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#
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"""Test code for vision package"""
from __future__ import print_function
import numpy as np
import tvm
from tvm import te
import topi
import topi.testing

_argsort_implement = {
    "generic": (topi.argsort, topi.generic.schedule_argsort),
    "gpu": (topi.cuda.argsort, topi.cuda.schedule_argsort),
}

_topk_implement = {
    "generic": (topi.topk, topi.generic.schedule_topk),
    "gpu": (topi.cuda.topk, topi.cuda.schedule_topk),
}

def verify_argsort(axis, is_ascend):
    dshape = (20, 100)
    data_dtype = "float32"
    data = te.placeholder(dshape, name="data", dtype=data_dtype)

    perm = np.arange(dshape[0] * dshape[1], dtype=data_dtype)
    np.random.shuffle(perm)
    np_data = perm.reshape(dshape)

    if is_ascend:
        np_indices = np.argsort(np_data, axis=axis)
    else:
        np_indices = np.argsort(-np_data, axis=axis)

    if axis == 0:
        np_indices = np_indices[:dshape[axis], :]
    else:
        np_indices = np_indices[:, :dshape[axis]]

    def check_device(device):
        ctx = tvm.context(device, 0)
        if not ctx.exist:
            print("Skip because %s is not enabled" % device)
            return
        print("Running on target: %s" % device)
        with tvm.target.create(device):
            fcompute, fschedule = topi.testing.dispatch(device, _argsort_implement)
            out = fcompute(data, axis=axis, is_ascend=is_ascend)
            s = fschedule(out)

        tvm_data = tvm.nd.array(np_data, ctx)
        tvm_out = tvm.nd.array(np.zeros(dshape, dtype=data_dtype), ctx)
        f = tvm.build(s, [data, out], device)
        f(tvm_data, tvm_out)
        tvm.testing.assert_allclose(tvm_out.asnumpy(), np_indices.astype(data_dtype), rtol=1e0)

    for device in ['llvm', 'cuda', 'opencl']:
        check_device(device)


def verify_topk(k, axis, ret_type, is_ascend, dtype):
    shape = (20, 100)
    data_dtype = "float32"
    data = te.placeholder(shape, name="data", dtype=data_dtype)

    np_data = np.random.uniform(size=shape).astype(data_dtype)
    if is_ascend:
        np_indices = np.argsort(np_data, axis=axis)
    else:
        np_indices = np.argsort(-np_data, axis=axis)
    kk = k if k >= 1 else shape[axis]
    if axis == 0:
        np_indices = np_indices[:kk, :]
        np_values = np.zeros(np_indices.shape).astype(data_dtype)
        for i in range(shape[1]):
            np_values[:, i] = np_data[np_indices[:, i], i]
    else:
        np_indices = np_indices[:, :kk]
        np_values = np.zeros(np_indices.shape).astype(data_dtype)
        for i in range(shape[0]):
            np_values[i, :] = np_data[i, np_indices[i, :]]
    np_indices = np_indices.astype(dtype)

    def check_device(device):
        ctx = tvm.context(device, 0)
        if not ctx.exist:
            print("Skip because %s is not enabled" % device)
            return
        print("Running on target: %s" % device)
        with tvm.target.create(device):
            fcompute, fschedule = topi.testing.dispatch(device, _topk_implement)
            outs = fcompute(data, k, axis, ret_type, is_ascend, dtype)
            outs = outs if isinstance(outs, list) else [outs]
            s = fschedule(outs)
        tvm_data = tvm.nd.array(np_data, ctx)
        tvm_res = []
        for t in outs:
            tvm_res.append(tvm.nd.empty(t.shape, dtype=t.dtype, ctx=ctx))
        f = tvm.build(s, [data] + outs, device)
        f(tvm_data, *tvm_res)
        if ret_type == "both":
            tvm.testing.assert_allclose(tvm_res[0].asnumpy(), np_values)
            tvm.testing.assert_allclose(tvm_res[1].asnumpy(), np_indices)
        elif ret_type == "values":
            tvm.testing.assert_allclose(tvm_res[0].asnumpy(), np_values)
        else:
            tvm.testing.assert_allclose(tvm_res[0].asnumpy(), np_indices)

    for device in ['llvm', 'cuda', 'opencl']:
        check_device(device)


def test_argsort():
    np.random.seed(0)
    for axis in [0, -1, 1]:
        verify_argsort(axis, True)
        verify_argsort(axis, False)


def test_topk():
    np.random.seed(0)
    for k in [0, 1, 5]:
        for axis in [0, -1, 1]:
            for ret_type in ["both", "values", "indices"]:
                verify_topk(k, axis, ret_type, True, "int64")
                verify_topk(k, axis, ret_type, False, "float32")


if __name__ == "__main__":
    test_argsort()
    test_topk()
