import torch
import numpy as np

def select_confident_samples(logits, top):
    batch_entropy = -(logits.softmax(1) * logits.log_softmax(1)).sum(1)
    idx = torch.argsort(batch_entropy, descending=False)[:int(batch_entropy.size()[0] * top)]
    return logits[idx], idx

def avg_entropy(outputs):
    logits = outputs - outputs.logsumexp(dim=-1, keepdim=True) # logits = outputs.log_softmax(dim=1) [N, 1000]
    avg_logits = logits.logsumexp(dim=0) - np.log(logits.shape[0]) # avg_logits = logits.mean(0) [1, 1000]
    min_real = torch.finfo(avg_logits.dtype).min
    avg_logits = torch.clamp(avg_logits, min=min_real)
    return -(avg_logits * torch.exp(avg_logits)).sum(dim=-1)

def test_time_tuning(model, inputs, optimizer, scaler, args):
    if args.cocoop:
        image_feature, pgen_ctx = inputs  # FIXME image_feature.shape is torch.Size([1, 1024])
        # pgen_ctx.requires_grad = True
        pgen_ctx = pgen_ctx.detach().to(torch.float32).requires_grad_(True)
        optimizer = torch.optim.AdamW([pgen_ctx], args.lr)

    selected_idx = None
    for j in range(args.tta_steps):
        with torch.cuda.amp.autocast():
            if args.cocoop:
                output = model((image_feature, pgen_ctx))
            else:
                output = model(inputs)

            if selected_idx is not None:
                output = output[selected_idx]
            else:
                output, selected_idx = select_confident_samples(output, args.selection_p)

            loss = avg_entropy(output)

        optimizer.zero_grad()
        # compute gradient and do SGD step
        scaler.scale(loss).backward()
        # Unscales the gradients of optimizer's assigned params in-place
        scaler.step(optimizer)
        scaler.update()
    if args.cocoop:
        return pgen_ctx

    return