# Copyright (c) 2022 PaddlePaddle 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.

import paddle
import paddle.nn as nn
import paddle.nn.functional as F


def avg_max_reduce_channel_helper(x, use_concat=True):
    # Reduce hw by avg and max, only support single input
    assert not isinstance(x, (list, tuple))
    mean_value = paddle.mean(x, axis=1, keepdim=True)
    max_value = paddle.max(x, axis=1, keepdim=True)

    if use_concat:
        res = paddle.concat([mean_value, max_value], axis=1)
    else:
        res = [mean_value, max_value]
    return res


def avg_max_reduce_channel(x):
    # Reduce hw by avg and max
    # Return cat([avg_ch_0, max_ch_0, avg_ch_1, max_ch_1, ...])
    if not isinstance(x, (list, tuple)):
        return avg_max_reduce_channel_helper(x)
    elif len(x) == 1:
        return avg_max_reduce_channel_helper(x[0])
    else:
        res = []
        for xi in x:
            res.extend(avg_max_reduce_channel_helper(xi, False))
        return paddle.concat(res, axis=1)