setup-python/node_modules/fast-levenshtein/levenshtein.js

(function() {
  'use strict';
  
  var collator;
  try {
    collator = (typeof Intl !== "undefined" && typeof Intl.Collator !== "undefined") ? Intl.Collator("generic", { sensitivity: "base" }) : null;
  } catch (err){
    console.log("Collator could not be initialized and wouldn't be used");
  }
  // arrays to re-use
  var prevRow = [],
    str2Char = [];
  
  /**
   * Based on the algorithm at http://en.wikipedia.org/wiki/Levenshtein_distance.
   */
  var Levenshtein = {
    /**
     * Calculate levenshtein distance of the two strings.
     *
     * @param str1 String the first string.
     * @param str2 String the second string.
     * @param [options] Additional options.
     * @param [options.useCollator] Use `Intl.Collator` for locale-sensitive string comparison.
     * @return Integer the levenshtein distance (0 and above).
     */
    get: function(str1, str2, options) {
      var useCollator = (options && collator && options.useCollator);
      
      var str1Len = str1.length,
        str2Len = str2.length;
      
      // base cases
      if (str1Len === 0) return str2Len;
      if (str2Len === 0) return str1Len;

      // two rows
      var curCol, nextCol, i, j, tmp;

      // initialise previous row
      for (i=0; i<str2Len; ++i) {
        prevRow[i] = i;
        str2Char[i] = str2.charCodeAt(i);
      }
      prevRow[str2Len] = str2Len;

      var strCmp;
      if (useCollator) {
        // calculate current row distance from previous row using collator
        for (i = 0; i < str1Len; ++i) {
          nextCol = i + 1;

          for (j = 0; j < str2Len; ++j) {
            curCol = nextCol;

            // substution
            strCmp = 0 === collator.compare(str1.charAt(i), String.fromCharCode(str2Char[j]));

            nextCol = prevRow[j] + (strCmp ? 0 : 1);

            // insertion
            tmp = curCol + 1;
            if (nextCol > tmp) {
              nextCol = tmp;
            }
            // deletion
            tmp = prevRow[j + 1] + 1;
            if (nextCol > tmp) {
              nextCol = tmp;
            }

            // copy current col value into previous (in preparation for next iteration)
            prevRow[j] = curCol;
          }

          // copy last col value into previous (in preparation for next iteration)
          prevRow[j] = nextCol;
        }
      }
      else {
        // calculate current row distance from previous row without collator
        for (i = 0; i < str1Len; ++i) {
          nextCol = i + 1;

          for (j = 0; j < str2Len; ++j) {
            curCol = nextCol;

            // substution
            strCmp = str1.charCodeAt(i) === str2Char[j];

            nextCol = prevRow[j] + (strCmp ? 0 : 1);

            // insertion
            tmp = curCol + 1;
            if (nextCol > tmp) {
              nextCol = tmp;
            }
            // deletion
            tmp = prevRow[j + 1] + 1;
            if (nextCol > tmp) {
              nextCol = tmp;
            }

            // copy current col value into previous (in preparation for next iteration)
            prevRow[j] = curCol;
          }

          // copy last col value into previous (in preparation for next iteration)
          prevRow[j] = nextCol;
        }
      }
      return nextCol;
    }

  };

  // amd
  if (typeof define !== "undefined" && define !== null && define.amd) {
    define(function() {
      return Levenshtein;
    });
  }
  // commonjs
  else if (typeof module !== "undefined" && module !== null && typeof exports !== "undefined" && module.exports === exports) {
    module.exports = Levenshtein;
  }
  // web worker
  else if (typeof self !== "undefined" && typeof self.postMessage === 'function' && typeof self.importScripts === 'function') {
    self.Levenshtein = Levenshtein;
  }
  // browser main thread
  else if (typeof window !== "undefined" && window !== null) {
    window.Levenshtein = Levenshtein;
  }
}());