# code in this file is modified from https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
from torch import Tensor
from typing import Any, List, Tuple


class _DenseLayer(nn.Module):
    def __init__(self,
                 num_input_features: int,
                 growth_rate: int,
                 bn_size: int,
                 drop_rate: float,
                 memory_efficient: bool = False
                 ) -> None:
        super(_DenseLayer, self).__init__()
        self.norm1: nn.BatchNorm2d
        self.add_module('norm1', nn.BatchNorm2d(num_input_features))
        self.relu1: nn.ReLU
        self.add_module('relu1', nn.ReLU(inplace=True))
        self.conv1: nn.Conv2d
        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
                                           growth_rate, kernel_size=1, stride=1,
                                           bias=False))
        self.norm2: nn.BatchNorm2d
        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate))
        self.relu2: nn.ReLU
        self.add_module('relu2', nn.ReLU(inplace=True))
        self.conv2: nn.Conv2d
        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
                                           kernel_size=3, stride=1, padding=1,
                                           bias=False))
        self.drop_rate = float(drop_rate)
        self.memory_efficient = memory_efficient

    def bn_function(self, inputs: List[Tensor]) -> Tensor:
        concated_features = torch.cat(inputs, 1)
        bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features)))  # noqa: T484
        return bottleneck_output

    # todo: rewrite when torchscript supports any
    def any_requires_grad(self, input: List[Tensor]) -> bool:
        for tensor in input:
            if tensor.requires_grad:
                return True
        return False

    @torch.jit.unused  # noqa: T484
    def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor:
        def closure(*inputs):
            return self.bn_function(inputs)

        return cp.checkpoint(closure, *input)

    @torch.jit._overload_method  # noqa: F811
    def forward(self, input: List[Tensor]) -> Tensor:
        pass

    @torch.jit._overload_method  # noqa: F811
    def forward(self, input: Tensor) -> Tensor:
        pass

    # torchscript does not yet support *args, so we overload method
    # allowing it to take either a List[Tensor] or single Tensor
    def forward(self, input: Tensor) -> Tensor:  # noqa: F811
        if isinstance(input, Tensor):
            prev_features = [input]
        else:
            prev_features = input

        if self.memory_efficient and self.any_requires_grad(prev_features):
            if torch.jit.is_scripting():
                raise Exception("Memory Efficient not supported in JIT")

            bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
        else:
            bottleneck_output = self.bn_function(prev_features)

        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
        if self.drop_rate > 0:
            new_features = F.dropout(new_features, p=self.drop_rate,
                                     training=self.training)
        return new_features


class _DenseBlock(nn.ModuleDict):
    _version = 2

    def __init__(
        self,
        num_layers: int,
        num_input_features: int,
        bn_size: int,
        growth_rate: int,
        drop_rate: float,
        memory_efficient: bool = False
    ) -> None:
        super(_DenseBlock, self).__init__()
        for i in range(num_layers):
            layer = _DenseLayer(
                num_input_features + i * growth_rate,
                growth_rate=growth_rate,
                bn_size=bn_size,
                drop_rate=drop_rate,
                memory_efficient=memory_efficient,
            )
            self.add_module('denselayer%d' % (i + 1), layer)

    def forward(self, init_features: Tensor) -> Tensor:
        features = [init_features]
        for name, layer in self.items():
            new_features = layer(features)
            features.append(new_features)
        return torch.cat(features, 1)


class _Transition(nn.Sequential):
    def __init__(self, num_input_features: int, num_output_features: int) -> None:
        super(_Transition, self).__init__()
        self.add_module('norm', nn.BatchNorm2d(num_input_features))
        self.add_module('relu', nn.ReLU(inplace=True))
        self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
                                          kernel_size=1, stride=1, bias=False))
        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))


class DenseNet(nn.Module):
    r"""Densenet-BC model class, based on
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
          (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
    """

    def __init__(self,
                 num_classes: int,
                 in_channels: int,
                 growth_rate: int = 32,
                 block_config: Tuple[int, int, int, int] = (6, 12, 24, 16),
                 num_init_features: int = 64,
                 bn_size: int = 4,
                 drop_rate: float = 0,
                 memory_efficient: bool = False,
                 return_features: bool = False,
                 ) -> None:
        super(DenseNet, self).__init__()

        # First convolution
        self.features = nn.Sequential(OrderedDict([
            ('conv0', nn.Conv2d(in_channels, num_init_features, kernel_size=7, stride=2,
                                padding=3, bias=False)),
            ('norm0', nn.BatchNorm2d(num_init_features)),
            ('relu0', nn.ReLU(inplace=True)),
            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
        ]))

        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            block = _DenseBlock(
                num_layers=num_layers,
                num_input_features=num_features,
                bn_size=bn_size,
                growth_rate=growth_rate,
                drop_rate=drop_rate,
                memory_efficient=memory_efficient
            )
            self.features.add_module('denseblock%d' % (i + 1), block)
            num_features = num_features + num_layers * growth_rate
            if i != len(block_config) - 1:
                trans = _Transition(num_input_features=num_features,
                                    num_output_features=num_features // 2)
                self.features.add_module('transition%d' % (i + 1), trans)
                num_features = num_features // 2

        # Final batch norm
        self.features.add_module('norm5', nn.BatchNorm2d(num_features))

        # Linear layer
        self.classifier = nn.Linear(num_features, num_classes)

        # Official init from torch repo.
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.constant_(m.bias, 0)
        self.return_features = return_features

    def forward(self, x: Tensor) -> Tensor:
        features = self.features(x)
        out = F.relu(features, inplace=True)
        out = F.adaptive_avg_pool2d(out, (1, 1))
        out = torch.flatten(out, 1)
        out = self.classifier(out)
        if self.return_features:
            return out, features
        return out


def _densenet(num_classes: int, in_channels: int, growth_rate: int, block_config: Tuple[int, int, int, int],
              num_init_features: int, **kwargs: Any) -> DenseNet:
    model = DenseNet(num_classes, in_channels, growth_rate, block_config, num_init_features, **kwargs)
    return model


def densenet121(num_classes: int, in_channels: int, **kwargs: Any) -> DenseNet:
    r"""Densenet-121 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
    memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
    but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
    """
    return _densenet(num_classes, in_channels, 32, (6, 12, 24, 16), 64, **kwargs)