import torch

from torch.autograd import Variable

import numpy as np

"""

tools to convert specified type

"""

def tonumpy(data):

    if data is None:

        return None

    if isinstance(data, np.ndarray):

        return data

    if isinstance(data, torch._TensorBase):

        return data.cpu().numpy()

    if isinstance(data, torch.autograd.Variable):

        return tonumpy(data.data)

    if isinstance(data, np.int32):

        return np.array(data)

    if isinstance(data, list):

        return np.array(data)

def totensor(data, cuda=True):

    if isinstance(data, np.ndarray):

        tensor = torch.from_numpy(data)

    if isinstance(data, torch._TensorBase):

        tensor = data

    if isinstance(data, torch.autograd.Variable):

        tensor = data.data

    if cuda:

        tensor = tensor.cuda()

    return tensor

def tovariable(data):

    if isinstance(data, np.ndarray):

        return tovariable(totensor(data))

    if isinstance(data, torch._TensorBase):

        return torch.autograd.Variable(data)

    if isinstance(data, torch.autograd.Variable):

        return data

    else:

        raise ValueError("UnKnow data type: %s, input should be {np.ndarray,Tensor,Variable}" %type(data))

def scalar(data):

    if isinstance(data, np.ndarray):

        return data.reshape(1)[0]

    if isinstance(data, torch._TensorBase):

        return data.view(1)[0]

    if isinstance(data, torch.autograd.Variable):

        return data.data.view(1)[0]

# Test

if __name__ == '__main__':

    x = torch.randn(3, 3)

    y = torch.randn(9)

    z = Variable(x)

    print(type(x))

    print(x.type())

    print(z.type())

    if isinstance(z, torch.Tensor):

        print('yes')