import torch
import numpy as np

# initialize a tensor directly as data
data = [[1,2],[3,4]]
x_data = torch.tensor(data).to('xpu')

print(x_data)

# from a numpy array
arr = np.array(data)
x_np = torch.from_numpy(arr).to('xpu')

print(x_np)

# from another tensor (could override the shape, datatype)
x_ones = torch.ones_like(x_data) # retains properties of x_data
print(f"Ones Tensor: \n {x_ones} \n")

x_rand = torch.rand_like(x_data, dtype=torch.float) # overrides datatype
print(f"Random Tensor: \n {x_rand} \n")

# use `shape` to define dimentions
shape = (3,5)
rand_tensor = torch.rand(shape)
ones_tensor = torch.ones(shape)
zeros_tensor = torch.zeros(shape)

print(f"Random: \n {rand_tensor} \n")
print(f"Ones: \n {ones_tensor} \n")
print(f"Zeros: \n {zeros_tensor} \n")

# tensors have attributes

tensor_attr = torch.rand(3,4).to('xpu')

# print(f"Shape: {tensor_attr.shape}")
# print(f"Datatype: {tensor_attr.dtype}")
# print(f"Device: {tensor_attr.device}")

# indexing & slicing

tensor = torch.ones(4,4).to('xpu')
print(f"First row: {tensor[0]}")