# Copyright 2020 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.
# ============================================================================
"""TimeMonitor Callback class."""

import time

from mindspore import log as logger
from ._callback import Callback


class TimeMonitor(Callback):
    """
    Monitor the time in training.

    Args:
        data_size (int): Dataset size. Default: None.
    """

    def __init__(self, data_size=None):
        super(TimeMonitor, self).__init__()
        self.data_size = data_size

    def epoch_begin(self, run_context):
        self.epoch_time = time.time()

    def epoch_end(self, run_context):
        epoch_seconds = (time.time() - self.epoch_time) * 1000
        step_size = self.data_size
        cb_params = run_context.original_args()
        if hasattr(cb_params, "batch_num"):
            batch_num = cb_params.batch_num
            if isinstance(batch_num, int) and batch_num > 0:
                step_size = cb_params.batch_num

        if not isinstance(step_size, int) or step_size < 1:
            logger.error("data_size must be positive int.")
            return

        step_seconds = epoch_seconds / step_size
        print("Epoch time: {:5.3f}, per step time: {:5.3f}".format(epoch_seconds, step_seconds), flush=True)
