import torch
from torch.nn import Conv2d, Sequential, ModuleList, BatchNorm2d
from torch import nn
from ..nn.mobilenet_v2 import MobileNetV2, InvertedResidual

from .ssd import SSD, GraphPath
from .predictor import Predictor
from .config import mobilenetv1_ssd_config as config


def SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, onnx_compatible=False):
    """Replace Conv2d with a depthwise Conv2d and Pointwise Conv2d."""
    ReLU = nn.ReLU if onnx_compatible else nn.ReLU6
    return Sequential(
        Conv2d(
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=kernel_size,
            groups=in_channels,
            stride=stride,
            padding=padding,
        ),
        BatchNorm2d(in_channels),
        ReLU(),
        Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1),
    )


def create_mobilenetv2_ssd_lite(num_classes, width_mult=1.0, use_batch_norm=True, onnx_compatible=False, is_test=False):
    base_net = MobileNetV2(
        width_mult=width_mult, use_batch_norm=use_batch_norm, onnx_compatible=onnx_compatible
    ).features

    source_layer_indexes = [
        GraphPath(14, "conv", 3),
        19,
    ]
    extras = ModuleList(
        [
            InvertedResidual(1280, 512, stride=2, expand_ratio=0.2),
            InvertedResidual(512, 256, stride=2, expand_ratio=0.25),
            InvertedResidual(256, 256, stride=2, expand_ratio=0.5),
            InvertedResidual(256, 64, stride=2, expand_ratio=0.25),
        ]
    )

    regression_headers = ModuleList(
        [
            SeperableConv2d(
                in_channels=round(576 * width_mult), out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False
            ),
            SeperableConv2d(in_channels=1280, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
            SeperableConv2d(in_channels=512, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
            SeperableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
            SeperableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
            Conv2d(in_channels=64, out_channels=6 * 4, kernel_size=1),
        ]
    )

    classification_headers = ModuleList(
        [
            SeperableConv2d(
                in_channels=round(576 * width_mult), out_channels=6 * num_classes, kernel_size=3, padding=1
            ),
            SeperableConv2d(in_channels=1280, out_channels=6 * num_classes, kernel_size=3, padding=1),
            SeperableConv2d(in_channels=512, out_channels=6 * num_classes, kernel_size=3, padding=1),
            SeperableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1),
            SeperableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1),
            Conv2d(in_channels=64, out_channels=6 * num_classes, kernel_size=1),
        ]
    )

    return SSD(
        num_classes,
        base_net,
        source_layer_indexes,
        extras,
        classification_headers,
        regression_headers,
        is_test=is_test,
        config=config,
    )


def create_mobilenetv2_ssd_lite_predictor(net, candidate_size=200, nms_method=None, sigma=0.5, device=None):
    predictor = Predictor(
        net,
        config.image_size,
        config.image_mean,
        config.image_std,
        nms_method=nms_method,
        iou_threshold=config.iou_threshold,
        candidate_size=candidate_size,
        sigma=sigma,
        device=device,
    )
    return predictor