"""Honest latency benchmark: warm up first, then time repeated runs.

The first CUDA generation pays for kernel autotuning and cache allocation, so a
single cold measurement makes any model look far worse than it is in service.
"""
import logging
import time
from threading import Thread

import numpy as np
import torch

logging.basicConfig(level=logging.INFO, format="%(message)s")
log = logging.getLogger("bench")
DEV = "cuda" if torch.cuda.is_available() else "cpu"

log.info("device=%s  gpu=%s", DEV, torch.cuda.get_device_name(0) if DEV == "cuda" else "-")

# ── STT ──────────────────────────────────────────────────────────────────────
from transformers import AutoModel

stt = AutoModel.from_pretrained("ai4bharat/indic-conformer-600m-multilingual", trust_remote_code=True)
stt = stt.to(DEV).eval()

# Where does it actually run? A model wrapping ONNX ignores .to(cuda).
params = list(stt.parameters())
log.info("STT param device: %s  (%d tensors)", params[0].device if params else "NO TORCH PARAMS", len(params))
log.info("STT type: %s", type(stt).__name__)

wav = torch.from_numpy((np.random.randn(16000 * 4) * 0.02).astype(np.float32)).unsqueeze(0).to(DEV)
with torch.inference_mode():
    stt(wav, "ta", "ctc")                      # warmup
times = []
for _ in range(3):
    t0 = time.perf_counter()
    with torch.inference_mode():
        stt(wav, "ta", "ctc")
    times.append((time.perf_counter() - t0) * 1000)
log.info("STT  4000 ms audio → %.0f / %.0f / %.0f ms   (RTF %.2fx)",
         *times, (sum(times) / len(times)) / 4000)

# ── TTS ──────────────────────────────────────────────────────────────────────
from parler_tts import ParlerTTSForConditionalGeneration, ParlerTTSStreamer
from transformers import AutoTokenizer

dtype = torch.float16 if DEV == "cuda" else torch.float32
tts = ParlerTTSForConditionalGeneration.from_pretrained("ai4bharat/indic-parler-tts", torch_dtype=dtype).to(DEV).eval()
tok = AutoTokenizer.from_pretrained("ai4bharat/indic-parler-tts")
dtok = AutoTokenizer.from_pretrained(tts.config.text_encoder._name_or_path)
SR = tts.config.sampling_rate
log.info("TTS sampling_rate=%d  frame_rate=%s", SR, getattr(tts.audio_encoder.config, "frame_rate", "?"))

desc = "Jaya speaks in a warm, clear, professional tone at a natural pace. The recording is very high quality with no background noise."
d = dtok(desc, return_tensors="pt").to(DEV)


def run(text, stream=True):
    p = tok(text, return_tensors="pt").to(DEV)
    kw = dict(input_ids=d.input_ids, attention_mask=d.attention_mask,
              prompt_input_ids=p.input_ids, prompt_attention_mask=p.attention_mask)
    t0 = time.perf_counter()
    if stream:
        fr = int(getattr(tts.audio_encoder.config, "frame_rate", 86) / 2)
        s = ParlerTTSStreamer(tts, device=DEV, play_steps=fr)
        Thread(target=tts.generate, kwargs={**kw, "streamer": s}, daemon=True).start()
        first, n = None, 0
        for c in s:
            if c is None or len(c) == 0:
                continue
            if first is None:
                first = (time.perf_counter() - t0) * 1000
            n += len(c)
    else:
        with torch.inference_mode():
            g = tts.generate(**kw)
        n = g.shape[-1]
        first = None
    total = (time.perf_counter() - t0) * 1000
    return first, total, 1000 * n / SR


SHORT = "மூவாயிரம் நானூறு லீட்கள் உள்ளன."
LONG = "புதிய லீட்கள் மூவாயிரம் நானூற்று இருபத்தேழு. இதில் எழுபத்தாறு சதவீதம் இன்னும் தொடர்பு கொள்ளப்படவில்லை."

run(SHORT)   # warmup
log.info("")
for label, text in (("short", SHORT), ("long", LONG)):
    first, total, audio = run(text)
    log.info("TTS %-5s %2d chars → first %.0f ms | total %.0f ms | audio %.0f ms | RTF %.2fx",
             label, len(text), first or -1, total, audio, total / max(audio, 1))

if DEV == "cuda":
    log.info("\nVRAM peak reserved: %.2f GB of %.1f GB",
             torch.cuda.max_memory_reserved() / 1e9,
             torch.cuda.get_device_properties(0).total_memory / 1e9)