": 0.8
                    }
                }
                Default settings balance speed and compression:
                optimized for localhost @ 0.0
                - MP3: constant bitrate, no compression (0.0)
                - OPUS: no compression (0.0)
                - FLAC: no compression (0.0)

        Returns:
            Bytes of the converted audio
        """
        buffer = BytesIO()

        try:
            # Always normalize audio to ensure proper amplitude scaling
            if normalizer is None:
                normalizer = AudioNormalizer()
            normalized_audio = await normalizer.normalize(audio_data)

            if output_format == "pcm":
                # Raw 16-bit PCM samples, no header
                buffer.write(normalized_audio.tobytes())
            elif output_format == "wav":
                # Write the WAV header ourselves so that we can specify a "fake" data size.
                # This is necessary for streaming responses to work properly: if we simply
                # concatenated individual WAV files then the initial chunk's header length
                # would be shorter than the full file length and subsequent chunks' RIFF
                # headers would appear in the middle of the audio data.
                if is_first_chunk:
                    # Modified from Python stdlib's wave.py module:
                    buffer.write(b'RIFF')
                    buffer.write(struct.pack('<L4s4sLHHLLHH4s',
                        0xFFFFFFFF,  # total size (set to max)
                        b'WAVE',
                        b'fmt ',
                        16,
                        1,  # PCM format
                        1,  # channels
                        sample_rate,
                        sample_rate * 2,  # byte rate
                        2,  # block align
                        16,  # bits per sample
                        b'data'
                    ))
                    buffer.write(struct.pack('<L', 0xFFFFFFFF))  # data size (set to max)
                # write raw PCM data
                buffer.write(normalized_audio.tobytes())
            elif output_format == "mp3":
                # MP3 format with proper framing
                settings = format_settings.get("mp3", {}) if format_settings else {}
                settings = {**AudioService.DEFAULT_SETTINGS["mp3"], **settings}
                sf.write(
                    buffer, normalized_audio, sample_rate, format="MP3", **settings
                )
            elif output_format == "opus":
                # Opus format in OGG container
                settings = format_settings.get("opus", {}) if format_settings else {}
                settings = {**AudioService.DEFAULT_SETTINGS["opus"], **settings}
                sf.write(
                    buffer,
                    normalized_audio,
                    sample_rate,
                    format="OGG",
                    subtype="OPUS",
                    **settings,
                )
            elif output_format == "flac":
                # FLAC format with proper framing
                if is_first_chunk:
                    logger.info("Starting FLAC stream...")
                settings = format_settings.get("flac", {}) if format_settings else {}
                settings = {**AudioService.DEFAULT_SETTINGS["flac"], **settings}
                sf.write(
                    buffer,
                    normalized_audio,
                    sample_rate,
                    format="FLAC",
                    subtype="PCM_16",
                    **settings,
                )
            elif output_format == "aac":           
                # Convert numpy array directly to AAC using pydub
                audio_segment = AudioSegment(
                    normalized_audio.tobytes(), 
                    frame_rate=sample_rate,
                    sample_width=normalized_audio.dtype.itemsize,
                    channels=1 if len(normalized_audio.shape) == 1 else normalized_audio.shape[1]
                )
                
                settings = format_settings.get("aac", {}) if format_settings else {}
                settings = {**AudioService.DEFAULT_SETTINGS["aac"], **settings}
                
                audio_segment.export(
                    buffer,
                    format="adts",  # ADTS is a common AAC container format
                    bitrate=settings["bitrate"]
                )
            else:
                raise ValueError(
                    f"Format {output_format} not supported. Supported formats are: wav, mp3, opus, flac, pcm, aac."
                )

            buffer.seek(0)
            return buffer.getvalue()

        except Exception as e:
            logger.error(f"Error converting audio to {output_format}: {str(e)}")
            raise ValueError(f"Failed to convert audio to {output_format}: {str(e)}")