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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)
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