"""
Config for the redesigned run: small custom vocab (not GPT-2's 50k) so
embedding overhead doesn't dominate the parameter budget, sized to hit a
genuine ~20:1 token:param ratio on a free-tier T4.

Run `python model.py` after building this to confirm the exact param count
before launching a long run.
"""
from dataclasses import dataclass


@dataclass
class ModelConfig:
    vocab_size: int = 8192          # custom BPE, trained on YOUR corpus (tokenizer_train.py)
                                      # -- NOT GPT-2's 50304. At small model sizes, a 50k vocab's
                                      # embedding table alone eats 60-75% of total params, leaving
                                      # almost nothing for actual transformer capacity. 8192 keeps
                                      # embedding overhead to ~17-20% of total.
    context_len: int = 256           # countdown prompts are short; halving context vs. the previous
                                      # 512 also halves the quadratic attention cost, for free
    d_model: int = 384
    n_layer: int = 10
    n_head: int = 6
    n_kv_head: int = 2               # GQA
    d_ff: int = 1024                 # SwiGLU inner dim
    rope_theta: float = 10000.0
    dropout: float = 0.0
    tie_embeddings: bool = True
    # -> this config lands at ~18.9M params, verified by model.py


@dataclass
class TrainConfig:
    data_dir: str = "data"
    train_bin: str = "data/train.bin"
    val_bin: str = "data/val.bin"

    # ---- the ratio that actually matters ----
    target_tokens: int = 380_000_000   # ~20:1 tokens:params -- genuinely Chinchilla-optimal,
                                         # not a compromise like the 1.3:1 ratio last time

    # ---- optimization ----
    micro_batch_size: int = 32          # smaller model + shorter context = bigger batch fits
    grad_accum_steps: int = 4           # effective batch = 32*4*256 = 32,768 tokens/step
    max_lr: float = 8e-4                # slightly higher than the 116M run's 6e-4 -- smaller
                                          # models generally tolerate a higher LR
    min_lr: float = 8e-5
    warmup_steps: int = 300
    weight_decay: float = 0.1
    grad_clip: float = 1.0
    beta1: float = 0.9
    beta2: float = 0.95

    precision: str = "fp16"             # T4 = Turing, no bf16 tensor cores -- same reasoning as before

    ckpt_dir: str = "checkpoints"
    log_path: str = "logs/train_log.csv"
    save_every_steps: int = 250
    eval_every_steps: int = 250
    eval_iters: int = 50
    log_every_steps: int = 20

    seed: int = 1337