#!/usr/bin/env bash
set -euo pipefail

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
# shellcheck source=lib.sh
source "${SCRIPT_DIR}/lib.sh"

MODEL_ID="${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct}"

PPO_DATA_DIR="${SHARED_ROOT}/datasets/gsm8k"
SFT_DATA_DIR="${SHARED_ROOT}/datasets/gsm8k_sft"

CODE_SNAPSHOT_DIR="${SHARED_ROOT}/common/code/verl/verl_repo"

echo "[head] ensure dataset dirs exist"
dexec "${HEAD_CONTAINER}" bash -lc "mkdir -p '${PPO_DATA_DIR}' '${SFT_DATA_DIR}'"

echo "[head] prepare PPO dataset (gsm8k RL parquet) -> ${PPO_DATA_DIR}"
dexec "${HEAD_CONTAINER}" bash -lc "if [[ -f '${PPO_DATA_DIR}/train.parquet' && -f '${PPO_DATA_DIR}/test.parquet' ]]; then echo 'ppo_dataset_exists: skip'; else python3 /workspace/verl/examples/data_preprocess/gsm8k.py --local_save_dir '${PPO_DATA_DIR}'; fi"

echo "[head] prepare SFT dataset (gsm8k messages parquet) -> ${SFT_DATA_DIR}"
if dexec "${HEAD_CONTAINER}" bash -lc "test -f '${SFT_DATA_DIR}/train.parquet'"; then
  echo "[head] sft_dataset_exists: skip"
else
  SFT_PY_CODE="$(cat <<'PY'
import os

import pandas as pd
from datasets import load_dataset

out_dir = os.environ["SFT_DATA_DIR"]
os.makedirs(out_dir, exist_ok=True)

ds = load_dataset("openai/gsm8k", "main")

instruction = "Let's think step by step and output the final answer after \"####\"."

def to_messages(example):
    q = example["question"].strip() + " " + instruction
    a = example["answer"]
    return {
        "messages": [
            {"role": "user", "content": q},
            {"role": "assistant", "content": a},
        ]
    }

train = ds["train"].map(to_messages, remove_columns=ds["train"].column_names)
test = ds["test"].map(to_messages, remove_columns=ds["test"].column_names)

pd.DataFrame(train).to_parquet(os.path.join(out_dir, "train.parquet"), index=False)
pd.DataFrame(test).to_parquet(os.path.join(out_dir, "test.parquet"), index=False)

print("sft_dataset_written_ok:", out_dir)
PY
)"
  printf "%s\n" "${SFT_PY_CODE}" | dexec "${HEAD_CONTAINER}" bash -lc "SFT_DATA_DIR='${SFT_DATA_DIR}' python3 -"
fi

echo "[head] ensure model cached to persistent HF_HOME (idempotent) -> ${MODEL_ID}"
PY_CODE="$(cat <<'PY'
import os

model_id = os.environ["MODEL_ID"]

hf_home = os.environ.get("HF_HOME", "/private/hf")
os.environ.setdefault("HF_HOME", hf_home)
os.environ.setdefault("HUGGINGFACE_HUB_CACHE", os.path.join(hf_home, "hub"))
os.environ.setdefault("TRANSFORMERS_CACHE", os.path.join(hf_home, "transformers"))

from huggingface_hub import snapshot_download

try:
    snapshot_download(repo_id=model_id, local_files_only=True)
    print("model_cache_exists: skip", model_id)
except Exception:
    print("model_cache_missing: downloading", model_id)
    snapshot_download(repo_id=model_id)
    print("model_cached_ok:", model_id)
PY
)"

printf "%s\n" "${PY_CODE}" | dexec "${HEAD_CONTAINER}" bash -lc "MODEL_ID='${MODEL_ID}' python3 -"

echo "[head] snapshot verl repo into shared common code path (idempotent best-effort) -> ${CODE_SNAPSHOT_DIR}"
dexec "${HEAD_CONTAINER}" bash -lc "mkdir -p '${CODE_SNAPSHOT_DIR}' && if command -v rsync >/dev/null 2>&1; then rsync -a --delete /workspace/verl/ '${CODE_SNAPSHOT_DIR}/'; else rm -rf '${CODE_SNAPSHOT_DIR:?}/'* && cp -a /workspace/verl/. '${CODE_SNAPSHOT_DIR}/'; fi && echo 'code_snapshot_ok'"