# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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.
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import setproctitle
from datetime import datetime
from pathlib import Path
from imnet_finetune import TrainerConfig, ClusterConfig, Trainer
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter

os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '29688'

def run(input_sizes,epochs,learning_rate,batch,imnet_path,architecture,resnet_weight_path,workers,shared_folder_path,job_id,local_rank,global_rank,num_tasks,EfficientNet_models):
    cluster_cfg = ClusterConfig(dist_backend="hccl", dist_url="env://")
    shared_folder=None
    data_folder_Path=None
    if Path(str(shared_folder_path)).is_dir():
        shared_folder=Path(shared_folder_path+"/finetune/")
    else:
        raise RuntimeError("No shared folder available")
    if Path(str(imnet_path)).is_dir():
        data_folder_Path=Path(str(imnet_path))
    else:
        raise RuntimeError("No shared folder available")
    train_cfg = TrainerConfig(
                    data_folder=str(data_folder_Path),
                    epochs=epochs,
                    lr=learning_rate,
                    input_size=input_sizes,
                    batch_per_gpu=batch,
                    save_folder=str(shared_folder),
                    imnet_path=imnet_path,
                    architecture=architecture,
                    resnet_weight_path=resnet_weight_path,
                    workers=workers,
                    local_rank=local_rank,
                    global_rank=global_rank,
                    num_tasks=num_tasks,
                    job_id=job_id,
                    EfficientNet_models=EfficientNet_models,
                    
                )
        
    # Create the executor
    os.makedirs(str(shared_folder), exist_ok=True)
    init_file = shared_folder / datetime.now().strftime("%Y%m%d-%H%M%S")
    if init_file.exists():
        os.remove(str(init_file))
        
    trainer = Trainer(train_cfg, cluster_cfg)
    
    #The code should be launch on each GPUs
    try:    
        if local_rank==0:
            val_accuracy = trainer.__call__()
            print(f"Validation accuracy: {val_accuracy}")
        else:
            trainer.__call__()
    except:
      print("Job failed")


if __name__ == "__main__":
    parser = ArgumentParser(description="Fine-tune script for FixRes models",formatter_class=ArgumentDefaultsHelpFormatter)
    parser.add_argument('--learning_rate', default=0.01, type=float, help='base learning rate')
    parser.add_argument('--epochs', default=1, type=int, help='epochs')
    parser.add_argument('--input_size', default=384, type=int, help='images input size')
    parser.add_argument('--batch', default=64, type=int, help='Batch by GPU')
    parser.add_argument('--imnet_path', default='/opt/npu/imagenet', type=str, help='Image Net dataset path')
    parser.add_argument('--architecture', default='ResNet50', type=str,choices=['ResNet50', 'PNASNet' , 'IGAM_Resnext101_32x48d','EfficientNet'], help='Neural network architecture')
    parser.add_argument('--resnet_weight_path', default='./train_cache/20211028-114243/checkpoint.pth', type=str, help='Neural network weights (only for ResNet50)')
    parser.add_argument('--workers', default=10, type=int, help='Numbers of CPUs')
    parser.add_argument('--local_rank', default=0, type=int, help='GPU: Local rank')
    parser.add_argument('--global_rank', default=0, type=int, help='GPU: glocal rank')
    parser.add_argument('--num_tasks', default=32, type=int, help='How many GPUs are used')
    parser.add_argument('--shared_folder_path', default='./train_cache', type=str, help='Shared Folder')
    parser.add_argument('--EfficientNet_models', default='tf_efficientnet_b0_ap', type=str, help='EfficientNet Models')
    parser.add_argument("--addr", default="127.0.0.1", type=str)

    args = parser.parse_args()
    setproctitle.setproctitle('FIXRES - Finetune')
    args.job_id = datetime.now().strftime("%Y%m%d-%H%M%S")
    os.environ['MASTER_ADDR'] = args.addr
    os.environ['MASTER_PORT'] = '29688'
    run(args.input_size,args.epochs,args.learning_rate,args.batch,args.imnet_path,args.architecture,args.resnet_weight_path,args.workers,args.shared_folder_path,args.job_id,args.local_rank,args.global_rank,args.num_tasks,args.EfficientNet_models)