# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
"""Test code for transposed convolution."""
import numpy as np
import itertools
import tvm
from tvm import te
from tvm import topi
import tvm.topi.testing
from tvm.contrib.pickle_memoize import memoize
from tvm.topi.utils import get_const_tuple
import tvm.testing

_conv1d_transpose_ncw_implement = {
    "generic": (topi.nn.conv1d_transpose_ncw, topi.generic.schedule_conv1d_transpose_ncw),
    "gpu": (topi.cuda.conv1d_transpose_ncw, topi.cuda.schedule_conv1d_transpose_ncw),
}


def verify_conv1d_transpose_ncw(
    batch, in_channel, in_size, num_filter, kernel, stride, padding, output_padding
):
    in_width = in_size
    A = te.placeholder((batch, in_channel, in_width), name="A")
    W = te.placeholder((in_channel, num_filter, kernel), name="W")

    a_shape = get_const_tuple(A.shape)
    w_shape = get_const_tuple(W.shape)
    dtype = A.dtype

    @memoize("topi.tests.test_topi_conv1d_transpose.verify_conv1d_transpose_ncw")
    def get_ref_data():
        a_np = np.random.uniform(size=a_shape).astype(dtype)
        w_np = np.random.uniform(size=w_shape).astype(dtype)
        b_np = tvm.topi.testing.conv1d_transpose_ncw_python(
            a_np, w_np, stride, padding, output_padding
        )
        c_np = np.maximum(b_np, 0)
        return a_np, w_np, b_np, c_np

    a_np, w_np, b_np, c_np = get_ref_data()

    def check_device(device, ctx):
        ctx = tvm.context(device, 0)
        with tvm.target.Target(device):
            fcompute, fschedule = tvm.topi.testing.dispatch(device, _conv1d_transpose_ncw_implement)
            B = fcompute(A, W, stride, padding, A.dtype, output_padding)
            C = topi.nn.relu(B)
            s1 = fschedule([B])
            s2 = fschedule([C])
        a = tvm.nd.array(a_np, ctx)
        w = tvm.nd.array(w_np, ctx)
        b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
        c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx)

        func1 = tvm.build(s1, [A, W, B], device)
        func2 = tvm.build(s2, [A, W, C], device)
        func1(a, w, b)
        func2(a, w, c)
        tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)

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


@tvm.testing.uses_gpu
def test_conv1d_transpose_ncw():
    verify_conv1d_transpose_ncw(1, 3, 224, 32, 5, 1, 0, (0,))
    verify_conv1d_transpose_ncw(1, 3, 224, 32, 7, 1, 2, (0,))
    verify_conv1d_transpose_ncw(1, 3, 224, 32, 5, 2, 1, (0,))
    verify_conv1d_transpose_ncw(1, 3, 224, 32, 5, 2, 1, (1,))
    verify_conv1d_transpose_ncw(1, 3, 224, 32, 5, 2, 0, (0,))
    verify_conv1d_transpose_ncw(1, 32, 32, 128, 5, 1, 0, (0,))
    verify_conv1d_transpose_ncw(1, 32, 32, 128, 5, 2, 1, (0,))
    verify_conv1d_transpose_ncw(1, 1, 1024, 1, 512, 1, 256, (0,))
    verify_conv1d_transpose_ncw(1, 1, 1024, 1, 512, 2, 256, (0,))
    verify_conv1d_transpose_ncw(1, 1, 1024, 1, 512, 5, 256, (0,))
    verify_conv1d_transpose_ncw(1, 1, 1024, 1, 512, 5, 256, (3,))
    verify_conv1d_transpose_ncw(1, 2, 1024, 1, 128, 128, 0, (0,))
    verify_conv1d_transpose_ncw(1, 1, 1024, 2, 128, 128, 0, (0,))
    verify_conv1d_transpose_ncw(1, 1, 1024, 2, 2, 2, 0, (0,))
    verify_conv1d_transpose_ncw(1, 1, 10, 1, 5, 1, (0, 3), (0,))
    verify_conv1d_transpose_ncw(1, 1, 10, 1, 5, 1, (1, 3), (0,))
    verify_conv1d_transpose_ncw(1, 1, 10, 1, 5, 1, (2, 3), (0,))
    verify_conv1d_transpose_ncw(1, 257, 128, 1, 512, 128, 256, (0,))


if __name__ == "__main__":
    test_conv1d_transpose_ncw()
