"""Central config for the Armenian LLM TPU project.

Everything tunable lives here so train_tpu.py / prepare_data.py / launch.py stay
in sync. Values are chosen for a SINGLE TPU v5e/v6e chip (16GB HBM) training from
scratch.
"""
from __future__ import annotations

from dataclasses import dataclass, asdict, field
import json
import os


def _default_compute_dtype() -> str:
    """Compute dtype, chosen by backend at import time.

    TPU (v5e/v6e, Colab supervisor path) uses bfloat16 — that's the proven,
    still-running config; leave it untouched. GPU (Kaggle T4x2) has NO bf16
    tensor cores (Turing sm_75), so bf16 there de-optimizes to fp32-upcast
    paths. The Kaggle launcher sets COMPUTE_DTYPE=float16 to use T4's native
    fp16 tensor cores instead. Env override keeps this file backend-safe: the
    SAME config.py is pulled from HF CODE_REPO by BOTH the TPU supervisor and
    the Kaggle kernel, so the split must be by env, never a hardcoded change.
    """
    dt = os.environ.get("COMPUTE_DTYPE", "bfloat16").strip().lower()
    if dt not in ("bfloat16", "float16", "float32"):
        raise ValueError(f"COMPUTE_DTYPE must be bfloat16|float16|float32, got {dt!r}")
    return dt


def _default_micro_batch() -> int:
    """Per-device micro-batch, chosen by backend at import time.

    TPU v5e1 (16GB HBM, single device, no pmap) fits micro=8 -> 8*ga(4) = 32
    seq/step. Kaggle T4x2 runs DATA-PARALLEL (pmap) across 2 GPUs, so the same
    micro=8 would be 8*ga(4)*2 = 64 seq/step AND each T4 tried to allocate an
    ~8.85GB fp16 buffer -> RESOURCE_EXHAUSTED OOM (T4 has 16GB but XLA-GPU
    overhead + NCCL clique + fp16 activations eat the headroom). Setting
    MICRO_BATCH=4 on GPU halves the per-device activation footprint AND makes
    4*ga(4)*2dev = 32 seq/step = EXACTLY the TPU baseline, so the LR schedule
    and the resumed checkpoint stay in sync (identical effective batch/math).
    Env override keeps config.py backend-safe (same file pulled by both paths).
    """
    mb = os.environ.get("MICRO_BATCH", "8").strip()
    try:
        v = int(mb)
    except ValueError:
        raise ValueError(f"MICRO_BATCH must be a positive int, got {mb!r}")
    if v < 1:
        raise ValueError(f"MICRO_BATCH must be >= 1, got {v}")
    return v


# ------------------------------------------------------------------
# Hugging Face repositories (durable storage across dead sessions)
# ------------------------------------------------------------------
# Verified via HfApi.whoami() against the live HF_TOKEN (write scope).
HF_USER = "ArthurYeghinyan"

# Where the pretokenized corpus goes (dataset repo) and where code is pulled from.
DATA_REPO = f"{HF_USER}/armenian-llm-data"          # repo_type="dataset"
CODE_REPO = f"{HF_USER}/armenian-llm-code"          # repo_type="dataset" (holds train_tpu.py, model.py, config.py)
# Checkpoint repo. Env-overridable so a run can be ISOLATED in its own repo — e.g.
# a fresh from-scratch run must not share a repo with a still-live older kernel,
# whose save() deletes older ckpts and whose high step numbers would hijack resume.
CKPT_REPO = os.environ.get(
    "CKPT_REPO", f"{HF_USER}/armenian-llm-124m")    # repo_type="model"  (checkpoints + final)

# ------------------------------------------------------------------
# Source dataset (phase 1: Wikipedia -> later swap to CulturaX)
# ------------------------------------------------------------------
# Phase 1 default: cleaned Armenian Wikipedia.
SOURCE_DATASET = "HuggingFaceFW/finewiki"
SOURCE_CONFIG = "hy"
SOURCE_SPLIT = "train"
# For phase 2 scale-up, switch to:
#   SOURCE_DATASET = "uonlp/CulturaX"; SOURCE_CONFIG = "hy"; streaming=True


@dataclass(frozen=True)
class TokenizerConfig:
    vocab_size: int = 32000
    model_type: str = "unigram"      # SentencePiece unigram = strong for morphologically rich Armenian
    character_coverage: float = 0.9998
    # Cap how much text is used to TRAIN the tokenizer (not the LM).
    train_sample_rows: int = 400_000
    max_chars_per_row: int = 20_000


@dataclass(frozen=True)
class ModelConfig:
    # ~124M params (GPT-2 small scale, modern Llama-style internals).
    vocab_size: int = 32000
    n_layer: int = 12
    n_head: int = 12
    n_kv_head: int = 12              # == n_head -> plain MHA; set < n_head for GQA
    n_embd: int = 768
    seq_len: int = 1024
    ffn_mult: float = 8 / 3          # SwiGLU: hidden = round to multiple of 256
    rope_theta: float = 10000.0
    rms_eps: float = 1e-5
    dtype: str = field(default_factory=_default_compute_dtype)  # bf16 on TPU, fp16 on GPU (see fn)
    tie_embeddings: bool = True

    def ffn_hidden(self) -> int:
        h = int(self.n_embd * self.ffn_mult)
        return ((h + 255) // 256) * 256


@dataclass(frozen=True)
class TrainConfig:
    # Global batch = micro_batch * grad_accum. Two separate OOMs were fixed here:
    #   1) HOST RAM at compile: the un-rematted jax.lax.scan grad-accum graph needed
    #      ~46GB and got cgroup-OOM-killed under JAX 0.10.2 (a silent SIGKILL, no
    #      traceback). Fixed by dropping scan for a python loop over a small jitted
    #      micro_grad (train_tpu.py) — matches the working Whisper-TPU reference.
    #   2) DEVICE HBM at run: the python-loop keeps an fp32 grad accumulator resident
    #      in HBM on top of params + adamw mu/nu + master weights, so micro=16 tipped
    #      jit_micro_grad over the edge (wanted 14.00G, only 13.35G free -> a REAL
    #      RESOURCE_EXHAUSTED with traceback, unlike the host kill). Fixed by halving
    #      micro_batch: micro=8 x ga=16 = SAME 128 seq = 131k tok/step, identical
    #      math, but half the per-micro activation footprint.
    micro_batch: int = field(default_factory=_default_micro_batch)  # 8 on TPU, 4 on GPU (see fn)
    grad_accum: int = 4              # global batch = 32 seq/step (TPU) or 4*4*2dev = 32 (GPU pmap)
    max_steps: int = 60_000
    warmup_steps: int = 500
    lr: float = 6e-4
    min_lr: float = 6e-5
    weight_decay: float = 0.1
    beta1: float = 0.9
    beta2: float = 0.95
    grad_clip: float = 1.0
    # Durability: how often to push a checkpoint to HF Hub.
    save_every: int = 1000
    eval_every: int = 1000
    eval_iters: int = 100
    log_every: int = 20
    seed: int = 1337


TOKENIZER = TokenizerConfig()
MODEL = ModelConfig()
TRAIN = TrainConfig()


def summary() -> dict:
    return {
        "data_repo": DATA_REPO,
        "code_repo": CODE_REPO,
        "ckpt_repo": CKPT_REPO,
        "source": f"{SOURCE_DATASET}:{SOURCE_CONFIG}",
        "tokenizer": asdict(TOKENIZER),
        "model": asdict(MODEL),
        "train": asdict(TRAIN),
        "ffn_hidden": MODEL.ffn_hidden(),
    }


if __name__ == "__main__":
    print(json.dumps(summary(), indent=2, ensure_ascii=False))