inputs-with-special-names.https.any.js

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This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/inputs-with-special-names.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/inputs-with-special-names.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/inputs-with-special-names.https.any.html?npu - WPT Dashboard Interop Dashboard

// META: title=test input with special character names

// META: global=window

// META: variant=?cpu

// META: variant=?gpu

// META: variant=?npu

// META: script=../resources/utils.js

// META: timeout=long

'use strict';

// https://www.w3.org/TR/webnn/#api-mlgraphbuilder-input

let mlContext;

// Skip tests if WebNN is unimplemented.

promise_setup(async () => {

  assert_implements(navigator.ml, 'missing navigator.ml');

  mlContext = await navigator.ml.createContext(contextOptions);

});

const specialNameArray = [

  '12-L#!.☺',

  '🤦🏼‍♂️124DS#!F',

  // Escape Sequence

  'hello\n\t\r\b\f\v\'\"\0\\webnn',

  // Hexadecimal Escape Sequences

  // '\x41'→ 'A'

  '\x41\x41\x41',

  // Unicode & Hexadecimal Characters

  //   "\u00A9" → "©"

  //   "\xA9" → "©"

  //   "\u2665" → "♥"

  //   "\u2026" → "…"

  //   "\U0001F600" → 😀 (Grinning Face Emoji)

  '\u00A9\xA9\u2665\u2026',

  '\U0001F600'

];

specialNameArray.forEach((name) => {

  promise_test(async () => {

    const builder = new MLGraphBuilder(mlContext);

    const inputOperand = builder.input(name, {dataType: 'float32', shape: [4]});

    const outputOperand = builder.abs(inputOperand);

    const [inputTensor, outputTensor, mlGraph] = await Promise.all([

      mlContext.createTensor({

        dataType: 'float32',

        shape: [4],

        readable: true,

        writable: true,

}),

      mlContext.createTensor({dataType: 'float32', shape: [4], readable: true}),

      builder.build({'output': outputOperand})

]);

    const inputData = Float32Array.from([-2, -1, 1, 2]);

    mlContext.writeTensor(inputTensor, inputData);

    const inputs = {};

    inputs[name] = inputTensor;

    mlContext.dispatch(mlGraph, inputs, {'output': outputTensor});

    // Wait for graph execution to complete.

    await mlContext.readTensor(outputTensor);

    assert_array_equals(

        new Float32Array(await mlContext.readTensor(outputTensor)),

        Float32Array.from([2, 1, 1, 2]));

  }, `abs input with special character name '${name}'`);

});