# The MIT License (MIT)
#
# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
# the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

: ${MAX_BATCH_SIZE:=${1:-64}}

load_models() {
  echo "Loading models..."
  python scripts/model-loader.py -u "${GRPC_ADDR}" load -m efficientnet_ensemble_gpu
  sleep 5
  echo "...models loaded"
}

unload_models() {
  echo "Unloading models..."
  python scripts/model-loader.py -u "${GRPC_ADDR}" unload -m efficientnet_ensemble_gpu
  sleep 5
  echo "...models unloaded"
}

# The BATCH_SIZES are assigned with powers of 2 lower or equal to MAX_BATCH_SIZE
echo "MAX BATCH SIZE: $MAX_BATCH_SIZE"
BATCH_SIZES=()
POWER_OF_2=1
while [ $POWER_OF_2 -le $MAX_BATCH_SIZE ]; do
  BATCH_SIZES+=($POWER_OF_2)
  POWER_OF_2=$((POWER_OF_2 * 2))
done

# Feel free to assign BATCH_SIZES manually...
#BATCH_SIZES=(1 2 4 8 16 32 64)

CONCURRENCY_RANGE=(1 16 32 64)
GRPC_ADDR=${GRPC_ADDR:-"localhost:8001"}
STABILITY_PERCENTAGE=50
PERF_ANALYZER_ARGS="-i grpc -u $GRPC_ADDR --request-count=64 --verbose-csv --collect-metrics"
INPUT_NAME="INPUT"
BENCH_DIR="bench-$(date +%Y%m%d_%H%M%S)"

mkdir -p "$BENCH_DIR/gpu"

echo "Batch size set: $BATCH_SIZES"
for BS in "${BATCH_SIZES[@]}"; do
  echo "Batch size: $BS"
done

for BS in "${BATCH_SIZES[@]}"; do
  for CONCURRENCY in "${CONCURRENCY_RANGE[@]}"; do
    echo "Benchmarking GPU preprocessing. Batch size: $BS"
    load_models
    perf_analyzer $PERF_ANALYZER_ARGS -m efficientnet_ensemble_gpu --input-data test_sample --shape $INPUT_NAME:$(stat --printf="%s" test_sample/$INPUT_NAME) --concurrency-range=$CONCURRENCY -b "$BS" -f "$BENCH_DIR/gpu/report-bs$BS-c$CONCURRENCY.csv"
    unload_models
  done
done

pip install -U pandas
python scripts/concatenate_results.py -p "$BENCH_DIR/gpu"
