# Copyright 2023 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.
# ============================================================================
"""Codegeex Train/Finetune/Eval/Predict scripts."""

import argparse

from mindformers import Trainer
from mindformers import init_context, ContextConfig, ParallelContextConfig
from mindformers.tools.utils import str2bool

# pylint: disable=W0611
import codegeex

def context_init(use_parallel=False, optimizer_parallel=False):
    """init context for mindspore."""
    context_config = ContextConfig(mode=0, device_target="Ascend", device_id=0, max_device_memory='57GB')
    parallel_config = None
    if use_parallel:
        parallel_config = ParallelContextConfig(parallel_mode='SEMI_AUTO_PARALLEL',
                                                gradients_mean=False,
                                                enable_parallel_optimizer=optimizer_parallel,
                                                full_batch=True)
    init_context(use_parallel=use_parallel,
                 context_config=context_config,
                 parallel_config=parallel_config)


def main(task='text_generation',
         config='run_codegeex.yaml',
         run_mode='train',
         use_parallel=False,
         ckpt=None,
         resume=False,
         train_dataset='',
         eval_dataset='',
         predict_data='',
         max_length=512,
         op=True):
    """main function."""

    # 环境初始化
    context_init(use_parallel, op)

    # 定义任务，预先准备好相应数据集

    if run_mode == 'train':
        trainer = Trainer(args=config,
                          task=task,
                          train_dataset=train_dataset)
        trainer.train(train_checkpoint=ckpt, resume=resume)
    elif run_mode == 'finetune':
        trainer = Trainer(args=config,
                          task=task,
                          train_dataset=train_dataset)
        trainer.finetune(finetune_checkpoint=ckpt, resume=resume)
    elif run_mode == 'eval':
        trainer = Trainer(args=config,
                          task=task,
                          eval_dataset=eval_dataset)
        trainer.evaluate(eval_checkpoint=ckpt)
    elif run_mode == 'predict':
        trainer = Trainer(args=config,
                          task=task)
        result = trainer.predict(input_data=predict_data,
                                 predict_checkpoint=ckpt, max_length=int(max_length))
        print(result)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--task', default='text_generation', type=str,
                        help='set task type.')
    parser.add_argument('--config', default='run_codegeex.yaml', type=str,
                        help='set task type.')
    parser.add_argument('--run_mode', default='train', type=str,
                        help='set run mode for model.')
    parser.add_argument('--use_parallel', default=True, type=str2bool,
                        help='open parallel for model.')
    parser.add_argument('--load_checkpoint', default="", type=str,
                        help='checkpoint name or dir to load.')
    parser.add_argument('--resume', default=False, type=str2bool,
                        help='whether resume training.')
    parser.add_argument('--train_dataset', default='', type=str,
                        help='set train dataset.')
    parser.add_argument('--eval_dataset', default='', type=str,
                        help='set eval dataset.')
    parser.add_argument('--predict_data', default='', type=str,
                        help='input predict data.')
    parser.add_argument('--predict_length', default=512, type=int,
                        help='max length for predict output.')
    parser.add_argument('--optimizer_parallel', default=True, type=str2bool,
                        help='whether use optimizer parallel. Default: None')
    args = parser.parse_args()

    main(task=args.task,
         config=args.config,
         run_mode=args.run_mode,
         use_parallel=args.use_parallel,
         ckpt=args.load_checkpoint,
         resume=args.resume,
         train_dataset=args.train_dataset,
         eval_dataset=args.eval_dataset,
         predict_data=args.predict_data,
         max_length=args.predict_length,
         op=args.optimizer_parallel)