import numpy as np

from easy_tpp.utils import is_torch_available


def is_tensor(x):
    """
    Tests if `x` is a `torch.Tensor` or `np.ndarray`.
    """
    if is_torch_available():
        import torch

        if isinstance(x, torch.Tensor):
            return True

    return isinstance(x, np.ndarray)


def _is_numpy(x):
    return isinstance(x, np.ndarray)


def is_numpy_array(x):
    """
    Tests if `x` is a numpy array or not.
    """
    return _is_numpy(x)


def _is_torch(x):
    import torch

    return isinstance(x, torch.Tensor)


def is_torch_tensor(x):
    """
    Tests if `x` is a torch tensor or not. Safe to call even if torch is not installed.
    """
    return False if not is_torch_available() else _is_torch(x)


def _is_torch_device(x):
    import torch

    return isinstance(x, torch.device)


def is_torch_device(x):
    """
    Tests if `x` is a torch device or not. Safe to call even if torch is not installed.
    """
    return False if not is_torch_available() else _is_torch_device(x)


def _is_torch_dtype(x):
    import torch

    if isinstance(x, str):
        if hasattr(torch, x):
            x = getattr(torch, x)
        else:
            return False
    return isinstance(x, torch.dtype)


def is_torch_dtype(x):
    """
    Tests if `x` is a torch dtype or not. Safe to call even if torch is not installed.
    """
    return False if not is_torch_available() else _is_torch_dtype(x)