David Snelling
bcd75b4b61
- Refactored `applyTensorFlowPatch` in `textEncoding.ts` to introduce a unified `Platform` class supporting both Node.js and browser environments with native `TextEncoder`/`TextDecoder`. - Modified global object handling to target `PlatformNode` in Node.js and `PlatformBrowser` in browser environments. - Added `getTextEncoder` and `getTextDecoder` utility functions to simplify text encoding/decoding across platforms. - Introduced `setup.ts` to apply environment patches before other modules load. - Updated CLI entry points (`cli.ts`, `rollup.config.js`) to ensure patching precedes TensorFlow.js usage. - Enhanced test coverage for TextEncoder compatibility with `test-fix.js`. - Adjusted imports in `test-tensorflow-textencoder.ts` to align with the updated `./utils/textEncoding.js`. - Streamlined constructor definitions in `Platform` for improved maintainability. This update ensures robust compatibility for TensorFlow.js by patching and standardizing text encoding/decoding functionality across environments.
102 lines
2.6 KiB
TypeScript
102 lines
2.6 KiB
TypeScript
/**
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* CLI Test for TensorFlow.js and TextEncoder
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*
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* This script tests TensorFlow.js and TextEncoder functionality in the CLI environment.
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*/
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import {
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getTextEncoder,
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getTextDecoder
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} from './utils/textEncoding.js'
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import * as tf from '@tensorflow/tfjs'
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import '@tensorflow/tfjs-backend-cpu'
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export async function testTensorFlowAndTextEncoder(): Promise<boolean> {
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console.log('Testing TensorFlow.js and TextEncoder in CLI environment...')
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try {
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// TensorFlow patch is automatically applied by the main package
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console.log('Using TensorFlow with automatic patching')
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// Test TextEncoder
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console.log('\n--- Testing TextEncoder ---')
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const encoder = getTextEncoder()
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const decoder = getTextDecoder()
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const testString = 'Hello, world! 👋'
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console.log(`Original string: "${testString}"`)
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const encoded = encoder.encode(testString)
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console.log(`Encoded: [${encoded}]`)
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const decoded = decoder.decode(encoded)
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console.log(`Decoded: "${decoded}"`)
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if (testString === decoded) {
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console.log('✅ TextEncoder/TextDecoder test passed!')
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} else {
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console.error('❌ TextEncoder/TextDecoder test failed!')
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return false
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}
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// Test TensorFlow.js
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console.log('\n--- Testing TensorFlow.js ---')
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// Create a simple tensor
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const tensor = tf.tensor2d([
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[1, 2],
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[3, 4]
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])
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console.log('Created tensor:')
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tensor.print()
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// Perform a simple operation
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const result = tensor.add(tf.scalar(1))
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console.log('Result of adding 1:')
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result.print()
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// Check the values
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const values = await result.array()
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const expected = [
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[2, 3],
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[4, 5]
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]
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console.log('Result values:', values)
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console.log('Expected values:', expected)
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// Compare values
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const match = JSON.stringify(values) === JSON.stringify(expected)
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if (match) {
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console.log('✅ TensorFlow.js test passed!')
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} else {
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console.error('❌ TensorFlow.js test failed!')
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return false
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}
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console.log('\nAll tests passed successfully!')
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return true
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} catch (error) {
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console.error('Error during test:', error)
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return false
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}
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}
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// This function can be called from the CLI
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export async function runTest(): Promise<void> {
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const success = await testTensorFlowAndTextEncoder()
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if (success) {
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console.log(
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'TensorFlow.js and TextEncoder verification completed successfully!'
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)
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process.exit(0)
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} else {
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console.error('TensorFlow.js and TextEncoder verification failed!')
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process.exit(1)
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}
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}
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// If this file is run directly
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if (typeof require !== 'undefined' && require.main === module) {
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runTest()
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}
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