"""
Faster-Whisper Transcriber

GPU-accelerated speech-to-text using faster-whisper (CTranslate2).
Supports streaming transcription with partial results.
"""

import numpy as np
from faster_whisper import WhisperModel
from typing import Iterator, Optional, List
import logging
import asyncio
from concurrent.futures import ThreadPoolExecutor

logger = logging.getLogger('whisper')


class WhisperTranscriber:
    """
    Faster-Whisper based transcription with streaming support.
    
    Runs on GPU (GTX 1660) with small model for balance of speed/quality.
    """
    
    def __init__(
        self,
        model_size: str = "small",
        device: str = "cuda",
        compute_type: str = "float16",
        language: str = "en",
        beam_size: int = 5
    ):
        """
        Initialize Whisper transcriber.
        
        Args:
            model_size: Model size (tiny, base, small, medium, large)
            device: Device to run on (cuda or cpu)
            compute_type: Compute precision (float16, int8, int8_float16)
            language: Language code for transcription
            beam_size: Beam search size (higher = better quality, slower)
        """
        self.model_size = model_size
        self.device = device
        self.compute_type = compute_type
        self.language = language
        self.beam_size = beam_size
        
        logger.info(f"Loading Faster-Whisper model: {model_size} on {device}...")
        
        # Load model
        self.model = WhisperModel(
            model_size,
            device=device,
            compute_type=compute_type,
            download_root="/models"
        )
        
        # Thread pool for blocking transcription calls
        self.executor = ThreadPoolExecutor(max_workers=2)
        
        logger.info(f"Whisper model loaded: {model_size} ({compute_type})")
    
    async def transcribe_async(
        self,
        audio: np.ndarray,
        sample_rate: int = 16000,
        initial_prompt: Optional[str] = None
    ) -> str:
        """
        Transcribe audio asynchronously (non-blocking).
        
        Args:
            audio: Audio data as numpy array (float32)
            sample_rate: Audio sample rate
            initial_prompt: Optional prompt to guide transcription
        
        Returns:
            Transcribed text
        """
        loop = asyncio.get_event_loop()
        
        # Run transcription in thread pool to avoid blocking
        result = await loop.run_in_executor(
            self.executor,
            self._transcribe_blocking,
            audio,
            sample_rate,
            initial_prompt
        )
        
        return result
    
    def _transcribe_blocking(
        self,
        audio: np.ndarray,
        sample_rate: int,
        initial_prompt: Optional[str]
    ) -> str:
        """
        Blocking transcription call (runs in thread pool).
        """
        # Convert to float32 if needed
        if audio.dtype != np.float32:
            audio = audio.astype(np.float32) / 32768.0
        
        # Transcribe
        segments, info = self.model.transcribe(
            audio,
            language=self.language,
            beam_size=self.beam_size,
            initial_prompt=initial_prompt,
            vad_filter=False,  # We handle VAD separately
            word_timestamps=False  # Can enable for word-level timing
        )
        
        # Collect all segments
        text_parts = []
        for segment in segments:
            text_parts.append(segment.text.strip())
        
        full_text = " ".join(text_parts).strip()
        
        logger.debug(f"Transcribed: '{full_text}' (language: {info.language}, "
                    f"probability: {info.language_probability:.2f})")
        
        return full_text
    
    async def transcribe_streaming(
        self,
        audio_stream: Iterator[np.ndarray],
        sample_rate: int = 16000,
        chunk_duration_s: float = 2.0
    ) -> Iterator[dict]:
        """
        Transcribe audio stream with partial results.
        
        Args:
            audio_stream: Iterator yielding audio chunks
            sample_rate: Audio sample rate
            chunk_duration_s: Duration of each chunk to transcribe
        
        Yields:
            {"type": "partial", "text": "partial transcript"}
            {"type": "final", "text": "complete transcript"}
        """
        accumulated_audio = []
        chunk_samples = int(chunk_duration_s * sample_rate)
        
        async for audio_chunk in audio_stream:
            accumulated_audio.append(audio_chunk)
            
            # Check if we have enough audio for transcription
            total_samples = sum(len(chunk) for chunk in accumulated_audio)
            
            if total_samples >= chunk_samples:
                # Concatenate accumulated audio
                audio_data = np.concatenate(accumulated_audio)
                
                # Transcribe current accumulated audio
                text = await self.transcribe_async(audio_data, sample_rate)
                
                if text:
                    yield {
                        "type": "partial",
                        "text": text,
                        "duration": total_samples / sample_rate
                    }
        
        # Final transcription of remaining audio
        if accumulated_audio:
            audio_data = np.concatenate(accumulated_audio)
            text = await self.transcribe_async(audio_data, sample_rate)
            
            if text:
                yield {
                    "type": "final",
                    "text": text,
                    "duration": len(audio_data) / sample_rate
                }
    
    def get_supported_languages(self) -> List[str]:
        """Get list of supported language codes."""
        return [
            "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr",
            "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi",
            "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no"
        ]
    
    def cleanup(self):
        """Cleanup resources."""
        self.executor.shutdown(wait=True)
        logger.info("Whisper transcriber cleaned up")