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"""Test code for relu activation"""
import os
import numpy as np
import tvm
from tvm import te
from tvm import topi
import tvm.topi.testing
from tvm.topi.utils import get_const_tuple
from tvm.contrib.nvcc import have_fp16

import tvm.testing


def verify_relu(m, n, dtype="float32"):
    A = te.placeholder((m, n), name="A", dtype=dtype)
    B = topi.nn.relu(A)

    a_np = np.random.uniform(low=-1.0, high=1.0, size=get_const_tuple(A.shape)).astype(A.dtype)
    b_np = a_np * (a_np > 0)

    def check_device(device, ctx):
        if dtype == "float16" and device == "cuda" and not have_fp16(tvm.gpu(0).compute_version):
            print("Skip because %s does not have fp16 support" % device)
            return
        print("Running on target: %s" % device)
        with tvm.target.Target(device):
            s = tvm.topi.testing.get_elemwise_schedule(device)(B)

        a = tvm.nd.array(a_np, ctx)
        b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
        foo = tvm.build(s, [A, B], device, name="relu")
        foo(a, b)
        tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)

    for device, ctx in tvm.testing.enabled_targets():
        check_device(device, ctx)


def verify_leaky_relu(m, alpha):
    A = te.placeholder((m,), name="A")
    B = topi.nn.leaky_relu(A, alpha)
    s = te.create_schedule([B.op])

    a_np = np.random.uniform(size=get_const_tuple(A.shape)).astype(A.dtype)
    b_np = a_np * (a_np > 0) + a_np * (a_np < 0) * alpha
    ctx = tvm.cpu(0)
    a = tvm.nd.array(a_np, ctx)
    b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
    foo = tvm.build(s, [A, B], "llvm", name="leaky_relu")
    foo(a, b)
    tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)


def verify_prelu(x, w, axis, weight_reshape):
    X = te.placeholder((x), name="X")
    W = te.placeholder((w), name="W")
    x_np = np.random.uniform(low=-1.0, high=1.0, size=get_const_tuple(X.shape)).astype(X.dtype)
    w_np = np.random.uniform(low=-1.0, high=1.0, size=get_const_tuple(W.shape)).astype(W.dtype)

    def _prelu_numpy(x, W):
        return (x < 0) * (x * W.reshape(weight_reshape)) + (x >= 0) * x

    B = topi.nn.prelu(X, W, axis)
    s = te.create_schedule([B.op])

    ctx = tvm.cpu(0)
    x_tvm = tvm.nd.array(x_np, ctx)
    w_tvm = tvm.nd.array(w_np, ctx)

    b = tvm.nd.array(np.zeros(get_const_tuple(X.shape), dtype=B.dtype), ctx)
    foo = tvm.build(s, [X, W, B], "llvm", name="prelu")
    foo(x_tvm, w_tvm, b)
    out_np = _prelu_numpy(x_np, w_np)
    tvm.testing.assert_allclose(b.asnumpy(), out_np, rtol=1e-5)


@tvm.testing.uses_gpu
def test_relu():
    verify_relu(10, 128, "float32")
    verify_relu(128, 64, "float16")


@tvm.testing.uses_gpu
def test_schedule_big_array():
    verify_relu(1024 * 100, 512)


def test_leaky_relu():
    verify_leaky_relu(100, 0.1)


def test_prelu():
    verify_prelu((1, 3, 2, 2), (3,), 1, (3, 1, 1))
    verify_prelu((1, 3, 2, 2), (2,), 2, (2, 1))
    verify_prelu((1, 3), (3,), 1, (3,))


if __name__ == "__main__":
    test_schedule_big_array()
    test_relu()
    test_leaky_relu()
    test_prelu()
