import librosa
from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch
import numpy as np
from pathlib import Path
import concurrent.futures

ASR_SAMPLING_RATE = 16_000
CHUNK_LENGTH_S = 60  # Increased to 60 seconds per chunk
MAX_CONCURRENT_CHUNKS = 4  # Adjust based on VRAM availability

ASR_LANGUAGES = {}
with open(f"data/asr/all_langs.tsv") as f:
    for line in f:
        iso, name = line.split(" ", 1)
        ASR_LANGUAGES[iso.strip()] = name.strip()

MODEL_ID = "facebook/mms-1b-all"

processor = AutoProcessor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

def load_audio(audio_data):
    if isinstance(audio_data, tuple):
        sr, audio_samples = audio_data
        audio_samples = (audio_samples / 32768.0).astype(np.float32)
        if sr != ASR_SAMPLING_RATE:
            audio_samples = librosa.resample(
                audio_samples, orig_sr=sr, target_sr=ASR_SAMPLING_RATE
            )
    elif isinstance(audio_data, np.ndarray):
        audio_samples = audio_data
    elif isinstance(audio_data, str):
        audio_samples = librosa.load(audio_data, sr=ASR_SAMPLING_RATE, mono=True)[0]
    else:
        raise ValueError(f"Invalid Audio Input Instance: {type(audio_data)}")
    return audio_samples

def process_chunk(chunk, device):
    inputs = processor(chunk, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model(**inputs).logits
    ids = torch.argmax(outputs, dim=-1)[0]
    return processor.decode(ids)

def transcribe(audio_data=None, lang="eng (English)"):
    if audio_data is None or (isinstance(audio_data, np.ndarray) and audio_data.size == 0):
        return "<<ERROR: Empty Audio Input>>"
    
    try:
        audio_samples = load_audio(audio_data)
    except Exception as e:
        return f"<<ERROR: {str(e)}>>"

    lang_code = lang.split()[0]
    processor.tokenizer.set_target_lang(lang_code)
    model.load_adapter(lang_code)

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)

    chunk_length = int(CHUNK_LENGTH_S * ASR_SAMPLING_RATE)
    chunks = [audio_samples[i:i+chunk_length] for i in range(0, len(audio_samples), chunk_length)]

    transcriptions = []
    
    with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_CONCURRENT_CHUNKS) as executor:
        future_to_chunk = {executor.submit(process_chunk, chunk, device): chunk for chunk in chunks}
        for future in concurrent.futures.as_completed(future_to_chunk):
            transcriptions.append(future.result())

    return " ".join(transcriptions)

# Example usage
ASR_EXAMPLES = [
    ["upload/english.mp3", "eng (English)"],
    # ["upload/tamil.mp3", "tam (Tamil)"],
    # ["upload/burmese.mp3",  "mya (Burmese)"],
]