"""
Source url: https://github.com/lukemelas/EfficientNet-PyTorch
Modified by Min Seok Lee, Wooseok Shin, Nikita Selin
License: Apache License 2.0
Changes:
    - Added support for extracting edge features
    - Added support for extracting object features at different levels
    - Refactored the code
"""
from typing import Any, List

import torch
from torch import nn
from torch.nn import functional as F

from .effi_utils import (
    get_same_padding_conv2d,
    calculate_output_image_size,
    MemoryEfficientSwish,
    drop_connect,
    round_filters,
    round_repeats,
    Swish,
    create_block_args,
)


class MBConvBlock(nn.Module):
    """Mobile Inverted Residual Bottleneck Block.

    Args:
        block_args (namedtuple): BlockArgs, defined in utils.py.
        global_params (namedtuple): GlobalParam, defined in utils.py.
        image_size (tuple or list): [image_height, image_width].

    References:
        [1] https://arxiv.org/abs/1704.04861 (MobileNet v1)
        [2] https://arxiv.org/abs/1801.04381 (MobileNet v2)
        [3] https://arxiv.org/abs/1905.02244 (MobileNet v3)
    """

    def __init__(self, block_args, global_params, image_size=None):
        super().__init__()
        self._block_args = block_args
        self._bn_mom = (
            1 - global_params.batch_norm_momentum
        )  # pytorch's difference from tensorflow
        self._bn_eps = global_params.batch_norm_epsilon
        self.has_se = (self._block_args.se_ratio is not None) and (
            0 < self._block_args.se_ratio <= 1
        )
        self.id_skip = (
            block_args.id_skip
        )  # whether to use skip connection and drop connect

        # Expansion phase (Inverted Bottleneck)
        inp = self._block_args.input_filters  # number of input channels
        oup = (
            self._block_args.input_filters * self._block_args.expand_ratio
        )  # number of output channels
        if self._block_args.expand_ratio != 1:
            Conv2d = get_same_padding_conv2d(image_size=image_size)
            self._expand_conv = Conv2d(
                in_channels=inp, out_channels=oup, kernel_size=1, bias=False
            )
            self._bn0 = nn.BatchNorm2d(
                num_features=oup, momentum=self._bn_mom, eps=self._bn_eps
            )
            # image_size = calculate_output_image_size(image_size, 1) <-- this wouldn't modify image_size

        # Depthwise convolution phase
        k = self._block_args.kernel_size
        s = self._block_args.stride
        Conv2d = get_same_padding_conv2d(image_size=image_size)
        self._depthwise_conv = Conv2d(
            in_channels=oup,
            out_channels=oup,
            groups=oup,  # groups makes it depthwise
            kernel_size=k,
            stride=s,
            bias=False,
        )
        self._bn1 = nn.BatchNorm2d(
            num_features=oup, momentum=self._bn_mom, eps=self._bn_eps
        )
        image_size = calculate_output_image_size(image_size, s)

        # Squeeze and Excitation layer, if desired
        if self.has_se:
            Conv2d = get_same_padding_conv2d(image_size=(1, 1))
            num_squeezed_channels = max(
                1, int(self._block_args.input_filters * self._block_args.se_ratio)
            )
            self._se_reduce = Conv2d(
                in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1
            )
            self._se_expand = Conv2d(
                in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1
            )

        # Pointwise convolution phase
        final_oup = self._block_args.output_filters
        Conv2d = get_same_padding_conv2d(image_size=image_size)
        self._project_conv = Conv2d(
            in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False
        )
        self._bn2 = nn.BatchNorm2d(
            num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps
        )
        self._swish = MemoryEfficientSwish()

    def forward(self, inputs, drop_connect_rate=None):
        """MBConvBlock's forward function.

        Args:
            inputs (tensor): Input tensor.
            drop_connect_rate (bool): Drop connect rate (float, between 0 and 1).

        Returns:
            Output of this block after processing.
        """

        # Expansion and Depthwise Convolution
        x = inputs
        if self._block_args.expand_ratio != 1:
            x = self._expand_conv(inputs)
            x = self._bn0(x)
            x = self._swish(x)

        x = self._depthwise_conv(x)
        x = self._bn1(x)
        x = self._swish(x)

        # Squeeze and Excitation
        if self.has_se:
            x_squeezed = F.adaptive_avg_pool2d(x, 1)
            x_squeezed = self._se_reduce(x_squeezed)
            x_squeezed = self._swish(x_squeezed)
            x_squeezed = self._se_expand(x_squeezed)
            x = torch.sigmoid(x_squeezed) * x

        # Pointwise Convolution
        x = self._project_conv(x)
        x = self._bn2(x)

        # Skip connection and drop connect
        input_filters, output_filters = (
            self._block_args.input_filters,
            self._block_args.output_filters,
        )
        if (
            self.id_skip
            and self._block_args.stride == 1
            and input_filters == output_filters
        ):
            # The combination of skip connection and drop connect brings about stochastic depth.
            if drop_connect_rate:
                x = drop_connect(x, p=drop_connect_rate, training=self.training)
            x = x + inputs  # skip connection
        return x

    def set_swish(self, memory_efficient=True):
        """Sets swish function as memory efficient (for training) or standard (for export).

        Args:
            memory_efficient (bool): Whether to use memory-efficient version of swish.
        """
        self._swish = MemoryEfficientSwish() if memory_efficient else Swish()


class EfficientNet(nn.Module):
    def __init__(self, blocks_args=None, global_params=None):
        super().__init__()
        assert isinstance(blocks_args, list), "blocks_args should be a list"
        assert len(blocks_args) > 0, "block args must be greater than 0"
        self._global_params = global_params
        self._blocks_args = blocks_args

        # Batch norm parameters
        bn_mom = 1 - self._global_params.batch_norm_momentum
        bn_eps = self._global_params.batch_norm_epsilon

        # Get stem static or dynamic convolution depending on image size
        image_size = global_params.image_size
        Conv2d = get_same_padding_conv2d(image_size=image_size)

        # Stem
        in_channels = 3  # rgb
        out_channels = round_filters(
            32, self._global_params
        )  # number of output channels
        self._conv_stem = Conv2d(
            in_channels, out_channels, kernel_size=3, stride=2, bias=False
        )
        self._bn0 = nn.BatchNorm2d(
            num_features=out_channels, momentum=bn_mom, eps=bn_eps
        )
        image_size = calculate_output_image_size(image_size, 2)

        # Build blocks
        self._blocks = nn.ModuleList([])
        for block_args in self._blocks_args:

            # Update block input and output filters based on depth multiplier.
            block_args = block_args._replace(
                input_filters=round_filters(
                    block_args.input_filters, self._global_params
                ),
                output_filters=round_filters(
                    block_args.output_filters, self._global_params
                ),
                num_repeat=round_repeats(block_args.num_repeat, self._global_params),
            )

            # The first block needs to take care of stride and filter size increase.
            self._blocks.append(
                MBConvBlock(block_args, self._global_params, image_size=image_size)
            )
            image_size = calculate_output_image_size(image_size, block_args.stride)
            if block_args.num_repeat > 1:  # modify block_args to keep same output size
                block_args = block_args._replace(
                    input_filters=block_args.output_filters, stride=1
                )
            for _ in range(block_args.num_repeat - 1):
                self._blocks.append(
                    MBConvBlock(block_args, self._global_params, image_size=image_size)
                )
                # image_size = calculate_output_image_size(image_size, block_args.stride)  # stride = 1

        self._swish = MemoryEfficientSwish()

    def set_swish(self, memory_efficient=True):
        """Sets swish function as memory efficient (for training) or standard (for export).

        Args:
            memory_efficient (bool): Whether to use memory-efficient version of swish.

        """
        self._swish = MemoryEfficientSwish() if memory_efficient else Swish()
        for block in self._blocks:
            block.set_swish(memory_efficient)

    def extract_endpoints(self, inputs):
        endpoints = dict()

        # Stem
        x = self._swish(self._bn0(self._conv_stem(inputs)))
        prev_x = x

        # Blocks
        for idx, block in enumerate(self._blocks):
            drop_connect_rate = self._global_params.drop_connect_rate
            if drop_connect_rate:
                drop_connect_rate *= float(idx) / len(
                    self._blocks
                )  # scale drop connect_rate
            x = block(x, drop_connect_rate=drop_connect_rate)
            if prev_x.size(2) > x.size(2):
                endpoints["reduction_{}".format(len(endpoints) + 1)] = prev_x
            prev_x = x

        # Head
        x = self._swish(self._bn1(self._conv_head(x)))
        endpoints["reduction_{}".format(len(endpoints) + 1)] = x

        return endpoints

    def _change_in_channels(self, in_channels):
        """Adjust model's first convolution layer to in_channels, if in_channels not equals 3.

        Args:
            in_channels (int): Input data's channel number.
        """
        if in_channels != 3:
            Conv2d = get_same_padding_conv2d(image_size=self._global_params.image_size)
            out_channels = round_filters(32, self._global_params)
            self._conv_stem = Conv2d(
                in_channels, out_channels, kernel_size=3, stride=2, bias=False
            )


class EfficientEncoderB7(EfficientNet):
    def __init__(self):
        super().__init__(
            *create_block_args(
                width_coefficient=2.0,
                depth_coefficient=3.1,
                dropout_rate=0.5,
                image_size=600,
            )
        )
        self._change_in_channels(3)
        self.block_idx = [10, 17, 37, 54]
        self.channels = [48, 80, 224, 640]

    def initial_conv(self, inputs):
        x = self._swish(self._bn0(self._conv_stem(inputs)))
        return x

    def get_blocks(self, x, H, W, block_idx):
        features = []
        for idx, block in enumerate(self._blocks):
            drop_connect_rate = self._global_params.drop_connect_rate
            if drop_connect_rate:
                drop_connect_rate *= float(idx) / len(
                    self._blocks
                )  # scale drop connect_rate
            x = block(x, drop_connect_rate=drop_connect_rate)
            if idx == block_idx[0]:
                features.append(x.clone())
            if idx == block_idx[1]:
                features.append(x.clone())
            if idx == block_idx[2]:
                features.append(x.clone())
            if idx == block_idx[3]:
                features.append(x.clone())

        return features

    def forward(self, inputs: torch.Tensor) -> List[Any]:
        B, C, H, W = inputs.size()
        x = self.initial_conv(inputs)  # Prepare input for the backbone
        return self.get_blocks(
            x, H, W, block_idx=self.block_idx
        )  # Get backbone features and edge maps
