#!/usr/bin/env python3
#
# Authors: Christoph Lehner 2020
#
# Desc.: Test small core features that are not sufficiently complex
#        to require a separate test file.  These tests need to be fast.
#
import gpt as g
import numpy as np
import sys, cgpt

# random
rng = g.random("test")

# grid
L = [8, 12, 24, 24]
for rb in [g.redblack, g.full]:
    grid_dp = g.grid(L, g.double, rb)
    grid_sp = g.grid(L, g.single, rb)

    # test fields
    l_dp = rng.cnormal(g.vcolor(grid_dp))
    l_sp = g.convert(l_dp, g.single)

    # and convert precision
    l_dp_prime = g.convert(l_sp, g.double)
    eps2 = g.norm2(l_dp - l_dp_prime) / g.norm2(l_dp)
    assert eps2 < 1e-14
    eps2 = g.norm2(l_dp[0, 0, 0, 0] - l_sp[0, 0, 0, 0])
    assert eps2 < 1e-14

    # communication test
    x = np.array([l_dp.grid.processor * 0.23 + 3], dtype=np.complex128)
    eps = np.linalg.norm(l_dp.grid.reduce(x, lambda a, b: a.__iadd__(b)) - l_dp.grid.globalsum(x))
    assert eps < 1e-14


################################################################################
# Test mview
################################################################################
c = g.coordinates(l_dp)
x = l_dp[c]
mv = g.mview(x)
assert mv.itemsize == 1 and mv.shape[0] == len(mv)
assert sys.getrefcount(x) == 3
del mv
assert sys.getrefcount(x) == 2


################################################################################
# Test pinning
################################################################################
l_v = g.complex(grid_sp)
pin = g.pin(l_v, g.accelerator)
del l_v
del pin


################################################################################
# Test assignments
################################################################################
pos = g.coordinates(l_dp)
lhs = g.lattice(l_dp)


def assign_copy():
    g.copy(lhs, l_dp)


def assign_pos():
    lhs[pos] = l_dp[pos]


def assign_pos_view():
    plan = g.copy_plan(lhs, l_dp)
    plan.destination += lhs.view[pos]
    plan.source += l_dp.view[pos]
    plan = plan()
    info = plan.info()
    for rank_dst, rank_src in info:
        assert rank_dst == rank_src
        assert rank_dst == lhs.grid.processor
        info_rank = info[(rank_dst, rank_src)]
        for index in info_rank:
            info_index = info_rank[index]
            # Make sure that after optimization only a single memcpy is needed
            assert info_index["blocks"] == 1
    plan(lhs, l_dp)


for method in [assign_copy, assign_pos, assign_pos_view]:
    lhs[:] = 0
    method()
    eps2 = g.norm2(lhs - l_dp) / g.norm2(l_dp)
    assert eps2 < 1e-25

################################################################################
# Test exp_ixp
################################################################################
# multiply momentum phase in l
p = 2.0 * np.pi * np.array([1, 2, 3, 4]) / L
exp_ixp = g.exp_ixp(p)

# Test one component
xc = (2, 3, 1, 5)
x = np.array(list(xc))
ref = np.exp(1j * np.dot(p, x)) * l_dp[xc]

val = g.eval(exp_ixp * l_dp)[xc]
eps = g.norm2(ref - val)
g.message("Reference value test: ", eps)
assert eps < 1e-25

# single/double
eps = g.norm2(exp_ixp * l_sp - g.convert(exp_ixp * l_dp, g.single)) / g.norm2(l_sp)
g.message("Momentum phase test single/double: ", eps)
assert eps < 1e-10

eps = g.norm2(g.inv(exp_ixp) * exp_ixp * l_dp - l_dp) / g.norm2(l_dp)
g.message("Momentum inverse test: ", eps)
assert eps < 1e-20

eps = g.norm2(g.adj(exp_ixp) * exp_ixp * l_dp - l_dp) / g.norm2(l_dp)
g.message("Momentum adj test: ", eps)
assert eps < 1e-20

eps = g.norm2(g.adj(exp_ixp * exp_ixp) * exp_ixp * exp_ixp * l_dp - l_dp) / g.norm2(l_dp)
g.message("Momentum adj test (2): ", eps)
assert eps < 1e-20

# arbitrary momentum / sinc reconstruction check
p_arbitrary = 2.0 * np.pi * np.array([1.5, 2.3, 3.2, 4.1]) / L
x_origin = np.array([7, 11, 4, 3])
exp_ixp_rel = g.exp_ixp(p_arbitrary, origin=x_origin)
xc = (2, 3, 1, 5)
x = np.array(list(xc)) - x_origin
for i in range(4):
    while x[i] < -L[i] / 2:
        x[i] += L[i]
ref = np.exp(1j * np.dot(p_arbitrary, x)) * l_dp[xc]
val = g.eval(exp_ixp_rel * l_dp)[xc]
eps = g.norm2(ref - val)
g.message("Reference value test (arbitrary momentum with origin): ", eps)
assert eps < 1e-25

################################################################################
# Test slice sums
################################################################################
for lattice_object in [
    g.complex(grid_dp),
    g.vcomplex(grid_dp, 10),
    g.vspin(grid_dp),
    g.vcolor(grid_dp),
    g.vspincolor(grid_dp),
    g.mspin(grid_dp),
    g.mcolor(grid_dp),
    g.mspincolor(grid_dp),
]:
    g.message(f"Testing slice with random {lattice_object.describe()}")
    obj_list = [g.copy(lattice_object) for _ in range(3)]
    rng.cnormal(obj_list)

    for dimension in range(4):
        tmp = g.slice(obj_list, dimension)
        full_sliced = np.array([[g.util.tensor_to_value(v) for v in obj] for obj in tmp])

        for n, obj in enumerate(obj_list):
            tmp = g.slice(obj, dimension)
            sliced = np.array([g.util.tensor_to_value(v) for v in tmp])
            assert np.allclose(full_sliced[n], sliced, atol=0.0, rtol=1e-13)

            sliced_numpy = np.array(
                [
                    np.sum(
                        obj[
                            slice(0, L[0]) if dimension != 0 else x,
                            slice(0, L[1]) if dimension != 1 else x,
                            slice(0, L[2]) if dimension != 2 else x,
                            slice(0, L[3]) if dimension != 3 else x,
                        ],
                        axis=0,
                    )
                    for x in range(L[dimension])
                ]
            )
            assert np.allclose(full_sliced[n], sliced_numpy, atol=0.0, rtol=1e-12)


################################################################################
# Test FFT
################################################################################
fft_l_sp = g.eval(g.fft() * l_sp)
eps = g.norm2(g.adj(g.fft()) * fft_l_sp - l_sp) / g.norm2(l_sp)
g.message("FFTinv * FFT:", eps)
assert eps < 1e-12

eps = g.norm2(g.sum(exp_ixp * l_sp) / np.prod(L) - fft_l_sp[1, 2, 3, 4])
g.message("FFT forward test:", eps)
assert eps < 1e-12

fft_mom_A = g.slice(g.exp_ixp(2.0 * np.pi * np.array([1, 2, 3, 0]) / L) * l_sp, 3) / np.prod(L[0:3])
fft_mom_B = [g.vcolor(x) for x in g.eval(g.fft([0, 1, 2]) * l_sp)[1, 2, 3, 0 : L[3]]]
for t in range(L[3]):
    eps = g.norm2(fft_mom_A[t] - fft_mom_B[t])
    assert eps < 1e-12


################################################################################
# Test correlate
################################################################################
def correlate_test_3d(a, b, x):
    # c[x] = (1/vol) sum_y a[y]*adj(b[y+x])
    bprime = g(g.adj(b))
    L = a.grid.gdimensions
    vol = L[0] * L[1] * L[2]
    for i in range(3):
        # see core test: dst = g.cshift(src, 0, 1) -> dst[x] = src[x+1]
        bprime = g.cshift(bprime, i, x[i])  # bprime[y] = b[y+x]
    return g.slice(a * bprime, 3)[x[3]] / vol


def correlate_test_4d(a, b, x):
    # c[x] = (1/vol) sum_y a[y]*adj(b[y+x])
    bprime = g(g.adj(b))
    L = a.grid.gdimensions
    vol = L[0] * L[1] * L[2] * L[3]
    for i in range(4):
        # see core test: dst = g.cshift(src, 0, 1) -> dst[x] = src[x+1]
        bprime = g.cshift(bprime, i, x[i])  # bprime[y] = b[y+x]
    return g.sum(a * bprime) / vol


A, B = rng.cnormal([g.complex(grid_dp) for i in range(2)])
eps = abs(g.correlate(A, B, [0, 1, 2])[1, 0, 3, 2] - correlate_test_3d(A, B, [1, 0, 3, 2]))
g.message(f"Test correlate 3d: {eps}")
assert eps < 1e-13
eps = abs(g.correlate(A, B)[1, 0, 3, 2] - correlate_test_4d(A, B, [1, 0, 3, 2]))
g.message(f"Test correlate 4d: {eps}")
assert eps < 1e-13

################################################################################
# Test vcomplex
################################################################################
va = g.vcomplex(grid_sp, 30)
vb = g.lattice(va)
va[:] = g.vcomplex([1] * 15 + [0.5] * 15, 30)
vb[:] = g.vcomplex([0.5] * 5 + [1.0] * 20 + [0.2] * 5, 30)
va @= 0.5 * va + 0.5 * vb
assert abs(va[0, 0, 0, 0][3] - 0.75) < 1e-6
assert abs(va[0, 0, 0, 0][18] - 0.75) < 1e-6
assert abs(va[0, 0, 0, 0][28] - 0.35) < 1e-6

################################################################################
# MPI
################################################################################
grid_sp.barrier()
nodes = grid_sp.globalsum(1)
assert nodes == grid_sp.Nprocessors
a = np.array([[1.0, 2.0, 3.0], [4, 5, 6j]], dtype=np.complex64)
b = np.copy(a)
grid_sp.globalsum(a)
eps = a / nodes - b
assert np.linalg.norm(eps) < 1e-7


################################################################################
# Test fast versus slow code paths to set all elements to a number
################################################################################
src = g.mspincolor(grid_sp)
new = g.mspincolor(grid_sp)
n = complex(3, 5)
src[:] = n
for x in range(L[0]):
    new[x, :, :, :] = n
assert abs(src[2, 3, 1, 5, 1, 2, 0, 1] - n) < 1e-7
assert g.norm2(src - new) / g.norm2(new) < 1e-7


################################################################################
# Test Cshifts
################################################################################
# create a complex lattice on the grid
src = g.complex(grid_sp)

# zero out all points and set the value at global position 0,0,0,0 to 2
src[:] = 0
src[0, 0, 0, 0] = complex(2, 1)

# create a new lattice that is compatible with another
new = g.lattice(src)

# create a new lattice that is a copy of another
original = g.copy(src)

# or copy the contents from one lattice to another
g.copy(new, src)

# cshift into a new lattice dst
dst = g.cshift(src, 0, 1)
# dst[x] = src[x+1] -> src[0] == dst[15]
assert abs(dst[7, 0, 0, 0] - complex(2, 1)) < 1e-6

################################################################################
# Test multi inner_product
################################################################################
for grid in [grid_dp, grid_sp]:
    for dtype in [g.vspincolor, lambda grid: g.vcomplex(grid, 12), g.complex]:
        left = [dtype(grid) for i in range(2)]
        right = [dtype(grid) for i in range(3)]
        rng.cnormal([left, right])
        host_result = g.rank_inner_product(left, right, False)
        acc_result = g.rank_inner_product(left, right, True)
        eps = np.linalg.norm(host_result - acc_result) / np.linalg.norm(host_result)
        g.message(f"Test multi inner product host<>accelerator: {eps}")
        assert eps < 1e-13
        for i in range(2):
            for j in range(3):
                host_result_individual = g.rank_inner_product(left[i], right[j], False)
                acc_result_individual = g.rank_inner_product(left[i], right[j], True)
                eps = abs(host_result_individual - host_result[i, j]) / abs(host_result[i, j])
                assert eps < 1e-13
                eps = abs(acc_result_individual - acc_result[i, j]) / abs(acc_result[i, j])
                assert eps < 1e-13
                if i == 0 and j == 0:
                    ref = np.vdot(
                        left[i][:].astype(np.complex128),
                        right[j][:].astype(np.complex128),
                    )
                    eps = abs(host_result_individual - ref) / abs(ref)
                    assert eps < 1e-12

################################################################################
# Test multi linear_combination against expression engine
################################################################################
for grid in [grid_sp, grid_dp]:
    nbasis = 7
    nblock = 3
    nvec = 2
    basis = [g.vcomplex(grid, 8) for i in range(nbasis)]
    rng.cnormal(basis)
    dst = [g.vcomplex(grid, 8) for i in range(nvec)]
    coef = [[rng.cnormal() for i in range(nbasis)] for j in range(nvec)]
    # multi
    g.linear_combination(dst, basis, coef, nblock)
    for j in range(nvec):
        ref = g.vcomplex(grid, 8)
        ref[:] = 0
        for i in range(nbasis):
            ref += coef[j][i] * basis[i]
        eps2 = g.norm2(dst[j] - ref) / g.norm2(ref)
        g.message(f"Test linear combination of vector {j}: {eps2}")
        assert eps2 < 1e-13


################################################################################
# Test bilinear_combination against expression engine
################################################################################
for grid in [grid_sp, grid_dp]:
    left = [g.complex(grid) for i in range(3)]
    right = [g.complex(grid) for i in range(3)]
    result_bilinear = [g.complex(grid) for i in range(3)]
    rng.cnormal([left, right])
    result = [
        g.eval(left[1] * right[2] - left[2] * right[1]),
        g.eval(left[2] * right[0] - left[0] * right[2]),
        g.eval(left[0] * right[1] + left[2] * right[0]),
    ]
    g.bilinear_combination(
        result_bilinear,
        left,
        right,
        [[1.0, -1.0], [1.0, -1.0], [1.0, 1.0]],
        [[1, 2], [2, 0], [0, 2]],
        [[2, 1], [0, 2], [1, 0]],
    )
    for j in range(len(result)):
        eps2 = g.norm2(result[j] - result_bilinear[j]) / g.norm2(result[j])
        g.message(f"Test bilinear combination of vector {j}: {eps2}")
        assert eps2 < 1e-13


################################################################################
# Test where
################################################################################
grid = grid_dp
sel = g.complex(grid)
rng.uniform_int(sel, min=0, max=1)

yes = g.vcomplex(grid, 8)
no = g.vcomplex(grid, 8)
rng.cnormal([yes, no])

w = g.where(sel, yes, no)

eps = np.linalg.norm(w[:] - np.where(sel[:] != 0.0, yes[:], no[:])) / np.linalg.norm(w[:])
g.message(f"Test gpt.where <> numpy.where with a selection of {g.norm2(sel)} points: {eps}")
assert eps == 0.0


################################################################################
# Test comparators
################################################################################
a = g.complex(grid)
b = g.complex(grid)
rng.cnormal([a, b])

c = a < b
eps = np.linalg.norm(c[:] - (a[:] < b[:]).astype(np.int32)) / np.linalg.norm(c[:])
g.message(f"Test a < b from gpt<>numpy: {eps}")
assert eps == 0.0

eps = g.norm2((b < a) - (a > b)) ** 0.5
g.message(f"Test a < b compatible with b > a: {eps}")
assert eps == 0.0

################################################################################
# Test basis rotate against linear combination
################################################################################
a = [g.complex(grid) for i in range(3)]
b = [g.complex(grid) for i in range(3)]
rng.cnormal(a)
c = [g.copy(x) for x in a]
Qt = np.array([[1, 2, 3], [9, 7, 13], [15, 17, 19]], dtype=np.complex128)
for i in range(3):
    g.linear_combination(b[i], a, Qt[i])
g.rotate(a, Qt, 0, 3, 0, 3, True)
g.rotate(c, Qt, 0, 3, 0, 3, False)
for i in range(3):
    eps = g.norm2(a[i] - b[i]) / g.norm2(a[i])
    g.message(f"Test basis rotate {i} on accelerator: {eps}")
    assert eps < 1e-13
    eps = g.norm2(c[i] - b[i]) / g.norm2(a[i])
    g.message(f"Test basis rotate {i} on host: {eps}")
    assert eps < 1e-13


################################################################################
# Test sparse domain
################################################################################
for l in [l_dp, l_sp]:
    nsparse = int(0.01 * l.grid.gsites / l.grid.Nprocessors)
    sdomain = g.domain.sparse(
        l.grid,
        rng.choice(g.coordinates(l), nsparse),
    )

    # test project/promote
    s = g(sdomain.project * l)
    l_prime = g(sdomain.promote * s)

    # test weight (choice draws with replacement) and its caching
    sweight = sdomain.weight()
    sweight2 = sdomain.weight()
    assert sweight is sweight2

    nsparse_global = g.sum(sweight)
    assert abs(nsparse_global - nsparse * l.grid.Nprocessors) < l.grid.precision.eps * 100

    eps = g.norm2(sweight * (l - l_prime)) ** 0.5
    g.message(f"Test sparse reconstruction: {eps}")
    assert eps < l.grid.precision.eps * 100

    ft_original = g.sum(sweight * exp_ixp * l)
    ft_sparse = g.sum(sdomain.exp_ixp(p) * s)
    eps = g.norm2(ft_original - ft_sparse) ** 0.5
    g.message(f"Test sparse momentum implementation: {eps}")
    assert eps < l.grid.precision.eps * 1e4

    ft_original = g.sum(sweight * exp_ixp_rel * l)
    ft_sparse = g.sum(sdomain.exp_ixp(p_arbitrary, origin=x_origin) * s)
    eps = g.norm2(ft_original - ft_sparse) ** 0.5
    g.message(f"Test sparse momentum implementation (arbitrary with origin): {eps}")
    assert eps < l.grid.precision.eps * 1e4

    sl_original = g.slice(sweight * l, 3)
    sl_sparse = sdomain.slice(s, 3)
    eps = sum([g.norm2(x - y) ** 0.5 for x, y in zip(sl_original, sl_sparse)])
    g.message(f"Test sparse slice: {eps}")
    assert eps < l.grid.precision.eps * 1e3

################################################################################
# Test mem_report
################################################################################
g.mem_report()
