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

script_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
exp_root="$(cd "${script_dir}/.." && pwd)"

role_name="${ROLE_NAME:-writer}"
deepspeed_config_name="${DEEPSPEED_CONFIG_NAME:-${exp_root}/config/ds.json}"
dataset_sub_name="${DATASET_SUB_NAME:-}"
dataset_split="${DATASET_SPLIT:-train}"
system_prompt="${SYSTEM_PROMPT:-}"

output_root="${OUTPUT_ROOT:-${exp_root}/output/${role_name}}"
reward_base_model_path="${REWARD_BASE_MODEL_PATH:-${output_root}/sft_full}"
preference_dataset_name="${PREFERENCE_DATASET_NAME:-${exp_root}/data/${role_name}_preference.jsonl}"
reward_model_lora_path="${output_root}/reward_model_lora"

mkdir -p "${output_root}"

if [ ! -f "${preference_dataset_name}" ] && [ ! -d "${preference_dataset_name}" ]; then
  echo "Missing preference dataset: ${preference_dataset_name}"
  echo "See ${exp_root}/README.md and the sample JSONL files."
  exit 1
fi

deepspeed "${exp_root}/reward_model.py" \
  --dataset_name="${preference_dataset_name}" \
  --dataset_sub_name="${dataset_sub_name}" \
  --dataset_split="${dataset_split}" \
  --system_prompt="${system_prompt}" \
  --model_name="${reward_base_model_path}" \
  --seq_length="${SEQ_LENGTH:-1024}" \
  --batch_size="${BATCH_SIZE:-8}" \
  --output_name="${reward_model_lora_path}" \
  --use_QLora="${USE_QLORA:-True}" \
  --use_flash_attention_2="${USE_FLASH_ATTENTION_2:-True}" \
  --deepspeed_config_name="${deepspeed_config_name}" \
  --num_train_epochs="${NUM_TRAIN_EPOCHS:-2}" \
  --gradient_accumulation_steps="${GRADIENT_ACCUMULATION_STEPS:-4}" \
  --learning_rate="${LEARNING_RATE:-2e-5}"