#!/bin/bash
# =============================================================================
# RunPod: download scriptwriter dataset from HF → LoRA train in tmux → upload
# =============================================================================
# Designed for your 2x A40 pod (~$0.98/hr). Run once on the pod:
#
#   export HF_TOKEN=hf_...
#   bash runpod_train_tmux.sh
#
# Optional overrides:
#   HF_DATASET_REPO   (default: datamatters24/scriptwriter-corpus-ia)
#   HF_MODEL_REPO     (default: datamatters24/scriptwriter-lora-ia)
#   BASE_MODEL        (default: meta-llama/Llama-3.2-3B-Instruct)
#   TMUX_SESSION      (default: scriptwriter-train)
#   WORKDIR           (default: /workspace/scriptwriter-trainer)
#
# Prerequisites on the pod:
#   - This repo present at WORKDIR (scripts/ + config/ at minimum)
#   - HF account has accepted the Llama 3.2 license for BASE_MODEL
#   - nvidia-smi works
# =============================================================================

set -euo pipefail

TMUX_SESSION="${TMUX_SESSION:-scriptwriter-train}"
WORKDIR="${WORKDIR:-/workspace/scriptwriter-trainer}"
HF_DATASET_REPO="${HF_DATASET_REPO:-datamatters24/scriptwriter-corpus-ia}"
HF_MODEL_REPO="${HF_MODEL_REPO:-datamatters24/scriptwriter-lora-ia}"
BASE_MODEL="${BASE_MODEL:-meta-llama/Llama-3.2-3B-Instruct}"
DATA_DIR="${DATA_DIR:-/workspace/data/processed}"
OUT_DIR="${OUTPUT_DIR:-/workspace/models/lora}"
LOG_DIR="${LOG_DIR:-/workspace/logs}"
TIMESTAMP="$(date +%Y%m%d_%H%M%S)"
TRAIN_LOG="${LOG_DIR}/train_${TIMESTAMP}.log"

# Resolve repo root: prefer script location, then WORKDIR, then /workspace
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
if [[ -f "${SCRIPT_DIR}/train_runpod.py" ]]; then
  ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"
elif [[ -f "${WORKDIR}/scripts/train_runpod.py" ]]; then
  ROOT="${WORKDIR}"
elif [[ -f /workspace/scripts/train_runpod.py ]]; then
  ROOT="/workspace"
else
  echo "ERROR: cannot find scripts/train_runpod.py"
  echo "Copy the scriptwriter-trainer repo to ${WORKDIR} (need scripts/ and config/),"
  echo "then re-run:  bash ${WORKDIR}/scripts/runpod_train_tmux.sh"
  exit 1
fi

cd "${ROOT}"
mkdir -p "${DATA_DIR}" "${OUT_DIR}" "${LOG_DIR}"

echo "========== Scriptwriter RunPod trainer =========="
echo "ROOT=${ROOT}"
echo "HF_DATASET_REPO=${HF_DATASET_REPO}"
echo "HF_MODEL_REPO=${HF_MODEL_REPO}"
echo "BASE_MODEL=${BASE_MODEL}"
echo "DATA_DIR=${DATA_DIR}"
echo "OUT_DIR=${OUT_DIR}"
echo "LOG=${TRAIN_LOG}"
echo

if [[ -z "${HF_TOKEN:-}" ]]; then
  echo "ERROR: HF_TOKEN is not set."
  echo "  export HF_TOKEN=hf_xxxxxxxx"
  exit 1
fi

if ! command -v nvidia-smi >/dev/null 2>&1; then
  echo "WARNING: nvidia-smi not found — training will be very slow/CPU."
else
  nvidia-smi -L || true
fi

echo "=== Installing Python deps (if needed) ==="
python3 -m pip install -q --upgrade pip
if [[ -f "${ROOT}/requirements-runpod.txt" ]]; then
  python3 -m pip install -q -r "${ROOT}/requirements-runpod.txt"
else
  python3 -m pip install -q \
    torch transformers datasets peft trl accelerate bitsandbytes \
    huggingface_hub pyyaml tqdm sentencepiece protobuf
fi
python3 -m pip install -q "huggingface_hub>=0.24.0"

export HF_TOKEN
export HUGGING_FACE_HUB_TOKEN="${HF_TOKEN}"
export HF_HOME="${HF_HOME:-/workspace/.cache/huggingface}"
export CONFIG_PATH="${CONFIG_PATH:-${ROOT}/config/training.yaml}"
export HF_DATASET_REPO HF_MODEL_REPO BASE_MODEL
export OUTPUT_DIR="${OUT_DIR}"
# train_runpod.py looks under ROOT/data/processed — symlink pod data there
mkdir -p "${ROOT}/data"
if [[ ! -e "${ROOT}/data/processed" ]]; then
  ln -sfn "${DATA_DIR}" "${ROOT}/data/processed"
elif [[ ! -L "${ROOT}/data/processed" && "${ROOT}/data/processed" != "${DATA_DIR}" ]]; then
  # Prefer downloading into DATA_DIR and also ensure ROOT sees train.jsonl
  mkdir -p "${ROOT}/data/processed"
fi

echo
echo "=== Downloading dataset: ${HF_DATASET_REPO} ==="
python3 - <<PY
import os
from pathlib import Path
from huggingface_hub import snapshot_download, login

login(token=os.environ["HF_TOKEN"], add_to_git_credential=False)
dest = Path(os.environ.get("DATA_DIR", "/workspace/data/processed"))
dest.mkdir(parents=True, exist_ok=True)
snapshot_download(
    repo_id=os.environ["HF_DATASET_REPO"],
    repo_type="dataset",
    local_dir=str(dest),
    token=os.environ["HF_TOKEN"],
)
print(f"Downloaded to {dest}")
for name in sorted(dest.iterdir()):
    if name.is_file():
        print(f"  {name.name:30s} {name.stat().st_size:10d} bytes")
PY

echo
echo "=== Ensuring train.jsonl / val.jsonl for train_runpod.py ==="
python3 - <<PY
from pathlib import Path
import shutil
import os

candidates = [
    Path(os.environ.get("DATA_DIR", "/workspace/data/processed")),
    Path("${ROOT}/data/processed"),
]
# Deduplicate while preserving order
seen = set()
dirs = []
for d in candidates:
    key = str(d.resolve()) if d.exists() else str(d)
    if key in seen:
        continue
    seen.add(key)
    dirs.append(d)

def ensure_split(data_dir: Path) -> None:
    data_dir.mkdir(parents=True, exist_ok=True)
    train = data_dir / "train.jsonl"
    if not train.exists():
        for alt in ("train-ia.jsonl", "train-local.jsonl", "checkpoint-ia.jsonl"):
            src = data_dir / alt
            if src.exists() and src.stat().st_size > 0:
                shutil.copyfile(src, train)
                print(f"Created {train} from {alt}")
                break
    val = data_dir / "val.jsonl"
    if not val.exists():
        for alt in ("val-ia.jsonl", "val-local.jsonl"):
            src = data_dir / alt
            if src.exists() and src.stat().st_size > 0:
                shutil.copyfile(src, val)
                print(f"Created {val} from {alt}")
                break
    if not train.exists():
        raise SystemExit(f"No train.jsonl (or train-ia/train-local) in {data_dir}")
    n = sum(1 for line in train.open() if line.strip())
    print(f"{data_dir}: train.jsonl -> {n} examples")

for d in dirs:
    ensure_split(d)

# Keep ROOT/data/processed in sync if it is a real directory separate from DATA_DIR
root_proc = Path("${ROOT}/data/processed")
data_dir = Path(os.environ.get("DATA_DIR", "/workspace/data/processed"))
if root_proc.resolve() != data_dir.resolve():
    for name in ("train.jsonl", "val.jsonl"):
        src = data_dir / name
        if src.exists():
            shutil.copyfile(src, root_proc / name)
            print(f"Copied {name} -> {root_proc / name}")
PY

WORKER="${LOG_DIR}/_train_worker_${TIMESTAMP}.sh"
cat > "${WORKER}" <<EOF
#!/bin/bash
set -euo pipefail
cd "${ROOT}"
export HF_TOKEN='${HF_TOKEN}'
export HUGGING_FACE_HUB_TOKEN='${HF_TOKEN}'
export HF_HOME='${HF_HOME}'
export CONFIG_PATH='${CONFIG_PATH}'
export BASE_MODEL='${BASE_MODEL}'
export OUTPUT_DIR='${OUT_DIR}'
export HF_DATASET_REPO='${HF_DATASET_REPO}'
export HF_MODEL_REPO='${HF_MODEL_REPO}'

exec > >(tee -a '${TRAIN_LOG}') 2>&1

echo "========== TRAIN START \$(date -Is) =========="
nvidia-smi || true
echo "train lines: \$(wc -l < '${ROOT}/data/processed/train.jsonl')"
python3 '${ROOT}/scripts/train_runpod.py'

echo
echo "========== UPLOAD ADAPTER \$(date -Is) =========="
python3 '${ROOT}/scripts/sync_hf.py' upload-model \\
  --repo '${HF_MODEL_REPO}' \\
  --folder '${OUT_DIR}'

echo
echo "========== DONE \$(date -Is) =========="
echo "Adapter: https://huggingface.co/${HF_MODEL_REPO}"
echo "STOP THE POD in the RunPod console to stop billing."
echo
read -r -p "Press enter to close tmux pane..." _
EOF
chmod +x "${WORKER}"

if tmux has-session -t "${TMUX_SESSION}" 2>/dev/null; then
  echo "tmux session '${TMUX_SESSION}' already exists."
  echo "  Attach: tmux attach -t ${TMUX_SESSION}"
  echo "  Kill:   tmux kill-session -t ${TMUX_SESSION}"
  exit 1
fi

tmux new-session -d -s "${TMUX_SESSION}" -n train "bash '${WORKER}'"
tmux new-window -t "${TMUX_SESSION}" -n monitor
tmux send-keys -t "${TMUX_SESSION}:monitor" \
  "watch -n 15 'echo === GPUs ===; nvidia-smi --query-gpu=index,name,memory.used,utilization.gpu --format=csv; echo; echo === log ===; tail -20 ${TRAIN_LOG} 2>/dev/null'" Enter
tmux select-window -t "${TMUX_SESSION}:train"

echo
echo "Started training in tmux '${TMUX_SESSION}'"
echo "  Attach:  tmux attach -t ${TMUX_SESSION}"
echo "  Detach:  Ctrl-b then d"
echo "  Log:     ${TRAIN_LOG}"
echo "  Model →  https://huggingface.co/${HF_MODEL_REPO}"
echo
echo "When finished (or if something fails): STOP THE POD."