"""
 Copyright (c) 2023, NVIDIA CORPORATION.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at

     http://www.apache.org/licenses/LICENSE-2.0

 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
"""

import hugectr
import json
import sys
import argparse
import threading
from mpi4py import MPI

DATA_READER_TYPE = {
    "Parquet": hugectr.DataReaderType_t.Parquet,
}
CHECK_TYPE = {"Sum": hugectr.Check_t.Sum, "None": hugectr.Check_t.Non}
OPTIMIZER_TYPE = {
    "Adam": hugectr.Optimizer_t.Adam,
    "MomentumSGD": hugectr.Optimizer_t.MomentumSGD,
    "Nesterov": hugectr.Optimizer_t.Nesterov,
    "SGD": hugectr.Optimizer_t.SGD,
}
UPDATE_TYPE = {
    "Global": hugectr.Update_t.Global,
    "LazyGlobal": hugectr.Update_t.LazyGlobal,
    "Local": hugectr.Update_t.Local,
}


def parse_args(parser):
    args = parser.parse_args()
    json_config = json.load(open(args.json_file, "rb"))
    solver_config = json_config["solver"]
    optimizer_config = json_config["optimizer"]
    data_config = json_config["layers"][0]
    args.source = data_config["source"]
    args.eval_source = data_config["eval_source"]
    if "format" not in data_config:
        raise TypeError("Data format must be parquet")
    else:
        args.data_reader_type = DATA_READER_TYPE[data_config.get("format", "Parquet")]
    args.check_type = CHECK_TYPE[data_config["check"]]
    args.cache_eval_data = data_config.get("cache_eval_data", 0)
    args.num_samples = data_config.get("num_samples", 0)
    args.eval_num_samples = data_config.get("eval_num_samples", 0)
    args.float_label_dense = data_config.get("float_label_dense", False)
    args.num_workers = data_config.get("num_workers", 16)
    args.slot_size_array = data_config.get("slot_size_array", [])
    args.optimizer_type = OPTIMIZER_TYPE[optimizer_config["type"]]
    args.update_type = UPDATE_TYPE[optimizer_config["update_type"]]
    args.learning_rate = 0.001
    args.beta1 = 0.9
    args.beta2 = 0.999
    args.epsilon = 0.0000001
    args.initial_accu_value = 0.0
    args.momentum_factor = 0.0
    args.atomic_update = True
    args.warmup_steps = 1
    args.decay_start = 0
    args.decay_steps = 1
    args.decay_power = 2.0
    args.end_lr = 0.0
    if "adam_hparam" in optimizer_config:
        args.learning_rate = optimizer_config["adam_hparam"]["learning_rate"]
        args.beta1 = optimizer_config["adam_hparam"]["beta1"]
        args.beta2 = optimizer_config["adam_hparam"]["beta2"]
        args.epsilon = optimizer_config["adam_hparam"]["epsilon"]
    if "adagrad_hparam" in optimizer_config:
        args.initial_accu_value = optimizer_config["adagrad_hparam"]["initial_accu_value"]
        args.epsilon = optimizer_config["adagrad_hparam"]["epsilon"]
    if "momentum_sgd_hparam" in optimizer_config:
        args.learning_rate = optimizer_config["momentum_sgd_hparam"]["learning_rate"]
        args.momentum_factor = optimizer_config["momentum_sgd_hparam"]["momentum_factor"]
    if "nesterov_hparam" in optimizer_config:
        args.learning_rate = optimizer_config["nesterov_hparam"]["learning_rate"]
        args.momentum_factor = optimizer_config["nesterov_hparam"]["momentum_factor"]
    if "sgd_hparam" in optimizer_config:
        args.learning_rate = optimizer_config["sgd_hparam"]["learning_rate"]
        args.warmup_steps = optimizer_config["sgd_hparam"].get("warmup_steps", 1)
        args.decay_start = optimizer_config["sgd_hparam"].get("decay_start", 0)
        args.decay_steps = optimizer_config["sgd_hparam"].get("decay_steps", 1)
        args.decay_power = optimizer_config["sgd_hparam"].get("decay_power", 2.0)
        args.end_lr = optimizer_config["sgd_hparam"].get("end_lr", 0)
    args.batchsize = solver_config.get("batchsize", 2048)
    args.batchsize_eval = solver_config.get("batchsize_eval", args.batchsize)
    args.max_eval_batches = solver_config.get("max_eval_batches", 100)
    args.max_iter = solver_config.get("max_iter", 10000)
    args.eval_interval = solver_config.get("eval_interval", 1000)
    args.display = solver_config.get("display", 200)
    vvgpu = solver_config["gpu"]
    if isinstance(vvgpu[0], list):
        args.vvgpu = vvgpu
    else:
        args.vvgpu = [vvgpu]
    args.use_mixed_precision = False
    args.scaler = 1.0
    if "mixed_precision" in solver_config:
        args.use_mixed_precision = True
        args.scaler = solver_config["mixed_precision"]
    args.i64_input_key = False
    if "input_key_type" in solver_config and solver_config["input_key_type"] == "I64":
        args.i64_input_key = True
    if "auc_threshold" in solver_config:
        args.auc_threshold = solver_config["auc_threshold"]
        args.auc_check = True
    else:
        args.auc_threshold = 0.5
        args.auc_check = False
    return args


def train(model, max_iter, display, max_eval_batches, eval_interval, auc_threshold):
    model.start_data_reading()
    lr_sch = model.get_learning_rate_scheduler()
    reach_auc_threshold = False
    for iter in range(max_iter):
        lr = lr_sch.get_next()
        model.set_learning_rate(lr)
        model.train()
        if iter % display == 0:
            loss = model.get_current_loss()
            if rank == 0:
                print("[HUGECTR][INFO] iter: {}; loss: {}".format(iter, loss))
        if iter % eval_interval == 0 and iter != 0:
            for _ in range(max_eval_batches):
                model.eval()
            metrics = model.get_eval_metrics()
            print("[HUGECTR][INFO] iter: {}, metrics: {}, rank: {} ".format(iter, metrics, rank))
            if metrics[0][1] > auc_threshold:
                reach_auc_threshold = True
                break
    if reach_auc_threshold == False:
        raise RuntimeError("Cannot reach the AUC threshold {}".format(auc_threshold))
        sys.exit(1)
    else:
        print("Successfully reach the AUC threshold {}".format(auc_threshold))


def multi_node_test(args):
    solver = hugectr.CreateSolver(
        max_eval_batches=args.max_eval_batches,
        batchsize_eval=args.batchsize_eval,
        batchsize=args.batchsize,
        vvgpu=args.vvgpu,
        lr=args.learning_rate,
        warmup_steps=args.warmup_steps,
        decay_start=args.decay_start,
        decay_steps=args.decay_steps,
        decay_power=args.decay_power,
        end_lr=args.end_lr,
        i64_input_key=args.i64_input_key,
        use_mixed_precision=args.use_mixed_precision,
        scaler=args.scaler,
    )
    reader = hugectr.DataReaderParams(
        data_reader_type=args.data_reader_type,
        source=[args.source],
        eval_source=args.eval_source,
        check_type=args.check_type,
        cache_eval_data=args.cache_eval_data,
        num_samples=args.num_samples,
        eval_num_samples=args.eval_num_samples,
        float_label_dense=args.float_label_dense,
        num_workers=args.num_workers,
        slot_size_array=args.slot_size_array,
    )
    optimizer = hugectr.CreateOptimizer(
        optimizer_type=args.optimizer_type,
        beta1=args.beta1,
        beta2=args.beta2,
        epsilon=args.epsilon,
        update_type=args.update_type,
        momentum_factor=args.momentum_factor,
        atomic_update=args.atomic_update,
    )
    model = hugectr.Model(solver, reader, optimizer)
    model.construct_from_json(graph_config_file=args.json_file, include_dense_network=True)
    model.compile()
    model.summary()
    if args.auc_check:
        train(
            model,
            args.max_iter,
            args.display,
            args.max_eval_batches,
            args.eval_interval,
            args.auc_threshold,
        )
    else:
        model.fit(max_iter=args.max_iter, display=args.display, eval_interval=args.eval_interval)
    return


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--json-file", type=str, required=True, help="JSON configuration file")
    args = parse_args(parser)
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    thread = threading.Thread(target=multi_node_test, args=(args,), name="[rank-%d train]" % rank)
    current_thread = threading.currentThread()
    print("[HUGECTR][INFO] before: rank %d " % (rank))
    # start the thread
    thread.start()
    # wait for terminate
    thread.join()
    print("[HUGECTR][INFO] after: rank %d " % (rank))
