import time
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F


class AdaptiveSupervisedPatchNCELoss(nn.Module):

    def __init__(self, opt):
        super().__init__()
        self.opt = opt
        self.cross_entropy_loss = torch.nn.CrossEntropyLoss(reduction='none')
        self.mask_dtype = torch.bool
        self.total_epochs = opt.n_epochs + opt.n_epochs_decay

    def forward(self, feat_q, feat_k, current_epoch=-1):
        num_patches = feat_q.shape[0]
        dim = feat_q.shape[1]
        feat_k = feat_k.detach()

        # pos logit
        l_pos = torch.bmm(
            feat_q.view(num_patches, 1, -1), feat_k.view(num_patches, -1, 1))
        l_pos = l_pos.view(num_patches, 1)

        # neg logit

        # Should the negatives from the other samples of a minibatch be utilized?
        # In CUT and FastCUT, we found that it's best to only include negatives
        # from the same image. Therefore, we set
        # --nce_includes_all_negatives_from_minibatch as False
        # However, for single-image translation, the minibatch consists of
        # crops from the "same" high-resolution image.
        # Therefore, we will include the negatives from the entire minibatch.
        if self.opt.nce_includes_all_negatives_from_minibatch:
            # reshape features as if they are all negatives of minibatch of size 1.
            batch_dim_for_bmm = 1
        else:
            batch_dim_for_bmm = self.opt.batch_size

        # reshape features to batch size
        feat_q = feat_q.view(batch_dim_for_bmm, -1, dim)
        feat_k = feat_k.view(batch_dim_for_bmm, -1, dim)
        npatches = feat_q.size(1)
        l_neg_curbatch = torch.bmm(feat_q, feat_k.transpose(2, 1))

        # diagonal entries are similarity between same features, and hence meaningless.
        # just fill the diagonal with very small number, which is exp(-10) and almost zero
        diagonal = torch.eye(npatches, device=feat_q.device, dtype=self.mask_dtype)[None, :, :]
        l_neg_curbatch.masked_fill_(diagonal, -10.0)
        l_neg = l_neg_curbatch.view(-1, npatches)

        out = torch.cat((l_pos, l_neg), dim=1) / self.opt.nce_T

        loss = self.cross_entropy_loss(out, torch.zeros(out.size(0), dtype=torch.long,
                                                        device=feat_q.device))

        if self.opt.asp_loss_mode == 'none':
            return loss

        scheduler, lookup = self.opt.asp_loss_mode.split('_')[:2]
        # Compute scheduling
        t = (current_epoch - 1) / self.total_epochs
        if scheduler == 'sigmoid':
            p = 1 / (1 + np.exp((t - 0.5) * 10))
        elif scheduler == 'linear':
            p = 1 - t
        elif scheduler == 'lambda':
            k = 1 - self.opt.n_epochs_decay / self.total_epochs
            m = 1 / (1 - k)
            p = m - m * t if t >= k else 1.0
        elif scheduler == 'zero':
            p = 1.0
        else:
            raise ValueError(f"Unrecognized scheduler: {scheduler}")
        # Weight lookups
        w0 = 1.0
        x = l_pos.squeeze().detach()
        if lookup == 'top':
            x = torch.where(x > 0.0, x, torch.zeros_like(x))
            w1 = torch.sqrt(1 - (x - 1) ** 2)
        elif lookup == 'linear':
            w1 = torch.relu(x)
        elif lookup == 'bell':
            sigma, mu, sc = 1, 0, 4
            w1 = 1 / (sigma * np.sqrt(2 * torch.pi)) * torch.exp(-((x - 0.5) * sc - mu) ** 2 / (2 * sigma ** 2))
        elif lookup == 'uniform':
            w1 = torch.ones_like(x)
        else:
            raise ValueError(f"Unrecognized lookup: {lookup}")
        # Apply weights with schedule
        w = p * w0 + (1 - p) * w1
        # Normalize
        w = w / w.sum() * len(w)
        loss = loss * w
        return loss