"""Offline, no-PyTorch, full-context smoke checks for every converted variant."""
import json
import sys
import time
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0,str(ROOT))
import numpy as np
import mlx.core as mx
from timestral_mlx import TimestralForecaster

records=[]
for size in ("5M","28M","123M","209M"):
    fc=TimestralForecaster.from_pretrained(ROOT/'models'/size)
    t=np.arange(32768+17,dtype=np.float32)
    x=np.sin(t*2*np.pi/24)+0.0001*t
    start=time.perf_counter()
    a=fc.forecast(x,32,symmetric_averaging=False,clip_nonnegative=False).quantiles
    elapsed=time.perf_counter()-start
    b=fc.forecast(x[-32768:],32,symmetric_averaging=False,clip_nonnegative=False).quantiles
    assert a.shape==(1,1,32,21) and np.isfinite(a).all() and (np.diff(a,axis=-1)>=0).all()
    np.testing.assert_array_equal(a,b)
    assert 'torch' not in sys.modules
    record=dict(variant=size,context_steps=32768,input_steps=32785,horizon=32,finite=True,
                monotone=True,truncation_exact=True,torch_imported=False,seconds=elapsed,passed=True)
    records.append(record)
    print(json.dumps(record),flush=True)
    del fc; mx.clear_cache()
(ROOT/'reports'/'full-context.json').write_text(json.dumps(records,indent=2)+'\n')