#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright (c) 2019, Open-MMLab. 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.

from collections import OrderedDict
import tensorflow as tf

import numpy as np


class LogBuffer(object):

    def __init__(self):
        self.val_history = OrderedDict()
        self.n_history = OrderedDict()
        self.output = OrderedDict()
        self.ready = False

    def clear(self):
        self.val_history.clear()
        self.n_history.clear()
        self.clear_output()

    def clear_output(self):
        self.output.clear()
        self.ready = False

    def update(self, vars, count=1):
        assert isinstance(vars, dict)
        for key, var in vars.items():
            if key not in self.val_history:
                self.val_history[key] = []
                self.n_history[key] = []
            if tf.is_tensor(var):
                var = var.numpy()
            self.val_history[key].append(var)
            self.n_history[key].append(count)

    def average(self, n=0):
        """Average latest n values or all values"""
        assert n >= 0
        for key in self.val_history:
            if 'time' in key:
                self.output[key] = self.val_history[key][-1]
            elif 'image' not in key: # skip images
                values = np.array(self.val_history[key][-n:])
                nums = np.array(self.n_history[key][-n:])
                avg = np.sum(values * nums) / np.sum(nums)
                self.output[key] = avg
        self.ready = True