# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# 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.

"""Utils for yolo task."""
import tensorflow as tf


class ListMetrics:
  """Private class used to cleanly place the matric values for each level."""

  def __init__(self, metric_names, name="ListMetrics"):
    self.name = name
    self._metric_names = metric_names
    self._metrics = self.build_metric()
    return

  def build_metric(self):
    metric_names = self._metric_names
    metrics = []
    for name in metric_names:
      metrics.append(tf.keras.metrics.Mean(name, dtype=tf.float32))
    return metrics

  def update_state(self, loss_metrics):
    metrics = self._metrics
    for m in metrics:
      m.update_state(loss_metrics[m.name])
    return

  def result(self):
    logs = dict()
    metrics = self._metrics
    for m in metrics:
      logs.update({m.name: m.result()})
    return logs

  def reset_states(self):
    metrics = self._metrics
    for m in metrics:
      m.reset_states()
    return