#!/usr/bin/python3

# Halide tutorial lesson 13: Tuples

# This lesson describes how to write Funcs that evaluate to multiple
# values.

# This lesson can be built by invoking the command:
#    make test_tutorial_lesson_13_tuples
# in a shell with the current directory at python_bindings/

import math

import halide as hl
import numpy as np


def main():
    # So far Funcs (such as the one below) have evaluated to a single
    # scalar value for each point in their domain.
    single_valued = hl.Func()
    x, y = hl.Var("x"), hl.Var("y")
    single_valued[x, y] = x + y

    # One way to write a hl.Func that returns a collection of values is
    # to add an additional dimension which indexes that
    # collection. This is how we typically deal with color. For
    # example, the hl.Func below represents a collection of three values
    # for every x, y coordinate indexed by c.
    color_image = hl.Func()
    c = hl.Var("c")
    color_image[x, y, c] = hl.select(
        c == 0,
        245,  # Red value
        c == 1,
        42,  # Green value
        132,  # Blue value
    )

    # Since this pattern appears quite often, Halide provides a
    # syntatic sugar to write the code above as the following,
    # using the "mux" function.
    # color_image[x, y, c] = hl.mux(c, [245, 42, 132]);

    # This method is often convenient because it makes it easy to
    # operate on this hl.Func in a way that treats each item in the
    # collection equally:
    brighter = hl.Func()
    brighter[x, y, c] = color_image[x, y, c] + 10

    # However this method is also inconvenient for three reasons.
    #
    # 1) Funcs are defined over an infinite domain, so users of this
    # hl.Func can for example access color_image(x, y, -17), which is
    # not a meaningful value and is probably indicative of a bug.
    #
    # 2) It requires a hl.select, which can impact performance if not
    # bounded and unrolled:
    # brighter.bound(c, 0, 3).unroll(c)
    #
    # 3) With this method, all values in the collection must have the
    # same type. While the above two issues are merely inconvenient,
    # this one is a hard limitation that makes it impossible to
    # express certain things in this way.

    # It is also possible to represent a collection of values as a
    # collection of Funcs:
    func_array = [hl.Func() for i in range(3)]
    func_array[0][x, y] = x + y
    func_array[1][x, y] = hl.sin(x)
    func_array[2][x, y] = hl.cos(y)

    # This method avoids the three problems above, but introduces a
    # new annoyance. Because these are separate Funcs, it is
    # difficult to schedule them so that they are all computed
    # together inside a single loop over x, y.

    # A third alternative is to define a hl.Func as evaluating to a
    # Tuple instead of an hl.Expr. A Tuple is a fixed-size collection of
    # Exprs which may have different type. The following function
    # evaluates to an integer value (x+y), and a floating point value
    # (hl.sin(x*y)).
    multi_valued = hl.Func("multi_valued")
    multi_valued[x, y] = (x + y, hl.sin(x * y))

    # Realizing a tuple-valued hl.Func returns a collection of
    # Buffers. We call this a Realization. It's equivalent to a
    # std::vector of hl.Buffer/Image objects:
    if True:
        im1, im2 = multi_valued.realize([80, 60])
        assert im1.type() == hl.Int(32)
        assert im2.type() == hl.Float(32)
        assert im1[30, 40] == 30 + 40
        assert np.isclose(im2[30, 40], math.sin(30 * 40))

    # You can also pass a tuple of pre-allocated buffers to realize()
    # rather than having new ones created. (The Buffers must have the correct
    # types and have identical sizes.)
    if True:
        im1, im2 = hl.Buffer(hl.Int(32), [80, 60]), hl.Buffer(hl.Float(32), [80, 60])
        multi_valued.realize((im1, im2))
        assert im1[30, 40] == 30 + 40
        assert np.isclose(im2[30, 40], math.sin(30 * 40))

    # All Tuple elements are evaluated together over the same domain
    # in the same loop nest, but stored in distinct allocations. The
    # equivalent C++ code to the above is:
    if True:
        multi_valued_0 = np.empty((80 * 60), dtype=np.int32)
        multi_valued_1 = np.empty((80 * 60), dtype=np.int32)

        for yy in range(80):
            for xx in range(60):
                multi_valued_0[xx + 60 * yy] = xx + yy
                multi_valued_1[xx + 60 * yy] = math.sin(xx * yy)

    # When compiling ahead-of-time, a Tuple-valued hl.Func evaluates
    # into multiple distinct output halide_buffer_t structs. These appear in
    # order at the end of the function signature:
    # int multi_valued(...input buffers and params..., halide_buffer_t
    # *output_1, halide_buffer_t *output_2)

    # You can construct a Tuple by passing multiple Exprs to the
    # Tuple constructor as we did above. Perhaps more elegantly, you
    # can also take advantage of initializer lists and just
    # enclose your Exprs in braces:
    multi_valued_2 = hl.Func("multi_valued_2")
    multi_valued_2[x, y] = (x + y, hl.sin(x * y))

    # Calls to a multi-valued hl.Func cannot be treated as Exprs. The
    # following is a syntax error:
    # hl.Func consumer
    # consumer[x, y] = multi_valued_2[x, y] + 10

    # Instead you must index the returned object with square brackets
    # to retrieve the individual Exprs:
    integer_part = multi_valued_2[x, y][0]
    floating_part = multi_valued_2[x, y][1]
    assert type(integer_part) is hl.FuncTupleElementRef
    assert type(floating_part) is hl.FuncTupleElementRef

    consumer = hl.Func()
    consumer[x, y] = (integer_part + 10, floating_part + 10.0)

    # Tuple reductions.
    if True:
        # Tuples are particularly useful in reductions, as they allow
        # the reduction to maintain complex state as it walks along
        # its domain. The simplest example is an argmax.

        # First we create an Image to take the argmax over.
        input_func = hl.Func()
        input_func[x] = hl.sin(x)
        input = input_func.realize([100])
        assert input.type() == hl.Float(32)

        # Then we defined a 2-valued Tuple which tracks the maximum value
        # its index.
        arg_max = hl.Func()

        # Pure definition.
        # (using [()] for zero-dimensional Funcs is a convention of this python interface)
        arg_max[()] = (0, input[0])

        # Update definition.
        r = hl.RDom([(1, 99)])
        old_index = arg_max[()][0]
        old_max = arg_max[()][1]
        new_index = hl.select(old_max > input[r], r, old_index)
        new_max = hl.max(input[r], old_max)
        arg_max[()] = (new_index, new_max)

        # The equivalent C++ is:
        arg_max_0 = 0
        arg_max_1 = float(input[0])
        for r in range(1, 100):
            old_index = arg_max_0
            old_max = arg_max_1
            new_index = r if (old_max > input[r]) else old_index
            new_max = max(input[r], old_max)
            # In a tuple update definition, all loads and computation
            # are done before any stores, so that all Tuple elements
            # are updated atomically with respect to recursive calls
            # to the same hl.Func.
            arg_max_0 = new_index
            arg_max_1 = new_max

        # Let's verify that the Halide and C++ found the same maximum
        # value and index.
        if True:
            r0, r1 = arg_max.realize()

            assert r0.type() == hl.Int(32)
            assert r1.type() == hl.Float(32)
            assert arg_max_0 == r0[()]
            assert np.isclose(arg_max_1, r1[()])

        # Halide provides argmax and hl.argmin as built-in reductions
        # similar to sum, product, maximum, and minimum. They return
        # a Tuple consisting of the point in the reduction domain
        # corresponding to that value, and the value itself. In the
        # case of ties they return the first value found. We'll use
        # one of these in the following section.

    # Tuples for user-defined types.
    if True:
        # Tuples can also be a convenient way to represent compound
        # objects such as complex numbers. Defining an object that
        # can be converted to and from a Tuple is one way to extend
        # Halide's type system with user-defined types.
        class Complex:
            def __init__(self, r, i=None):
                if type(r) is float and type(i) is float:
                    self.real = hl.Expr(r)
                    self.imag = hl.Expr(i)
                elif i is not None:
                    self.real = r
                    self.imag = i
                else:
                    self.real = r[0]
                    self.imag = r[1]

            def as_tuple(self):
                "Convert to a Tuple"
                return (self.real, self.imag)

            def __add__(self, other):
                "Complex addition"
                return Complex(self.real + other.real, self.imag + other.imag)

            def __mul__(self, other):
                "Complex multiplication"
                return Complex(
                    self.real * other.real - self.imag * other.imag,
                    self.real * other.imag + self.imag * other.real,
                )

            def __getitem__(self, idx):
                return (self.real, self.imag)[idx]

            def __len__(self):
                return 2

            def magnitude(self):
                "Complex magnitude"
                return (self.real * self.real) + (self.imag * self.imag)

            # Other complex operators would go here. The above are
            # sufficient for this example.

        # Let's use the Complex struct to compute a Mandelbrot set.
        mandelbrot = hl.Func()

        # The initial complex value corresponding to an x, y coordinate
        # in our hl.Func.
        initial = Complex(x / 15.0 - 2.5, y / 6.0 - 2.0)

        # Pure definition.
        t = hl.Var("t")
        mandelbrot[x, y, t] = Complex(0.0, 0.0)

        # We'll use an update definition to take 12 steps.
        r = hl.RDom([(1, 12)])
        current = Complex(mandelbrot[x, y, r - 1])

        # The following line uses the complex multiplication and
        # addition we defined above.
        mandelbrot[x, y, r] = Complex(current * current) + initial

        # We'll use another tuple reduction to compute the iteration
        # number where the value first escapes a circle of radius 4.
        # This can be expressed as an hl.argmin of a boolean - we want
        # the index of the first time the given boolean expression is
        # false (we consider false to be less than true).  The argmax
        # would return the index of the first time the expression is
        # true.

        escape_condition = Complex(mandelbrot[x, y, r]).magnitude() < 16.0
        first_escape = hl.argmin(escape_condition)
        assert type(first_escape) is tuple
        # We only want the index, not the value, but hl.argmin returns
        # both, so we'll index the hl.argmin Tuple expression using
        # square brackets to get the hl.Expr representing the index.
        escape = hl.Func()
        escape[x, y] = first_escape[0]

        # Realize the pipeline and print the result as ascii art.
        result = escape.realize([61, 25])
        assert result.type() == hl.Int(32)
        code = " .:-~*={&%#@"
        for yy in range(result.height()):
            for xx in range(result.width()):
                index = result[xx, yy]
                if index < len(code):
                    print(f"{code[index]}", end="")
                else:
                    pass  # is lesson 13 cpp version buggy ?
            print()

    print("Success!")

    return 0


if __name__ == "__main__":
    main()