#!/bin/bash
FILENAME=$1
source test_tipc/common_func.sh

# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer', 'klquant_whole_infer']
MODE=$2

dataline=$(cat ${FILENAME})

# parser params
IFS=$'\n'
lines=(${dataline})

# The training params
model_name=$(func_parser_value "${lines[1]}")
python=$(func_parser_value "${lines[2]}")
gpu_list=$(func_parser_value "${lines[3]}")
train_use_gpu_key=$(func_parser_key "${lines[4]}")
train_use_gpu_value=$(func_parser_value "${lines[4]}")
autocast_list=$(func_parser_value "${lines[5]}")
autocast_key=$(func_parser_key "${lines[5]}")
epoch_key=$(func_parser_key "${lines[6]}")
epoch_num=$(func_parser_params "${lines[6]}")
save_model_key=$(func_parser_key "${lines[7]}")
train_batch_key=$(func_parser_key "${lines[8]}")
train_batch_value=$(func_parser_value "${lines[8]}")
pretrain_model_key=$(func_parser_key "${lines[9]}")
pretrain_model_value=$(func_parser_value "${lines[9]}")
train_model_name=$(func_parser_value "${lines[10]}")
train_infer_img_dir=$(func_parser_value "${lines[11]}")
train_param_key1=$(func_parser_key "${lines[12]}")
train_param_value1=$(func_parser_value "${lines[12]}")

trainer_list=$(func_parser_value "${lines[14]}")
trainer_norm=$(func_parser_key "${lines[15]}")
norm_trainer=$(func_parser_value "${lines[15]}")
pact_key=$(func_parser_key "${lines[16]}")
pact_trainer=$(func_parser_value "${lines[16]}")
fpgm_key=$(func_parser_key "${lines[17]}")
fpgm_trainer=$(func_parser_value "${lines[17]}")
distill_key=$(func_parser_key "${lines[18]}")
distill_trainer=$(func_parser_value "${lines[18]}")
to_static_key=$(func_parser_key "${lines[19]}")
to_static_trainer=$(func_parser_value "${lines[19]}")
trainer_key2=$(func_parser_key "${lines[20]}")
trainer_value2=$(func_parser_value "${lines[20]}")

eval_py=$(func_parser_value "${lines[23]}")
eval_key1=$(func_parser_key "${lines[24]}")
eval_value1=$(func_parser_value "${lines[24]}")

save_infer_key=$(func_parser_key "${lines[27]}")
export_weight=$(func_parser_key "${lines[28]}")
norm_export=$(func_parser_value "${lines[29]}")
pact_export=$(func_parser_value "${lines[30]}")
fpgm_export=$(func_parser_value "${lines[31]}")
distill_export=$(func_parser_value "${lines[32]}")
kl_quant_cmd_key=$(func_parser_key "${lines[33]}")
kl_quant_cmd_value=$(func_parser_value "${lines[33]}")
export_key2=$(func_parser_key "${lines[34]}")
export_value2=$(func_parser_value "${lines[34]}")

# parser inference model 
infer_model_dir_list=$(func_parser_value "${lines[36]}")
infer_export_flag=$(func_parser_value "${lines[37]}")
infer_is_quant=$(func_parser_value "${lines[38]}")

# parser inference 
inference_py=$(func_parser_value "${lines[39]}")
use_gpu_key=$(func_parser_key "${lines[40]}")
use_gpu_list=$(func_parser_value "${lines[40]}")
use_mkldnn_key=$(func_parser_key "${lines[41]}")
use_mkldnn_list=$(func_parser_value "${lines[41]}")
cpu_threads_key=$(func_parser_key "${lines[42]}")
cpu_threads_list=$(func_parser_value "${lines[42]}")
batch_size_key=$(func_parser_key "${lines[43]}")
batch_size_list=$(func_parser_value "${lines[43]}")
use_trt_key=$(func_parser_key "${lines[44]}")
use_trt_list=$(func_parser_value "${lines[44]}")
precision_key=$(func_parser_key "${lines[45]}")
precision_list=$(func_parser_value "${lines[45]}")
infer_model_key=$(func_parser_key "${lines[46]}")
image_dir_key=$(func_parser_key "${lines[47]}")
infer_img_dir=$(func_parser_value "${lines[47]}")
save_log_key=$(func_parser_key "${lines[48]}")
benchmark_key=$(func_parser_key "${lines[49]}")
benchmark_value=$(func_parser_value "${lines[49]}")
infer_key1=$(func_parser_key "${lines[50]}")
infer_value1=$(func_parser_value "${lines[50]}")
if [ ! $epoch_num ]; then
  epoch_num=2
fi
if [ $MODE = 'benchmark_train' ]; then
  epoch_num=1
fi

LOG_PATH="./test_tipc/output/${model_name}"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_python.log"

function func_inference(){
    IFS='|'
    _python=$1
    _script=$2
    _model_dir=$3
    _log_path=$4
    _img_dir=$5
    _flag_quant=$6
    # inference 
    for use_gpu in ${use_gpu_list[*]}; do
        if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
            for use_mkldnn in ${use_mkldnn_list[*]}; do
                if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
                    continue
                fi
                for threads in ${cpu_threads_list[*]}; do
                    for batch_size in ${batch_size_list[*]}; do
                        _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
                        set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
                        eval $command
                        last_status=${PIPESTATUS[0]}
                        eval "cat ${_save_log_path}"
                        status_check $last_status "${command}" "../${status_log}"
                    done
                done
            done
        elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
            for use_trt in ${use_trt_list[*]}; do
                for precision in ${precision_list[*]}; do
                    if [ ${precision} = "True" ] && [ ${use_trt} = "False" ]; then
                        continue
                    fi
                    if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
                        continue
                    fi
                    for batch_size in ${batch_size_list[*]}; do
                        _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
                        set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
                        set_precision=$(func_set_params "${precision_key}" "${precision}")
                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
                        eval $command
                        last_status=${PIPESTATUS[0]}
                        eval "cat ${_save_log_path}"
                        status_check $last_status "${command}" "../${status_log}"
                    done
                done
            done
        else
            echo "Does not support hardware other than CPU and GPU Currently!"
        fi
    done
}

if [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ]; then
   IFS="|"
   infer_export_flag=(${infer_export_flag})
   if [ ${infer_export_flag} != "null" ]  && [ ${infer_export_flag} != "False" ]; then
	rm -rf ${infer_model_dir_list/..\//}
	export_cmd="${python} ${norm_export} -o Global.pretrained_model=${model_name}_pretrained -o Global.save_inference_dir=${infer_model_dir_list/..\//}"
	eval $export_cmd
   fi
fi

if [ ${MODE} = "whole_infer" ]; then
    GPUID=$3
    if [ ${#GPUID} -le 0 ];then
        env=" "
    else
        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
    fi
    # set CUDA_VISIBLE_DEVICES
    eval $env
    export Count=0
    cd deploy
    for infer_model in ${infer_model_dir_list[*]}; do
        #run inference
        is_quant=${infer_quant_flag[Count]}
        echo "is_quant: ${is_quant}"
        func_inference "${python}" "${inference_py}" "${infer_model}" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
        Count=$(($Count + 1))
    done
    cd ..

elif [ ${MODE} = "klquant_whole_infer" ]; then
    # for kl_quant
    if [ ${kl_quant_cmd_value} != "null" ] && [ ${kl_quant_cmd_value} != "False" ]; then
	echo "kl_quant"
	command="${python} ${kl_quant_cmd_value}"
	eval $command
	last_status=${PIPESTATUS[0]}
	status_check $last_status "${command}" "${status_log}"
	cd inference/quant_post_static_model
	ln -s __model__ inference.pdmodel
	ln -s __params__ inference.pdiparams
	cd ../../deploy
	is_quant=True
        func_inference "${python}" "${inference_py}" "${infer_model_dir_list}/quant_post_static_model" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
	cd ..
    fi
else
    IFS="|"
    export Count=0
    USE_GPU_KEY=(${train_use_gpu_value})
    for gpu in ${gpu_list[*]}; do
        train_use_gpu=${USE_GPU_KEY[Count]}
        Count=$(($Count + 1))
        ips=""
        if [ ${gpu} = "-1" ];then
            env=""
        elif [ ${#gpu} -le 1 ];then
            env="export CUDA_VISIBLE_DEVICES=${gpu}"
            eval ${env}
        elif [ ${#gpu} -le 15 ];then
            IFS=","
            array=(${gpu})
            env="export CUDA_VISIBLE_DEVICES=${array[0]}"
            IFS="|"
        else
            IFS=";"
            array=(${gpu})
            ips=${array[0]}
            gpu=${array[1]}
            IFS="|"
            env=" "
        fi
        for autocast in ${autocast_list[*]}; do
            for trainer in ${trainer_list[*]}; do 
                flag_quant=False
                if [ ${trainer} = ${pact_key} ]; then
                    run_train=${pact_trainer}
                    run_export=${pact_export}
                    flag_quant=True
                elif [ ${trainer} = "${fpgm_key}" ]; then
                    run_train=${fpgm_trainer}
                    run_export=${fpgm_export}
                elif [ ${trainer} = "${distill_key}" ]; then
                    run_train=${distill_trainer}
                    run_export=${distill_export}
                # In case of @to_static, we re-used norm_traier,
                # but append "-o Global.to_static=True" for config
                # to trigger "apply_to_static" logic in 'engine.py'
                elif [ ${trainer} = "${to_static_key}" ]; then
                    run_train="${norm_trainer}  ${to_static_trainer}"
                    run_export=${norm_export}
                elif [[ ${trainer} = ${trainer_key2} ]]; then
                    run_train=${trainer_value2}
                    run_export=${export_value2}
                else
                    run_train=${norm_trainer}
                    run_export=${norm_export}
                fi

                if [ ${run_train} = "null" ]; then
                    continue
                fi
                
                set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
                set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
                set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
                set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
                set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
                set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu_value}")
                if [ ${#ips} -le 26 ];then
                    save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
                    nodes=1
                else
                    IFS=","
                    ips_array=(${ips})
                    IFS="|"
                    nodes=${#ips_array[@]}
                    save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
                fi
                
                # load pretrain from norm training if current trainer is pact or fpgm trainer
                # if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
                #    set_pretrain="${load_norm_train_model}"
                # fi

                set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
                if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
                    cmd="${python} ${run_train} ${set_use_gpu}  ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} "
                elif [ ${#ips} -le 26 ];then  # train with multi-gpu
                    cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}"
                else     # train with multi-machine
                    cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}"
                fi
                # run train
		eval "unset CUDA_VISIBLE_DEVICES"
		# export FLAGS_cudnn_deterministic=True
		sleep 5
                eval $cmd
                status_check $? "${cmd}" "${status_log}"
                sleep 5
		
		if [[ $FILENAME == *GeneralRecognition* ]]; then
		    set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
		else
                    set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${model_name}/${train_model_name}")
		fi
                # save norm trained models to set pretrain for pact training and fpgm training 
                if [ ${trainer} = ${trainer_norm} ]; then
                    load_norm_train_model=${set_eval_pretrain}
                fi
                # run eval 
                if [ ${eval_py} != "null" ]; then
                    set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
                    eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}" 
                    eval $eval_cmd
                    status_check $? "${eval_cmd}" "${status_log}"
                    sleep 5
                fi
                # run export model
                if [ ${run_export} != "null" ]; then 
                    # run export model
                    save_infer_path="${save_log}"
		    if [[ $FILENAME == *GeneralRecognition* ]]; then
		        set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
		    else
		        set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${model_name}/${train_model_name}")
		    fi
                    set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
                    export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
                    eval $export_cmd
                    status_check $? "${export_cmd}" "${status_log}"

                    #run inference
                    eval $env
                    save_infer_path="${save_log}"
		    cd deploy
                    func_inference "${python}" "${inference_py}" "../${save_infer_path}" "../${LOG_PATH}" "${infer_img_dir}" "${flag_quant}"
		    cd ..
                fi
                eval "unset CUDA_VISIBLE_DEVICES"
            done  # done with:    for trainer in ${trainer_list[*]}; do 
        done      # done with:    for autocast in ${autocast_list[*]}; do 
    done          # done with:    for gpu in ${gpu_list[*]}; do
fi  # end if [ ${MODE} = "infer" ]; then