# Copyright 2021 Samsung Electronics 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.
# =============================================================================

import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F

class ReLUConvBN(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, affine, track_running_stats=True, use_bn=True, name='ReLUConvBN'):
        super(ReLUConvBN, self).__init__()
        self.name = name
        if use_bn:
            self.op = nn.Sequential(
                nn.ReLU(inplace=False),
                nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine),
                nn.BatchNorm2d(out_channels, affine=affine, track_running_stats=track_running_stats)
                )
        else:
            self.op = nn.Sequential(
                nn.ReLU(inplace=False),
                nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine)
                )

    def forward(self, x):
        return self.op(x)

class Identity(nn.Module):
    def __init__(self, name='Identity'):
        self.name = name
        super(Identity, self).__init__()

    def forward(self, x):
        return x

class Zero(nn.Module):

  def __init__(self, stride, name='Zero'):
    self.name = name
    super(Zero, self).__init__()
    self.stride = stride

  def forward(self, x):
    if self.stride == 1:
      return x.mul(0.)
    return x[:,:,::self.stride,::self.stride].mul(0.)

class POOLING(nn.Module):
    def __init__(self, kernel_size, stride, padding, name='POOLING'):
        super(POOLING, self).__init__()
        self.name = name
        self.avgpool = nn.AvgPool2d(kernel_size=kernel_size, stride=1, padding=1, count_include_pad=False)

    def forward(self, x):
        return self.avgpool(x)


class reduction(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(reduction, self).__init__()
        self.residual = nn.Sequential(
                            nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
                            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, bias=False))

        self.conv_a = ReLUConvBN(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=2, padding=1, dilation=1, affine=True, track_running_stats=True)
        self.conv_b = ReLUConvBN(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, dilation=1, affine=True, track_running_stats=True)

    def forward(self, x):
        basicblock = self.conv_a(x)
        basicblock = self.conv_b(basicblock)
        residual = self.residual(x)
        return residual + basicblock

class stem(nn.Module):
    def __init__(self, out_channels, use_bn=True):
        super(stem, self).__init__()
        if use_bn:
            self.net = nn.Sequential(
                    nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=3, padding=1, bias=False),
                    nn.BatchNorm2d(out_channels))
        else:
            self.net = nn.Sequential(
                    nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=3, padding=1, bias=False)
            )

    def forward(self, x):
        return self.net(x)

class top(nn.Module):
    # def __init__(self, in_dims, num_classes, use_bn=True):
    def __init__(self, in_dims, use_bn=True):
        super(top, self).__init__()
        if use_bn:
            self.lastact = nn.Sequential(nn.BatchNorm2d(in_dims), nn.ReLU(inplace=True))
        else:
            self.lastact = nn.ReLU(inplace=True)
        self.global_pooling = nn.AdaptiveAvgPool2d(1)
        # self.classifier = nn.Linear(in_dims, num_classes)

    def forward(self, x):
        x = self.lastact(x)
        x = self.global_pooling(x)
        x = x.view(x.size(0), -1)
        # logits = self.classifier(x)
        # return logits
        return x


class SearchCell(nn.Module):

    def __init__(self, in_channels, out_channels, stride, affine, track_running_stats, use_bn=True, num_nodes=4, keep_mask=None):
        super(SearchCell, self).__init__()
        self.num_nodes = num_nodes
        self.options = nn.ModuleList()
        for curr_node in range(self.num_nodes-1):
            for prev_node in range(curr_node+1): 
                for _op_name in OPS.keys():
                    op = OPS[_op_name](in_channels, out_channels, stride, affine, track_running_stats, use_bn)
                    self.options.append(op)

        if keep_mask is not None:
            self.keep_mask = keep_mask
        else:
            self.keep_mask = [True]*len(self.options)

    def forward(self, x):
        outs = [x]

        idx = 0
        for curr_node in range(self.num_nodes-1):
            edges_in = []
            for prev_node in range(curr_node+1): # n-1 prev nodes
                for op_idx in range(len(OPS.keys())):
                    if self.keep_mask[idx]:
                        edges_in.append(self.options[idx](outs[prev_node]))
                    idx += 1
            node_output = sum(edges_in)
            outs.append(node_output)
        
        return outs[-1]



OPS = {
    'none' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: Zero(stride, name='none'),
    'avg_pool_3x3' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: POOLING(3, 1, 1, name='avg_pool_3x3'),
    'nor_conv_3x3' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: ReLUConvBN(in_channels, out_channels, 3, 1, 1, 1, affine, track_running_stats, use_bn, name='nor_conv_3x3'),
    'nor_conv_1x1' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: ReLUConvBN(in_channels, out_channels, 1, 1, 0, 1, affine, track_running_stats, use_bn, name='nor_conv_1x1'),
    'skip_connect' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: Identity(name='skip_connect'),
}


