#!/usr/bin/env bash
# ======================================================================
#  Vanilla Video Reward Pipeline — vLLM/OpenAI-compatible backend,
#  per-dimension scoring (3 dims):
#      - instruction_following
#      - visual_quality
#      - world_consistency
#  Definitions live in ../prompts/definitions.md.
#
#  For every video, the model is called once per scoring dimension and
#  asked to first reason, then assign an integer score in 1..5.
#
#  Input data: a point-wise benchmark JSON whose ``video_path`` field is
#  resolved relative to the JSON's directory.
#
#  Start a vLLM OpenAI-compatible server in another terminal first.
#
#  Usage:
#    bash infer.sh
#    bash infer.sh 50
#    bash infer.sh all 12 my_run
#    bash infer.sh all 12 my_run /path/data.json
#
#    $1 num_samples        : number of videos or 'all' (default: all)
#    $2 concurrency        : default 32
#    $3 output tag         : default infer
#    $4 data json path     : default ../data/firm-video-bench.json
#    $5 vLLM base URL      : default http://127.0.0.1:8000/v1
#    $6 served model name  : default Qwen3-VL-8B-Instruct
# ======================================================================

set -euo pipefail

NUM_SAMPLES="${1:-all}"
CONCURRENCY="${2:-32}"
TAG="${3:-infer}"
DATA="${4:-}"
VLLM_BASE_URL="${5:-${VLLM_BASE_URL:-http://127.0.0.1:8000/v1}}"
VLLM_MODEL="${6:-${VLLM_MODEL:-Qwen3-VL-8B-Instruct}}"
VLLM_API_KEY="${VLLM_API_KEY:-EMPTY}"
VLLM_MAX_TOKENS="${VLLM_MAX_TOKENS:-4096}"
VLLM_TEMPERATURE="${VLLM_TEMPERATURE:-0}"
VLLM_REQUEST_INTERVAL="${VLLM_REQUEST_INTERVAL:-0.0}"
VLLM_MAX_RETRIES="${VLLM_MAX_RETRIES:-3}"
VLLM_RETRY_BASE_DELAY="${VLLM_RETRY_BASE_DELAY:-2.0}"
VLLM_REQUEST_TIMEOUT="${VLLM_REQUEST_TIMEOUT:-300}"

if [[ "${VLLM_BASE_URL}" != */v1 ]]; then
    VLLM_BASE_URL="${VLLM_BASE_URL%/}/v1"
fi

SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
PROJECT_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"

if [ -z "${DATA}" ]; then
    DATA="${PROJECT_ROOT}/data/firm-video-bench.json"
fi

RESULTS_DIR="${PROJECT_ROOT}/results"
SCORE_JSON="${RESULTS_DIR}/${TAG}_scores.json"

mkdir -p "${RESULTS_DIR}"

if command -v curl >/dev/null 2>&1; then
    if ! curl -fsS "${VLLM_BASE_URL}/models" >/dev/null; then
        echo "Cannot reach vLLM endpoint: ${VLLM_BASE_URL}" >&2
        echo "Please start a vLLM OpenAI-compatible server first." >&2
        exit 1
    fi
fi

echo "============================================================"
echo "  Vanilla Pipeline (vLLM/OpenAI-compatible, 3 dims, per-dim calls)"
echo "============================================================"
echo "  vLLM base:    ${VLLM_BASE_URL}"
echo "  Model:        ${VLLM_MODEL}"
echo "  Data:         ${DATA}"
echo "  Num samples:  ${NUM_SAMPLES} (videos)"
echo "  Concurrency:  ${CONCURRENCY}"
echo "  Max tokens:   ${VLLM_MAX_TOKENS}"
echo "  Temperature:  ${VLLM_TEMPERATURE}"
echo "  Scores:       ${SCORE_JSON}"
echo "============================================================"

python "${SCRIPT_DIR}/infer.py" \
    --data "${DATA}" \
    --score_output "${SCORE_JSON}" \
    --num_samples "${NUM_SAMPLES}" \
    --concurrency "${CONCURRENCY}" \
    --vllm_base_url "${VLLM_BASE_URL}" \
    --model "${VLLM_MODEL}" \
    --api_key "${VLLM_API_KEY}" \
    --max_tokens "${VLLM_MAX_TOKENS}" \
    --temperature "${VLLM_TEMPERATURE}" \
    --request_interval "${VLLM_REQUEST_INTERVAL}" \
    --max_retries "${VLLM_MAX_RETRIES}" \
    --retry_base_delay "${VLLM_RETRY_BASE_DELAY}" \
    --request_timeout "${VLLM_REQUEST_TIMEOUT}"

echo ""
echo "Done!"
echo "  Scores:  ${SCORE_JSON}"