"""HYDRA autoresearch training entry point.

Thin shim over the `hydra/` package (W1 modularization). The heavy lifting
lives in:
  hydra/config.py     — PostSemClawConfig dataclass + env var constants
  hydra/engram.py     — GPUEngram (conditional memory, Hebbian writes)
  hydra/optimizer.py  — MuonAdamW + fused Muon/AdamW step kernels
  hydra/model.py      — PostSemClawModel assembly + forward
  hydra/eval.py       — factual probes + factual English scoring
  hydra/training.py   — training loop + main()

Public API is re-exported below for back-compat with tests/ and scripts/
that still `from train import ...`.

Usage: `uv run train.py`
"""

from __future__ import annotations

# Re-exports for back-compat. Importing hydra.model is safe (no side effects).
from hydra.config import PostSemClawConfig
from hydra.engram import GPUEngram
from hydra.model import PostSemClawModel, norm
from hydra.optimizer import (
    MuonAdamW,
    adamw_step_fused,
    muon_step_fused,
    polar_express_coeffs,
)

# MAX_SEQ_LEN is often imported from train by tooling; forward from prepare.
from prepare import MAX_SEQ_LEN  # noqa: F401

__all__ = [
    "PostSemClawConfig",
    "PostSemClawModel",
    "GPUEngram",
    "MuonAdamW",
    "adamw_step_fused",
    "muon_step_fused",
    "polar_express_coeffs",
    "norm",
    "MAX_SEQ_LEN",
]


if __name__ == "__main__":
    from hydra.training import main
    main()