import torch
import numpy as np

data = [[1,2],[3,4]]
x_data = torch.tensor(data)

print(x_data)

np_array = np.array(data)
x_np = torch.from_numpy(np_array)


x_ones = torch.ones_like(x_data)
print(f"Ones Tensor: \n {x_ones}\n")
print(x_ones.dtype)

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

shape=(2, 3)
rand_tensor = torch.rand(shape)
ones_tensor = torch.ones(shape)
zeros_tensor = torch.zeros(shape)

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


tensor = torch.rand(3, 4)
print(f"Shape of tensor: {tensor.shape}")
print(f"Datatype of tensor: {tensor.dtype}")
print(f"Device tensor is stored on: {tensor.device}")

tensor = torch.ones(4, 4)
if torch.cuda.is_available():
	tensor = tensor.to("cuda")

print(f"First row: {tensor[0]}")
print(f"First column: {tensor[:, 0]}")
print(f"Last Column: {tensor[..., -1]}")
tensor[:, 1] = 0
print(tensor)

t1 = torch.cat([tensor, tensor, tensor], dim=1)
print(t1)


y1 = tensor @ tensor.T
y2 = tensor.matmul(tensor.T)
print(y1)
print(y2)
y3 = torch.rand_like(y1)
torch.matmul(tensor, tensor.T, out=y3)
print(y3)

print(y3.cpu().numpy())

agg = y3.sum()
print(agg)
print(agg.item())