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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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# software distributed under the License is distributed on an
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# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
import sys
import numpy as np
import pytest
import tvm
from tvm import relay
from tests.python.relay.aot.aot_test_utils import (
    AOTTestModel,
    AOT_CORSTONE300_RUNNER,
    generate_ref_data,
    compile_and_run,
)


@tvm.testing.requires_corstone300
@pytest.mark.parametrize(
    "data_shape_nhwc, kernel_size, num_filter, strides, padding, dilation",
    [
        ((1, 32, 32, 1), (3, 3), 12, 1, 0, 1),
        ((1, 32, 10, 3), (3, 3), 16, 1, 0, 1),
        ((1, 49, 10, 1), (10, 4), 64, (2, 1), (4, 1, 5, 1), 1),
        ((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 1),
        ((1, 32, 32, 16), (3, 3), 16, 1, 0, 1),
        ((1, 32, 32, 16), (3, 3), 16, 1, 0, 1),
        ((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 2),
        ((1, 32, 32, 16), (3, 3), 16, 1, (1, 1, 2, 2), 2),
        # bug https://github.com/apache/tvm/issues/9226
        ((1, 49, 10, 1), (10, 4), 64, (2, 2), (4, 1, 5, 1), 1),
        # from Visual Wake Word model
        ((1, 96, 96, 3), (3, 3), 8, (2, 2), (0, 0, 1, 1), 1),
        # from Image Classification model (one of the MLPerfTiny models)
        ((1, 16, 16, 32), (1, 1), 64, (2, 2), 0, 1),
        ((4, 16, 16, 8), (5, 5), 8, 2, (0, 4, 4, 0), 1),
        ((4, 16, 16, 8), (5, 5), 16, 2, (0, 4, 4, 0), 1),
        ((4, 16, 16, 8), (5, 5), 8, 2, 0, 1),
        ((4, 16, 16, 8), (5, 5), 16, 2, 0, 1),
        ((1, 16, 16, 8), (3, 3), 16, 2, (0, 0, 1, 1), 1),
        ((1, 16, 16, 8), (3, 3), 16, 2, (1, 1, 2, 2), 1),
        ((1, 16, 16, 8), (5, 5), 16, 2, (3, 3, 2, 2), 1),
        ((1, 16, 16, 8), (3, 3), 16, 2, (0, 1, 2, 3), 1),
    ],
)
@pytest.mark.parametrize("dtype", ["int8", "int16"])
def test_conv2d(data_shape_nhwc, kernel_size, num_filter, strides, padding, dilation, dtype):
    """Test a subgraph with a single conv2d operator."""
    ishape = data_shape_nhwc
    wshape = (*kernel_size, data_shape_nhwc[-1], num_filter)

    weight_data = np.random.randint(low=-10, high=10, size=wshape, dtype=dtype)

    input0 = relay.var("input", relay.TensorType(ishape, dtype))
    weight0 = relay.const(weight_data)
    out0 = relay.op.nn.conv2d(
        input0,
        weight0,
        kernel_size=kernel_size,
        strides=strides,
        padding=padding,
        dilation=(dilation, dilation),
        data_layout="NHWC",
        kernel_layout="HWIO",
        out_dtype="int32",
        out_layout="NHWC",
    )
    ref_mod = tvm.IRModule.from_expr(relay.Function([input0], out0))

    input1 = relay.var("input", relay.TensorType(ishape, dtype))
    weight1 = relay.const(np.moveaxis(weight_data, 2, -1))
    out1 = relay.op.nn.conv2d(
        input1,
        weight1,
        kernel_size=kernel_size,
        strides=strides,
        padding=padding,
        dilation=(dilation, dilation),
        data_layout="NHWC",
        kernel_layout="HWOI",
        out_dtype="int32",
        out_layout="NHWC",
    )
    mod = tvm.IRModule.from_expr(relay.Function([input1], out1))

    inputs = {"input": np.random.randint(low=-128, high=127, size=ishape, dtype=dtype)}
    output_list = generate_ref_data(ref_mod, inputs)

    compile_and_run(
        AOTTestModel(module=mod, inputs=inputs, outputs=output_list),
        runner=AOT_CORSTONE300_RUNNER,
        interface_api="c",
        use_unpacked_api=True,
        target_opts={
            "-keys": "arm_cpu",
            "-mcpu": "cortex-m7",
        },
    )


@tvm.testing.requires_corstone300
@pytest.mark.parametrize(
    "data_shape_nwc, kernel_size, num_filter, strides, padding",
    [
        ((1, 32, 12), 3, 16, 1, 0),
        ((3, 12, 10), 4, 24, 1, 0),
        ((1, 7, 7), 3, 5, 1, 0),
        ((1, 10, 2), 4, 4, 2, (1, 1)),
        ((1, 20, 2), 4, 4, 2, (0, 1)),
        ((1, 16, 4), 1, 12, 1, (1, 0)),
        ((1, 24, 16), 1, 32, 3, (2, 2)),
    ],
)
@pytest.mark.parametrize("dtype", ["int8", "int16"])
def test_conv1d(data_shape_nwc, kernel_size, num_filter, strides, padding, dtype):
    """Test a subgraph with a single conv1d operator."""
    ishape = data_shape_nwc
    wshape = (kernel_size, data_shape_nwc[-1], num_filter)

    weight_data = np.random.randint(low=-10, high=10, size=wshape, dtype=dtype)

    input0 = relay.var("input", relay.TensorType(ishape, dtype))
    weight0 = relay.const(weight_data)
    out0 = relay.op.nn.conv1d(
        input0,
        weight0,
        strides=strides,
        padding=padding,
        data_layout="NWC",
        kernel_layout="WIO",
        out_dtype="int32",
        out_layout="NWC",
    )
    ref_mod = tvm.IRModule.from_expr(relay.Function([input0], out0))

    input1 = relay.var("input", relay.TensorType(ishape, dtype))
    weight1 = relay.const(np.moveaxis(weight_data, 1, -1))
    out1 = relay.op.nn.conv1d(
        input1,
        weight1,
        strides=strides,
        padding=padding,
        data_layout="NWC",
        kernel_layout="WOI",
        out_dtype="int32",
        out_layout="NWC",
    )
    mod = tvm.IRModule.from_expr(relay.Function([input1], out1))

    inputs = {"input": np.random.randint(low=-128, high=127, size=ishape, dtype=dtype)}
    output_list = generate_ref_data(ref_mod, inputs)

    compile_and_run(
        AOTTestModel(module=mod, inputs=inputs, outputs=output_list),
        runner=AOT_CORSTONE300_RUNNER,
        interface_api="c",
        use_unpacked_api=True,
        target_opts={
            "-keys": "arm_cpu",
            "-mcpu": "cortex-m7",
        },
    )


@tvm.testing.requires_corstone300
@pytest.mark.parametrize(
    "M, K, N",
    [
        (1, 32, 64),
        (3, 12, 10),
    ],
)
def test_dense(M, K, N):
    """Test a subgraph with a single dense operator."""
    ishape = (M, K)
    wshape = (N, K)

    input0 = relay.var("input", relay.TensorType(ishape, "int8"))
    dense_f = relay.op.nn.batch_flatten(input0)
    weight0 = relay.const(np.random.randint(low=-10, high=10, size=wshape, dtype="int8"))
    out = relay.op.nn.dense(dense_f, weight0, out_dtype="int32")

    mod = tvm.IRModule.from_expr(relay.Function([input0], out))
    inputs = {"input": np.random.randint(low=-128, high=127, size=ishape, dtype="int8")}
    output_list = generate_ref_data(mod, inputs)

    compile_and_run(
        AOTTestModel(module=mod, inputs=inputs, outputs=output_list),
        runner=AOT_CORSTONE300_RUNNER,
        interface_api="c",
        use_unpacked_api=True,
        target_opts={
            "-keys": "arm_cpu",
            "-mcpu": "cortex-m7",
        },
    )


@tvm.testing.requires_corstone300
@pytest.mark.parametrize(
    "data_shape_nhwc, pool_size, strides, padding",
    [
        ((1, 32, 32, 1), (3, 3), 1, 0),
        ((1, 32, 20, 4), (3, 3), (2, 2), 0),
    ],
)
def test_maxpool_2d(data_shape_nhwc, pool_size, strides, padding):
    """Test a subgraph with a single maxpool_2d operator."""

    ishape = data_shape_nhwc

    input0 = relay.var("input", relay.TensorType(ishape, "int8"))
    out = relay.op.nn.max_pool2d(input0, pool_size, layout="NHWC", strides=strides, padding=padding)

    mod = tvm.IRModule.from_expr(relay.Function([input0], out))
    inputs = {"input": np.random.randint(low=-128, high=127, size=ishape, dtype="int8")}
    output_list = generate_ref_data(mod, inputs)

    compile_and_run(
        AOTTestModel(module=mod, inputs=inputs, outputs=output_list),
        runner=AOT_CORSTONE300_RUNNER,
        interface_api="c",
        use_unpacked_api=True,
        target_opts={
            "-keys": "arm_cpu",
            "-mcpu": "cortex-m7",
        },
    )


@tvm.testing.requires_corstone300
@pytest.mark.parametrize(
    "data_shape_nwc, pool_size, strides, padding",
    [
        ((1, 32, 1), 3, 1, 0),
        ((1, 20, 4), 3, 2, 0),
    ],
)
def test_maxpool_1d(data_shape_nwc, pool_size, strides, padding):
    """Test a subgraph with a single maxpool_1d operator."""
    ishape = data_shape_nwc

    input0 = relay.var("input", relay.TensorType(ishape, "int8"))
    out = relay.op.nn.max_pool1d(input0, pool_size, layout="NWC", strides=strides, padding=padding)

    mod = tvm.IRModule.from_expr(relay.Function([input0], out))
    inputs = {"input": np.random.randint(low=-128, high=127, size=ishape, dtype="int8")}
    output_list = generate_ref_data(mod, inputs)

    compile_and_run(
        AOTTestModel(module=mod, inputs=inputs, outputs=output_list),
        runner=AOT_CORSTONE300_RUNNER,
        interface_api="c",
        use_unpacked_api=True,
        target_opts={
            "-keys": "arm_cpu",
            "-mcpu": "cortex-m7",
        },
    )


@tvm.testing.requires_corstone300
@pytest.mark.parametrize(
    "data_shape_nchw, pool_size, strides, padding",
    [
        ((1, 1, 32, 32), (3, 3), 1, 0),
        ((1, 4, 32, 20), (3, 3), (2, 2), 0),
    ],
)
def test_avgpool_2d(data_shape_nchw, pool_size, strides, padding):
    """Test a subgraph with a single avgpool_2d operator."""

    ishape = data_shape_nchw

    input0 = relay.var("input", relay.TensorType(ishape, "int32"))
    out0 = relay.nn.avg_pool2d(input0, pool_size, layout="NCHW", strides=strides, padding=padding)
    ref_mod = tvm.IRModule.from_expr(relay.Function([input0], out0))

    input1 = relay.var("input", relay.TensorType(ishape, "int16"))
    out1 = relay.op.nn.avg_pool2d(
        input1, pool_size, layout="NCHW", strides=strides, padding=padding
    )
    mod = tvm.IRModule.from_expr(relay.Function([input1], out1))

    input_data = np.random.randint(low=-128, high=127, size=ishape, dtype="int32")
    inputs = {"input": input_data}
    output_list = generate_ref_data(ref_mod, inputs)

    compile_and_run(
        AOTTestModel(
            module=mod, inputs={"input": input_data.astype(dtype="int16")}, outputs=output_list
        ),
        runner=AOT_CORSTONE300_RUNNER,
        interface_api="c",
        use_unpacked_api=True,
        target_opts={
            "-keys": "arm_cpu",
            "-mcpu": "cortex-m7",
        },
    )


@tvm.testing.requires_corstone300
@pytest.mark.parametrize(
    "data_shape_ncw, pool_size, strides, padding",
    [
        ((1, 1, 32), 3, 1, 0),
        ((1, 4, 20), 3, 2, 2),
    ],
)
def test_avgpool_1d(data_shape_ncw, pool_size, strides, padding):
    """Test a subgraph with a single avgpool_1d operator."""

    ishape = data_shape_ncw

    input0 = relay.var("input", relay.TensorType(ishape, "int32"))
    out0 = relay.op.nn.avg_pool1d(input0, pool_size, layout="NCW", strides=strides, padding=padding)
    ref_mod = tvm.IRModule.from_expr(relay.Function([input0], out0))

    input1 = relay.var("input", relay.TensorType(ishape, "int16"))
    out1 = relay.op.nn.avg_pool1d(input1, pool_size, layout="NCW", strides=strides, padding=padding)
    mod = tvm.IRModule.from_expr(relay.Function([input1], out1))

    input_data = np.random.randint(low=-10, high=10, size=ishape, dtype="int32")
    inputs = {"input": input_data}
    output_list = generate_ref_data(ref_mod, inputs)

    compile_and_run(
        AOTTestModel(
            module=mod, inputs={"input": input_data.astype(dtype="int16")}, outputs=output_list
        ),
        runner=AOT_CORSTONE300_RUNNER,
        interface_api="c",
        use_unpacked_api=True,
        target_opts={
            "-keys": "arm_cpu",
            "-mcpu": "cortex-m7",
        },
    )


if __name__ == "__main__":
    sys.exit(pytest.main([__file__] + sys.argv[1:]))