import argparse

import librosa
import torch
from transformers import AutoProcessor, Wav2Vec2ForCTC

from _common import load_clips, write_results

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

# MMS's own ISO 639-3 language codes.
MMS_LANG_CODE = {
    "igbo": "ibo",
    "yoruba": "yor",
    "hausa": "hau",
    "pidgin": "eng",  # no dedicated Pidgin adapter — fall back to English,
                       # same reasoning as Whisper's Pidgin fallback.
}


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--language", default="igbo")
    args = parser.parse_args()

    target_lang = MMS_LANG_CODE.get(args.language, "eng")
    if args.language == "pidgin":
        print("[warn] MMS has no dedicated Pidgin adapter — using 'eng'. "
              "Note this substitution in the report.")

    clips = load_clips(args.language)
    print(f"[info] Loading {MODEL_ID} with '{target_lang}' adapter "
          f"(first run downloads it — this model is large)…")
    processor = AutoProcessor.from_pretrained(MODEL_ID, target_lang=target_lang)
    model = Wav2Vec2ForCTC.from_pretrained(
        MODEL_ID, target_lang=target_lang, ignore_mismatched_sizes=True
    )
    model.load_adapter(target_lang)
    print(f"[info] MMS: {len(clips)} clips, language '{target_lang}'")

    rows = []
    for i, (clip_id, wav_path) in enumerate(clips, 1):
        audio, sr = librosa.load(wav_path, sr=16000)
        inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
        with torch.no_grad():
            logits = model(**inputs).logits
        ids = torch.argmax(logits, dim=-1)[0]
        transcript = processor.decode(ids).strip()
        rows.append((clip_id, transcript))
        print(f"  [{i}/{len(clips)}] {clip_id} ← \"{transcript[:60]}…\"")

    write_results("mms", rows)


if __name__ == "__main__":
    main()