# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.nn as nn

class MoCo(nn.Module):
    """
    Build a MoCo model with: a query encoder, a key encoder, and a queue
    https://arxiv.org/abs/1911.05722
    """
    def __init__(self, base_encoder, dim=128, K=65536, m=0.999, T=0.07, weight_func=None):
        """
        dim: feature dimension (default: 128)
        K: queue size; number of negative keys (default: 65536)
        m: moco momentum of updating key encoder (default: 0.999)
        T: softmax temperature (default: 0.07)
        """
        super(MoCo, self).__init__()

        self.K = K
        self.m = m
        self.T = T

        # create the encoders
        # num_classes is the output fc dimension
        self.encoder_q = base_encoder(classes=dim)
        self.encoder_k = base_encoder(classes=dim)

        for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):
            param_k.data.copy_(param_q.data)  # initialize
            param_k.requires_grad = False  # not update by gradient

        # create the queue
        self.register_buffer("queue", torch.randn(dim, K))
        self.queue = nn.functional.normalize(self.queue, dim=0)
        self.register_buffer("queue_pseudolabel", torch.zeros(K))

        self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long))

        self.weight_func=weight_func

    @torch.no_grad()
    def _momentum_update_key_encoder(self):
        """
        Momentum update of the key encoder
        """
        for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):
            param_k.data = param_k.data * self.m + param_q.data * (1. - self.m)

    @torch.no_grad()
    def _dequeue_and_enqueue(self, keys, pseudolabels):
        # # gather keys before updating queue
        # keys = concat_all_gather(keys)

        batch_size = keys.shape[0]

        ptr = int(self.queue_ptr)
        assert self.K % batch_size == 0  # for simplicity

        # replace the keys at ptr (dequeue and enqueue)
        self.queue[:, ptr:ptr + batch_size] = keys.T
        self.queue_pseudolabel[ptr:ptr + batch_size] = pseudolabels.T
        ptr = (ptr + batch_size) % self.K  # move pointer

        self.queue_ptr[0] = ptr

    @torch.no_grad()
    def _batch_shuffle_ddp(self, x):
        """
        Batch shuffle, for making use of BatchNorm.
        *** Only support DistributedDataParallel (DDP) model. ***
        """
        # gather from all gpus
        batch_size_this = x.shape[0]
        x_gather = concat_all_gather(x)
        batch_size_all = x_gather.shape[0]

        num_gpus = batch_size_all // batch_size_this

        # random shuffle index
        idx_shuffle = torch.randperm(batch_size_all).cuda()

        # broadcast to all gpus
        torch.distributed.broadcast(idx_shuffle, src=0)

        # index for restoring
        idx_unshuffle = torch.argsort(idx_shuffle)

        # shuffled index for this gpu
        gpu_idx = torch.distributed.get_rank()
        idx_this = idx_shuffle.view(num_gpus, -1)[gpu_idx]

        return x_gather[idx_this], idx_unshuffle

    @torch.no_grad()
    def _batch_unshuffle_ddp(self, x, idx_unshuffle):
        """
        Undo batch shuffle.
        *** Only support DistributedDataParallel (DDP) model. ***
        """
        # gather from all gpus
        batch_size_this = x.shape[0]
        x_gather = concat_all_gather(x)
        batch_size_all = x_gather.shape[0]

        num_gpus = batch_size_all // batch_size_this

        # restored index for this gpu
        gpu_idx = torch.distributed.get_rank()
        idx_this = idx_unshuffle.view(num_gpus, -1)[gpu_idx]

        return x_gather[idx_this]

    def forward(self, im_q, im_k, pseudo_label, vanilla=False):
        """
        Input:
            im_q: a batch of query images
            im_k: a batch of key images
        Output:
            logits, targets
        """

        # compute query features
        q = self.encoder_q(im_q)  # queries: NxC
        q = nn.functional.normalize(q, dim=1)

        k = self.encoder_k(im_k)  # keys: NxC
        k = nn.functional.normalize(k, dim=1)

        # compute logits
        # Einstein sum is more intuitive
        # positive logits: Nx1
        l_pos = torch.einsum('nc,nc->n', [q, k]).unsqueeze(-1)
        # negative logits: NxK
        l_neg = torch.einsum('nc,ck->nk', [q, self.queue.clone().detach()])

        # logits: Nx(1+K)
        logits = torch.cat([l_pos, l_neg], dim=1)

        # apply temperature
        logits /= self.T

        # if we want to do an vanilla moco
        if vanilla:
            # labels: positive key indicators
            criterion = nn.CrossEntropyLoss().cuda()
            labels = torch.zeros(logits.shape[0], dtype=torch.long).cuda()
            loss = criterion(logits, labels)
            self._dequeue_and_enqueue(k,pseudo_label)
        # if we want to use the proposed CL-TCI-MoCo
        else:
            # mask: NxK, logits_mask: Nx(1+K)
            mask = torch.eq(pseudo_label.unsqueeze(-1), self.queue_pseudolabel.clone().detach().unsqueeze(-1).T).float().cuda()
            pos_mask = torch.ones(logits.shape[0], dtype=torch.float).cuda().unsqueeze(-1)
            logits_mask = torch.cat([pos_mask, mask], dim=1)
            log_prob = logits - torch.log(torch.exp(logits).sum(1, keepdim=True))
            mean_log_prob_pos = (logits_mask * log_prob).sum(1) / (logits_mask).sum(1)
            loss = -1 * mean_log_prob_pos.mean()
            # dequeue and enqueue
            self._dequeue_and_enqueue(k,pseudo_label)

        return loss


# utils
@torch.no_grad()
def concat_all_gather(tensor):
    """
    Performs all_gather operation on the provided tensors.
    *** Warning ***: torch.distributed.all_gather has no gradient.
    """
    tensors_gather = [torch.ones_like(tensor)
        for _ in range(torch.distributed.get_world_size())]
    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)

    output = torch.cat(tensors_gather, dim=0)
    return output
