topic = "pytorch"
lesson = 2

from n import *
home, models_path = get_project_dir("FashionMNIST")
print(home)

import torch
import numpy as np

# directly from data
data = [[1, 2], [3, 4]]

x_data = torch.tensor(data)
print_(f"x_data={x_data}")

# from NumPy array

data = [[1, 2], [3, 4]]

np_array = np.array(data)
print_(f"np_array={np_array}")

x_np = torch.from_numpy(np_array)
print_(f"x_np={x_np}")

# from another array

data = [[1, 2], [3, 4]]

x_data = torch.tensor(data)
print_(f"x_data={x_data}")

# retains the properties (shape, datatype)
x_ones = torch.ones_like(x_data)

# retains shape but overrides the datatype
x_rand = torch.rand_like(x_data, dtype=torch.float) 


print_("Ones Tensor:", x_ones)
#print(x_ones)

print_("Random Tensor:", x_rand)
#print(x_rand)

# With random or constant values
shape = (2,3,)

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

print_("Random Tensor:", rand_tensor)

print_("Ones Tensor:", ones_tensor)

print_("Zeros Tensor:", zeros_tensor)

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}")

print_("https://pytorch.org/docs/stable/torch.html")

# Move to the GPU if available
tensor = torch.rand(3,4)
print_(f"tensor", tensor)

if torch.cuda.is_available():
    tensor = tensor.to("cuda")
print_(f"tensor", tensor)

tensor = tensor.to("cpu")
print_(f"tensor", tensor)

# Standard numpy-like indexing and slicing:

tensor = torch.ones(4, 4)

print_("tensor", tensor)

print_(f"First row: {tensor[0]}")

print_(f"First column: {tensor[:, 0]}") # column = 0

print_(f"Last column: {tensor[..., -1]}") # column = -1

tensor[:, 1] = 0 # 1st index 1 and any zeroth index = 0

print_("tensor", tensor)

# Joining tensors

tensor = torch.ones(4, 4)

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

t0 = torch.cat([tensor, tensor, tensor], dim=0)

print_("tensor", tensor)

print_("dim=1", t1)

print_("dim=0", t0)

# Matrix Multiplications

tensor = torch.ones(4, 4)
print_("tensor")
print(tensor)

# Matrix multiplication between two tensors
y1 = tensor @ tensor.T
print_("tensor @ tensor.T")
print(y1)

y2 = tensor.matmul(tensor.T)
print_("tensor.matmul(tensor.T)")
print(y2)


out = torch.rand(4,4)
y3 = torch.matmul(tensor, tensor.T, out=out)
print_("torch.matmul(tensor, tensor.T, out=torch.rand(4,4))")
print(y3)

# Element-wise product

tensor = torch.ones(4, 4)
print_("tensor")
print(tensor)

z1 = tensor * tensor
print_("tensor * tensor")
print(z1)

z2 = tensor.mul(tensor)
print_("tensor.mul(tensor)")
print(z2)

out = torch.rand(4,4)
z3 = torch.mul(tensor, tensor, out=out)
print_("torch.mul(tensor, tensor, out=torch.rand(4,4))")
print(z3)

tensor = torch.ones(4, 4)
print_("tensor")
print(tensor)

# One element tensor
print_("tensor.sum()")
print(tensor.sum())

# One element tensor converted to numpy
print_("tensor.sum().item()")
print(tensor.sum().item())

tensor = torch.ones(4, 4)
print_("tensor")
print(tensor)

tensor = tensor.add(3)
print_("tensor")
print(tensor)

tensor.add_(3)
print_("tensor")
print(tensor)

t = torch.ones(5)
print_(f"t = {t}")

n = t.numpy()
print_(f"n = {n}")

# change in the tensor reflects in the NumPy array
t.add_(1)

print_(f"t = {t}")
print_(f"n = {n}")

# NumPy array to Tensor

n = np.ones(5)
t = torch.from_numpy(n)

np.add(n, 3, out=n)

print_(f"n = {n}")
print_(f"t = {t}")

n = np.ones(5)
t = torch.from_numpy(n)

t.add_(3)

print_(f"n = {n}")
print_(f"t = {t}")