"""
DhVaani-0.5 local CPU benchmark.

Loads ARTPARK-IISc/DhVaani-0.5 via transformers AutoModel, runs a few
synthesis calls, and records wall-clock latency + real-time factor (RTF)
for each. Writes .wav (native) and .mp3 (via ffmpeg/pydub) outputs, plus
a latency.json summary.

Usage:
    source venv/bin/activate
    python benchmark.py
"""
import json
import os
import time

os.environ.setdefault("HF_TOKEN", os.environ.get("HF_TOKEN", ""))

import torch
import soundfile as sf
from transformers import AutoModel

HERE = os.path.dirname(os.path.abspath(__file__))
OUT_DIR = os.path.join(HERE, "outputs")
os.makedirs(OUT_DIR, exist_ok=True)

DEV = "cuda" if torch.cuda.is_available() else "cpu"
REFERENCE_WAV = os.path.join(HERE, "reference.wav")
# We don't have a ground-truth transcript for samples/malayalam.wav from the
# model repo, so this is an approximate placeholder — good enough to prove
# the pipeline runs and to measure latency, not tuned for max clone fidelity.
REFERENCE_TEXT = "ഇത് ഒരു മാതൃകാ ശബ്ദമാണ്."

TEST_CASES = [
    {"name": "malayalam_greeting", "text": "നമസ്‌കാരം, സുഖമാണോ?"},
    {"name": "malayalam_longer", "text": "ഇന്ന് കാലാവസ്ഥ വളരെ നല്ലതാണ്. നമുക്ക് പുറത്തു പോകാം."},
]


def main():
    print(f"[info] device = {DEV}")
    print("[info] loading ARTPARK-IISc/DhVaani-0.5 (AutoModel, trust_remote_code=True) ...")

    t0 = time.perf_counter()
    model = AutoModel.from_pretrained(
        "ARTPARK-IISc/DhVaani-0.5", trust_remote_code=True
    ).to(DEV).eval()
    load_s = time.perf_counter() - t0
    print(f"[info] model loaded in {load_s:.2f}s")

    sr = model.sampling_rate
    results = {
        "device": DEV,
        "model": "ARTPARK-IISc/DhVaani-0.5",
        "model_load_seconds": round(load_s, 3),
        "sampling_rate": sr,
        "runs": [],
    }

    for case in TEST_CASES:
        name, text = case["name"], case["text"]
        print(f"\n[run] {name!r}: {text!r}")

        t0 = time.perf_counter()
        audio = model.synthesize(
            text=text,
            prompt_wav=REFERENCE_WAV,
            prompt_text=REFERENCE_TEXT,
        )
        gen_s = time.perf_counter() - t0

        audio_duration_s = len(audio) / sr
        rtf = gen_s / audio_duration_s if audio_duration_s > 0 else float("nan")

        wav_path = os.path.join(OUT_DIR, f"{name}.wav")
        sf.write(wav_path, audio, sr)

        mp3_path = os.path.join(OUT_DIR, f"{name}.mp3")
        try:
            from pydub import AudioSegment

            AudioSegment.from_wav(wav_path).export(mp3_path, format="mp3", bitrate="192k")
            mp3_ok = True
        except Exception as e:
            print(f"[warn] mp3 export failed: {e}")
            mp3_ok = False

        print(
            f"[result] gen_time={gen_s:.3f}s  audio_len={audio_duration_s:.3f}s  "
            f"RTF={rtf:.3f}  (RTF<1 means faster than real-time)"
        )

        results["runs"].append(
            {
                "name": name,
                "text": text,
                "generation_seconds": round(gen_s, 3),
                "audio_duration_seconds": round(audio_duration_s, 3),
                "real_time_factor": round(rtf, 4),
                "wav_path": os.path.relpath(wav_path, HERE),
                "mp3_path": os.path.relpath(mp3_path, HERE) if mp3_ok else None,
            }
        )

    summary_path = os.path.join(OUT_DIR, "latency.json")
    with open(summary_path, "w") as f:
        json.dump(results, f, indent=2, ensure_ascii=False)

    print(f"\n[done] summary written to {summary_path}")


if __name__ == "__main__":
    main()