#! /usr/bin/env python3
#! _*_ coding: utf-8 _*_


"""
Tensors
^^^^^^^
Tensors are similar to NumPy’s ndarrays, with the addition being that
Tensors can also be used on a GPU to accelerate computing.
"""

from __future__ import print_function
import torch
import numpy as np

# Construct a 5x3 matrix, uninitialized:
x = torch.empty(5, 3)
print(x)

# Construct a randomly initialized matrix:
x = torch.rand(5, 3)
print(x)

# Construct a matrix filled zeros and of dtype long:
x = torch.zeros(5, 3, dtype=torch.long)
print(x)

# Construct a tensor directly from data:
x = torch.tensor([5.5, 3])
print(x)

# or create a tensor based on an existing tensor. These methods
# will reuse properties of the input tensor, e.g. dtype, unless
# new values are provided by user
x = x.new_ones(5, 3, dtype=torch.double)      # new_* methods take in sizes
print(x)

x = torch.randn_like(x, dtype=torch.float)    # override dtype!
print(x)                                      # result has the same size

# Get its size:
print(x.size())

# Addition: syntax 1
y = torch.rand(5, 3)
print(x + y)

# Addition: syntax 2
print(torch.add(x, y))

# Addition: providing an output tensor as argument
result = torch.empty(5, 3)
torch.add(x, y, out=result)
print(result)

# Addition: in-place, adds x to y
y.add_(x)
print(y)

# x.copy_(y)
# print(x)
# x.t_()
# print(x)

# You can use standard NumPy-like indexing with all bells and whistles!
print(y[:, 1])

# Resizing: If you want to resize/reshape tensor, you can use ``torch.view``:
x = torch.randn(4, 4)
y = x.view(16)
z = x.view(-1, 8)  # the size -1 is inferred from other dimensions
print(x)
print(y)
print(z)
print(x.size(), y.size(), z.size())

# If you have a one element tensor, use ``.item()`` to get the value as a
# Python number
x = torch.randn(1)
print(x)
print(x.item())

# Converting a Torch Tensor to a NumPy Array
a = torch.ones(5)
print(a)
b = a.numpy()
print(b)
# See how the numpy array changed in value.
a.add_(1)
print(a)
print(b)

# Converting NumPy Array to Torch Tensor
# See how changing the np array changed the Torch Tensor automatically
a = np.ones(5)
b = torch.from_numpy(a)
np.add(a, 1, out=a)
print(a)
print(b)

# All the Tensors on the CPU except a CharTensor support converting to
# NumPy and back.
#
# CUDA Tensors
# ------------
#
# Tensors can be moved onto any device using the ``.to`` method.

# let us run this cell only if CUDA is available
# We will use ``torch.device`` objects to move tensors in and out of GPU
if torch.cuda.is_available():
    device = torch.device("cuda")          # a CUDA device object
    y = torch.ones_like(x, device=device)  # directly create a tensor on GPU
    x = x.to(device)                       # or just use strings ``.to("cuda")``
    z = x + y
    print(z)
    print(z.to("cpu", torch.double))       # ``.to`` can also change dtype together!