# flake8: noqa

from __future__ import print_function

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


def SoftCE(predicted, target):
    logprobs = torch.nn.functional.log_softmax(predicted, dim=1)
    return -(target * logprobs).sum() / predicted.shape[0]


class CrossEntropyLabelSmooth(nn.Module):
    """Cross entropy loss with label smoothing regularizer.

    Reference:
    Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
    Equation: y = (1 - epsilon) * y + epsilon / K.

    Args:
        num_classes (int): number of classes.
        epsilon (float): weight.
    """

    def __init__(self, num_classes, epsilon=0.1):
        super(CrossEntropyLabelSmooth, self).__init__()
        self.num_classes = num_classes
        self.epsilon = epsilon
        self.logsoftmax = nn.LogSoftmax(dim=1).cuda()

    def forward(self, inputs, targets):
        """
        Args:
            inputs: prediction matrix (before softmax) with shape (batch_size, num_classes)
            targets: ground truth labels with shape (num_classes)
        """
        log_probs = self.logsoftmax(inputs)
        targets = torch.zeros_like(log_probs).scatter_(1, targets.unsqueeze(1), 1)
        targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
        loss = (- targets * log_probs).mean(0).sum()
        return loss


class SoftEntropy(nn.Module):
    def __init__(self):
        super(SoftEntropy, self).__init__()
        self.logsoftmax = nn.LogSoftmax(dim=1)

    def forward(self, inputs, targets):
        log_probs = self.logsoftmax(inputs)
        loss = (- F.softmax(targets, dim=1).detach() * log_probs).mean(0).sum()
        return loss


class QuadrupletLoss(torch.nn.Module):
    """
    Quadruplet loss function.
    Builds on the Triplet Loss and takes 4 data input: one anchor, one positive
    and two negative examples. The negative examples needs not to be matching
    the anchor, the positive and each other.
    """

    def __init__(self, margin1=2.0, margin2=1.0):
        super(QuadrupletLoss, self).__init__()
        self.margin1 = margin1
        self.margin2 = margin2

    def forward(self, anchor, positive, negative1, negative2):

        squarred_distance_pos = (anchor - positive).pow(2).sum(1)
        squarred_distance_neg = (anchor - negative1).pow(2).sum(1)
        squarred_distance_neg_b = (negative1 - negative2).pow(2).sum(1)

        quadruplet_loss = \
            F.relu(self.margin1 + squarred_distance_pos - squarred_distance_neg) \
            + F.relu(self.margin2 + squarred_distance_pos - squarred_distance_neg_b)

        return quadruplet_loss.mean()


def simCLR_loss(features, temperature=0.07, device='cuda'):
    batch_size, n_views = features.shape[0], features.shape[1]
    labels = torch.cat([torch.arange(batch_size) for i in range(n_views)], dim=0)
    labels = (labels.unsqueeze(0) == labels.unsqueeze(1)).float()
    labels = labels.to(device)
    # print(labels)
    if len(features.shape) > 3:
        features = features.contiguous().view(features.shape[0], features.shape[1], -1)
    features = torch.cat(torch.unbind(features, dim=1), dim=0)
    features = F.normalize(features, dim=1)

    similarity_matrix = torch.matmul(features, features.T)
    # assert similarity_matrix.shape == (
    #     self.args.n_views * self.args.batch_size, self.args.n_views * self.args.batch_size)
    # assert similarity_matrix.shape == labels.shape

    # discard the main diagonal from both: labels and similarities matrix
    mask = torch.eye(labels.shape[0], dtype=torch.bool).to(device)
    labels = labels[~mask].view(labels.shape[0], -1)
    similarity_matrix = similarity_matrix[~mask].view(similarity_matrix.shape[0], -1)
    # assert similarity_matrix.shape == labels.shape

    # select and combine multiple positives
    positives = similarity_matrix[labels.bool()].view(labels.shape[0], -1)

    # select only the negatives the negatives
    negatives = similarity_matrix[~labels.bool()].view(similarity_matrix.shape[0], -1)

    logits = torch.cat([positives, negatives], dim=1)
    labels = torch.zeros(logits.shape[0], dtype=torch.long).to(device)

    logits = logits / temperature
    return logits, labels


class SupConLoss(nn.Module):
    """Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
    It also supports the unsupervised contrastive loss in SimCLR"""

    def __init__(self, temperature=0.07, contrast_mode='all',
                 base_temperature=0.07):
        super(SupConLoss, self).__init__()
        self.temperature = temperature
        self.contrast_mode = contrast_mode
        self.base_temperature = base_temperature

    def forward(self, features, labels=None, mask=None):
        """Compute loss for model. If both `labels` and `mask` are None,
        it degenerates to SimCLR unsupervised loss:
        https://arxiv.org/pdf/2002.05709.pdf

        Args:
            features: hidden vector of shape [bsz, n_views, ...].
            labels: ground truth of shape [bsz].
            mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j
                has the same class as sample i. Can be asymmetric.
        Returns:
            A loss scalar.
        """
        device = (torch.device('cuda')
                  if features.is_cuda
                  else torch.device('cpu'))

        if len(features.shape) < 3:
            raise ValueError('`features` needs to be [bsz, n_views, ...],'
                             'at least 3 dimensions are required')
        if len(features.shape) > 3:
            features = features.view(features.shape[0], features.shape[1], -1)

        batch_size = features.shape[0]
        if labels is not None and mask is not None:
            raise ValueError('Cannot define both `labels` and `mask`')
        elif labels is None and mask is None:
            mask = torch.eye(batch_size, dtype=torch.float32).to(device)
        elif labels is not None:
            labels = labels.contiguous().view(-1, 1)
            if labels.shape[0] != batch_size:
                raise ValueError('Num of labels does not match num of features')
            mask = torch.eq(labels, labels.T).float().to(device)
        else:
            mask = mask.float().to(device)

        contrast_count = features.shape[1]
        contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)
        if self.contrast_mode == 'one':
            anchor_feature = features[:, 0]
            anchor_count = 1
        elif self.contrast_mode == 'all':
            anchor_feature = contrast_feature
            anchor_count = contrast_count
        else:
            raise ValueError('Unknown mode: {}'.format(self.contrast_mode))

        # compute logits
        anchor_dot_contrast = torch.div(
            torch.matmul(anchor_feature, contrast_feature.T),
            self.temperature)
        # for numerical stability
        logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
        logits = anchor_dot_contrast - logits_max.detach()

        # tile mask
        mask = mask.repeat(anchor_count, contrast_count)
        # mask-out self-contrast cases
        logits_mask = torch.scatter(
            torch.ones_like(mask),
            1,
            torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
            0
        )
        mask = mask * logits_mask

        # compute log_prob
        exp_logits = torch.exp(logits) * logits_mask
        log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))

        # compute mean of log-likelihood over positive
        mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)

        # loss
        loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
        loss = loss.view(anchor_count, batch_size).mean()

        return loss


class SoftSupConLoss(nn.Module):
    """Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
    It also supports the unsupervised contrastive loss in SimCLR"""

    def __init__(self, temperature=0.07, contrast_mode='all',
                 base_temperature=0.07):
        super(SupConLoss, self).__init__()
        self.temperature = temperature
        self.contrast_mode = contrast_mode
        self.base_temperature = base_temperature

    def forward(self, features, labels=None, soft_mask=None):
        """Compute loss for model. If both `labels` and `mask` are None,
        it degenerates to SimCLR unsupervised loss:
        https://arxiv.org/pdf/2002.05709.pdf

        Args:
            features: hidden vector of shape [bsz, n_views, ...].
            labels: ground truth of shape [bsz].
            mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j
                has the same class as sample i. Can be asymmetric.
        Returns:
            A loss scalar.
        """
        device = (torch.device('cuda')
                  if features.is_cuda
                  else torch.device('cpu'))

        if len(features.shape) < 3:
            raise ValueError('`features` needs to be [bsz, n_views, ...],'
                             'at least 3 dimensions are required')
        if len(features.shape) > 3:
            features = features.view(features.shape[0], features.shape[1], -1)

        batch_size = features.shape[0]
        if labels is not None and soft_mask is not None:
            raise ValueError('Cannot define both `labels` and `mask`')
        elif labels is None and soft_mask is None:
            soft_mask = torch.eye(batch_size, dtype=torch.float32).to(device)
        elif labels is not None:
            labels = labels.contiguous().view(-1, 1)
            if labels.shape[0] != batch_size:
                raise ValueError('Num of labels does not match num of features')
            soft_mask = torch.eq(labels, labels.T).float().to(device)
        else:
            soft_mask = soft_mask.float().to(device)

        contrast_count = features.shape[1]
        contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)
        if self.contrast_mode == 'one':
            anchor_feature = features[:, 0]
            anchor_count = 1
        elif self.contrast_mode == 'all':
            anchor_feature = contrast_feature
            anchor_count = contrast_count
        else:
            raise ValueError('Unknown mode: {}'.format(self.contrast_mode))

        # compute logits
        anchor_dot_contrast = torch.div(
            torch.matmul(anchor_feature, contrast_feature.T),
            self.temperature)
        # for numerical stability
        logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
        logits = anchor_dot_contrast - logits_max.detach()

        # tile mask
        soft_mask = soft_mask.repeat(anchor_count, contrast_count)
        # mask-out self-contrast cases
        logits_mask = torch.scatter(
            torch.ones_like(soft_mask),
            1,
            torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
            0
        )
        soft_mask = soft_mask * logits_mask

        # compute log_prob
        exp_logits = torch.exp(logits) * logits_mask
        log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))
        # log_prob = logits - torch.nn.functional.log_softmax(logits, dim = 1)

        # compute mean of log-likelihood over positive
        mean_log_prob_pos = (soft_mask * log_prob).sum(1) / soft_mask.sum(1)
        # mean_log_prob_pos = (soft_mask * log_prob).sum(1) / logits.shape[0]

        # loss
        loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
        loss = loss.view(anchor_count, batch_size).mean()

        return loss