from __future__ import division

__copyright__ = "Copyright (C) 2012 Andreas Kloeckner"

__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""


import sys
import numpy as np
import loopy as lp
import pyopencl as cl
import pyopencl.clrandom  # noqa
import pytest

import logging
logger = logging.getLogger(__name__)

try:
    import faulthandler
except ImportError:
    pass
else:
    faulthandler.enable()

from pyopencl.tools import pytest_generate_tests_for_pyopencl \
        as pytest_generate_tests

__all__ = [
        "pytest_generate_tests",
        "cl"  # 'cl.create_some_context'
    ]


def test_complicated_subst(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i]: 0<=i<n}",
            """
                f(x) := x*a[x]
                g(x) := 12 + f(x)
                h(x) := 1 + g(x) + 20*g$two(x)

                a[i] = h$one(i) * h$two(i)
                """,
            [
                lp.GlobalArg("a", np.float32, shape=("n",)),
                lp.ValueArg("n", np.int32),
                ])

    from loopy.subst import expand_subst
    knl = expand_subst(knl, "g$two < h$two")

    print knl

    sr_keys = knl.substitutions.keys()
    for letter, how_many in [
            ("f", 1),
            ("g", 1),
            ("h", 2)
            ]:
        substs_with_letter = sum(1 for k in sr_keys if k.startswith(letter))
        assert substs_with_letter == how_many


def test_type_inference_no_artificial_doubles(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i]: 0<=i<n}",
            """
                <> bb = a[i] - b[i]
                c[i] = bb
                """,
            [
                lp.GlobalArg("a", np.float32, shape=("n",)),
                lp.GlobalArg("b", np.float32, shape=("n",)),
                lp.GlobalArg("c", np.float32, shape=("n",)),
                lp.ValueArg("n", np.int32),
                ],
            assumptions="n>=1")

    for k in lp.generate_loop_schedules(knl):
        code = lp.generate_code(k)
        assert "double" not in code


def test_sized_and_complex_literals(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i]: 0<=i<n}",
            """
                <> aa = 5jf
                <> bb = 5j
                a[i] = imag(aa)
                b[i] = imag(bb)
                c[i] = 5f
                """,
            [
                lp.GlobalArg("a", np.float32, shape=("n",)),
                lp.GlobalArg("b", np.float32, shape=("n",)),
                lp.GlobalArg("c", np.float32, shape=("n",)),
                lp.ValueArg("n", np.int32),
                ],
            assumptions="n>=1")

    lp.auto_test_vs_ref(knl, ctx, knl, parameters=dict(n=5))


def test_simple_side_effect(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i,j]: 0<=i,j<100}",
            """
                a[i] = a[i] + 1
                """,
            [lp.GlobalArg("a", np.float32, shape=(100,))]
            )

    kernel_gen = lp.generate_loop_schedules(knl)

    for gen_knl in kernel_gen:
        print gen_knl
        compiled = lp.CompiledKernel(ctx, gen_knl)
        print compiled.code


def test_nonsense_reduction(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i]: 0<=i<100}",
            """
                a[i] = sum(i, 2)
                """,
            [lp.GlobalArg("a", np.float32, shape=(100,))]
            )

    import pytest
    with pytest.raises(RuntimeError):
        list(lp.generate_loop_schedules(knl))


def test_owed_barriers(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i]: 0<=i<100}",
            [
                "<float32> z[i] = a[i]"
                ],
            [lp.GlobalArg("a", np.float32, shape=(100,))]
            )

    knl = lp.tag_inames(knl, dict(i="l.0"))

    kernel_gen = lp.generate_loop_schedules(knl)

    for gen_knl in kernel_gen:
        compiled = lp.CompiledKernel(ctx, gen_knl)
        print compiled.code


def test_wg_too_small(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i]: 0<=i<100}",
            [
                "<float32> z[i] = a[i] {id=copy}"
                ],
            [lp.GlobalArg("a", np.float32, shape=(100,))],
            local_sizes={0: 16})

    knl = lp.tag_inames(knl, dict(i="l.0"))

    kernel_gen = lp.generate_loop_schedules(knl)

    import pytest
    for gen_knl in kernel_gen:
        with pytest.raises(RuntimeError):
            lp.CompiledKernel(ctx, gen_knl).get_code()


def test_join_inames(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i,j]: 0<=i,j<16}",
            [
                "b[i,j] = 2*a[i,j]"
                ],
            [
                lp.GlobalArg("a", np.float32, shape=(16, 16,)),
                lp.GlobalArg("b", np.float32, shape=(16, 16,))
                ],
            )

    ref_knl = knl

    knl = lp.add_prefetch(knl, "a", sweep_inames=["i", "j"])
    knl = lp.join_inames(knl, ["a_dim_0", "a_dim_1"])

    lp.auto_test_vs_ref(ref_knl, ctx, knl)


def test_divisibility_assumption(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "[n] -> {[i]: 0<=i<n}",
            [
                "b[i] = 2*a[i]"
                ],
            [
                lp.GlobalArg("a", np.float32, shape=("n",)),
                lp.GlobalArg("b", np.float32, shape=("n",)),
                lp.ValueArg("n", np.int32),
                ],
            assumptions="n>=1 and (exists zz: n = 16*zz)")

    ref_knl = knl

    knl = lp.split_iname(knl, "i", 16)

    for k in lp.generate_loop_schedules(knl):
        code = lp.generate_code(k)
        assert "if" not in code

    lp.auto_test_vs_ref(ref_knl, ctx, knl,
            parameters={"n": 16**3})


def test_multi_cse(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i]: 0<=i<100}",
            [
                "<float32> z[i] = a[i] + a[i]**2"
                ],
            [lp.GlobalArg("a", np.float32, shape=(100,))],
            local_sizes={0: 16})

    knl = lp.split_iname(knl, "i", 16, inner_tag="l.0")
    knl = lp.add_prefetch(knl, "a", [])

    kernel_gen = lp.generate_loop_schedules(knl)

    for gen_knl in kernel_gen:
        compiled = lp.CompiledKernel(ctx, gen_knl)
        print compiled.code


def test_stencil(ctx_factory):
    ctx = ctx_factory()

    # n=32 causes corner case behavior in size calculations for temprorary (a
    # non-unifiable, two-constant-segments PwAff as the base index)

    n = 256
    knl = lp.make_kernel(ctx.devices[0],
            "{[i,j]: 0<= i,j < %d}" % n,
            [
                "a_offset(ii, jj) := a[ii+1, jj+1]",
                "z[i,j] = -2*a_offset(i,j)"
                    " + a_offset(i,j-1)"
                    " + a_offset(i,j+1)"
                    " + a_offset(i-1,j)"
                    " + a_offset(i+1,j)"
                ],
            [
                lp.GlobalArg("a", np.float32, shape=(n+2, n+2,)),
                lp.GlobalArg("z", np.float32, shape=(n+2, n+2,))
                ])

    ref_knl = knl

    def variant_1(knl):
        knl = lp.split_iname(knl, "i", 16, outer_tag="g.1", inner_tag="l.1")
        knl = lp.split_iname(knl, "j", 16, outer_tag="g.0", inner_tag="l.0")
        knl = lp.add_prefetch(knl, "a", ["i_inner", "j_inner"])
        knl = lp.set_loop_priority(knl, ["a_dim_0_outer", "a_dim_1_outer"])
        return knl

    def variant_2(knl):
        knl = lp.split_iname(knl, "i", 16, outer_tag="g.1", inner_tag="l.1")
        knl = lp.split_iname(knl, "j", 16, outer_tag="g.0", inner_tag="l.0")
        knl = lp.add_prefetch(knl, "a", ["i_inner", "j_inner"],
                fetch_bounding_box=True)
        knl = lp.set_loop_priority(knl, ["a_dim_0_outer", "a_dim_1_outer"])
        return knl

    for variant in [variant_1, variant_2]:
        lp.auto_test_vs_ref(ref_knl, ctx, variant(knl),
                fills_entire_output=False, print_ref_code=False,
                op_count=[n*n], op_label=["cells"])


def test_stencil_with_overfetch(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i,j]: 0<= i,j < n}",
            [
                "a_offset(ii, jj) := a[ii+2, jj+2]",
                "z[i,j] = -2*a_offset(i,j)"
                    " + a_offset(i,j-1)"
                    " + a_offset(i,j+1)"
                    " + a_offset(i-1,j)"
                    " + a_offset(i+1,j)"

                    " + a_offset(i,j-2)"
                    " + a_offset(i,j+2)"
                    " + a_offset(i-2,j)"
                    " + a_offset(i+2,j)"
                ],
            assumptions="n>=1")

    knl = lp.add_and_infer_dtypes(knl, dict(a=np.float32))

    ref_knl = knl

    def variant_overfetch(knl):
        knl = lp.split_iname(knl, "i", 16, outer_tag="g.1", inner_tag="l.1",
                slabs=(1, 1))
        knl = lp.split_iname(knl, "j", 16, outer_tag="g.0", inner_tag="l.0",
               slabs=(1, 1))
        knl = lp.add_prefetch(knl, "a", ["i_inner", "j_inner"],
                fetch_bounding_box=True)
        knl = lp.set_loop_priority(knl, ["a_dim_0_outer", "a_dim_1_outer"])
        return knl

    for variant in [variant_overfetch]:
        n = 200
        lp.auto_test_vs_ref(ref_knl, ctx, variant(knl),
                fills_entire_output=False, print_ref_code=False,
                op_count=[n*n], parameters=dict(n=n), op_label=["cells"])


def test_eq_constraint(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i,j]: 0<= i,j < 32}",
            [
                "a[i] = b[i]"
                ],
            [
                lp.GlobalArg("a", np.float32, shape=(1000,)),
                lp.GlobalArg("b", np.float32, shape=(1000,))
                ])

    knl = lp.split_iname(knl, "i", 16, outer_tag="g.0")
    knl = lp.split_iname(knl, "i_inner", 16, outer_tag=None, inner_tag="l.0")

    kernel_gen = lp.generate_loop_schedules(knl)

    for knl in kernel_gen:
        print lp.generate_code(knl)


def test_argmax(ctx_factory):
    dtype = np.dtype(np.float32)
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)
    order = "C"

    n = 10000

    knl = lp.make_kernel(ctx.devices[0],
            "{[i]: 0<=i<%d}" % n,
            [
                "<> result = argmax(i, fabs(a[i]))",
                "max_idx = result.index",
                "max_val = result.value",
                ],
            [
                lp.GlobalArg("a", dtype, shape=(n,), order=order),
                lp.GlobalArg("max_idx", np.int32, shape=(), order=order),
                lp.GlobalArg("max_val", dtype, shape=(), order=order),
                ])

    a = np.random.randn(10000).astype(dtype)
    cknl = lp.CompiledKernel(ctx, knl)
    evt, (max_idx, max_val) = cknl(queue, a=a, out_host=True)
    assert max_val == np.max(np.abs(a))
    assert max_idx == np.where(np.abs(a) == max_val)[-1]


# {{{ code generator fuzzing

def make_random_value():
    from random import randrange, uniform
    v = randrange(3)
    if v == 0:
        while True:
            z = randrange(-1000, 1000)
            if z:
                return z

    elif v == 1:
        return uniform(-10, 10)
    else:
        cval = uniform(-10, 10) + 1j*uniform(-10, 10)
        if randrange(0, 2) == 0:
            return np.complex128(cval)
        else:
            return np.complex128(cval)


def make_random_expression(var_values, size):
    from random import randrange
    import pymbolic.primitives as p
    v = randrange(1500)
    size[0] += 1
    if v < 500 and size[0] < 40:
        term_count = randrange(2, 5)
        if randrange(2) < 1:
            cls = p.Sum
        else:
            cls = p.Product
        return cls(tuple(
            make_random_expression(var_values, size)
            for i in range(term_count)))
    elif v < 750:
        return make_random_value()
    elif v < 1000:
        var_name = "var_%d" % len(var_values)
        assert var_name not in var_values
        var_values[var_name] = make_random_value()
        return p.Variable(var_name)
    elif v < 1250:
        # Cannot use '-' because that destroys numpy constants.
        return p.Sum((
            make_random_expression(var_values, size),
            - make_random_expression(var_values, size)))
    elif v < 1500:
        # Cannot use '/' because that destroys numpy constants.
        return p.Quotient(
                make_random_expression(var_values, size),
                make_random_expression(var_values, size))


def generate_random_fuzz_examples(count):
    for i in xrange(count):
        size = [0]
        var_values = {}
        expr = make_random_expression(var_values, size)
        yield expr, var_values


def test_fuzz_code_generator(ctx_factory):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    #from expr_fuzz import get_fuzz_examples
    for expr, var_values in generate_random_fuzz_examples(50):
    #for expr, var_values in get_fuzz_examples():
        from pymbolic import evaluate
        try:
            true_value = evaluate(expr, var_values)
        except ZeroDivisionError:
            continue

        def get_dtype(x):
            if isinstance(x, (complex, np.complexfloating)):
                return np.complex128
            else:
                return np.float64

        knl = lp.make_kernel(ctx.devices[0], "{ : }",
                [lp.ExpressionInstruction("value", expr)],
                [lp.GlobalArg("value", np.complex128, shape=())]
                + [
                    lp.ValueArg(name, get_dtype(val))
                    for name, val in var_values.iteritems()
                    ])
        ck = lp.CompiledKernel(ctx, knl)
        evt, (lp_value,) = ck(queue, out_host=True, **var_values)
        err = abs(true_value-lp_value)/abs(true_value)
        if abs(err) > 1e-10:
            print 80*"-"
            print "WRONG: rel error=%g" % err
            print "true=%r" % true_value
            print "loopy=%r" % lp_value
            print 80*"-"
            print ck.code
            print 80*"-"
            print var_values
            print 80*"-"
            print repr(expr)
            print 80*"-"
            print expr
            print 80*"-"
            1/0

# }}}


def test_empty_reduction(ctx_factory):
    dtype = np.dtype(np.float32)
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(ctx.devices[0],
            [
                "{[i]: 0<=i<20}",
                "[i] -> {[j]: 0<=j<0}"
                ],
            [
                "a[i] = sum(j, j)",
                ],
            [
                lp.GlobalArg("a", dtype, (20,)),
                ])
    cknl = lp.CompiledKernel(ctx, knl)

    evt, (a,) = cknl(queue)

    assert (a.get() == 0).all()


def test_nested_dependent_reduction(ctx_factory):
    dtype = np.dtype(np.int32)
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(ctx.devices[0],
            [
                "{[i]: 0<=i<n}",
                "{[j]: 0<=j<i+sumlen}"
                ],
            [
                "<> sumlen = l[i]",
                "a[i] = sum(j, j)",
                ],
            [
                lp.ValueArg("n", np.int32),
                lp.GlobalArg("a", dtype, ("n",)),
                lp.GlobalArg("l", np.int32, ("n",)),
                ])

    cknl = lp.CompiledKernel(ctx, knl)

    n = 330
    l = np.arange(n, dtype=np.int32)
    evt, (a,) = cknl(queue, l=l, n=n, out_host=True)

    tgt_result = (2*l-1)*2*l/2
    assert (a == tgt_result).all()


def test_multi_nested_dependent_reduction(ctx_factory):
    dtype = np.dtype(np.int32)
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            [
                "{[itgt]: 0 <= itgt < ntgts}",
                "{[isrc_box]: 0 <= isrc_box < nboxes}",
                "{[isrc]: 0 <= isrc < npart}"
                ],
            [
                "<> npart = nparticles_per_box[isrc_box]",
                "a[itgt] = sum((isrc_box, isrc), 1)",
                ],
            [
                lp.ValueArg("n", np.int32),
                lp.GlobalArg("a", dtype, ("n",)),
                lp.GlobalArg("nparticles_per_box", np.int32, ("nboxes",)),
                lp.ValueArg("ntgts", np.int32),
                lp.ValueArg("nboxes", np.int32),
                ],
            assumptions="ntgts>=1")

    cknl = lp.CompiledKernel(ctx, knl)
    print cknl.code
    # FIXME: Actually test functionality.


def test_recursive_nested_dependent_reduction(ctx_factory):
    dtype = np.dtype(np.int32)
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            [
                "{[itgt]: 0 <= itgt < ntgts}",
                "{[isrc_box]: 0 <= isrc_box < nboxes}",
                "{[isrc]: 0 <= isrc < npart}"
                ],
            [
                "<> npart = nparticles_per_box[isrc_box]",
                "<> boxsum = sum(isrc, isrc+isrc_box+itgt)",
                "a[itgt] = sum(isrc_box, boxsum)",
                ],
            [
                lp.ValueArg("n", np.int32),
                lp.GlobalArg("a", dtype, ("n",)),
                lp.GlobalArg("nparticles_per_box", np.int32, ("nboxes",)),
                lp.ValueArg("ntgts", np.int32),
                lp.ValueArg("nboxes", np.int32),
                ],
            assumptions="ntgts>=1")

    cknl = lp.CompiledKernel(ctx, knl)
    print cknl.get_code()
    # FIXME: Actually test functionality.


def test_dependent_loop_bounds(ctx_factory):
    dtype = np.dtype(np.float32)
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            [
                "{[i]: 0<=i<n}",
                "{[jj]: 0<=jj<row_len}",
                ],
            [
                "<> row_len = a_rowstarts[i+1] - a_rowstarts[i]",
                "a_sum[i] = sum(jj, a_values[[a_rowstarts[i]+jj]])",
                ],
            [
                lp.GlobalArg("a_rowstarts", np.int32, shape=lp.auto),
                lp.GlobalArg("a_indices", np.int32, shape=lp.auto),
                lp.GlobalArg("a_values", dtype),
                lp.GlobalArg("a_sum", dtype, shape=lp.auto),
                lp.ValueArg("n", np.int32),
                ],
            assumptions="n>=1 and row_len>=1")

    cknl = lp.CompiledKernel(ctx, knl)
    print "---------------------------------------------------"
    print cknl.get_highlighted_code()
    print "---------------------------------------------------"


def test_dependent_loop_bounds_2(ctx_factory):
    dtype = np.dtype(np.float32)
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            [
                "{[i]: 0<=i<n}",
                "{[jj]: 0<=jj<row_len}",
                ],
            [
                "<> row_start = a_rowstarts[i]",
                "<> row_len = a_rowstarts[i+1] - row_start",
                "ax[i] = sum(jj, a_values[[row_start+jj]])",
                ],
            [
                lp.GlobalArg("a_rowstarts", np.int32, shape=lp.auto),
                lp.GlobalArg("a_indices", np.int32, shape=lp.auto),
                lp.GlobalArg("a_values", dtype, strides=(1,)),
                lp.GlobalArg("ax", dtype, shape=lp.auto),
                lp.ValueArg("n", np.int32),
                ],
            assumptions="n>=1 and row_len>=1")

    knl = lp.split_iname(knl, "i", 128, outer_tag="g.0",
            inner_tag="l.0")
    cknl = lp.CompiledKernel(ctx, knl)
    print "---------------------------------------------------"
    print cknl.get_highlighted_code()
    print "---------------------------------------------------"


def test_dependent_loop_bounds_3(ctx_factory):
    # The point of this test is that it shows a dependency between
    # domains that is exclusively mediated by the row_len temporary.
    # It also makes sure that row_len gets read before any
    # conditionals use it.

    dtype = np.dtype(np.float32)
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            [
                "{[i]: 0<=i<n}",
                "{[jj]: 0<=jj<row_len}",
                ],
            [
                "<> row_len = a_row_lengths[i]",
                "a[i,jj] = 1",
                ],
            [
                lp.GlobalArg("a_row_lengths", np.int32, shape=lp.auto),
                lp.GlobalArg("a", dtype, shape=("n,n"), order="C"),
                lp.ValueArg("n", np.int32),
                ])

    assert knl.parents_per_domain()[1] == 0

    knl = lp.split_iname(knl, "i", 128, outer_tag="g.0",
            inner_tag="l.0")

    cknl = lp.CompiledKernel(ctx, knl)
    print "---------------------------------------------------"
    print cknl.get_highlighted_code()
    print "---------------------------------------------------"

    knl_bad = lp.split_iname(knl, "jj", 128, outer_tag="g.1",
            inner_tag="l.1")

    import pytest
    with pytest.raises(RuntimeError):
        list(lp.generate_loop_schedules(knl_bad))


def test_independent_multi_domain(ctx_factory):
    dtype = np.dtype(np.float32)
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(ctx.devices[0],
            [
                "{[i]: 0<=i<n}",
                "{[j]: 0<=j<n}",
                ],
            [
                "a[i] = 1",
                "b[j] = 2",
                ],
            [
                lp.GlobalArg("a", dtype, shape=("n"), order="C"),
                lp.GlobalArg("b", dtype, shape=("n"), order="C"),
                lp.ValueArg("n", np.int32),
                ])

    knl = lp.split_iname(knl, "i", 16, outer_tag="g.0",
            inner_tag="l.0")
    knl = lp.split_iname(knl, "j", 16, outer_tag="g.0",
            inner_tag="l.0")
    assert knl.parents_per_domain() == 2*[None]

    n = 50
    cknl = lp.CompiledKernel(ctx, knl)
    evt, (a, b) = cknl(queue, n=n, out_host=True)

    assert a.shape == (50,)
    assert b.shape == (50,)
    assert (a == 1).all()
    assert (b == 2).all()


def test_bare_data_dependency(ctx_factory):
    dtype = np.dtype(np.float32)
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(ctx.devices[0],
            [
                "[znirp] -> {[i]: 0<=i<znirp}",
                ],
            [
                "<> znirp = n",
                "a[i] = 1",
                ],
            [
                lp.GlobalArg("a", dtype, shape=("n"), order="C"),
                lp.ValueArg("n", np.int32),
                ])

    cknl = lp.CompiledKernel(ctx, knl)
    n = 20000
    evt, (a,) = cknl(queue, n=n, out_host=True)

    assert a.shape == (n,)
    assert (a == 1).all()


def test_equality_constraints(ctx_factory):
    dtype = np.float32
    ctx = ctx_factory()

    order = "C"

    n = 10

    knl = lp.make_kernel(ctx.devices[0], [
            "[n] -> {[i,j]: 0<=i,j<n }",
            "{[k]: k =i+5 and k < n}",
            ],
            [
                "a[i,j] = 5 {id=set_all}",
                "a[i,k] = 22 {dep=set_all}",
                ],
            [
                lp.GlobalArg("a", dtype, shape="n, n", order=order),
                lp.ValueArg("n", np.int32, approximately=1000),
                ],
            name="equality_constraints", assumptions="n>=1")

    seq_knl = knl

    knl = lp.split_iname(knl, "i", 16, outer_tag="g.0", inner_tag="l.0")
    knl = lp.split_iname(knl, "j", 16, outer_tag="g.1", inner_tag="l.1")
    #print knl
    #print knl.domains[0].detect_equalities()

    lp.auto_test_vs_ref(seq_knl, ctx, knl,
            parameters=dict(n=n), print_ref_code=True)


def test_stride(ctx_factory):
    dtype = np.float32
    ctx = ctx_factory()

    order = "C"

    n = 10

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i]: 0<=i<n and (exists l: i = 2*l)}",
            ],
            [
                "a[i] = 5",
                ],
            [
                lp.GlobalArg("a", dtype, shape="n", order=order),
                lp.ValueArg("n", np.int32, approximately=1000),
                ],
            assumptions="n>=1")

    seq_knl = knl

    lp.auto_test_vs_ref(seq_knl, ctx, knl,
            parameters=dict(n=n), fills_entire_output=False)


def test_domain_dependency_via_existentially_quantified_variable(ctx_factory):
    dtype = np.float32
    ctx = ctx_factory()

    order = "C"

    n = 10

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i]: 0<=i<n }",
            "{[k]: k=i and (exists l: k = 2*l) }",
            ],
            [
                "a[i] = 5 {id=set}",
                "a[k] = 6 {dep=set}",
                ],
            [
                lp.GlobalArg("a", dtype, shape="n", order=order),
                lp.ValueArg("n", np.int32, approximately=1000),
                ],
            assumptions="n>=1")

    seq_knl = knl

    lp.auto_test_vs_ref(seq_knl, ctx, knl,
            parameters=dict(n=n), )


def test_double_sum(ctx_factory):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    n = 20

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j]: 0<=i,j<n }",
            ],
            [
                "a = sum((i,j), i*j)",
                "b = sum(i, sum(j, i*j))",
                ],
            assumptions="n>=1")

    cknl = lp.CompiledKernel(ctx, knl)

    evt, (a, b) = cknl(queue, n=n)

    ref = sum(i*j for i in xrange(n) for j in xrange(n))
    assert a.get() == ref
    assert b.get() == ref


# {{{ test race detection

@pytest.mark.skipif("sys.version_info < (2,6)")
def test_ilp_write_race_detection_global(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "[n] -> {[i,j]: 0<=i,j<n }",
            ],
            [
                "a[i] = 5+i+j",
                ],
            [
                lp.GlobalArg("a", np.float32),
                lp.ValueArg("n", np.int32, approximately=1000),
                ],
            assumptions="n>=1")

    knl = lp.tag_inames(knl, dict(j="ilp"))

    from loopy.diagnostic import WriteRaceConditionWarning
    from warnings import catch_warnings
    with catch_warnings(record=True) as warn_list:
        list(lp.generate_loop_schedules(knl))

        assert any(isinstance(w.message, WriteRaceConditionWarning)
                for w in warn_list)


def test_ilp_write_race_avoidance_local(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[i,j]: 0<=i<16 and 0<=j<17 }",
            [
                "<> a[i] = 5+i+j",
                ],
            [])

    knl = lp.tag_inames(knl, dict(i="l.0", j="ilp"))

    for k in lp.generate_loop_schedules(knl):
        assert k.temporary_variables["a"].shape == (16, 17)


def test_ilp_write_race_avoidance_private(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{[j]: 0<=j<16 }",
            [
                "<> a = 5+j",
                ],
            [])

    knl = lp.tag_inames(knl, dict(j="ilp"))

    for k in lp.generate_loop_schedules(knl):
        assert k.temporary_variables["a"].shape == (16,)

# }}}


def test_write_parameter(ctx_factory):
    dtype = np.float32
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j]: 0<=i,j<n }",
            ],
            """
                a = sum((i,j), i*j)
                b = sum(i, sum(j, i*j))
                n = 15
                """,
            [
                lp.GlobalArg("a", dtype, shape=()),
                lp.GlobalArg("b", dtype, shape=()),
                lp.ValueArg("n", np.int32, approximately=1000),
                ],
            assumptions="n>=1")

    import pytest
    with pytest.raises(RuntimeError):
        lp.CompiledKernel(ctx, knl).get_code()


# {{{ arg guessing

def test_arg_shape_guessing(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j]: 0<=i,j<n }",
            ],
            """
                a = 1.5 + sum((i,j), i*j)
                b[i, j] = i*j
                c[i+j, j] = b[j,i]
                """,
            [
                lp.GlobalArg("a", shape=lp.auto),
                lp.GlobalArg("b", shape=lp.auto),
                lp.GlobalArg("c", shape=lp.auto),
                lp.ValueArg("n"),
                ],
            assumptions="n>=1")

    print knl
    print lp.CompiledKernel(ctx, knl).get_highlighted_code()


def test_arg_guessing(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j]: 0<=i,j<n }",
            ],
            """
                a = 1.5 + sum((i,j), i*j)
                b[i, j] = i*j
                c[i+j, j] = b[j,i]
                """,
            assumptions="n>=1")

    print knl
    print lp.CompiledKernel(ctx, knl).get_highlighted_code()


def test_arg_guessing_with_reduction(ctx_factory):
    #logging.basicConfig(level=logging.DEBUG)
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j]: 0<=i,j<n }",
            ],
            """
                a = 1.5 + sum((i,j), i*j)
                d = 1.5 + sum((i,j), b[i,j])
                b[i, j] = i*j
                c[i+j, j] = b[j,i]
                """,
            assumptions="n>=1")

    print knl
    print lp.CompiledKernel(ctx, knl).get_highlighted_code()

# }}}


def test_nonlinear_index(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j]: 0<=i,j<n }",
            ],
            """
                a[i*i] = 17
                """,
            [
                lp.GlobalArg("a", shape="n"),
                lp.ValueArg("n"),
                ],
            assumptions="n>=1")

    print knl
    print lp.CompiledKernel(ctx, knl).get_highlighted_code()


def test_triangle_domain(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j]: 0<=i,j<n and i <= j}",
            ],
            "a[i,j] = 17",
            assumptions="n>=1")

    print knl
    print lp.CompiledKernel(ctx, knl).get_highlighted_code()


def test_offsets_and_slicing(ctx_factory):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    n = 20

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j]: 0<=i<n and 0<=j<m }",
            ],
            """
                b[i,j] = 2*a[i,j]
                """,
            assumptions="n>=1 and m>=1",
            default_offset=lp.auto)

    knl = lp.tag_data_axes(knl, "a,b", "stride:auto,stride:1")

    cknl = lp.CompiledKernel(ctx, knl)

    a_full = cl.clrandom.rand(queue, (n, n), np.float64)
    a_full_h = a_full.get()
    b_full = cl.clrandom.rand(queue, (n, n), np.float64)
    b_full_h = b_full.get()

    a_sub = (slice(3, 10), slice(5, 10))
    a = a_full[a_sub]

    b_sub = (slice(3+3, 10+3), slice(5+4, 10+4))
    b = b_full[b_sub]

    b_full_h[b_sub] = 2*a_full_h[a_sub]

    print cknl.get_highlighted_code({"a": a.dtype})
    cknl(queue, a=a, b=b)

    import numpy.linalg as la
    assert la.norm(b_full.get() - b_full_h) < 1e-13


def test_vector_ilp_with_prefetch(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{ [i]: 0<=i<n }",
            "out[i] = 2*a[i]",
            [
                # Tests that comma'd arguments interoperate with
                # argument guessing.
                lp.GlobalArg("out,a", np.float32, shape=lp.auto),
                "..."
                ])

    knl = lp.split_iname(knl, "i", 128, inner_tag="l.0")
    knl = lp.split_iname(knl, "i_outer", 4, outer_tag="g.0", inner_tag="ilp")
    knl = lp.add_prefetch(knl, "a", ["i_inner", "i_outer_inner"])

    cknl = lp.CompiledKernel(ctx, knl)
    cknl.cl_kernel_info()

    import re
    code = cknl.get_code()
    assert len(list(re.finditer("barrier", code))) == 1


# {{{ convolutions

def test_convolution(ctx_factory):
    ctx = ctx_factory()

    dtype = np.float32

    knl = lp.make_kernel(ctx.devices[0],
        "{ [iimg, ifeat, icolor, im_x, im_y, f_x, f_y]: \
                -f_w <= f_x,f_y <= f_w \
                and 0 <= im_x < im_w and 0 <= im_y < im_h \
                and 0<=iimg<=nimgs and 0<=ifeat<nfeats and 0<=icolor<ncolors \
                }",
        """
        out[iimg, ifeat, im_x, im_y] = sum((f_x, f_y, icolor), \
            img[iimg, f_w+im_x-f_x, f_w+im_y-f_y, icolor] \
            * f[ifeat, f_w+f_x, f_w+f_y, icolor])
        """,
        [
            lp.GlobalArg("f", dtype, shape=lp.auto),
            lp.GlobalArg("img", dtype, shape=lp.auto),
            lp.GlobalArg("out", dtype, shape=lp.auto),
            "..."
            ],
        assumptions="f_w>=1 and im_w, im_h >= 2*f_w+1 and nfeats>=1 and nimgs>=0",
        flags="annotate_inames",
        defines=dict(ncolors=3))

    ref_knl = knl

    f_w = 3

    def variant_0(knl):
        #knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
        knl = lp.set_loop_priority(knl, "iimg,im_x,im_y,ifeat,f_x,f_y")
        return knl

    def variant_1(knl):
        knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
        knl = lp.set_loop_priority(knl, "iimg,im_x_outer,im_y,ifeat,f_x,f_y")
        return knl

    def variant_2(knl):
        knl = lp.split_iname(knl, "im_x", 16, outer_tag="g.0", inner_tag="l.0")
        knl = lp.split_iname(knl, "im_y", 16, outer_tag="g.1", inner_tag="l.1")
        knl = lp.fix_parameters(knl, f_w=f_w)
        knl = lp.tag_inames(knl, dict(ifeat="g.2"))
        knl = lp.add_prefetch(knl, "f[ifeat,:,:,:]")
        knl = lp.add_prefetch(knl, "img", "im_x_inner, im_y_inner, f_x, f_y")
        return knl

    for variant in [
            variant_0,
            variant_1,
            variant_2
            ]:
        lp.auto_test_vs_ref(ref_knl, ctx, variant(knl),
                parameters=dict(
                    im_w=128, im_h=128, f_w=f_w,
                    nfeats=3, nimgs=3
                    ))


def test_convolution_with_nonzero_base(ctx_factory):
    # This is kept alive as a test for domains that don't start at zero.
    # These are a bad idea for split_iname, which places its origin at zero
    # and therefore produces a first block that is odd-sized.
    #
    # Therefore, for real tests, check test_convolution further up.

    ctx = ctx_factory()

    dtype = np.float32

    knl = lp.make_kernel(ctx.devices[0],
        "{ [iimg, ifeat, icolor, im_x, im_y, f_x, f_y]: \
                -f_w <= f_x,f_y <= f_w \
                and f_w <= im_x < im_w-f_w and f_w <= im_y < im_h-f_w \
                and 0<=iimg<=nimgs and 0<=ifeat<nfeats and 0<=icolor<ncolors \
                }",
        """
        out[iimg, ifeat, im_x-f_w, im_y-f_w] = sum((f_x, f_y, icolor), \
            img[iimg, im_x-f_x, im_y-f_y, icolor] \
            * f[ifeat, f_w+f_x, f_w+f_y, icolor])
        """,
        [
            lp.GlobalArg("f", dtype, shape=lp.auto),
            lp.GlobalArg("img", dtype, shape=lp.auto),
            lp.GlobalArg("out", dtype, shape=lp.auto),
            "..."
            ],
        assumptions="f_w>=1 and im_w, im_h >= 2*f_w+1 and nfeats>=1 and nimgs>=0",
        flags="annotate_inames",
        defines=dict(ncolors=3))

    ref_knl = knl

    f_w = 3

    def variant_0(knl):
        #knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
        knl = lp.set_loop_priority(knl, "iimg,im_x,im_y,ifeat,f_x,f_y")
        return knl

    def variant_1(knl):
        knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
        knl = lp.set_loop_priority(knl, "iimg,im_x_outer,im_y,ifeat,f_x,f_y")
        return knl

    for variant in [
            variant_0,
            variant_1,
            ]:
        lp.auto_test_vs_ref(ref_knl, ctx, variant(knl),
                parameters=dict(
                    im_w=128, im_h=128, f_w=f_w,
                    nfeats=12, nimgs=17
                    ))

# }}}


def test_c_instruction(ctx_factory):
    #logging.basicConfig(level=logging.DEBUG)
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j]: 0<=i,j<n }",
            ],
            [
                lp.CInstruction("i", """
                    x = sin((float) i);
                    """, assignees="x"),
                "a[i*i] = x",
                ],
            [
                lp.GlobalArg("a", shape="n"),
                lp.ValueArg("n"),
                lp.TemporaryVariable("x", np.float32),
                ],
            assumptions="n>=1")

    knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")

    print knl
    print lp.CompiledKernel(ctx, knl).get_highlighted_code()


def test_dependent_domain_insn_iname_finding(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "{[isrc_box]: 0<=isrc_box<nsrc_boxes}",
            "{[isrc,idim]: isrc_start<=isrc<isrc_end and 0<=idim<dim}",
            ],
            """
                <> src_ibox = source_boxes[isrc_box]
                <> isrc_start = box_source_starts[src_ibox]
                <> isrc_end = isrc_start+box_source_counts_nonchild[src_ibox]
                <> strength = strengths[isrc] {id=set_strength}
                """,
            [
                lp.GlobalArg("box_source_starts,box_source_counts_nonchild",
                    None, shape=None),
                lp.GlobalArg("strengths",
                    None, shape="nsources"),
                "..."])

    print knl
    assert "isrc_box" in knl.insn_inames("set_strength")

    print lp.CompiledKernel(ctx, knl).get_highlighted_code(
            dict(
                source_boxes=np.int32,
                box_source_starts=np.int32,
                box_source_counts_nonchild=np.int32,
                strengths=np.float64,
                nsources=np.int32,
                ))


def test_inames_deps_from_write_subscript(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j]: 0<=i,j<n}",
            ],
            """
                <> src_ibox = source_boxes[i]
                <int32> something = 5
                a[src_ibox] = sum(j, something) {id=myred}
                """,
            [
                lp.GlobalArg("box_source_starts,box_source_counts_nonchild,a",
                    None, shape=None),
                "..."])

    print knl
    assert "i" in knl.insn_inames("myred")


def test_split_reduction(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j,k]: 0<=i,j,k<n}",
            ],
            """
                b = sum((i,j,k), a[i,j,k])
                """,
            [
                lp.GlobalArg("box_source_starts,box_source_counts_nonchild,a",
                    None, shape=None),
                "..."])

    knl = lp.split_reduction_outward(knl, "j,k")
    # FIXME: finish test


def test_modulo_indexing(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0], [
            "{[i,j]: 0<=i<n and 0<=j<5}",
            ],
            """
                b[i] = sum(j, a[(i+j)%n])
                """,
            [
                lp.GlobalArg("a", None, shape="n"),
                "..."
                ]
            )

    print knl
    print lp.CompiledKernel(ctx, knl).get_highlighted_code(
            dict(
                a=np.float32,
                ))


def test_rob_stroud_bernstein(ctx_factory):
    ctx = ctx_factory()

    # NOTE: tmp would have to be zero-filled beforehand

    knl = lp.make_kernel(ctx.devices[0],
            "{[el, i2, alpha1,alpha2]: \
                    0 <= el < nels and \
                    0 <= i2 < nqp1d and \
                    0 <= alpha1 <= deg and 0 <= alpha2 <= deg-alpha1 }",
            """
                <> xi = qpts[1, i2] {inames=el}
                <> s = 1-xi
                <> r = xi/s
                <> aind = 0 {id=aind_init,inames=i2:el}

                <> w = s**(deg-alpha1) {id=init_w}

                tmp[el,alpha1,i2] = tmp[el,alpha1,i2] + w * coeffs[aind] \
                        {id=write_tmp,inames=alpha2}
                w = w * r * ( deg - alpha1 - alpha2 ) / (1 + alpha2) \
                        {id=update_w,dep=init_w:write_tmp}
                aind = aind + 1 \
                        {id=aind_incr,\
                        dep=aind_init:write_tmp:update_w, \
                        inames=el:i2:alpha1:alpha2}
                """,
            [
                # Must declare coeffs to have "no" shape, to keep loopy
                # from trying to figure it out the shape automatically.

                lp.GlobalArg("coeffs", None, shape=None),
                "..."
                ],
            assumptions="deg>=0 and nels>=1"
            )

    knl = lp.fix_parameters(knl, nqp1d=7, deg=4)
    knl = lp.split_iname(knl, "el", 16, inner_tag="l.0")
    knl = lp.split_iname(knl, "el_outer", 2, outer_tag="g.0", inner_tag="ilp",
            slabs=(0, 1))
    knl = lp.tag_inames(knl, dict(i2="l.1", alpha1="unr", alpha2="unr"))

    print lp.CompiledKernel(ctx, knl).get_highlighted_code(
            dict(
                qpts=np.float32,
                coeffs=np.float32,
                tmp=np.float32,
                ))


@pytest.mark.parametrize("vec_len", [2, 3, 4, 8, 16])
def test_vector_types(ctx_factory, vec_len):
    ctx = ctx_factory()

    knl = lp.make_kernel(ctx.devices[0],
            "{ [i,j]: 0<=i<n and 0<=j<vec_len }",
            "out[i,j] = 2*a[i,j]",
            [
                lp.GlobalArg("a", np.float32, shape=lp.auto),
                lp.GlobalArg("out", np.float32, shape=lp.auto),
                "..."
                ])

    knl = lp.fix_parameters(knl, vec_len=vec_len)

    ref_knl = knl

    knl = lp.tag_data_axes(knl, "out", "c,vec")
    knl = lp.tag_inames(knl, dict(j="unr"))

    knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")

    lp.auto_test_vs_ref(ref_knl, ctx, knl,
            parameters=dict(
                n=20000
                ),
            fills_entire_output=False)


def test_conditional(ctx_factory):
    #logging.basicConfig(level=logging.DEBUG)
    ctx = ctx_factory()

    knl = lp.make_kernel(
            ctx.devices[0],
            "{ [i,j]: 0<=i,j<n }",
            """
                <> my_a = a[i,j] {id=read_a}
                <> a_less_than_zero = my_a < 0 {dep=read_a,inames=i:j}
                my_a = 2*my_a {id=twice_a,dep=read_a,if=a_less_than_zero}
                my_a = my_a+1 {id=aplus,dep=twice_a,if=a_less_than_zero}
                out[i,j] = 2*my_a {dep=aplus}
                """,
            [
                lp.GlobalArg("a", np.float32, shape=lp.auto),
                lp.GlobalArg("out", np.float32, shape=lp.auto),
                "..."
                ])

    ref_knl = knl

    lp.auto_test_vs_ref(ref_knl, ctx, knl,
            parameters=dict(
                n=200
                ))


if __name__ == "__main__":
    if len(sys.argv) > 1:
        exec(sys.argv[1])
    else:
        from py.test.cmdline import main
        main([__file__])

# vim: foldmethod=marker