"""De-risk before building around these models: do they load, fit, and run fast enough?"""
import logging
import time

import numpy as np
import torch

logging.basicConfig(level=logging.INFO, format="%(message)s")
log = logging.getLogger("probe")

DEV = "cuda" if torch.cuda.is_available() else "cpu"
def vram(tag):
    if DEV == "cuda":
        log.info("   VRAM %-10s alloc %.2f GB | reserved %.2f GB", tag,
                 torch.cuda.memory_allocated() / 1e9, torch.cuda.memory_reserved() / 1e9)

log.info("device=%s", DEV)
if DEV == "cuda":
    log.info("gpu=%s total=%.1f GB", torch.cuda.get_device_name(0),
             torch.cuda.get_device_properties(0).total_memory / 1e9)

# ── STT ──────────────────────────────────────────────────────────────────────
log.info("\n[1/2] loading IndicConformer…")
t0 = time.perf_counter()
from transformers import AutoModel
stt = AutoModel.from_pretrained("ai4bharat/indic-conformer-600m-multilingual", trust_remote_code=True)
stt = stt.to(DEV).eval()
log.info("   loaded in %.1fs", time.perf_counter() - t0)
vram("after STT")

# 3 s of quiet noise — we only care that a forward pass runs and how long it takes.
wav = torch.from_numpy((np.random.randn(16000 * 3) * 0.01).astype(np.float32)).unsqueeze(0).to(DEV)
for i in range(2):
    t0 = time.perf_counter()
    with torch.inference_mode():
        out = stt(wav, "ta", "ctc")
    log.info("   pass %d: %.0f ms  -> %r", i + 1, (time.perf_counter() - t0) * 1000, str(out)[:60])

# ── TTS ──────────────────────────────────────────────────────────────────────
log.info("\n[2/2] loading Indic Parler-TTS…")
t0 = time.perf_counter()
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)
log.info("   loaded in %.1fs  sr=%d", time.perf_counter() - t0, tts.config.sampling_rate)
vram("after TTS")

desc = "Jaya speaks in a warm, clear, professional tone at a natural pace. The recording is very high quality with no background noise."
prompt = "உங்கள் புதிய லீட்கள் மூன்று ஆயிரம் நானூறு."

d = dtok(desc, return_tensors="pt").to(DEV)
p = tok(prompt, 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)

# Streaming: what the user actually experiences is time-to-first-audio.
frame_rate = getattr(tts.audio_encoder.config, "frame_rate", 86)
streamer = ParlerTTSStreamer(tts, device=DEV, play_steps=int(frame_rate / 2))
from threading import Thread
t0 = time.perf_counter()
Thread(target=tts.generate, kwargs={**kw, "streamer": streamer}, daemon=True).start()

first_ms, total = None, 0
for chunk in streamer:
    if chunk is None or len(chunk) == 0:
        continue
    if first_ms is None:
        first_ms = (time.perf_counter() - t0) * 1000
    total += len(chunk)
gen_ms = (time.perf_counter() - t0) * 1000
audio_ms = 1000 * total / tts.config.sampling_rate

log.info("   time to FIRST audio : %.0f ms", first_ms or -1)
log.info("   full generation     : %.0f ms for %.0f ms of audio", gen_ms, audio_ms)
log.info("   realtime factor     : %.2fx  (<1 means faster than realtime)", gen_ms / max(audio_ms, 1))
vram("peak")
if DEV == "cuda":
    log.info("   peak reserved: %.2f GB", torch.cuda.max_memory_reserved() / 1e9)