import torch
from collections import OrderedDict

import torch
import torch.nn as nn

def make_layers(block, no_relu_layers):
    layers = []
    for layer_name, v in block.items():
        if 'pool' in layer_name:
            layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
                                    padding=v[2])
            layers.append((layer_name, layer))
        else:
            conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],
                               kernel_size=v[2], stride=v[3],
                               padding=v[4])
            layers.append((layer_name, conv2d))
            if layer_name not in no_relu_layers:
                layers.append(('relu_'+layer_name, nn.ReLU(inplace=True)))

    return nn.Sequential(OrderedDict(layers))

class bodypose_model(nn.Module):
    def __init__(self):
        super(bodypose_model, self).__init__()

        # these layers have no relu layer
        no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\
                          'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\
                          'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\
                          'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
        blocks = {}
        block0 = OrderedDict([
                      ('conv1_1', [3, 64, 3, 1, 1]),
                      ('conv1_2', [64, 64, 3, 1, 1]),
                      ('pool1_stage1', [2, 2, 0]),
                      ('conv2_1', [64, 128, 3, 1, 1]),
                      ('conv2_2', [128, 128, 3, 1, 1]),
                      ('pool2_stage1', [2, 2, 0]),
                      ('conv3_1', [128, 256, 3, 1, 1]),
                      ('conv3_2', [256, 256, 3, 1, 1]),
                      ('conv3_3', [256, 256, 3, 1, 1]),
                      ('conv3_4', [256, 256, 3, 1, 1]),
                      ('pool3_stage1', [2, 2, 0]),
                      ('conv4_1', [256, 512, 3, 1, 1]),
                      ('conv4_2', [512, 512, 3, 1, 1]),
                      ('conv4_3_CPM', [512, 256, 3, 1, 1]),
                      ('conv4_4_CPM', [256, 128, 3, 1, 1])
                  ])


        # Stage 1
        block1_1 = OrderedDict([
                        ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]),
                        ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
                        ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]),
                        ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
                        ('conv5_5_CPM_L1', [512, 38, 1, 1, 0])
                    ])

        block1_2 = OrderedDict([
                        ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]),
                        ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
                        ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]),
                        ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
                        ('conv5_5_CPM_L2', [512, 19, 1, 1, 0])
                    ])
        blocks['block1_1'] = block1_1
        blocks['block1_2'] = block1_2

        self.model0 = make_layers(block0, no_relu_layers)

        # Stages 2 - 6
        for i in range(2, 7):
            blocks['block%d_1' % i] = OrderedDict([
                    ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
                    ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                    ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                    ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                    ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                    ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
                    ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])
                ])

            blocks['block%d_2' % i] = OrderedDict([
                    ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
                    ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                    ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                    ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                    ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                    ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
                    ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])
                ])

        for k in blocks.keys():
            blocks[k] = make_layers(blocks[k], no_relu_layers)

        self.model1_1 = blocks['block1_1']
        self.model2_1 = blocks['block2_1']
        self.model3_1 = blocks['block3_1']
        self.model4_1 = blocks['block4_1']
        self.model5_1 = blocks['block5_1']
        self.model6_1 = blocks['block6_1']

        self.model1_2 = blocks['block1_2']
        self.model2_2 = blocks['block2_2']
        self.model3_2 = blocks['block3_2']
        self.model4_2 = blocks['block4_2']
        self.model5_2 = blocks['block5_2']
        self.model6_2 = blocks['block6_2']


    def forward(self, x):

        out1 = self.model0(x)

        out1_1 = self.model1_1(out1)
        out1_2 = self.model1_2(out1)
        out2 = torch.cat([out1_1, out1_2, out1], 1)

        out2_1 = self.model2_1(out2)
        out2_2 = self.model2_2(out2)
        out3 = torch.cat([out2_1, out2_2, out1], 1)

        out3_1 = self.model3_1(out3)
        out3_2 = self.model3_2(out3)
        out4 = torch.cat([out3_1, out3_2, out1], 1)

        out4_1 = self.model4_1(out4)
        out4_2 = self.model4_2(out4)
        out5 = torch.cat([out4_1, out4_2, out1], 1)

        out5_1 = self.model5_1(out5)
        out5_2 = self.model5_2(out5)
        out6 = torch.cat([out5_1, out5_2, out1], 1)

        out6_1 = self.model6_1(out6)
        out6_2 = self.model6_2(out6)

        return out6_1, out6_2

class handpose_model(nn.Module):
    def __init__(self):
        super(handpose_model, self).__init__()

        # these layers have no relu layer
        no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\
                          'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6']
        # stage 1
        block1_0 = OrderedDict([
                ('conv1_1', [3, 64, 3, 1, 1]),
                ('conv1_2', [64, 64, 3, 1, 1]),
                ('pool1_stage1', [2, 2, 0]),
                ('conv2_1', [64, 128, 3, 1, 1]),
                ('conv2_2', [128, 128, 3, 1, 1]),
                ('pool2_stage1', [2, 2, 0]),
                ('conv3_1', [128, 256, 3, 1, 1]),
                ('conv3_2', [256, 256, 3, 1, 1]),
                ('conv3_3', [256, 256, 3, 1, 1]),
                ('conv3_4', [256, 256, 3, 1, 1]),
                ('pool3_stage1', [2, 2, 0]),
                ('conv4_1', [256, 512, 3, 1, 1]),
                ('conv4_2', [512, 512, 3, 1, 1]),
                ('conv4_3', [512, 512, 3, 1, 1]),
                ('conv4_4', [512, 512, 3, 1, 1]),
                ('conv5_1', [512, 512, 3, 1, 1]),
                ('conv5_2', [512, 512, 3, 1, 1]),
                ('conv5_3_CPM', [512, 128, 3, 1, 1])
            ])

        block1_1 = OrderedDict([
            ('conv6_1_CPM', [128, 512, 1, 1, 0]),
            ('conv6_2_CPM', [512, 22, 1, 1, 0])
        ])

        blocks = {}
        blocks['block1_0'] = block1_0
        blocks['block1_1'] = block1_1

        # stage 2-6
        for i in range(2, 7):
            blocks['block%d' % i] = OrderedDict([
                    ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]),
                    ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]),
                    ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]),
                    ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]),
                    ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]),
                    ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]),
                    ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0])
                ])

        for k in blocks.keys():
            blocks[k] = make_layers(blocks[k], no_relu_layers)

        self.model1_0 = blocks['block1_0']
        self.model1_1 = blocks['block1_1']
        self.model2 = blocks['block2']
        self.model3 = blocks['block3']
        self.model4 = blocks['block4']
        self.model5 = blocks['block5']
        self.model6 = blocks['block6']

    def forward(self, x):
        out1_0 = self.model1_0(x)
        out1_1 = self.model1_1(out1_0)
        concat_stage2 = torch.cat([out1_1, out1_0], 1)
        out_stage2 = self.model2(concat_stage2)
        concat_stage3 = torch.cat([out_stage2, out1_0], 1)
        out_stage3 = self.model3(concat_stage3)
        concat_stage4 = torch.cat([out_stage3, out1_0], 1)
        out_stage4 = self.model4(concat_stage4)
        concat_stage5 = torch.cat([out_stage4, out1_0], 1)
        out_stage5 = self.model5(concat_stage5)
        concat_stage6 = torch.cat([out_stage5, out1_0], 1)
        out_stage6 = self.model6(concat_stage6)
        return out_stage6
