cleanup some code

from modules.textual_inversion.learn_schedule import LearnRateScheduler

from torch import einsum

from collections import defaultdict, deque

from statistics import stdev, mean

class HypernetworkModule(torch.nn.Module):

    return torch.stack(conds)

def log_statistics(loss_info:dict, key, value):

    if key not in loss_info:

        loss_info[key] = [value]

    else:

        loss_info[key].append(value)

        if len(loss_info[key]) > 1024:

            loss_info[key].pop(0)

def statistics(data):

    total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"

    recent_data = data[-32:]

        weight.requires_grad = True

    size = len(ds.indexes)

    loss_dict = {}

    loss_dict = defaultdict(lambda : deque(maxlen = 1024))

    losses = torch.zeros((size,))

    previous_mean_loss = 0

    print("Mean loss of {} elements".format(size))

            losses[hypernetwork.step % losses.shape[0]] = loss.item()

            for entry in entries:

                log_statistics(loss_dict, entry.filename, loss.item())

                loss_dict[entry.filename].append(loss.item())

            optimizer.zero_grad()

            weights[0].grad = None