import torch


def to_view(data, i):
    return {k + i: v for k, v in data.items()}


def get_view(data, i):
    data_g = {k: v for k, v in data.items() if not k[-1].isnumeric()}
    data_i = {k[:-1]: v for k, v in data.items() if k[-1] == i}
    return {**data_g, **data_i}


def get_twoview(data, idx):
    li = idx[0]
    ri = idx[-1]
    assert idx == f"{li}to{ri}"
    data_lr = {k[:-4] + "0to1": v for k, v in data.items() if k[-4:] == f"{li}to{ri}"}
    data_rl = {k[:-4] + "1to0": v for k, v in data.items() if k[-4:] == f"{ri}ito{li}"}
    data_l = {
        k[:-1] + "0": v for k, v in data.items() if k[-1:] == li and k[-3:-1] != "to"
    }
    data_r = {
        k[:-1] + "1": v for k, v in data.items() if k[-1:] == ri and k[-3:-1] != "to"
    }
    return {**data_lr, **data_rl, **data_l, **data_r}


def stack_twoviews(data, indices=["0to1", "0to2", "1to2"]):
    idx0 = indices[0]
    m_data = data[idx0] if idx0 in data else get_twoview(data, idx0)
    # stack on dim=0
    for idx in indices[1:]:
        data_i = data[idx] if idx in data else get_twoview(data, idx)
        for k, v in data_i.items():
            m_data[k] = torch.cat([m_data[k], v], dim=0)
    return m_data


def unstack_twoviews(data, B, indices=["0to1", "0to2", "1to2"]):
    out = {}
    for i, idx in enumerate(indices):
        out[idx] = {k: v[i * B : (i + 1) * B] for k, v in data.items()}
    return out