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node_modules/sucrase/dist/esm/parser/tokenizer/readWord.js
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64
node_modules/sucrase/dist/esm/parser/tokenizer/readWord.js
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import {input, state} from "../traverser/base";
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import {charCodes} from "../util/charcodes";
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import {IS_IDENTIFIER_CHAR} from "../util/identifier";
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import {finishToken} from "./index";
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import {READ_WORD_TREE} from "./readWordTree";
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import {TokenType as tt} from "./types";
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/**
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* Read an identifier, producing either a name token or matching on one of the existing keywords.
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* For performance, we pre-generate big decision tree that we traverse. Each node represents a
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* prefix and has 27 values, where the first value is the token or contextual token, if any (-1 if
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* not), and the other 26 values are the transitions to other nodes, or -1 to stop.
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*/
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export default function readWord() {
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let treePos = 0;
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let code = 0;
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let pos = state.pos;
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while (pos < input.length) {
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code = input.charCodeAt(pos);
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if (code < charCodes.lowercaseA || code > charCodes.lowercaseZ) {
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break;
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}
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const next = READ_WORD_TREE[treePos + (code - charCodes.lowercaseA) + 1];
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if (next === -1) {
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break;
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} else {
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treePos = next;
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pos++;
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}
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}
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const keywordValue = READ_WORD_TREE[treePos];
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if (keywordValue > -1 && !IS_IDENTIFIER_CHAR[code]) {
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state.pos = pos;
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if (keywordValue & 1) {
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finishToken(keywordValue >>> 1);
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} else {
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finishToken(tt.name, keywordValue >>> 1);
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}
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return;
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}
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while (pos < input.length) {
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const ch = input.charCodeAt(pos);
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if (IS_IDENTIFIER_CHAR[ch]) {
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pos++;
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} else if (ch === charCodes.backslash) {
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// \u
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pos += 2;
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if (input.charCodeAt(pos) === charCodes.leftCurlyBrace) {
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while (pos < input.length && input.charCodeAt(pos) !== charCodes.rightCurlyBrace) {
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pos++;
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}
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pos++;
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}
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} else if (ch === charCodes.atSign && input.charCodeAt(pos + 1) === charCodes.atSign) {
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pos += 2;
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} else {
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break;
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}
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}
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state.pos = pos;
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finishToken(tt.name);
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}
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