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

try:
    from torchvision.models.utils import load_state_dict_from_url
except ImportError:
    from torch.utils.model_zoo import load_url as load_state_dict_from_url

# Inception weights ported to Pytorch from
# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'


#class InceptionV3(nn.Module):
    # """Pretrained InceptionV3 network returning feature maps"""
    #
    # # Index of default block of inception to return,
    # # corresponds to output of final average pooling
    # DEFAULT_BLOCK_INDEX = 3
    #
    # # Maps feature dimensionality to their output blocks indices
    # BLOCK_INDEX_BY_DIM = {
    #     64: 0,      # First max pooling features
    #     192: 1,     # Second max pooling featurs
    #     768: 2,     # Pre-aux classifier features
    #     2048: 3,    # Final average pooling features
    #     'prob': 4,  # softmax layer
    # }
    #
    # def __init__(self,
    #              output_blocks=[DEFAULT_BLOCK_INDEX],
    #              resize_input=True,
    #              normalize_input=True,
    #              requires_grad=False,
    #              use_fid_inception=True):
    #     """Build pretrained InceptionV3
    #
    #     Parameters
    #     ----------
    #     output_blocks : list of int
    #         Indices of blocks to return features of. Possible values are:
    #             - 0: corresponds to output of first max pooling
    #             - 1: corresponds to output of second max pooling
    #             - 2: corresponds to output which is fed to aux classifier
    #             - 3: corresponds to output of final average pooling
    #     resize_input : bool
    #         If true, bilinearly resizes input to width and height 299 before
    #         feeding input to model. As the network without fully connected
    #         layers is fully convolutional, it should be able to handle inputs
    #         of arbitrary size, so resizing might not be strictly needed
    #     normalize_input : bool
    #         If true, scales the input from range (0, 1) to the range the
    #         pretrained Inception network expects, namely (-1, 1)
    #     requires_grad : bool
    #         If true, parameters of the model require gradients. Possibly useful
    #         for finetuning the network
    #     use_fid_inception : bool
    #         If true, uses the pretrained Inception model used in Tensorflow's
    #         FID implementation. If false, uses the pretrained Inception model
    #         available in torchvision. The FID Inception model has different
    #         weights and a slightly different structure from torchvision's
    #         Inception model. If you want to compute FID scores, you are
    #         strongly advised to set this parameter to true to get comparable
    #         results.
    #     """
    #     super(InceptionV3, self).__init__()
    #
    #     self.resize_input = resize_input
    #     self.normalize_input = normalize_input
    #     self.output_blocks = sorted(output_blocks)
    #     self.last_needed_block = max(output_blocks)
    #
    #     # assert self.last_needed_block <= 3, \
    #     #     'Last possible output block index is 3'
    #
    #     self.blocks = nn.ModuleList()
    #
    #     if use_fid_inception:
    #         inception = fid_inception_v3()
    #     else:
    #         inception = models.inception_v3(pretrained=True)
    #
    #     # Block 0: input to maxpool1
    #     block0 = [
    #         inception.Conv2d_1a_3x3,
    #         inception.Conv2d_2a_3x3,
    #         inception.Conv2d_2b_3x3,
    #         nn.MaxPool2d(kernel_size=3, stride=2)
    #     ]
    #     self.blocks.append(nn.Sequential(*block0))
    #
    #     # Block 1: maxpool1 to maxpool2
    #     if self.last_needed_block >= 1:
    #         block1 = [
    #             inception.Conv2d_3b_1x1,
    #             inception.Conv2d_4a_3x3,
    #             nn.MaxPool2d(kernel_size=3, stride=2)
    #         ]
    #         self.blocks.append(nn.Sequential(*block1))
    #
    #     # Block 2: maxpool2 to aux classifier
    #     if self.last_needed_block >= 2:
    #         block2 = [
    #             inception.Mixed_5b,
    #             inception.Mixed_5c,
    #             inception.Mixed_5d,
    #             inception.Mixed_6a,
    #             inception.Mixed_6b,
    #             inception.Mixed_6c,
    #             inception.Mixed_6d,
    #             inception.Mixed_6e,
    #         ]
    #         self.blocks.append(nn.Sequential(*block2))
    #
    #     # Block 3: aux classifier to final avgpool
    #     if self.last_needed_block >= 3:
    #         block3 = [
    #             inception.Mixed_7a,
    #             inception.Mixed_7b,
    #             inception.Mixed_7c,
    #             nn.AdaptiveAvgPool2d(output_size=(1, 1))
    #         ]
    #         self.blocks.append(nn.Sequential(*block3))
    #
    #     if self.last_needed_block >= 4:
    #         self.fc = inception.fc
    #         self.fc.bias = None
    #
    #     for param in self.parameters():
    #         param.requires_grad = requires_grad
    #
    # def forward(self, inp):
    #     """Get Inception feature maps
    #
    #     Parameters
    #     ----------
    #     inp : torch.autograd.Variable
    #         Input tensor of shape Bx3xHxW. Values are expected to be in
    #         range (0, 1)
    #
    #     Returns
    #     -------
    #     List of torch.autograd.Variable, corresponding to the selected output
    #     block, sorted ascending by index
    #     """
    #     outp = []
    #     x = inp
    #
    #     if self.resize_input:
    #         x = F.interpolate(x,
    #                           size=(299, 299),
    #                           mode='bilinear',
    #                           align_corners=False)
    #
    #     if self.normalize_input:
    #         x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
    #
    #     for idx, block in enumerate(self.blocks):
    #         x = block(x)
    #         if idx in self.output_blocks:
    #             outp.append(x)
    #
    #         if idx == self.last_needed_block:
    #             break
    #
    #     if self.last_needed_block >= 4:
    #         x = F.dropout(x, training=self.training)
    #         # N x 2048 x 1 x 1
    #         x = torch.flatten(x, 1)
    #         # N x 2048
    #         x = self.fc(x)
    #         x = F.softmax(x, dim=1)
    #         outp.append(x)
    #
    #     return outp

class InceptionV3(nn.Module):
    """Pretrained InceptionV3 network returning feature maps"""

    # Index of default block of inception to return,
    # corresponds to output of final average pooling
    def __init__(self,
                 resize_input=True,
                 normalize_input=False,
                 requires_grad=False):
        """Build pretrained InceptionV3
        Parameters
        ----------
        resize_input : bool
            If true, bilinearly resizes input to width and height 299 before
            feeding input to model. As the network without fully connected
            layers is fully convolutional, it should be able to handle inputs
            of arbitrary size, so resizing might not be strictly needed
        normalize_input : bool
            If true, scales the input from range (0, 1) to the range the
            pretrained Inception network expects, namely (-1, 1)
        requires_grad : bool
            If true, parameters of the model require gradients. Possibly useful
            for finetuning the network
        """
        super(InceptionV3, self).__init__()

        self.resize_input = resize_input
        self.normalize_input = normalize_input
        self.blocks = nn.ModuleList()

        state_dict, inception = fid_inception_v3()

        # Block 0: input to maxpool1
        block0 = [
            inception.Conv2d_1a_3x3,
            inception.Conv2d_2a_3x3,
            inception.Conv2d_2b_3x3,
            nn.MaxPool2d(kernel_size=3, stride=2)
        ]
        self.blocks.append(nn.Sequential(*block0))

        # Block 1: maxpool1 to maxpool2
        block1 = [
            inception.Conv2d_3b_1x1,
            inception.Conv2d_4a_3x3,
            nn.MaxPool2d(kernel_size=3, stride=2)
        ]
        self.blocks.append(nn.Sequential(*block1))

        # Block 2: maxpool2 to aux classifier
        block2 = [
            inception.Mixed_5b,
            inception.Mixed_5c,
            inception.Mixed_5d,
            inception.Mixed_6a,
            inception.Mixed_6b,
            inception.Mixed_6c,
            inception.Mixed_6d,
            inception.Mixed_6e,
        ]
        self.blocks.append(nn.Sequential(*block2))

        # Block 3: aux classifier to final avgpool
        block3 = [
            inception.Mixed_7a,
            inception.Mixed_7b,
            inception.Mixed_7c,
            nn.AdaptiveAvgPool2d(output_size=(1, 1))
        ]
        self.blocks.append(nn.Sequential(*block3))

        with torch.no_grad():
            self.fc = nn.Linear(2048, 1008, bias=True)
            self.fc.weight.copy_(state_dict['fc.weight'])
            self.fc.bias.copy_(state_dict['fc.bias'])

        for param in self.parameters():
            param.requires_grad = requires_grad


    def forward(self, inp):
        """Get Inception feature maps
        Parameters
        ----------
        inp : torch.autograd.Variable
            Input tensor of shape Bx3xHxW. Values are expected to be in
            range (0, 1)
        Returns
        -------
        List of torch.autograd.Variable, corresponding to the selected output
        block, sorted ascending by index
        """
        x = inp

        if self.resize_input:
            x = F.interpolate(x,
                              size=(299, 299),
                              mode='bilinear',
                              align_corners=False)

        if self.normalize_input:
            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)

        for idx, block in enumerate(self.blocks):
            x = block(x)

        x = F.dropout(x, training=False)
        x = torch.flatten(x, 1)
        logit = self.fc(x)
        return x, logit
def fid_inception_v3():
    """Build pretrained Inception model for FID computation

    The Inception model for FID computation uses a different set of weights
    and has a slightly different structure than torchvision's Inception.

    This method first constructs torchvision's Inception and then patches the
    necessary parts that are different in the FID Inception model.
    """
    inception = models.inception_v3(num_classes=1008,
                                    aux_logits=False,
                                    pretrained=False)
    inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
    inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
    inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
    inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
    inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
    inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
    inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
    inception.Mixed_7b = FIDInceptionE_1(1280)
    inception.Mixed_7c = FIDInceptionE_2(2048)

    state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
    inception.load_state_dict(state_dict)
    return inception


class FIDInceptionA(models.inception.InceptionA):
    """InceptionA block patched for FID computation"""
    def __init__(self, in_channels, pool_features):
        super(FIDInceptionA, self).__init__(in_channels, pool_features)

    def forward(self, x):
        branch1x1 = self.branch1x1(x)

        branch5x5 = self.branch5x5_1(x)
        branch5x5 = self.branch5x5_2(branch5x5)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)

        # Patch: Tensorflow's average pool does not use the padded zero's in
        # its average calculation
        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
                                   count_include_pad=False)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
        return torch.cat(outputs, 1)


class FIDInceptionC(models.inception.InceptionC):
    """InceptionC block patched for FID computation"""
    def __init__(self, in_channels, channels_7x7):
        super(FIDInceptionC, self).__init__(in_channels, channels_7x7)

    def forward(self, x):
        branch1x1 = self.branch1x1(x)

        branch7x7 = self.branch7x7_1(x)
        branch7x7 = self.branch7x7_2(branch7x7)
        branch7x7 = self.branch7x7_3(branch7x7)

        branch7x7dbl = self.branch7x7dbl_1(x)
        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)

        # Patch: Tensorflow's average pool does not use the padded zero's in
        # its average calculation
        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
                                   count_include_pad=False)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
        return torch.cat(outputs, 1)


class FIDInceptionE_1(models.inception.InceptionE):
    """First InceptionE block patched for FID computation"""
    def __init__(self, in_channels):
        super(FIDInceptionE_1, self).__init__(in_channels)

    def forward(self, x):
        branch1x1 = self.branch1x1(x)

        branch3x3 = self.branch3x3_1(x)
        branch3x3 = [
            self.branch3x3_2a(branch3x3),
            self.branch3x3_2b(branch3x3),
        ]
        branch3x3 = torch.cat(branch3x3, 1)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = [
            self.branch3x3dbl_3a(branch3x3dbl),
            self.branch3x3dbl_3b(branch3x3dbl),
        ]
        branch3x3dbl = torch.cat(branch3x3dbl, 1)

        # Patch: Tensorflow's average pool does not use the padded zero's in
        # its average calculation
        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
                                   count_include_pad=False)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
        return torch.cat(outputs, 1)


class FIDInceptionE_2(models.inception.InceptionE):
    """Second InceptionE block patched for FID computation"""
    def __init__(self, in_channels):
        super(FIDInceptionE_2, self).__init__(in_channels)

    def forward(self, x):
        branch1x1 = self.branch1x1(x)

        branch3x3 = self.branch3x3_1(x)
        branch3x3 = [
            self.branch3x3_2a(branch3x3),
            self.branch3x3_2b(branch3x3),
        ]
        branch3x3 = torch.cat(branch3x3, 1)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = [
            self.branch3x3dbl_3a(branch3x3dbl),
            self.branch3x3dbl_3b(branch3x3dbl),
        ]
        branch3x3dbl = torch.cat(branch3x3dbl, 1)

        # Patch: The FID Inception model uses max pooling instead of average
        # pooling. This is likely an error in this specific Inception
        # implementation, as other Inception models use average pooling here
        # (which matches the description in the paper).
        branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
        return torch.cat(outputs, 1)
