#  -------------------------------------------------------------------------------------------

#  Copyright (c) Microsoft Corporation. All rights reserved.

#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.

#  -------------------------------------------------------------------------------------------

from typing import Any, List, Tuple, Type, Union

import torch

from torch.hub import load_state_dict_from_url

from torchvision.models.resnet import model_urls, ResNet, BasicBlock, Bottleneck

TypeSkipConnections = Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]

class ResNetHIML(ResNet):

    """Wrapper class of the original torchvision ResNet model.

    The forward function is updated to return the penultimate layer

    activations, which are required to obtain image patch embeddings.

    """

    def __init__(self, **kwargs: Any) -> None:

        super().__init__(**kwargs)

    def forward(self, x: torch.Tensor,

                return_intermediate_layers: bool = False) -> Union[torch.Tensor, TypeSkipConnections]:

        """ResNetHIML forward pass. Optionally returns intermediate layers using the

        ``return_intermediate_layers`` argument.

        :param return_intermediate_layers: If ``True``, return layers x0-x4 as a tuple,

            otherwise return x4 only.

        """

        x0 = self.conv1(x)

        x0 = self.bn1(x0)

        x0 = self.relu(x0)

        x0 = self.maxpool(x0)

        x1 = self.layer1(x0)

        x2 = self.layer2(x1)

        x3 = self.layer3(x2)

        x4 = self.layer4(x3)

        if return_intermediate_layers:

            return x0, x1, x2, x3, x4

        else:

            return x4

def _resnet(arch: str, block: Type[Union[BasicBlock, Bottleneck]], layers: List[int],

            pretrained: bool, progress: bool, **kwargs: Any) -> ResNetHIML:

    """Instantiate a custom :class:`ResNet` model.

    Adapted from :mod:`torchvision.models.resnet`.

    """

    model = ResNetHIML(block=block, layers=layers, **kwargs)

    if pretrained:

        state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)

        model.load_state_dict(state_dict)

    return model

def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNetHIML:

    r"""ResNet-18 model from

    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.

    :param pretrained: If ``True``, returns a model pre-trained on ImageNet.

    :param progress: If ``True``, displays a progress bar of the download to ``stderr``.

    """

    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs)

def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNetHIML:

    r"""ResNet-50 model from

    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.

    :param pretrained: If ``True``, returns a model pre-trained on ImageNet

    :param progress: If ``True``, displays a progress bar of the download to ``stderr``.

    """

    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs)