""" PyTorch Mixed Convolution
Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595)
Hacked together by / Copyright 2020 Ross Wightman
"""
# Copyright (c) 2021, NVIDIA CORPORATION. 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.

# Copyright 2019-2022 Ross Wightman
#
# 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 torch
from torch import nn as nn

from .conv2d_same import create_conv2d_pad


def _split_channels(num_chan, num_groups):
    split = [num_chan // num_groups for _ in range(num_groups)]
    split[0] += num_chan - sum(split)
    return split


class MixedConv2d(nn.ModuleDict):
    """ Mixed Grouped Convolution
    Based on MDConv and GroupedConv in MixNet impl:
      https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py
    """
    def __init__(self, in_channels, out_channels, kernel_size=3,
                 stride=1, padding='', dilation=1, depthwise=False, **kwargs):
        super(MixedConv2d, self).__init__()

        kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size]
        num_groups = len(kernel_size)
        in_splits = _split_channels(in_channels, num_groups)
        out_splits = _split_channels(out_channels, num_groups)
        self.in_channels = sum(in_splits)
        self.out_channels = sum(out_splits)
        for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)):
            conv_groups = out_ch if depthwise else 1
            # use add_module to keep key space clean
            self.add_module(
                str(idx),
                create_conv2d_pad(
                    in_ch, out_ch, k, stride=stride,
                    padding=padding, dilation=dilation, groups=conv_groups, **kwargs)
            )
        self.splits = in_splits

    def forward(self, x):
        x_split = torch.split(x, self.splits, 1)
        x_out = [c(x_split[i]) for i, c in enumerate(self.values())]
        x = torch.cat(x_out, 1)
        return x