<|file_name|>test_relu.py<|end_file_name|><｜fim▁begin｜># Licensed to the Apache Software Foundation (ASF) under one
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"""Arm(R) Ethos(TM)-N integration relu tests"""

import numpy as np
import pytest
import tvm
from tvm import relay
from tvm.testing import requires_ethosn
from . import infrastructure as tei


def _get_model(shape, dtype, a_min, a_max):
    assert a_min >= np.iinfo(dtype).min and a_max <= np.iinfo(dtype).max
    a = relay.var("a", shape=shape, dtype=dtype)
    relu = relay.clip(a, a_min=a_min, a_max=a_max)
    return relu


@requires_ethosn
@pytest.mark.parametrize("dtype", ["uint8", "int8"])
def test_relu(dtype):
    trials = [
        ((1, 4, 4, 4), 65, 178, "uint8"),
        ((1, 8, 4, 2), 1, 254, "uint8"),
        ((1, 16), 12, 76, "uint8"),
        ((1, 4, 4, 4), 65, 125, "int8"),
        ((1, 8, 4, 2), -100, 100, "int8"),
        ((1, 16), -120, -20, "int8"),
    ]

    np.random.seed(0)
    for shape, a_min, a_max, trial_dtype in trials:
        if trial_dtype == dtype:
                        <|fim_middle|>



@requires_ethosn
def test_relu_failure():
    trials = [
        ((1, 4, 4, 4, 4), "uint8", 65, 78, "dimensions=5, dimensions must be <= 4"),
        ((1, 8, 4, 2), "int16", 1, 254, "dtype='int16', dtype must be either uint8, int8 or int32"),
        ((1, 8, 4, 2), "uint8", 254, 1, "Relu has lower bound > upper bound"),
        ((2, 2, 2, 2), "uint8", 1, 63, "batch size=2, batch size must = 1; "),
    ]

    for shape, dtype, a_min, a_max, err_msg in trials:
        model = _get_model(shape, dtype, a_min, a_max)
        mod = tei.make_ethosn_partition(model)
        tei.test_error(mod, {}, err_msg)
<｜fim▁end｜>
            inputs = {
                "a": tvm.nd.array(
                    np.random.randint(
                        low=np.iinfo(dtype).min,
                        high=np.iinfo(dtype).max + 1,
                        size=shape,
                        dtype=dtype,
                    )
                ),
            }
            outputs = []
            for npu in [False, True]:
                model = _get_model(inputs["a"].shape, dtype, a_min, a_max)
                mod = tei.make_module(model, {})
                outputs.append(tei.build_and_run(mod, inputs, 1, {}, npu=npu))

            tei.verify(outputs, dtype, 1)