import aesara
from aesara import tensor as at

# Declare two symbolic floating-point scalars
a = at.dscalar("a")
b = at.dscalar("b")

# Create a simple example expression
c = a + b

# Convert the expression into a callable object that takes `(a, b)`
# values as input and computes the value of `c`.
f_c = aesara.function([a, b], c)

assert f_c(1.5, 2.5) == 4.0

# Compute the gradient of the example expression with respect to `a`
dc = aesara.grad(c, a)

f_dc = aesara.function([a, b], dc)

assert f_dc(1.5, 2.5) == 1.0

# Compiling functions with `aesara.function` also optimizes
# expression graphs by removing unnecessary operations and
# replacing computations with more efficient ones.

v = at.vector("v")
M = at.matrix("M")

d = a/a + (M + a).dot(v)

aesara.dprint(d)
# Elemwise{add,no_inplace} [id A] ''
#  |InplaceDimShuffle{x} [id B] ''
#  | |Elemwise{true_div,no_inplace} [id C] ''
#  |   |a [id D]
#  |   |a [id D]
#  |dot [id E] ''
#    |Elemwise{add,no_inplace} [id F] ''
#    | |M [id G]
#    | |InplaceDimShuffle{x,x} [id H] ''
#    |   |a [id D]
#    |v [id I]

f_d = aesara.function([a, v, M], d)

# `a/a` -> `1` and the dot product is replaced with a BLAS function
# (i.e. CGemv)
aesara.dprint(f_d)
# Elemwise{Add}[(0, 1)] [id A] ''   5
#  |TensorConstant{(1,) of 1.0} [id B]
#  |CGemv{inplace} [id C] ''   4
#    |AllocEmpty{dtype='float64'} [id D] ''   3
#    | |Shape_i{0} [id E] ''   2
#    |   |M [id F]
#    |TensorConstant{1.0} [id G]
#    |Elemwise{add,no_inplace} [id H] ''   1
#    | |M [id F]
#    | |InplaceDimShuffle{x,x} [id I] ''   0
#    |   |a [id J]
#    |v [id K]
#    |TensorConstant{0.0} [id L]