gmp.umd.js

/*!
 * gmp-wasm v1.1.0 (https://www.npmjs.com/package/gmp-wasm)
 * (c) Dani Biro
 * @license LGPL-3.0
 */

function gmpWasm() {

(function (global, factory) {
    typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
    typeof define === 'function' && define.amd ? define(['exports'], factory) :
    (global = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.gmp = {}));
})(this, (function (exports) { 'use strict';

    /*! *****************************************************************************
    Copyright (c) Microsoft Corporation.

    Permission to use, copy, modify, and/or distribute this software for any
    purpose with or without fee is hereby granted.

    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
    LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
    OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
    PERFORMANCE OF THIS SOFTWARE.
    ***************************************************************************** */

    function __awaiter(thisArg, _arguments, P, generator) {
        function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
        return new (P || (P = Promise))(function (resolve, reject) {
            function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
            function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
            function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
            step((generator = generator.apply(thisArg, _arguments || [])).next());
        });
    }

    function isUint32(num) {
        return Number.isSafeInteger(num) && num >= 0 && num < Math.pow(2, 32);
    }
    function assertUint32(num) {
        if (!isUint32(num)) {
            throw new Error('Invalid number specified: uint32_t is required');
        }
    }
    function isInt32(num) {
        return Number.isSafeInteger(num) && num >= -Math.pow(2, 31) && num < Math.pow(2, 31);
    }
    function assertInt32(num) {
        if (!isInt32(num)) {
            throw new Error('Invalid number specified: int32_t is required');
        }
    }
    function assertArray(arr) {
        if (!Array.isArray(arr)) {
            throw new Error('Invalid parameter specified. Array is required!');
        }
    }
    function isValidRadix(radix) {
        return Number.isSafeInteger(radix) && radix >= 2 && radix <= 36;
    }
    function assertValidRadix(radix) {
        if (!isValidRadix(radix)) {
            throw new Error('radix must have a value between 2 and 36');
        }
    }
    const FLOAT_SPECIAL_VALUES = {
        '@NaN@': 'NaN',
        '@Inf@': 'Infinity',
        '-@Inf@': '-Infinity',
    };
    const FLOAT_SPECIAL_VALUE_KEYS = Object.keys(FLOAT_SPECIAL_VALUES);
    const trimTrailingZeros = (num) => {
        let pos = num.length - 1;
        while (pos >= 0) {
            if (num[pos] === '.') {
                pos--;
                break;
            }
            else if (num[pos] === '0') {
                pos--;
            }
            else {
                break;
            }
        }
        if (pos !== num.length - 1) {
            return num.slice(0, pos + 1);
        }
        if (num.length === 0) {
            return '0';
        }
        return num;
    };
    const insertDecimalPoint = (mantissa, pointPos) => {
        const isNegative = mantissa.startsWith('-');
        const mantissaWithoutSign = isNegative ? mantissa.slice(1) : mantissa;
        const sign = isNegative ? '-' : '';
        let hasDecimalPoint = false;
        if (pointPos <= 0) {
            const zeros = '0'.repeat(-pointPos);
            mantissa = `${sign}0.${zeros}${mantissaWithoutSign}`;
            hasDecimalPoint = true;
        }
        else if (pointPos < mantissaWithoutSign.length) {
            mantissa = `${sign}${mantissaWithoutSign.slice(0, pointPos)}.${mantissaWithoutSign.slice(pointPos)}`;
            hasDecimalPoint = true;
        }
        else {
            const zeros = '0'.repeat(pointPos - mantissaWithoutSign.length);
            mantissa = `${mantissa}${zeros}`;
        }
        // trim trailing zeros after decimal point
        if (hasDecimalPoint) {
            mantissa = trimTrailingZeros(mantissa);
        }
        return mantissa;
    };

    // matches mpfr_rnd_t
    /** Represents the different rounding modes. */
    exports.FloatRoundingMode = void 0;
    (function (FloatRoundingMode) {
        /** Round to nearest, with ties to even. MPFR_RNDN */
        FloatRoundingMode[FloatRoundingMode["ROUND_NEAREST"] = 0] = "ROUND_NEAREST";
        /** Round toward zero. MPFR_RNDZ */
        FloatRoundingMode[FloatRoundingMode["ROUND_TO_ZERO"] = 1] = "ROUND_TO_ZERO";
        /** Round toward +Infinity. MPFR_RNDU */
        FloatRoundingMode[FloatRoundingMode["ROUND_UP"] = 2] = "ROUND_UP";
        /** Round toward -Infinity. MPFR_RNDD */
        FloatRoundingMode[FloatRoundingMode["ROUND_DOWN"] = 3] = "ROUND_DOWN";
        /** Round away from zero. MPFR_RNDA */
        FloatRoundingMode[FloatRoundingMode["ROUND_FROM_ZERO"] = 4] = "ROUND_FROM_ZERO";
        // /** (Experimental) Faithful rounding. MPFR_RNDF */
        // ROUND_FAITHFUL = 5,
        // /** (Experimental) Round to nearest, with ties away from zero. MPFR_RNDNA */
        // ROUND_TO_NEAREST_AWAY_FROM_ZERO = -1,
    })(exports.FloatRoundingMode || (exports.FloatRoundingMode = {}));
    const INVALID_PARAMETER_ERROR$2 = 'Invalid parameter!';
    function getFloatContext(gmp, ctx, ctxOptions) {
        var _a, _b, _c;
        const mpfr_t_arr = [];
        const isInteger = (val) => ctx.intContext.isInteger(val);
        const isRational = (val) => ctx.rationalContext.isRational(val);
        const isFloat = (val) => ctx.floatContext.isFloat(val);
        const globalRndMode = ((_a = ctxOptions.roundingMode) !== null && _a !== void 0 ? _a : exports.FloatRoundingMode.ROUND_NEAREST);
        const globalPrecisionBits = (_b = ctxOptions.precisionBits) !== null && _b !== void 0 ? _b : 52;
        const globalRadix = (_c = ctxOptions.radix) !== null && _c !== void 0 ? _c : 10;
        assertUint32(globalPrecisionBits);
        assertValidRadix(globalRadix);
        const compare = (mpfr_t, val) => {
            if (typeof val === 'number') {
                assertInt32(val);
                return gmp.mpfr_cmp_si(mpfr_t, val);
            }
            if (typeof val === 'string') {
                const f = FloatFn(val, ctxOptions);
                return gmp.mpfr_cmp(mpfr_t, f.mpfr_t);
            }
            if (isInteger(val)) {
                return gmp.mpfr_cmp_z(mpfr_t, val.mpz_t);
            }
            if (isRational(val)) {
                return gmp.mpfr_cmp_q(mpfr_t, val.mpq_t);
            }
            if (isFloat(val)) {
                return gmp.mpfr_cmp(mpfr_t, val.mpfr_t);
            }
            throw new Error(INVALID_PARAMETER_ERROR$2);
        };
        const mergeFloatOptions = (options1, options2) => {
            var _a, _b, _c, _d, _e, _f;
            const precisionBits1 = (_a = options1 === null || options1 === void 0 ? void 0 : options1.precisionBits) !== null && _a !== void 0 ? _a : globalPrecisionBits;
            const precisionBits2 = (_b = options2 === null || options2 === void 0 ? void 0 : options2.precisionBits) !== null && _b !== void 0 ? _b : globalPrecisionBits;
            return {
                precisionBits: Math.max(precisionBits1, precisionBits2),
                roundingMode: (_d = (_c = options2 === null || options2 === void 0 ? void 0 : options2.roundingMode) !== null && _c !== void 0 ? _c : options1.roundingMode) !== null && _d !== void 0 ? _d : ctxOptions.roundingMode,
                radix: (_f = (_e = options2 === null || options2 === void 0 ? void 0 : options2.radix) !== null && _e !== void 0 ? _e : options1.radix) !== null && _f !== void 0 ? _f : ctxOptions.radix,
            };
        };
        const FloatPrototype = {
            mpfr_t: 0,
            precisionBits: -1,
            rndMode: -1,
            radix: -1,
            type: 'float',
            get options() {
                var _a, _b, _c;
                return {
                    precisionBits: (_a = this.precisionBits) !== null && _a !== void 0 ? _a : globalPrecisionBits,
                    roundingMode: (_b = this.rndMode) !== null && _b !== void 0 ? _b : globalRndMode,
                    radix: (_c = this.radix) !== null && _c !== void 0 ? _c : globalRadix,
                };
            },
            get setOptions() {
                return {
                    precisionBits: this.precisionBits,
                    roundingMode: this.rndMode,
                    radix: this.radix,
                };
            },
            /** Returns the sum of this number and the given one. */
            add(val) {
                if (typeof val === 'number') {
                    const n = FloatFn(null, this.options);
                    gmp.mpfr_add_d(n.mpfr_t, this.mpfr_t, val, this.rndMode);
                    return n;
                }
                if (typeof val === 'string') {
                    const n = FloatFn(val, this.options);
                    gmp.mpfr_add(n.mpfr_t, this.mpfr_t, n.mpfr_t, this.rndMode);
                    return n;
                }
                if (isFloat(val)) {
                    const n = FloatFn(null, mergeFloatOptions(this.setOptions, val.setOptions));
                    gmp.mpfr_add(n.mpfr_t, this.mpfr_t, val.mpfr_t, this.rndMode);
                    return n;
                }
                if (isRational(val)) {
                    const n = FloatFn(null, this.options);
                    gmp.mpfr_add_q(n.mpfr_t, this.mpfr_t, val.mpq_t, this.rndMode);
                    return n;
                }
                if (isInteger(val)) {
                    const n = FloatFn(null, this.options);
                    gmp.mpfr_add_z(n.mpfr_t, this.mpfr_t, val.mpz_t, this.rndMode);
                    return n;
                }
                throw new Error(INVALID_PARAMETER_ERROR$2);
            },
            /** Returns the difference of this number and the given one. */
            sub(val) {
                if (typeof val === 'number') {
                    const n = FloatFn(null, this.options);
                    gmp.mpfr_sub_d(n.mpfr_t, this.mpfr_t, val, this.rndMode);
                    return n;
                }
                if (typeof val === 'string') {
                    const n = FloatFn(val, this.options);
                    gmp.mpfr_sub(n.mpfr_t, this.mpfr_t, n.mpfr_t, this.rndMode);
                    return n;
                }
                if (isFloat(val)) {
                    const n = FloatFn(null, mergeFloatOptions(this.setOptions, val.setOptions));
                    gmp.mpfr_sub(n.mpfr_t, this.mpfr_t, val.mpfr_t, this.rndMode);
                    return n;
                }
                if (isRational(val)) {
                    const n = FloatFn(null, this.options);
                    gmp.mpfr_sub_q(n.mpfr_t, this.mpfr_t, val.mpq_t, this.rndMode);
                    return n;
                }
                if (isInteger(val)) {
                    const n = FloatFn(null, this.options);
                    gmp.mpfr_sub_z(n.mpfr_t, this.mpfr_t, val.mpz_t, this.rndMode);
                    return n;
                }
                throw new Error(INVALID_PARAMETER_ERROR$2);
            },
            /** Returns the product of this number and the given one. */
            mul(val) {
                if (typeof val === 'number') {
                    const n = FloatFn(null, this.options);
                    if (isInt32(val)) {
                        gmp.mpfr_mul_si(n.mpfr_t, this.mpfr_t, val, this.rndMode);
                    }
                    else {
                        gmp.mpfr_mul_d(n.mpfr_t, this.mpfr_t, val, this.rndMode);
                    }
                    return n;
                }
                if (typeof val === 'string') {
                    const n = FloatFn(val, this.options);
                    gmp.mpfr_mul(n.mpfr_t, this.mpfr_t, n.mpfr_t, this.rndMode);
                    return n;
                }
                if (isFloat(val)) {
                    const n = FloatFn(null, mergeFloatOptions(this.setOptions, val.setOptions));
                    gmp.mpfr_mul(n.mpfr_t, this.mpfr_t, val.mpfr_t, this.rndMode);
                    return n;
                }
                if (isRational(val)) {
                    const n = FloatFn(null, this.options);
                    gmp.mpfr_mul_q(n.mpfr_t, this.mpfr_t, val.mpq_t, this.rndMode);
                    return n;
                }
                if (isInteger(val)) {
                    const n = FloatFn(null, this.options);
                    gmp.mpfr_mul_z(n.mpfr_t, this.mpfr_t, val.mpz_t, this.rndMode);
                    return n;
                }
                throw new Error(INVALID_PARAMETER_ERROR$2);
            },
            /** Returns the result of the division of this number by the given one. */
            div(val) {
                if (typeof val === 'number') {
                    const n = FloatFn(null, this.options);
                    gmp.mpfr_div_d(n.mpfr_t, this.mpfr_t, val, this.rndMode);
                    return n;
                }
                if (typeof val === 'string') {
                    const n = FloatFn(val, this.options);
                    gmp.mpfr_div(n.mpfr_t, this.mpfr_t, n.mpfr_t, this.rndMode);
                    return n;
                }
                if (isFloat(val)) {
                    const n = FloatFn(null, mergeFloatOptions(this.setOptions, val.setOptions));
                    gmp.mpfr_div(n.mpfr_t, this.mpfr_t, val.mpfr_t, this.rndMode);
                    return n;
                }
                if (isRational(val)) {
                    const n = FloatFn(null, this.options);
                    gmp.mpfr_div_q(n.mpfr_t, this.mpfr_t, val.mpq_t, this.rndMode);
                    return n;
                }
                if (isInteger(val)) {
                    const n = FloatFn(null, this.options);
                    gmp.mpfr_div_z(n.mpfr_t, this.mpfr_t, val.mpz_t, this.rndMode);
                    return n;
                }
                throw new Error(INVALID_PARAMETER_ERROR$2);
            },
            /** Returns the square root. */
            sqrt() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_sqrt(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the reciprocal square root. */
            invSqrt() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_rec_sqrt(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the cube root. */
            cbrt() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_cbrt(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the n-th root. */
            nthRoot(nth) {
                const n = FloatFn(null, this.options);
                assertUint32(nth);
                gmp.mpfr_rootn_ui(n.mpfr_t, this.mpfr_t, nth, this.rndMode);
                return n;
            },
            /** Returns the number with inverted sign. */
            neg() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_neg(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the absolute value. */
            abs() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_abs(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the factorial */
            factorial() {
                const n = FloatFn(null, this.options);
                if (gmp.mpfr_fits_uint_p(this.mpfr_t, this.rndMode) === 0) {
                    throw new Error('Invalid value for factorial()');
                }
                const value = gmp.mpfr_get_ui(this.mpfr_t, this.rndMode);
                gmp.mpfr_fac_ui(n.mpfr_t, value, this.rndMode);
                return n;
            },
            /** Returns true if the number is an integer */
            isInteger() {
                return gmp.mpfr_integer_p(this.mpfr_t) !== 0;
            },
            /** Returns true if the number is zero */
            isZero() {
                return gmp.mpfr_zero_p(this.mpfr_t) !== 0;
            },
            /** Returns true if the number is a regular number (i.e., neither NaN, nor an infinity nor zero) */
            isRegular() {
                return gmp.mpfr_regular_p(this.mpfr_t) !== 0;
            },
            /** Return true if the number is an ordinary number (i.e., neither NaN nor an infinity) */
            isNumber() {
                return gmp.mpfr_number_p(this.mpfr_t) !== 0;
            },
            /** Returns true if the number is an infinity */
            isInfinite() {
                return gmp.mpfr_inf_p(this.mpfr_t) !== 0;
            },
            /** Returns true if the number is NaN */
            isNaN() {
                return gmp.mpfr_nan_p(this.mpfr_t) !== 0;
            },
            /** Returns true if the current number is equal to the provided number */
            isEqual(val) {
                return compare(this.mpfr_t, val) === 0;
            },
            /** Returns true if the current number is less than the provided number */
            lessThan(val) {
                return compare(this.mpfr_t, val) < 0;
            },
            /** Returns true if the current number is less than or equal to the provided number */
            lessOrEqual(val) {
                return compare(this.mpfr_t, val) <= 0;
            },
            /** Returns true if the current number is greater than the provided number */
            greaterThan(val) {
                return compare(this.mpfr_t, val) > 0;
            },
            /** Returns true if the current number is greater than or equal to the provided number */
            greaterOrEqual(val) {
                return compare(this.mpfr_t, val) >= 0;
            },
            /** Returns the natural logarithm */
            ln() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_log(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the base 2 logarithm */
            log2() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_log2(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the base 10 logarithm */
            log10() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_log10(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the exponential (e^x) */
            exp() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_exp(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns 2 to the power of current number (2^x) */
            exp2() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_exp2(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns 10 to the power of current number (10^x) */
            exp10() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_exp10(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns this number exponentiated to the given value. */
            pow(val) {
                const n = FloatFn(null, this.options);
                if (typeof val === 'number') {
                    if (isInt32(val)) {
                        gmp.mpfr_pow_si(n.mpfr_t, this.mpfr_t, val, this.rndMode);
                    }
                    else {
                        gmp.mpfr_pow(n.mpfr_t, this.mpfr_t, FloatFn(val).mpfr_t, this.rndMode);
                    }
                }
                else {
                    gmp.mpfr_pow(n.mpfr_t, this.mpfr_t, val.mpfr_t, this.rndMode);
                }
                return n;
            },
            /** Returns the sine */
            sin() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_sin(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the cosine */
            cos() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_cos(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the tangent */
            tan() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_tan(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the secant */
            sec() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_sec(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the cosecant */
            csc() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_csc(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the cotangent */
            cot() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_cot(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the arc-cosine */
            acos() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_acos(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the arc-sine */
            asin() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_asin(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the arc-tangent */
            atan() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_atan(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the hyperbolic sine */
            sinh() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_sinh(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the hyperbolic cosine */
            cosh() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_cosh(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the hyperbolic tangent */
            tanh() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_tanh(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the hyperbolic secant */
            sech() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_sech(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the hyperbolic cosecant */
            csch() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_csch(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the hyperbolic cotangent */
            coth() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_coth(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the inverse hyperbolic cosine */
            acosh() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_acosh(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the inverse hyperbolic sine */
            asinh() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_asinh(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the inverse hyperbolic tangent */
            atanh() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_atanh(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate exponential integral */
            eint() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_eint(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the real part of the dilogarithm (the integral of -log(1-t)/t from 0 to op) */
            li2() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_li2(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the Gamma function. */
            gamma() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_gamma(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the logarithm of the absolute value of the Gamma function */
            lngamma() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_lngamma(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the Digamma (sometimes also called Psi) function */
            digamma() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_digamma(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the Beta function */
            beta(op2) {
                if (!isFloat(op2)) {
                    throw new Error('Only floats parameters are supported!');
                }
                const n = FloatFn(null, this.options);
                gmp.mpfr_beta(n.mpfr_t, this.mpfr_t, op2.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the Riemann Zeta function */
            zeta() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_zeta(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the error function */
            erf() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_erf(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the complementary error function */
            erfc() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_erfc(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the first kind Bessel function of order 0 */
            j0() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_j0(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the first kind Bessel function of order 1 */
            j1() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_j1(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the first kind Bessel function of order n */
            jn(n) {
                assertInt32(n);
                const rop = FloatFn(null, this.options);
                gmp.mpfr_jn(rop.mpfr_t, n, this.mpfr_t, this.rndMode);
                return rop;
            },
            /** Calculate the value of the second kind Bessel function of order 0 */
            y0() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_y0(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the second kind Bessel function of order 1 */
            y1() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_y1(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the second kind Bessel function of order n */
            yn(n) {
                assertInt32(n);
                const rop = FloatFn(null, this.options);
                gmp.mpfr_yn(rop.mpfr_t, n, this.mpfr_t, this.rndMode);
                return rop;
            },
            /** Calculate the arithmetic-geometric mean */
            agm(op2) {
                if (!isFloat(op2)) {
                    throw new Error('Only floats parameters are supported!');
                }
                const n = FloatFn(null, this.options);
                gmp.mpfr_agm(n.mpfr_t, this.mpfr_t, op2.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of the Airy function Ai on x */
            ai() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_ai(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Returns the sign of the current value (-1 or 0 or 1) */
            sign() {
                return gmp.mpfr_sgn(this.mpfr_t);
            },
            /** Converts current value into a JavaScript number */
            toNumber() {
                return gmp.mpfr_get_d(this.mpfr_t, this.rndMode);
            },
            /** Rounds to the next higher or equal representable integer */
            ceil() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_ceil(n.mpfr_t, this.mpfr_t);
                return n;
            },
            /** Rounds to the next lower or equal representable integer */
            floor() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_floor(n.mpfr_t, this.mpfr_t);
                return n;
            },
            /** Rounds to the nearest representable integer, rounding halfway cases away from zero */
            round() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_round(n.mpfr_t, this.mpfr_t);
                return n;
            },
            /** Rounds to the nearest representable integer, rounding halfway cases with the even-rounding rule */
            roundEven() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_roundeven(n.mpfr_t, this.mpfr_t);
                return n;
            },
            /** Rounds to the next representable integer toward zero */
            trunc() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_trunc(n.mpfr_t, this.mpfr_t);
                return n;
            },
            /** Round to precision */
            roundTo(prec) {
                assertUint32(prec);
                const n = FloatFn(this, this.options);
                gmp.mpfr_prec_round(this.mpfr_t, prec, this.rndMode);
                return n;
            },
            /** Returns the fractional part */
            frac() {
                const n = FloatFn(null, this.options);
                gmp.mpfr_frac(n.mpfr_t, this.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of x - ny, where n is the integer quotient of x divided by y; n is rounded toward zero */
            fmod(y) {
                if (!isFloat(y)) {
                    throw new Error('Only floats parameters are supported!');
                }
                const n = FloatFn(null, this.options);
                gmp.mpfr_fmod(n.mpfr_t, this.mpfr_t, y.mpfr_t, this.rndMode);
                return n;
            },
            /** Calculate the value of x - ny, where n is the integer quotient of x divided by y; n is rounded to the nearest integer (ties rounded to even) */
            remainder(y) {
                if (!isFloat(y)) {
                    throw new Error('Only floats parameters are supported!');
                }
                const n = FloatFn(null, this.options);
                gmp.mpfr_remainder(n.mpfr_t, this.mpfr_t, y.mpfr_t, this.rndMode);
                return n;
            },
            /** Return next value towards +∞ */
            nextAbove() {
                const n = FloatFn(this, this.options);
                gmp.mpfr_nextabove(n.mpfr_t);
                return n;
            },
            /** Return next value towards -∞ */
            nextBelow() {
                const n = FloatFn(this, this.options);
                gmp.mpfr_nextbelow(n.mpfr_t);
                return n;
            },
            /** Returns the exponent of x, assuming that x is a non-zero ordinary number and the significand is considered in [1/2, 1). */
            exponent() {
                return gmp.mpfr_get_exp(this.mpfr_t);
            },
            /** Converts the number to string */
            toString(radix) {
                radix = radix !== null && radix !== void 0 ? radix : this.options.radix;
                assertValidRadix(radix);
                const str = gmp.mpfr_to_string(this.mpfr_t, radix, this.rndMode);
                return str;
            },
            /** Formats the number using fixed-point notation */
            toFixed(digits = 0, radix) {
                assertUint32(digits);
                radix = radix !== null && radix !== void 0 ? radix : this.options.radix;
                assertValidRadix(radix);
                const str = this.toString(radix);
                if (Object.values(FLOAT_SPECIAL_VALUES).includes(str)) {
                    return str;
                }
                if (digits === 0) {
                    return ctx.intContext.Integer(this).toString(radix);
                }
                let multiplier = null;
                if (radix === 2) {
                    multiplier = FloatFn(digits).exp2();
                }
                else if (radix === 10) {
                    multiplier = FloatFn(digits).exp10();
                }
                else {
                    multiplier = FloatFn(radix).pow(digits);
                }
                const multiplied = this.mul(multiplier);
                const int = ctx.intContext.Integer(multiplied);
                const isNegative = int.sign() === -1;
                let intStr = int.abs().toString(radix);
                if (intStr.length < digits + 1) {
                    intStr = '0'.repeat(digits + 1 - intStr.length) + intStr;
                }
                return `${isNegative ? '-' : ''}${intStr.slice(0, -digits)}.${intStr.slice(-digits)}`;
            },
            /** Converts the number to an integer */
            toInteger() {
                return ctx.intContext.Integer(this);
            },
            /** Converts the number to a rational number */
            toRational() {
                return ctx.rationalContext.Rational(this);
            },
        };
        const setValue = (mpfr_t, rndMode, radix, val) => {
            if (typeof val === 'string') {
                const res = gmp.mpfr_set_string(mpfr_t, val, radix, rndMode);
                if (res !== 0) {
                    throw new Error('Invalid number provided!');
                }
                return;
            }
            if (typeof val === 'number') {
                if (isInt32(val)) {
                    gmp.mpfr_set_si(mpfr_t, val, rndMode);
                    if (Object.is(val, -0)) {
                        gmp.mpfr_neg(mpfr_t, mpfr_t, rndMode);
                    }
                }
                else {
                    gmp.mpfr_set_d(mpfr_t, val, rndMode);
                }
                return;
            }
            if (isFloat(val)) {
                gmp.mpfr_set(mpfr_t, val.mpfr_t, rndMode);
                return;
            }
            if (isRational(val)) {
                gmp.mpfr_set_q(mpfr_t, val.mpq_t, rndMode);
                return;
            }
            if (isInteger(val)) {
                gmp.mpfr_set_z(mpfr_t, val.mpz_t, rndMode);
                return;
            }
            throw new Error(INVALID_PARAMETER_ERROR$2);
        };
        const FloatFn = (val, options) => {
            var _a, _b, _c;
            const rndMode = ((_a = options === null || options === void 0 ? void 0 : options.roundingMode) !== null && _a !== void 0 ? _a : globalRndMode);
            const precisionBits = (_b = options === null || options === void 0 ? void 0 : options.precisionBits) !== null && _b !== void 0 ? _b : globalPrecisionBits;
            const radix = (_c = options === null || options === void 0 ? void 0 : options.radix) !== null && _c !== void 0 ? _c : globalRadix;
            assertValidRadix(radix);
            const instance = Object.create(FloatPrototype);
            instance.rndMode = rndMode;
            instance.precisionBits = precisionBits;
            instance.radix = radix;
            instance.mpfr_t = gmp.mpfr_t();
            gmp.mpfr_init2(instance.mpfr_t, precisionBits);
            if (val !== undefined && val !== null) {
                setValue(instance.mpfr_t, rndMode, radix, val);
            }
            mpfr_t_arr.push(instance.mpfr_t);
            return instance;
        };
        return {
            Float: FloatFn,
            isFloat: (val) => FloatPrototype.isPrototypeOf(val),
            Pi: (options) => {
                var _a;
                const n = FloatFn(null, options);
                gmp.mpfr_const_pi(n.mpfr_t, ((_a = options === null || options === void 0 ? void 0 : options.roundingMode) !== null && _a !== void 0 ? _a : globalRndMode));
                return n;
            },
            EulerConstant: (options) => {
                var _a;
                const n = FloatFn(null, options);
                gmp.mpfr_const_euler(n.mpfr_t, ((_a = options === null || options === void 0 ? void 0 : options.roundingMode) !== null && _a !== void 0 ? _a : globalRndMode));
                return n;
            },
            EulerNumber: (options) => {
                return FloatFn(1, options).exp();
            },
            Log2: (options) => {
                var _a;
                const n = FloatFn(null, options);
                gmp.mpfr_const_log2(n.mpfr_t, ((_a = options === null || options === void 0 ? void 0 : options.roundingMode) !== null && _a !== void 0 ? _a : globalRndMode));
                return n;
            },
            Catalan: (options) => {
                var _a;
                const n = FloatFn(null, options);
                gmp.mpfr_const_catalan(n.mpfr_t, ((_a = options === null || options === void 0 ? void 0 : options.roundingMode) !== null && _a !== void 0 ? _a : globalRndMode));
                return n;
            },
            destroy: () => {
                for (let i = mpfr_t_arr.length - 1; i >= 0; i--) {
                    gmp.mpfr_clear(mpfr_t_arr[i]);
                    gmp.mpfr_t_free(mpfr_t_arr[i]);
                }
                mpfr_t_arr.length = 0;
            }
        };
    }

    // DEFLATE is a complex format; to read this code, you should probably check the RFC first:

    // aliases for shorter compressed code (most minifers don't do this)
    var u8 = Uint8Array, u16 = Uint16Array, u32 = Uint32Array;
    // fixed length extra bits
    var fleb = new u8([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0, /* unused */ 0, 0, /* impossible */ 0]);
    // fixed distance extra bits
    // see fleb note
    var fdeb = new u8([0, 0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, /* unused */ 0, 0]);
    // code length index map
    var clim = new u8([16, 17, 18, 0, 8, 7, 9, 6, 10, 5, 11, 4, 12, 3, 13, 2, 14, 1, 15]);
    // get base, reverse index map from extra bits
    var freb = function (eb, start) {
        var b = new u16(31);
        for (var i = 0; i < 31; ++i) {
            b[i] = start += 1 << eb[i - 1];
        }
        // numbers here are at max 18 bits
        var r = new u32(b[30]);
        for (var i = 1; i < 30; ++i) {
            for (var j = b[i]; j < b[i + 1]; ++j) {
                r[j] = ((j - b[i]) << 5) | i;
            }
        }
        return [b, r];
    };
    var _a = freb(fleb, 2), fl = _a[0], revfl = _a[1];
    // we can ignore the fact that the other numbers are wrong; they never happen anyway
    fl[28] = 258, revfl[258] = 28;
    var _b = freb(fdeb, 0), fd = _b[0];
    // map of value to reverse (assuming 16 bits)
    var rev = new u16(32768);
    for (var i = 0; i < 32768; ++i) {
        // reverse table algorithm from SO
        var x = ((i & 0xAAAA) >>> 1) | ((i & 0x5555) << 1);
        x = ((x & 0xCCCC) >>> 2) | ((x & 0x3333) << 2);
        x = ((x & 0xF0F0) >>> 4) | ((x & 0x0F0F) << 4);
        rev[i] = (((x & 0xFF00) >>> 8) | ((x & 0x00FF) << 8)) >>> 1;
    }
    // create huffman tree from u8 "map": index -> code length for code index
    // mb (max bits) must be at most 15
    // TODO: optimize/split up?
    var hMap = (function (cd, mb, r) {
        var s = cd.length;
        // index
        var i = 0;
        // u16 "map": index -> # of codes with bit length = index
        var l = new u16(mb);
        // length of cd must be 288 (total # of codes)
        for (; i < s; ++i) {
            if (cd[i])
                ++l[cd[i] - 1];
        }
        // u16 "map": index -> minimum code for bit length = index
        var le = new u16(mb);
        for (i = 0; i < mb; ++i) {
            le[i] = (le[i - 1] + l[i - 1]) << 1;
        }
        var co;
        if (r) {
            // u16 "map": index -> number of actual bits, symbol for code
            co = new u16(1 << mb);
            // bits to remove for reverser
            var rvb = 15 - mb;
            for (i = 0; i < s; ++i) {
                // ignore 0 lengths
                if (cd[i]) {
                    // num encoding both symbol and bits read
                    var sv = (i << 4) | cd[i];
                    // free bits
                    var r_1 = mb - cd[i];
                    // start value
                    var v = le[cd[i] - 1]++ << r_1;
                    // m is end value
                    for (var m = v | ((1 << r_1) - 1); v <= m; ++v) {
                        // every 16 bit value starting with the code yields the same result
                        co[rev[v] >>> rvb] = sv;
                    }
                }
            }
        }
        else {
            co = new u16(s);
            for (i = 0; i < s; ++i) {
                if (cd[i]) {
                    co[i] = rev[le[cd[i] - 1]++] >>> (15 - cd[i]);
                }
            }
        }
        return co;
    });
    // fixed length tree
    var flt = new u8(288);
    for (var i = 0; i < 144; ++i)
        flt[i] = 8;
    for (var i = 144; i < 256; ++i)
        flt[i] = 9;
    for (var i = 256; i < 280; ++i)
        flt[i] = 7;
    for (var i = 280; i < 288; ++i)
        flt[i] = 8;
    // fixed distance tree
    var fdt = new u8(32);
    for (var i = 0; i < 32; ++i)
        fdt[i] = 5;
    // fixed length map
    var flrm = /*#__PURE__*/ hMap(flt, 9, 1);
    // fixed distance map
    var fdrm = /*#__PURE__*/ hMap(fdt, 5, 1);
    // find max of array
    var max = function (a) {
        var m = a[0];
        for (var i = 1; i < a.length; ++i) {
            if (a[i] > m)
                m = a[i];
        }
        return m;
    };
    // read d, starting at bit p and mask with m
    var bits = function (d, p, m) {
        var o = (p / 8) | 0;
        return ((d[o] | (d[o + 1] << 8)) >> (p & 7)) & m;
    };
    // read d, starting at bit p continuing for at least 16 bits
    var bits16 = function (d, p) {
        var o = (p / 8) | 0;
        return ((d[o] | (d[o + 1] << 8) | (d[o + 2] << 16)) >> (p & 7));
    };
    // get end of byte
    var shft = function (p) { return ((p + 7) / 8) | 0; };
    // typed array slice - allows garbage collector to free original reference,
    // while being more compatible than .slice
    var slc = function (v, s, e) {
        if (s == null || s < 0)
            s = 0;
        if (e == null || e > v.length)
            e = v.length;
        // can't use .constructor in case user-supplied
        var n = new (v.BYTES_PER_ELEMENT == 2 ? u16 : v.BYTES_PER_ELEMENT == 4 ? u32 : u8)(e - s);
        n.set(v.subarray(s, e));
        return n;
    };
    // error codes
    var ec = [
        'unexpected EOF',
        'invalid block type',
        'invalid length/literal',
        'invalid distance',
        'stream finished',
        'no stream handler',
        ,
        'no callback',
        'invalid UTF-8 data',
        'extra field too long',
        'date not in range 1980-2099',
        'filename too long',
        'stream finishing',
        'invalid zip data'
        // determined by unknown compression method
    ];
    var err = function (ind, msg, nt) {
        var e = new Error(msg || ec[ind]);
        e.code = ind;
        if (Error.captureStackTrace)
            Error.captureStackTrace(e, err);
        if (!nt)
            throw e;
        return e;
    };
    // expands raw DEFLATE data
    var inflt = function (dat, buf, st) {
        // source length
        var sl = dat.length;
        if (!sl || (st && st.f && !st.l))
            return buf || new u8(0);
        // have to estimate size
        var noBuf = !buf || st;
        // no state
        var noSt = !st || st.i;
        if (!st)
            st = {};
        // Assumes roughly 33% compression ratio average
        if (!buf)
            buf = new u8(sl * 3);
        // ensure buffer can fit at least l elements
        var cbuf = function (l) {
            var bl = buf.length;
            // need to increase size to fit
            if (l > bl) {
                // Double or set to necessary, whichever is greater
                var nbuf = new u8(Math.max(bl * 2, l));
                nbuf.set(buf);
                buf = nbuf;
            }
        };
        //  last chunk         bitpos           bytes
        var final = st.f || 0, pos = st.p || 0, bt = st.b || 0, lm = st.l, dm = st.d, lbt = st.m, dbt = st.n;
        // total bits
        var tbts = sl * 8;
        do {
            if (!lm) {
                // BFINAL - this is only 1 when last chunk is next
                final = bits(dat, pos, 1);
                // type: 0 = no compression, 1 = fixed huffman, 2 = dynamic huffman
                var type = bits(dat, pos + 1, 3);
                pos += 3;
                if (!type) {
                    // go to end of byte boundary
                    var s = shft(pos) + 4, l = dat[s - 4] | (dat[s - 3] << 8), t = s + l;
                    if (t > sl) {
                        if (noSt)
                            err(0);
                        break;
                    }
                    // ensure size
                    if (noBuf)
                        cbuf(bt + l);
                    // Copy over uncompressed data
                    buf.set(dat.subarray(s, t), bt);
                    // Get new bitpos, update byte count
                    st.b = bt += l, st.p = pos = t * 8, st.f = final;
                    continue;
                }
                else if (type == 1)
                    lm = flrm, dm = fdrm, lbt = 9, dbt = 5;
                else if (type == 2) {
                    //  literal                            lengths
                    var hLit = bits(dat, pos, 31) + 257, hcLen = bits(dat, pos + 10, 15) + 4;
                    var tl = hLit + bits(dat, pos + 5, 31) + 1;
                    pos += 14;
                    // length+distance tree
                    var ldt = new u8(tl);
                    // code length tree
                    var clt = new u8(19);
                    for (var i = 0; i < hcLen; ++i) {
                        // use index map to get real code
                        clt[clim[i]] = bits(dat, pos + i * 3, 7);
                    }
                    pos += hcLen * 3;
                    // code lengths bits
                    var clb = max(clt), clbmsk = (1 << clb) - 1;
                    // code lengths map
                    var clm = hMap(clt, clb, 1);
                    for (var i = 0; i < tl;) {
                        var r = clm[bits(dat, pos, clbmsk)];
                        // bits read
                        pos += r & 15;
                        // symbol
                        var s = r >>> 4;
                        // code length to copy
                        if (s < 16) {
                            ldt[i++] = s;
                        }
                        else {
                            //  copy   count
                            var c = 0, n = 0;
                            if (s == 16)
                                n = 3 + bits(dat, pos, 3), pos += 2, c = ldt[i - 1];
                            else if (s == 17)
                                n = 3 + bits(dat, pos, 7), pos += 3;
                            else if (s == 18)
                                n = 11 + bits(dat, pos, 127), pos += 7;
                            while (n--)
                                ldt[i++] = c;
                        }
                    }
                    //    length tree                 distance tree
                    var lt = ldt.subarray(0, hLit), dt = ldt.subarray(hLit);
                    // max length bits
                    lbt = max(lt);
                    // max dist bits
                    dbt = max(dt);
                    lm = hMap(lt, lbt, 1);
                    dm = hMap(dt, dbt, 1);
                }
                else
                    err(1);
                if (pos > tbts) {
                    if (noSt)
                        err(0);
                    break;
                }
            }
            // Make sure the buffer can hold this + the largest possible addition
            // Maximum chunk size (practically, theoretically infinite) is 2^17;
            if (noBuf)
                cbuf(bt + 131072);
            var lms = (1 << lbt) - 1, dms = (1 << dbt) - 1;
            var lpos = pos;
            for (;; lpos = pos) {
                // bits read, code
                var c = lm[bits16(dat, pos) & lms], sym = c >>> 4;
                pos += c & 15;
                if (pos > tbts) {
                    if (noSt)
                        err(0);
                    break;
                }
                if (!c)
                    err(2);
                if (sym < 256)
                    buf[bt++] = sym;
                else if (sym == 256) {
                    lpos = pos, lm = null;
                    break;
                }
                else {
                    var add = sym - 254;
                    // no extra bits needed if less
                    if (sym > 264) {
                        // index
                        var i = sym - 257, b = fleb[i];
                        add = bits(dat, pos, (1 << b) - 1) + fl[i];
                        pos += b;
                    }
                    // dist
                    var d = dm[bits16(dat, pos) & dms], dsym = d >>> 4;
                    if (!d)
                        err(3);
                    pos += d & 15;
                    var dt = fd[dsym];
                    if (dsym > 3) {
                        var b = fdeb[dsym];
                        dt += bits16(dat, pos) & ((1 << b) - 1), pos += b;
                    }
                    if (pos > tbts) {
                        if (noSt)
                            err(0);
                        break;
                    }
                    if (noBuf)
                        cbuf(bt + 131072);
                    var end = bt + add;
                    for (; bt < end; bt += 4) {
                        buf[bt] = buf[bt - dt];
                        buf[bt + 1] = buf[bt + 1 - dt];
                        buf[bt + 2] = buf[bt + 2 - dt];
                        buf[bt + 3] = buf[bt + 3 - dt];
                    }
                    bt = end;
                }
            }
            st.l = lm, st.p = lpos, st.b = bt, st.f = final;
            if (lm)
                final = 1, st.m = lbt, st.d = dm, st.n = dbt;
        } while (!final);
        return bt == buf.length ? buf : slc(buf, 0, bt);
    };
    // empty
    var et = /*#__PURE__*/ new u8(0);
    /**
     * Expands DEFLATE data with no wrapper
     * @param data The data to decompress
     * @param out Where to write the data. Saves memory if you know the decompressed size and provide an output buffer of that length.
     * @returns The decompressed version of the data
     */
    function inflateSync(data, out) {
        return inflt(data, out);
    }
    // text decoder
    var td = typeof TextDecoder != 'undefined' && /*#__PURE__*/ new TextDecoder();
    // text decoder stream
    var tds = 0;
    try {
        td.decode(et, { stream: true });
        tds = 1;
    }
    catch (e) { }

    const base64Chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/';
    const base64Lookup = new Uint8Array(256);
    for (let i = 0; i < base64Chars.length; i++) {
        base64Lookup[base64Chars.charCodeAt(i)] = i;
    }
    function getDecodeBase64Length(data) {
        let bufferLength = Math.floor(data.length * 0.75);
        const len = data.length;
        if (data[len - 1] === '=') {
            bufferLength -= 1;
            if (data[len - 2] === '=') {
                bufferLength -= 1;
            }
        }
        return bufferLength;
    }
    function decodeBase64(data) {
        const bufferLength = getDecodeBase64Length(data);
        const len = data.length;
        const bytes = new Uint8Array(bufferLength);
        let p = 0;
        for (let i = 0; i < len; i += 4) {
            const encoded1 = base64Lookup[data.charCodeAt(i)];
            const encoded2 = base64Lookup[data.charCodeAt(i + 1)];
            const encoded3 = base64Lookup[data.charCodeAt(i + 2)];
            const encoded4 = base64Lookup[data.charCodeAt(i + 3)];
            bytes[p] = (encoded1 << 2) | (encoded2 >> 4);
            p += 1;
            bytes[p] = ((encoded2 & 15) << 4) | (encoded3 >> 2);
            p += 1;
            bytes[p] = ((encoded3 & 3) << 6) | (encoded4 & 63);
            p += 1;
        }
        return bytes;
    }

    const gmpWasmLength = 560789;
    const gmpWasm = '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';

    let instance = null;
    let compiledModule = null;
    const decompressAndCompile = () => __awaiter(void 0, void 0, void 0, function* () {
        if (compiledModule)
            return;
        // console.time('decompress');
        const decoded = decodeBase64(gmpWasm);
        const decompressed = new Uint8Array(gmpWasmLength);
        inflateSync(decoded, decompressed);
        // console.timeEnd('decompress');
        // console.time('compile');
        compiledModule = yield WebAssembly.compile(decompressed);
        // console.timeEnd('compile');
    });
    const getBinding = (reset = false) => __awaiter(void 0, void 0, void 0, function* () {
        if (!reset && instance !== null) {
            return instance;
        }
        if (typeof WebAssembly === 'undefined') {
            throw new Error('WebAssembly is not supported in this environment!');
        }
        yield decompressAndCompile();
        const heap = { HEAP8: new Uint8Array(0) };
        const errorHandler = () => {
            throw new Error('Fatal error in gmp-wasm');
        };
        const wasmInstance = yield WebAssembly.instantiate(compiledModule, {
            env: {
                emscripten_notify_memory_growth: () => {
                    heap.HEAP8 = new Uint8Array(wasmInstance.exports.memory.buffer);
                },
            },
            wasi_snapshot_preview1: {
                proc_exit: errorHandler,
                fd_write: errorHandler,
                fd_seek: errorHandler,
                fd_close: errorHandler,
            },
        });
        const exports = wasmInstance.exports;
        exports._initialize();
        heap.HEAP8 = new Uint8Array(wasmInstance.exports.memory.buffer);
        instance = Object.assign({ heap }, exports);
        return instance;
    });

    const decoder = new TextDecoder();
    const encoder = new TextEncoder();
    const PREALLOCATED_STR_SIZE = 2 * 1024;
    function getGMPInterface() {
        return __awaiter(this, void 0, void 0, function* () {
            let gmp = yield getBinding();
            let strBuf = gmp.g_malloc(PREALLOCATED_STR_SIZE);
            let mpfr_exp_t_ptr = gmp.g_malloc(4);
            const getStringPointer = (input) => {
                const data = encoder.encode(input);
                let srcPtr = strBuf;
                if (data.length + 1 > PREALLOCATED_STR_SIZE) {
                    srcPtr = gmp.g_malloc(data.length + 1);
                }
                gmp.heap.HEAP8.set(data, srcPtr);
                gmp.heap.HEAP8[srcPtr + data.length] = 0;
                return srcPtr;
            };
            return {
                reset: () => __awaiter(this, void 0, void 0, function* () {
                    gmp = yield getBinding(true);
                    strBuf = gmp.g_malloc(PREALLOCATED_STR_SIZE);
                    mpfr_exp_t_ptr = gmp.g_malloc(4);
                }),
                malloc: (size) => gmp.g_malloc(size),
                malloc_cstr: (str) => {
                    const buf = encoder.encode(str);
                    const ptr = gmp.g_malloc(buf.length + 1);
                    gmp.heap.HEAP8.set(buf, ptr);
                    gmp.heap.HEAP8[ptr + buf.length] = 0;
                    return ptr;
                },
                free: (ptr) => gmp.g_free(ptr),
                get mem() { return gmp.heap.HEAP8; },
                get memView() { return new DataView(gmp.heap.HEAP8.buffer, gmp.heap.HEAP8.byteOffset, gmp.heap.HEAP8.byteLength); },
                /**************** Random number routines.  ****************/
                /** Initialize state with a default algorithm. */
                gmp_randinit_default: (state) => { gmp.g_randinit_default(state); },
                /** Initialize state with a linear congruential algorithm X = (aX + c) mod 2^m2exp. */
                gmp_randinit_lc_2exp: (state, a, c, m2exp) => { gmp.g_randinit_lc_2exp(state, a, c, m2exp); },
                /** Initialize state for a linear congruential algorithm as per gmp_randinit_lc_2exp. */
                gmp_randinit_lc_2exp_size: (state, size) => { return gmp.g_randinit_lc_2exp_size(state, size); },
                /** Initialize state for a Mersenne Twister algorithm. */
                gmp_randinit_mt: (state) => { gmp.g_randinit_mt(state); },
                /** Initialize rop with a copy of the algorithm and state from op. */
                gmp_randinit_set: (rop, op) => { gmp.g_randinit_set(rop, op); },
                /** Set an initial seed value into state. */
                gmp_randseed: (state, seed) => { gmp.g_randseed(state, seed); },
                /** Set an initial seed value into state. */
                gmp_randseed_ui: (state, seed) => { gmp.g_randseed_ui(state, seed); },
                /** Free all memory occupied by state. */
                gmp_randclear: (state) => { gmp.g_randclear(state); },
                /** Generate a uniformly distributed random number of n bits, i.e. in the range 0 to 2^n - 1 inclusive. */
                gmp_urandomb_ui: (state, n) => { return gmp.g_urandomb_ui(state, n); },
                /** Generate a uniformly distributed random number in the range 0 to n - 1, inclusive. */
                gmp_urandomm_ui: (state, n) => { return gmp.g_urandomm_ui(state, n); },
                /**************** Formatted output routines.  ****************/
                /**************** Formatted input routines.  ****************/
                /**************** Integer (i.e. Z) routines.  ****************/
                /** Get GMP limb size */
                mp_bits_per_limb: () => gmp.z_limb_size(),
                /** Allocates memory for the mpfr_t C struct and returns pointer */
                mpz_t: () => gmp.z_t(),
                /** Deallocates memory of a mpfr_t C struct */
                mpz_t_free: (ptr) => { gmp.z_t_free(ptr); },
                /** Deallocates memory of a NULL-terminated list of mpfr_t variables */
                mpz_t_frees: (...ptrs) => {
                    for (let i = 0; i < ptrs.length; i++) {
                        if (!ptrs[i])
                            return;
                        gmp.z_t_free(ptrs[i]);
                    }
                },
                /** Set rop to the absolute value of op. */
                mpz_abs: (rop, op) => { gmp.z_abs(rop, op); },
                /** Set rop to op1 + op2. */
                mpz_add: (rop, op1, op2) => { gmp.z_add(rop, op1, op2); },
                /** Set rop to op1 + op2. */
                mpz_add_ui: (rop, op1, op2) => { gmp.z_add_ui(rop, op1, op2); },
                /** Set rop to rop + op1 * op2. */
                mpz_addmul: (rop, op1, op2) => { gmp.z_addmul(rop, op1, op2); },
                /** Set rop to rop + op1 * op2. */
                mpz_addmul_ui: (rop, op1, op2) => { gmp.z_addmul_ui(rop, op1, op2); },
                /** Set rop to op1 bitwise-and op2. */
                mpz_and: (rop, op1, op2) => { gmp.z_and(rop, op1, op2); },
                /** Compute the binomial coefficient n over k and store the result in rop. */
                mpz_bin_ui: (rop, n, k) => { gmp.z_bin_ui(rop, n, k); },
                /** Compute the binomial coefficient n over k and store the result in rop. */
                mpz_bin_uiui: (rop, n, k) => { gmp.z_bin_uiui(rop, n, k); },
                /** Set the quotient q to ceiling(n / d). */
                mpz_cdiv_q: (q, n, d) => { gmp.z_cdiv_q(q, n, d); },
                /** Set the quotient q to ceiling(n / 2^b). */
                mpz_cdiv_q_2exp: (q, n, b) => { gmp.z_cdiv_q_2exp(q, n, b); },
                /** Set the quotient q to ceiling(n / d), and return the remainder r = | n - q * d |. */
                mpz_cdiv_q_ui: (q, n, d) => gmp.z_cdiv_q_ui(q, n, d),
                /** Set the quotient q to ceiling(n / d), and set the remainder r to n - q * d. */
                mpz_cdiv_qr: (q, r, n, d) => { gmp.z_cdiv_qr(q, r, n, d); },
                /** Set quotient q to ceiling(n / d), set the remainder r to n - q * d, and return | r |. */
                mpz_cdiv_qr_ui: (q, r, n, d) => gmp.z_cdiv_qr_ui(q, r, n, d),
                /** Set the remainder r to n - q * d where q = ceiling(n / d). */
                mpz_cdiv_r: (r, n, d) => { gmp.z_cdiv_r(r, n, d); },
                /** Set the remainder r to n - q * 2^b where q = ceiling(n / 2^b). */
                mpz_cdiv_r_2exp: (r, n, b) => { gmp.z_cdiv_r_2exp(r, n, b); },
                /** Set the remainder r to n - q * d where q = ceiling(n / d), and return | r |. */
                mpz_cdiv_r_ui: (r, n, d) => gmp.z_cdiv_r_ui(r, n, d),
                /** Return the remainder | r | where r = n - q * d, and where q = ceiling(n / d). */
                mpz_cdiv_ui: (n, d) => gmp.z_cdiv_ui(n, d),
                /** Free the space occupied by x. */
                mpz_clear: (x) => { gmp.z_clear(x); },
                /** Free the space occupied by a NULL-terminated list of mpz_t variables. */
                mpz_clears: (...ptrs) => {
                    for (let i = 0; i < ptrs.length; i++) {
                        if (!ptrs[i])
                            return;
                        gmp.z_clear(ptrs[i]);
                    }
                },
                /** Clear bit bit_index in rop. */
                mpz_clrbit: (rop, bit_index) => { gmp.z_clrbit(rop, bit_index); },
                /** Compare op1 and op2. */
                mpz_cmp: (op1, op2) => gmp.z_cmp(op1, op2),
                /** Compare op1 and op2. */
                mpz_cmp_d: (op1, op2) => gmp.z_cmp_d(op1, op2),
                /** Compare op1 and op2. */
                mpz_cmp_si: (op1, op2) => gmp.z_cmp_si(op1, op2),
                /** Compare op1 and op2. */
                mpz_cmp_ui: (op1, op2) => gmp.z_cmp_ui(op1, op2),
                /** Compare the absolute values of op1 and op2. */
                mpz_cmpabs: (op1, op2) => gmp.z_cmpabs(op1, op2),
                /** Compare the absolute values of op1 and op2. */
                mpz_cmpabs_d: (op1, op2) => gmp.z_cmpabs_d(op1, op2),
                /** Compare the absolute values of op1 and op2. */
                mpz_cmpabs_ui: (op1, op2) => gmp.z_cmpabs_ui(op1, op2),
                /** Set rop to the one’s complement of op. */
                mpz_com: (rop, op) => { gmp.z_com(rop, op); },
                /** Complement bit bitIndex in rop. */
                mpz_combit: (rop, bitIndex) => { gmp.z_combit(rop, bitIndex); },
                /** Return non-zero if n is congruent to c modulo d. */
                mpz_congruent_p: (n, c, d) => gmp.z_congruent_p(n, c, d),
                /** Return non-zero if n is congruent to c modulo 2^b. */
                mpz_congruent_2exp_p: (n, c, b) => gmp.z_congruent_2exp_p(n, c, b),
                /** Return non-zero if n is congruent to c modulo d. */
                mpz_congruent_ui_p: (n, c, d) => gmp.z_congruent_ui_p(n, c, d),
                /** Set q to n / d when it is known in advance that d divides n. */
                mpz_divexact: (q, n, d) => { gmp.z_divexact(q, n, d); },
                /** Set q to n / d when it is known in advance that d divides n. */
                mpz_divexact_ui: (q, n, d) => { gmp.z_divexact_ui(q, n, d); },
                /** Return non-zero if n is exactly divisible by d. */
                mpz_divisible_p: (n, d) => gmp.z_divisible_p(n, d),
                /** Return non-zero if n is exactly divisible by d. */
                mpz_divisible_ui_p: (n, d) => gmp.z_divisible_ui_p(n, d),
                /** Return non-zero if n is exactly divisible by 2^b. */
                mpz_divisible_2exp_p: (n, b) => gmp.z_divisible_2exp_p(n, b),
                /** Determine whether op is even. */
                mpz_even_p: (op) => { gmp.z_even_p(op); },
                /** Fill rop with word data from op. */
                mpz_export: (rop, countp, order, size, endian, nails, op) => gmp.z_export(rop, countp, order, size, endian, nails, op),
                /** Set rop to the factorial n!. */
                mpz_fac_ui: (rop, n) => { gmp.z_fac_ui(rop, n); },
                /** Set rop to the double-factorial n!!. */
                mpz_2fac_ui: (rop, n) => { gmp.z_2fac_ui(rop, n); },
                /** Set rop to the m-multi-factorial n!^(m)n. */
                mpz_mfac_uiui: (rop, n, m) => { gmp.z_mfac_uiui(rop, n, m); },
                /** Set rop to the primorial of n, i.e. the product of all positive prime numbers ≤ n. */
                mpz_primorial_ui: (rop, n) => { gmp.z_primorial_ui(rop, n); },
                /** Set the quotient q to floor(n / d). */
                mpz_fdiv_q: (q, n, d) => { gmp.z_fdiv_q(q, n, d); },
                /** Set the quotient q to floor(n / 2^b). */
                mpz_fdiv_q_2exp: (q, n, b) => { gmp.z_fdiv_q_2exp(q, n, b); },
                /** Set the quotient q to floor(n / d), and return the remainder r = | n - q * d |. */
                mpz_fdiv_q_ui: (q, n, d) => gmp.z_fdiv_q_ui(q, n, d),
                /** Set the quotient q to floor(n / d), and set the remainder r to n - q * d. */
                mpz_fdiv_qr: (q, r, n, d) => { gmp.z_fdiv_qr(q, r, n, d); },
                /** Set quotient q to floor(n / d), set the remainder r to n - q * d, and return | r |. */
                mpz_fdiv_qr_ui: (q, r, n, d) => gmp.z_fdiv_qr_ui(q, r, n, d),
                /** Set the remainder r to n - q * d where q = floor(n / d). */
                mpz_fdiv_r: (r, n, d) => { gmp.z_fdiv_r(r, n, d); },
                /** Set the remainder r to n - q * 2^b where q = floor(n / 2^b). */
                mpz_fdiv_r_2exp: (r, n, b) => { gmp.z_fdiv_r_2exp(r, n, b); },
                /** Set the remainder r to n - q * d where q = floor(n / d), and return | r |. */
                mpz_fdiv_r_ui: (r, n, d) => gmp.z_fdiv_r_ui(r, n, d),
                /** Return the remainder | r | where r = n - q * d, and where q = floor(n / d). */
                mpz_fdiv_ui: (n, d) => gmp.z_fdiv_ui(n, d),
                /** Sets fn to to F[n], the n’th Fibonacci number. */
                mpz_fib_ui: (fn, n) => { gmp.z_fib_ui(fn, n); },
                /** Sets fn to F[n], and fnsub1 to F[n - 1]. */
                mpz_fib2_ui: (fn, fnsub1, n) => { gmp.z_fib2_ui(fn, fnsub1, n); },
                /** Return non-zero iff the value of op fits in a signed 32-bit integer. Otherwise, return zero. */
                mpz_fits_sint_p: (op) => gmp.z_fits_sint_p(op),
                /** Return non-zero iff the value of op fits in a signed 32-bit integer. Otherwise, return zero. */
                mpz_fits_slong_p: (op) => gmp.z_fits_slong_p(op),
                /** Return non-zero iff the value of op fits in a signed 16-bit integer. Otherwise, return zero. */
                mpz_fits_sshort_p: (op) => gmp.z_fits_sshort_p(op),
                /** Return non-zero iff the value of op fits in an unsigned 32-bit integer. Otherwise, return zero. */
                mpz_fits_uint_p: (op) => gmp.z_fits_uint_p(op),
                /** Return non-zero iff the value of op fits in an unsigned 32-bit integer. Otherwise, return zero. */
                mpz_fits_ulong_p: (op) => gmp.z_fits_ulong_p(op),
                /** Return non-zero iff the value of op fits in an unsigned 16-bit integer. Otherwise, return zero. */
                mpz_fits_ushort_p: (op) => gmp.z_fits_ushort_p(op),
                /** Set rop to the greatest common divisor of op1 and op2. */
                mpz_gcd: (rop, op1, op2) => { gmp.z_gcd(rop, op1, op2); },
                /** Compute the greatest common divisor of op1 and op2. If rop is not null, store the result there. */
                mpz_gcd_ui: (rop, op1, op2) => gmp.z_gcd_ui(rop, op1, op2),
                /** Set g to the greatest common divisor of a and b, and in addition set s and t to coefficients satisfying a * s + b * t = g. */
                mpz_gcdext: (g, s, t, a, b) => { gmp.z_gcdext(g, s, t, a, b); },
                /** Convert op to a double, truncating if necessary (i.e. rounding towards zero). */
                mpz_get_d: (op) => gmp.z_get_d(op),
                /** Convert op to a double, truncating if necessary (i.e. rounding towards zero), and returning the exponent separately. */
                mpz_get_d_2exp: (exp, op) => gmp.z_get_d_2exp(exp, op),
                /** Return the value of op as an signed long. */
                mpz_get_si: (op) => gmp.z_get_si(op),
                /** Convert op to a string of digits in base base. */
                mpz_get_str: (str, base, op) => gmp.z_get_str(str, base, op),
                /** Return the value of op as an unsigned long. */
                mpz_get_ui: (op) => gmp.z_get_ui(op),
                /** Return limb number n from op. */
                mpz_getlimbn: (op, n) => gmp.z_getlimbn(op, n),
                /** Return the hamming distance between the two operands. */
                mpz_hamdist: (op1, op2) => gmp.z_hamdist(op1, op2),
                /** Set rop from an array of word data at op. */
                mpz_import: (rop, count, order, size, endian, nails, op) => { gmp.z_import(rop, count, order, size, endian, nails, op); },
                /** Initialize x, and set its value to 0. */
                mpz_init: (x) => { gmp.z_init(x); },
                /** Initialize a NULL-terminated list of mpz_t variables, and set their values to 0. */
                mpz_inits: (...ptrs) => {
                    for (let i = 0; i < ptrs.length; i++) {
                        if (!ptrs[i])
                            return;
                        gmp.z_init(ptrs[i]);
                    }
                },
                /** Initialize x, with space for n-bit numbers, and set its value to 0. */
                mpz_init2: (x, n) => { gmp.z_init2(x, n); },
                /** Initialize rop with limb space and set the initial numeric value from op. */
                mpz_init_set: (rop, op) => { gmp.z_init_set(rop, op); },
                /** Initialize rop with limb space and set the initial numeric value from op. */
                mpz_init_set_d: (rop, op) => { gmp.z_init_set_d(rop, op); },
                /** Initialize rop with limb space and set the initial numeric value from op. */
                mpz_init_set_si: (rop, op) => { gmp.z_init_set_si(rop, op); },
                /** Initialize rop and set its value like mpz_set_str. */
                mpz_init_set_str: (rop, str, base) => gmp.z_init_set_str(rop, str, base),
                /** Initialize rop with limb space and set the initial numeric value from op. */
                mpz_init_set_ui: (rop, op) => { gmp.z_init_set_ui(rop, op); },
                /** Compute the inverse of op1 modulo op2 and put the result in rop. */
                mpz_invert: (rop, op1, op2) => gmp.z_invert(rop, op1, op2),
                /** Set rop to op1 bitwise inclusive-or op2. */
                mpz_ior: (rop, op1, op2) => { gmp.z_ior(rop, op1, op2); },
                /** Calculate the Jacobi symbol (a/b). */
                mpz_jacobi: (a, b) => gmp.z_jacobi(a, b),
                /** Calculate the Jacobi symbol (a/b) with the Kronecker extension (a/2) = (2/a) when a odd, or (a/2) = 0 when a even. */
                mpz_kronecker: (a, b) => gmp.z_kronecker(a, b),
                /** Calculate the Jacobi symbol (a/b) with the Kronecker extension (a/2) = (2/a) when a odd, or (a/2) = 0 when a even. */
                mpz_kronecker_si: (a, b) => gmp.z_kronecker_si(a, b),
                /** Calculate the Jacobi symbol (a/b) with the Kronecker extension (a/2) = (2/a) when a odd, or (a/2) = 0 when a even. */
                mpz_kronecker_ui: (a, b) => gmp.z_kronecker_ui(a, b),
                /** Calculate the Jacobi symbol (a/b) with the Kronecker extension (a/2) = (2/a) when a odd, or (a/2) = 0 when a even. */
                mpz_si_kronecker: (a, b) => gmp.z_si_kronecker(a, b),
                /** Calculate the Jacobi symbol (a/b) with the Kronecker extension (a/2) = (2/a) when a odd, or (a/2) = 0 when a even. */
                mpz_ui_kronecker: (a, b) => gmp.z_ui_kronecker(a, b),
                /** Set rop to the least common multiple of op1 and op2. */
                mpz_lcm: (rop, op1, op2) => { gmp.z_lcm(rop, op1, op2); },
                /** Set rop to the least common multiple of op1 and op2. */
                mpz_lcm_ui: (rop, op1, op2) => { gmp.z_lcm_ui(rop, op1, op2); },
                /** Calculate the Legendre symbol (a/p). */
                mpz_legendre: (a, p) => gmp.z_legendre(a, p),
                /** Sets ln to to L[n], the n’th Lucas number. */
                mpz_lucnum_ui: (ln, n) => { gmp.z_lucnum_ui(ln, n); },
                /** Sets ln to L[n], and lnsub1 to L[n - 1]. */
                mpz_lucnum2_ui: (ln, lnsub1, n) => { gmp.z_lucnum2_ui(ln, lnsub1, n); },
                /** An implementation of the probabilistic primality test found in Knuth's Seminumerical Algorithms book. */
                mpz_millerrabin: (n, reps) => gmp.z_millerrabin(n, reps),
                /** Set r to n mod d. */
                mpz_mod: (r, n, d) => { gmp.z_mod(r, n, d); },
                /** Set r to n mod d. */
                mpz_mod_ui: (r, n, d) => { gmp.z_mod_ui(r, n, d); },
                /** Set rop to op1 * op2. */
                mpz_mul: (rop, op1, op2) => { gmp.z_mul(rop, op1, op2); },
                /** Set rop to op1 * 2^op2. */
                mpz_mul_2exp: (rop, op1, op2) => { gmp.z_mul_2exp(rop, op1, op2); },
                /** Set rop to op1 * op2. */
                mpz_mul_si: (rop, op1, op2) => { gmp.z_mul_si(rop, op1, op2); },
                /** Set rop to op1 * op2. */
                mpz_mul_ui: (rop, op1, op2) => { gmp.z_mul_ui(rop, op1, op2); },
                /** Set rop to -op. */
                mpz_neg: (rop, op) => { gmp.z_neg(rop, op); },
                /** Set rop to the next prime greater than op. */
                mpz_nextprime: (rop, op) => { gmp.z_nextprime(rop, op); },
                /** Determine whether op is odd. */
                mpz_odd_p: (op) => { gmp.z_odd_p(op); },
                /** Return non-zero if op is a perfect power, i.e., if there exist integers a and b, with b > 1, such that op = a^b. */
                mpz_perfect_power_p: (op) => gmp.z_perfect_power_p(op),
                /** Return non-zero if op is a perfect square, i.e., if the square root of op is an integer. */
                mpz_perfect_square_p: (op) => gmp.z_perfect_square_p(op),
                /** Return the population count of op. */
                mpz_popcount: (op) => gmp.z_popcount(op),
                /** Set rop to base^exp. The case 0^0 yields 1. */
                mpz_pow_ui: (rop, base, exp) => { gmp.z_pow_ui(rop, base, exp); },
                /** Set rop to (base^exp) modulo mod. */
                mpz_powm: (rop, base, exp, mod) => { gmp.z_powm(rop, base, exp, mod); },
                /** Set rop to (base^exp) modulo mod. */
                mpz_powm_sec: (rop, base, exp, mod) => { gmp.z_powm_sec(rop, base, exp, mod); },
                /** Set rop to (base^exp) modulo mod. */
                mpz_powm_ui: (rop, base, exp, mod) => { gmp.z_powm_ui(rop, base, exp, mod); },
                /** Determine whether n is prime. */
                mpz_probab_prime_p: (n, reps) => gmp.z_probab_prime_p(n, reps),
                /** Generate a random integer of at most maxSize limbs. */
                mpz_random: (rop, maxSize) => { gmp.z_random(rop, maxSize); },
                /** Generate a random integer of at most maxSize limbs, with long strings of zeros and ones in the binary representation. */
                mpz_random2: (rop, maxSize) => { gmp.z_random2(rop, maxSize); },
                /** Change the space allocated for x to n bits. */
                mpz_realloc2: (x, n) => { gmp.z_realloc2(x, n); },
                /** Remove all occurrences of the factor f from op and store the result in rop. */
                mpz_remove: (rop, op, f) => gmp.z_remove(rop, op, f),
                /** Set rop to the truncated integer part of the nth root of op. */
                mpz_root: (rop, op, n) => gmp.z_root(rop, op, n),
                /** Set root to the truncated integer part of the nth root of u. Set rem to the remainder, u - root^n. */
                mpz_rootrem: (root, rem, u, n) => { gmp.z_rootrem(root, rem, u, n); },
                /** Generate a random integer with long strings of zeros and ones in the binary representation. */
                mpz_rrandomb: (rop, state, n) => { gmp.z_rrandomb(rop, state, n); },
                /** Scan op for 0 bit. */
                mpz_scan0: (op, startingBit) => gmp.z_scan0(op, startingBit),
                /** Scan op for 1 bit. */
                mpz_scan1: (op, startingBit) => gmp.z_scan1(op, startingBit),
                /** Set the value of rop from op. */
                mpz_set: (rop, op) => { gmp.z_set(rop, op); },
                /** Set the value of rop from op. */
                mpz_set_d: (rop, op) => { gmp.z_set_d(rop, op); },
                /** Set the value of rop from op. */
                mpz_set_q: (rop, op) => { gmp.z_set_q(rop, op); },
                /** Set the value of rop from op. */
                mpz_set_si: (rop, op) => { gmp.z_set_si(rop, op); },
                /** Set the value of rop from str, a null-terminated C string in base base. */
                mpz_set_str: (rop, str, base) => gmp.z_set_str(rop, str, base),
                /** Set the value of rop from op. */
                mpz_set_ui: (rop, op) => { gmp.z_set_ui(rop, op); },
                /** Set bit bitIndex in rop. */
                mpz_setbit: (rop, bitIndex) => { gmp.z_setbit(rop, bitIndex); },
                /** Return +1 if op > 0, 0 if op = 0, and -1 if op < 0. */
                mpz_sgn: (op) => gmp.z_sgn(op),
                /** Return the size of op measured in number of limbs. */
                mpz_size: (op) => gmp.z_size(op),
                /** Return the size of op measured in number of digits in the given base. */
                mpz_sizeinbase: (op, base) => gmp.z_sizeinbase(op, base),
                /** Set rop to the truncated integer part of the square root of op. */
                mpz_sqrt: (rop, op) => { gmp.z_sqrt(rop, op); },
                /** Set rop1 to the truncated integer part of the square root of op, like mpz_sqrt. Set rop2 to the remainder op - rop1 * rop1, which will be zero if op is a perfect square. */
                mpz_sqrtrem: (rop1, rop2, op) => { gmp.z_sqrtrem(rop1, rop2, op); },
                /** Set rop to op1 - op2. */
                mpz_sub: (rop, op1, op2) => { gmp.z_sub(rop, op1, op2); },
                /** Set rop to op1 - op2. */
                mpz_sub_ui: (rop, op1, op2) => { gmp.z_sub_ui(rop, op1, op2); },
                /** Set rop to op1 - op2. */
                mpz_ui_sub: (rop, op1, op2) => { gmp.z_ui_sub(rop, op1, op2); },
                /** Set rop to rop - op1 * op2. */
                mpz_submul: (rop, op1, op2) => { gmp.z_submul(rop, op1, op2); },
                /** Set rop to rop - op1 * op2. */
                mpz_submul_ui: (rop, op1, op2) => { gmp.z_submul_ui(rop, op1, op2); },
                /** Swap the values rop1 and rop2 efficiently. */
                mpz_swap: (rop1, rop2) => { gmp.z_swap(rop1, rop2); },
                /** Return the remainder | r | where r = n - q * d, and where q = trunc(n / d). */
                mpz_tdiv_ui: (n, d) => gmp.z_tdiv_ui(n, d),
                /** Set the quotient q to trunc(n / d). */
                mpz_tdiv_q: (q, n, d) => { gmp.z_tdiv_q(q, n, d); },
                /** Set the quotient q to trunc(n / 2^b). */
                mpz_tdiv_q_2exp: (q, n, b) => { gmp.z_tdiv_q_2exp(q, n, b); },
                /** Set the quotient q to trunc(n / d), and return the remainder r = | n - q * d |. */
                mpz_tdiv_q_ui: (q, n, d) => gmp.z_tdiv_q_ui(q, n, d),
                /** Set the quotient q to trunc(n / d), and set the remainder r to n - q * d. */
                mpz_tdiv_qr: (q, r, n, d) => { gmp.z_tdiv_qr(q, r, n, d); },
                /** Set quotient q to trunc(n / d), set the remainder r to n - q * d, and return | r |. */
                mpz_tdiv_qr_ui: (q, r, n, d) => { return gmp.z_tdiv_qr_ui(q, r, n, d); },
                /** Set the remainder r to n - q * d where q = trunc(n / d). */
                mpz_tdiv_r: (r, n, d) => { gmp.z_tdiv_r(r, n, d); },
                /** Set the remainder r to n - q * 2^b where q = trunc(n / 2^b). */
                mpz_tdiv_r_2exp: (r, n, b) => { gmp.z_tdiv_r_2exp(r, n, b); },
                /** Set the remainder r to n - q * d where q = trunc(n / d), and return | r |. */
                mpz_tdiv_r_ui: (r, n, d) => gmp.z_tdiv_r_ui(r, n, d),
                /** Test bit bitIndex in op and return 0 or 1 accordingly. */
                mpz_tstbit: (op, bitIndex) => gmp.z_tstbit(op, bitIndex),
                /** Set rop to base^exp. The case 0^0 yields 1. */
                mpz_ui_pow_ui: (rop, base, exp) => { gmp.z_ui_pow_ui(rop, base, exp); },
                /** Generate a uniformly distributed random integer in the range 0 to 2^n - 1, inclusive. */
                mpz_urandomb: (rop, state, n) => { gmp.z_urandomb(rop, state, n); },
                /** Generate a uniform random integer in the range 0 to n - 1, inclusive. */
                mpz_urandomm: (rop, state, n) => { gmp.z_urandomm(rop, state, n); },
                /** Set rop to op1 bitwise exclusive-or op2. */
                mpz_xor: (rop, op1, op2) => { gmp.z_xor(rop, op1, op2); },
                /** Return a pointer to the limb array representing the absolute value of x. */
                mpz_limbs_read: (x) => gmp.z_limbs_read(x),
                /** Return a pointer to the limb array of x, intended for write access. */
                mpz_limbs_write: (x, n) => gmp.z_limbs_write(x, n),
                /** Return a pointer to the limb array of x, intended for write access. */
                mpz_limbs_modify: (x, n) => gmp.z_limbs_modify(x, n),
                /** Updates the internal size field of x. */
                mpz_limbs_finish: (x, s) => { gmp.z_limbs_finish(x, s); },
                /** Special initialization of x, using the given limb array and size. */
                mpz_roinit_n: (x, xp, xs) => gmp.z_roinit_n(x, xp, xs),
                /**************** Rational (i.e. Q) routines.  ****************/
                /** Allocates memory for the mpq_t C struct and returns pointer */
                mpq_t: () => gmp.q_t(),
                /** Deallocates memory of a mpq_t C struct */
                mpq_t_free: (mpq_ptr) => { gmp.q_t_free(mpq_ptr); },
                /** Deallocates memory of a NULL-terminated list of mpq_t variables */
                mpq_t_frees: (...ptrs) => {
                    for (let i = 0; i < ptrs.length; i++) {
                        if (!ptrs[i])
                            return;
                        gmp.q_t_free(ptrs[i]);
                    }
                },
                /** Set rop to the absolute value of op. */
                mpq_abs: (rop, op) => { gmp.q_abs(rop, op); },
                /** Set sum to addend1 + addend2. */
                mpq_add: (sum, addend1, addend2) => { gmp.q_add(sum, addend1, addend2); },
                /** Remove any factors that are common to the numerator and denominator of op, and make the denominator positive. */
                mpq_canonicalize: (op) => { gmp.q_canonicalize(op); },
                /** Free the space occupied by x. */
                mpq_clear: (x) => { gmp.q_clear(x); },
                /** Free the space occupied by a NULL-terminated list of mpq_t variables. */
                mpq_clears: (...ptrs) => {
                    for (let i = 0; i < ptrs.length; i++) {
                        if (!ptrs[i])
                            return;
                        gmp.q_clear(ptrs[i]);
                    }
                },
                /** Compare op1 and op2. */
                mpq_cmp: (op1, op2) => gmp.q_cmp(op1, op2),
                /** Compare op1 and num2 / den2. */
                mpq_cmp_si: (op1, num2, den2) => gmp.q_cmp_si(op1, num2, den2),
                /** Compare op1 and num2 / den2. */
                mpq_cmp_ui: (op1, num2, den2) => gmp.q_cmp_ui(op1, num2, den2),
                /** Compare op1 and op2. */
                mpq_cmp_z: (op1, op2) => gmp.q_cmp_z(op1, op2),
                /** Set quotient to dividend / divisor. */
                mpq_div: (quotient, dividend, divisor) => { gmp.q_div(quotient, dividend, divisor); },
                /** Set rop to op1 / 2^op2. */
                mpq_div_2exp: (rop, op1, op2) => { gmp.q_div_2exp(rop, op1, op2); },
                /** Return non-zero if op1 and op2 are equal, zero if they are non-equal. */
                mpq_equal: (op1, op2) => gmp.q_equal(op1, op2),
                /** Set numerator to the numerator of rational. */
                mpq_get_num: (numerator, rational) => { gmp.q_get_num(numerator, rational); },
                /** Set denominator to the denominator of rational. */
                mpq_get_den: (denominator, rational) => { gmp.q_get_den(denominator, rational); },
                /** Convert op to a double, truncating if necessary (i.e. rounding towards zero). */
                mpq_get_d: (op) => gmp.q_get_d(op),
                /** Convert op to a string of digits in base base. */
                mpq_get_str: (str, base, op) => gmp.q_get_str(str, base, op),
                /** Initialize x and set it to 0/1. */
                mpq_init: (x) => { gmp.q_init(x); },
                /** Initialize a NULL-terminated list of mpq_t variables, and set their values to 0/1. */
                mpq_inits: (...ptrs) => {
                    for (let i = 0; i < ptrs.length; i++) {
                        if (!ptrs[i])
                            return;
                        gmp.q_init(ptrs[i]);
                    }
                },
                /** Set inverted_number to 1 / number. */
                mpq_inv: (inverted_number, number) => { gmp.q_inv(inverted_number, number); },
                /** Set product to multiplier * multiplicand. */
                mpq_mul: (product, multiplier, multiplicand) => { gmp.q_mul(product, multiplier, multiplicand); },
                /** Set rop to op1 * 2*op2. */
                mpq_mul_2exp: (rop, op1, op2) => { gmp.q_mul_2exp(rop, op1, op2); },
                /** Set negated_operand to -operand. */
                mpq_neg: (negated_operand, operand) => { gmp.q_neg(negated_operand, operand); },
                /** Assign rop from op. */
                mpq_set: (rop, op) => { gmp.q_set(rop, op); },
                /** Set rop to the value of op. There is no rounding, this conversion is exact. */
                mpq_set_d: (rop, op) => { gmp.q_set_d(rop, op); },
                /** Set the denominator of rational to denominator. */
                mpq_set_den: (rational, denominator) => { gmp.q_set_den(rational, denominator); },
                /** Set the numerator of rational to numerator. */
                mpq_set_num: (rational, numerator) => { gmp.q_set_num(rational, numerator); },
                /** Set the value of rop to op1 / op2. */
                mpq_set_si: (rop, op1, op2) => { gmp.q_set_si(rop, op1, op2); },
                /** Set rop from a null-terminated string str in the given base. */
                mpq_set_str: (rop, str, base) => gmp.q_set_str(rop, str, base),
                /** Set the value of rop to op1 / op2. */
                mpq_set_ui: (rop, op1, op2) => { gmp.q_set_ui(rop, op1, op2); },
                /** Assign rop from op. */
                mpq_set_z: (rop, op) => { gmp.q_set_z(rop, op); },
                /** Return +1 if op > 0, 0 if op = 0, and -1 if op < 0. */
                mpq_sgn: (op) => gmp.q_sgn(op),
                /** Set difference to minuend - subtrahend. */
                mpq_sub: (difference, minuend, subtrahend) => { gmp.q_sub(difference, minuend, subtrahend); },
                /** Swap the values rop1 and rop2 efficiently. */
                mpq_swap: (rop1, rop2) => { gmp.q_swap(rop1, rop2); },
                /** Return a reference to the numerator of op. */
                mpq_numref: (op) => gmp.q_numref(op),
                /** Return a reference to the denominator of op. */
                mpq_denref: (op) => gmp.q_denref(op),
                /**************** MPFR  ****************/
                /** Allocates memory for the mpfr_t C struct and returns pointer */
                mpfr_t: () => gmp.r_t(),
                /** Deallocates memory of a mpfr_t C struct */
                mpfr_t_free: (mpfr_ptr) => { gmp.r_t_free(mpfr_ptr); },
                /** Deallocates memory of a NULL-terminated list of mpfr_t variables */
                mpfr_t_frees: (...ptrs) => {
                    for (let i = 0; i < ptrs.length; i++) {
                        if (!ptrs[i])
                            return;
                        gmp.r_t_free(ptrs[i]);
                    }
                },
                /** Return the MPFR version, as a null-terminated string. */
                mpfr_get_version: () => gmp.r_get_version(),
                /** Return a null-terminated string containing the ids of the patches applied to the MPFR library (contents of the PATCHES file), separated by spaces. */
                mpfr_get_patches: () => gmp.r_get_patches(),
                /** Return a non-zero value if MPFR was compiled as thread safe using compiler-level Thread Local Storage, return zero otherwise. */
                mpfr_buildopt_tls_p: () => gmp.r_buildopt_tls_p(),
                /** Return a non-zero value if MPFR was compiled with ‘__float128’ support, return zero otherwise. */
                mpfr_buildopt_float128_p: () => gmp.r_buildopt_float128_p(),
                /** Return a non-zero value if MPFR was compiled with decimal float support, return zero otherwise. */
                mpfr_buildopt_decimal_p: () => gmp.r_buildopt_decimal_p(),
                /** Return a non-zero value if MPFR was compiled with GMP internals, return zero otherwise. */
                mpfr_buildopt_gmpinternals_p: () => gmp.r_buildopt_gmpinternals_p(),
                /** Return a non-zero value if MPFR was compiled so that all threads share the same cache for one MPFR constant, return zero otherwise. */
                mpfr_buildopt_sharedcache_p: () => gmp.r_buildopt_sharedcache_p(),
                /** Return a string saying which thresholds file has been used at compile time. */
                mpfr_buildopt_tune_case: () => gmp.r_buildopt_tune_case(),
                /** Return the (current) smallest exponent allowed for a floating-point variable. */
                mpfr_get_emin: () => gmp.r_get_emin(),
                /** Set the smallest exponent allowed for a floating-point variable. */
                mpfr_set_emin: (exp) => gmp.r_set_emin(exp),
                /** Return the minimum exponent allowed for mpfr_set_emin. */
                mpfr_get_emin_min: () => gmp.r_get_emin_min(),
                /** Return the maximum exponent allowed for mpfr_set_emin. */
                mpfr_get_emin_max: () => gmp.r_get_emin_max(),
                /** Return the (current) largest exponent allowed for a floating-point variable. */
                mpfr_get_emax: () => gmp.r_get_emax(),
                /** Set the largest exponent allowed for a floating-point variable. */
                mpfr_set_emax: (exp) => gmp.r_set_emax(exp),
                /** Return the minimum exponent allowed for mpfr_set_emax. */
                mpfr_get_emax_min: () => gmp.r_get_emax_min(),
                /** Return the maximum exponent allowed for mpfr_set_emax. */
                mpfr_get_emax_max: () => gmp.r_get_emax_max(),
                /** Set the default rounding mode to rnd. */
                mpfr_set_default_rounding_mode: (rnd) => { gmp.r_set_default_rounding_mode(rnd); },
                /** Get the default rounding mode. */
                mpfr_get_default_rounding_mode: () => gmp.r_get_default_rounding_mode(),
                /** Return a string ("MPFR_RNDD", "MPFR_RNDU", "MPFR_RNDN", "MPFR_RNDZ", "MPFR_RNDA") corresponding to the rounding mode rnd, or a null pointer if rnd is an invalid rounding mode. */
                mpfr_print_rnd_mode: (rnd) => gmp.r_print_rnd_mode(rnd),
                /** Clear (lower) all global flags (underflow, overflow, divide-by-zero, invalid, inexact, erange). */
                mpfr_clear_flags: () => { gmp.r_clear_flags(); },
                /** Clear (lower) the underflow flag. */
                mpfr_clear_underflow: () => { gmp.r_clear_underflow(); },
                /** Clear (lower) the overflow flag. */
                mpfr_clear_overflow: () => { gmp.r_clear_overflow(); },
                /** Clear (lower) the divide-by-zero flag. */
                mpfr_clear_divby0: () => { gmp.r_clear_divby0(); },
                /** Clear (lower) the invalid flag. */
                mpfr_clear_nanflag: () => { gmp.r_clear_nanflag(); },
                /** Clear (lower) the inexact flag. */
                mpfr_clear_inexflag: () => { gmp.r_clear_inexflag(); },
                /** Clear (lower) the erange flag. */
                mpfr_clear_erangeflag: () => { gmp.r_clear_erangeflag(); },
                /** Set (raised) the underflow flag. */
                mpfr_set_underflow: () => { gmp.r_set_underflow(); },
                /** Set (raised) the overflow flag. */
                mpfr_set_overflow: () => { gmp.r_set_overflow(); },
                /** Set (raised) the divide-by-zero flag. */
                mpfr_set_divby0: () => { gmp.r_set_divby0(); },
                /** Set (raised) the invalid flag. */
                mpfr_set_nanflag: () => { gmp.r_set_nanflag(); },
                /** Set (raised) the inexact flag. */
                mpfr_set_inexflag: () => { gmp.r_set_inexflag(); },
                /** Set (raised) the erange flag. */
                mpfr_set_erangeflag: () => { gmp.r_set_erangeflag(); },
                /** Return the underflow flag, which is non-zero iff the flag is set. */
                mpfr_underflow_p: () => gmp.r_underflow_p(),
                /** Return the overflow flag, which is non-zero iff the flag is set. */
                mpfr_overflow_p: () => gmp.r_overflow_p(),
                /** Return the divide-by-zero flag, which is non-zero iff the flag is set. */
                mpfr_divby0_p: () => gmp.r_divby0_p(),
                /** Return the invalid flag, which is non-zero iff the flag is set. */
                mpfr_nanflag_p: () => gmp.r_nanflag_p(),
                /** Return the inexact flag, which is non-zero iff the flag is set. */
                mpfr_inexflag_p: () => gmp.r_inexflag_p(),
                /** Return the erange flag, which is non-zero iff the flag is set. */
                mpfr_erangeflag_p: () => gmp.r_erangeflag_p(),
                /** Clear (lower) the group of flags specified by mask. */
                mpfr_flags_clear: (mask) => { gmp.r_flags_clear(mask); },
                /** Set (raise) the group of flags specified by mask. */
                mpfr_flags_set: (mask) => { gmp.r_flags_set(mask); },
                /** Return the flags specified by mask. */
                mpfr_flags_test: (mask) => gmp.r_flags_test(mask),
                /** Return all the flags. */
                mpfr_flags_save: () => gmp.r_flags_save(),
                /** Restore the flags specified by mask to their state represented in flags. */
                mpfr_flags_restore: (flags, mask) => { gmp.r_flags_restore(flags, mask); },
                /** Check that x is within the current range of acceptable values. */
                mpfr_check_range: (x, t, rnd) => gmp.r_check_range(x, t, rnd),
                /** Initialize x, set its precision to be exactly prec bits and its value to NaN. */
                mpfr_init2: (x, prec) => { gmp.r_init2(x, prec); },
                /** Initialize all the mpfr_t variables of the given variable argument x, set their precision to be exactly prec bits and their value to NaN. */
                mpfr_inits2: (prec, ...ptrs) => {
                    for (let i = 0; i < ptrs.length; i++) {
                        if (!ptrs[i])
                            return;
                        gmp.r_init2(ptrs[i], prec);
                    }
                },
                /** Initialize x, set its precision to the default precision, and set its value to NaN. */
                mpfr_init: (x) => { gmp.r_init(x); },
                /** Initialize all the mpfr_t variables of the given list x, set their precision to the default precision and their value to NaN. */
                mpfr_inits: (...ptrs) => {
                    for (let i = 0; i < ptrs.length; i++) {
                        if (!ptrs[i])
                            return;
                        gmp.r_init(ptrs[i]);
                    }
                },
                /** Free the space occupied by the significand of x. */
                mpfr_clear: (x) => { gmp.r_clear(x); },
                /** Free the space occupied by all the mpfr_t variables of the given list x. */
                mpfr_clears: (...ptrs) => {
                    for (let i = 0; i < ptrs.length; i++) {
                        if (!ptrs[i])
                            return;
                        gmp.r_clear(ptrs[i]);
                    }
                },
                /** Round x according to rnd with precision prec, which must be an integer between MPFR_PREC_MIN and MPFR_PREC_MAX (otherwise the behavior is undefined). */
                mpfr_prec_round: (x, prec, rnd) => gmp.r_prec_round(x, prec, rnd),
                /** Return non-zero value if one is able to round correctly x to precision prec with the direction rnd2, and 0 otherwise. */
                mpfr_can_round: (b, err, rnd1, rnd2, prec) => gmp.r_can_round(b, err, rnd1, rnd2, prec),
                /** Return the minimal number of bits required to store the significand of x, and 0 for special values, including 0. */
                mpfr_min_prec: (x) => gmp.r_min_prec(x),
                /** Return the exponent of x, assuming that x is a non-zero ordinary number and the significand is considered in [1/2,1). */
                mpfr_get_exp: (x) => gmp.r_get_exp(x),
                /** Set the exponent of x if e is in the current exponent range. */
                mpfr_set_exp: (x, e) => gmp.r_set_exp(x, e),
                /** Return the precision of x, i.e., the number of bits used to store its significand. */
                mpfr_get_prec: (x) => gmp.r_get_prec(x),
                /** Reset the precision of x to be exactly prec bits, and set its value to NaN. */
                mpfr_set_prec: (x, prec) => { gmp.r_set_prec(x, prec); },
                /** Reset the precision of x to be exactly prec bits. */
                mpfr_set_prec_raw: (x, prec) => { gmp.r_set_prec_raw(x, prec); },
                /** Set the default precision to be exactly prec bits, where prec can be any integer between MPFR_PREC_MINand MPFR_PREC_MAX. */
                mpfr_set_default_prec: (prec) => { gmp.r_set_default_prec(prec); },
                /** Return the current default MPFR precision in bits. */
                mpfr_get_default_prec: () => gmp.r_get_default_prec(),
                /** Set the value of rop from op rounded toward the given direction rnd. */
                mpfr_set_d: (rop, op, rnd) => gmp.r_set_d(rop, op, rnd),
                /** Set the value of rop from op rounded toward the given direction rnd. */
                mpfr_set_z: (rop, op, rnd) => gmp.r_set_z(rop, op, rnd),
                /** Set the value of rop from op multiplied by two to the power e, rounded toward the given direction rnd. */
                mpfr_set_z_2exp: (rop, op, e, rnd) => gmp.r_set_z_2exp(rop, op, e, rnd),
                /** Set the variable x to NaN (Not-a-Number). */
                mpfr_set_nan: (x) => { gmp.r_set_nan(x); },
                /** Set the variable x to infinity. */
                mpfr_set_inf: (x, sign) => { gmp.r_set_inf(x, sign); },
                /** Set the variable x to zero. */
                mpfr_set_zero: (x, sign) => { gmp.r_set_zero(x, sign); },
                /** Set the value of rop from op rounded toward the given direction rnd. */
                mpfr_set_si: (rop, op, rnd) => gmp.r_set_si(rop, op, rnd),
                /** Set the value of rop from op rounded toward the given direction rnd. */
                mpfr_set_ui: (rop, op, rnd) => gmp.r_set_ui(rop, op, rnd),
                /** Set the value of rop from op multiplied by two to the power e, rounded toward the given direction rnd. */
                mpfr_set_si_2exp: (rop, op, e, rnd) => gmp.r_set_si_2exp(rop, op, e, rnd),
                /** Set the value of rop from op multiplied by two to the power e, rounded toward the given direction rnd. */
                mpfr_set_ui_2exp: (rop, op, e, rnd) => gmp.r_set_ui_2exp(rop, op, e, rnd),
                /** Set the value of rop from op rounded toward the given direction rnd. */
                mpfr_set_q: (rop, op, rnd) => gmp.r_set_q(rop, op, rnd),
                /** Set rop to op1 * op2 rounded in the direction rnd. */
                mpfr_mul_q: (rop, op1, op2, rnd) => gmp.r_mul_q(rop, op1, op2, rnd),
                /** Set rop to op1 / op2 rounded in the direction rnd. */
                mpfr_div_q: (rop, op1, op2, rnd) => gmp.r_div_q(rop, op1, op2, rnd),
                /** Set rop to op1 + op2 rounded in the direction rnd. */
                mpfr_add_q: (rop, op1, op2, rnd) => gmp.r_add_q(rop, op1, op2, rnd),
                /** Set rop to op1 - op2 rounded in the direction rnd. */
                mpfr_sub_q: (rop, op1, op2, rnd) => gmp.r_sub_q(rop, op1, op2, rnd),
                /** Compare op1 and op2. */
                mpfr_cmp_q: (op1, op2) => gmp.r_cmp_q(op1, op2),
                /** Convert op to a mpq_t. */
                mpfr_get_q: (rop, op) => gmp.r_get_q(rop, op),
                /** Set rop to the value of the string s in base base, rounded in the direction rnd. */
                mpfr_set_str: (rop, s, base, rnd) => gmp.r_set_str(rop, s, base, rnd),
                /** Initialize rop and set its value from op, rounded in the direction rnd. */
                mpfr_init_set: (rop, op, rnd) => gmp.r_init_set(rop, op, rnd),
                /** Initialize rop and set its value from op, rounded in the direction rnd. */
                mpfr_init_set_ui: (rop, op, rnd) => gmp.r_init_set_ui(rop, op, rnd),
                /** Initialize rop and set its value from op, rounded in the direction rnd. */
                mpfr_init_set_si: (rop, op, rnd) => gmp.r_init_set_si(rop, op, rnd),
                /** Initialize rop and set its value from op, rounded in the direction rnd. */
                mpfr_init_set_d: (rop, op, rnd) => gmp.r_init_set_d(rop, op, rnd),
                /** Initialize rop and set its value from op, rounded in the direction rnd. */
                mpfr_init_set_z: (rop, op, rnd) => gmp.r_init_set_z(rop, op, rnd),
                /** Initialize rop and set its value from op, rounded in the direction rnd. */
                mpfr_init_set_q: (rop, op, rnd) => gmp.r_init_set_q(rop, op, rnd),
                /** Initialize x and set its value from the string s in base base, rounded in the direction rnd. */
                mpfr_init_set_str: (x, s, base, rnd) => gmp.r_init_set_str(x, s, base, rnd),
                /** Set rop to the absolute value of op rounded in the direction rnd. */
                mpfr_abs: (rop, op, rnd) => gmp.r_abs(rop, op, rnd),
                /** Set the value of rop from op rounded toward the given direction rnd. */
                mpfr_set: (rop, op, rnd) => gmp.r_set(rop, op, rnd),
                /** Set rop to -op rounded in the direction rnd. */
                mpfr_neg: (rop, op, rnd) => gmp.r_neg(rop, op, rnd),
                /** Return a non-zero value iff op has its sign bit set (i.e., if it is negative, -0, or a NaN whose representation has its sign bit set). */
                mpfr_signbit: (op) => gmp.r_signbit(op),
                /** Set the value of rop from op, rounded toward the given direction rnd, then set (resp. clear) its sign bit if s is non-zero (resp. zero), even when op is a NaN. */
                mpfr_setsign: (rop, op, s, rnd) => gmp.r_setsign(rop, op, s, rnd),
                /** Set the value of rop from op1, rounded toward the given direction rnd, then set its sign bit to that of op2 (even when op1 or op2 is a NaN). */
                mpfr_copysign: (rop, op1, op2, rnd) => gmp.r_copysign(rop, op1, op2, rnd),
                /** Put the scaled significand of op (regarded as an integer, with the precision of op) into rop, and return the exponent exp (which may be outside the current exponent range) such that op = rop * 2^exp. */
                mpfr_get_z_2exp: (rop, op) => gmp.r_get_z_2exp(rop, op),
                /** Convert op to a double, using the rounding mode rnd. */
                mpfr_get_d: (op, rnd) => gmp.r_get_d(op, rnd),
                /** Return d and set exp such that 0.5 ≤ abs(d) <1 and d * 2^exp = op rounded to double precision, using the given rounding mode. */
                mpfr_get_d_2exp: (exp, op, rnd) => gmp.r_get_d_2exp(exp, op, rnd),
                /** Set exp and y such that 0.5 ≤ abs(y) < 1 and y * 2^exp = x rounded to the precision of y, using the given rounding mode. */
                mpfr_frexp: (exp, y, x, rnd) => gmp.r_frexp(exp, y, x, rnd),
                /** Convert op to a long after rounding it with respect to rnd. */
                mpfr_get_si: (op, rnd) => gmp.r_get_si(op, rnd),
                /** Convert op to an unsigned long after rounding it with respect to rnd. */
                mpfr_get_ui: (op, rnd) => gmp.r_get_ui(op, rnd),
                /** Return the minimal integer m such that any number of p bits, when output with m digits in radix b with rounding to nearest, can be recovered exactly when read again, still with rounding to nearest. More precisely, we have m = 1 + ceil(p*log(2)/log(b)), with p replaced by p-1 if b is a power of 2. */
                mpfr_get_str_ndigits: (b, p) => gmp.r_get_str_ndigits(b, p),
                /** Convert op to a string of digits in base b, with rounding in the direction rnd, where n is either zero (see below) or the number of significant digits output in the string; in the latter case, n must be greater or equal to 2. */
                mpfr_get_str: (str, expptr, base, n, op, rnd) => gmp.r_get_str(str, expptr, base, n, op, rnd),
                /** Convert op to a mpz_t, after rounding it with respect to rnd. */
                mpfr_get_z: (rop, op, rnd) => gmp.r_get_z(rop, op, rnd),
                /** Free a string allocated by mpfr_get_str using the unallocation function (see GNU MPFR - Memory Handling). */
                mpfr_free_str: (str) => { gmp.r_free_str(str); },
                /** Generate a uniformly distributed random float. */
                mpfr_urandom: (rop, state, rnd) => gmp.r_urandom(rop, state, rnd),
                /** Generate one random float according to a standard normal gaussian distribution (with mean zero and variance one). */
                mpfr_nrandom: (rop, state, rnd) => gmp.r_nrandom(rop, state, rnd),
                /** Generate one random float according to an exponential distribution, with mean one. */
                mpfr_erandom: (rop, state, rnd) => gmp.r_erandom(rop, state, rnd),
                /** Generate a uniformly distributed random float in the interval 0 ≤ rop < 1. */
                mpfr_urandomb: (rop, state) => gmp.r_urandomb(rop, state),
                /** Equivalent to mpfr_nexttoward where y is plus infinity. */
                mpfr_nextabove: (x) => { gmp.r_nextabove(x); },
                /** Equivalent to mpfr_nexttoward where y is minus infinity. */
                mpfr_nextbelow: (x) => { gmp.r_nextbelow(x); },
                /** Replace x by the next floating-point number in the direction of y. */
                mpfr_nexttoward: (x, y) => { gmp.r_nexttoward(x, y); },
                /** Set rop to op1 raised to op2, rounded in the direction rnd. */
                mpfr_pow: (rop, op1, op2, rnd) => gmp.r_pow(rop, op1, op2, rnd),
                /** Set rop to op1 raised to op2, rounded in the direction rnd. */
                mpfr_pow_si: (rop, op1, op2, rnd) => gmp.r_pow_si(rop, op1, op2, rnd),
                /** Set rop to op1 raised to op2, rounded in the direction rnd. */
                mpfr_pow_ui: (rop, op1, op2, rnd) => gmp.r_pow_ui(rop, op1, op2, rnd),
                /** Set rop to op1 raised to op2, rounded in the direction rnd. */
                mpfr_ui_pow_ui: (rop, op1, op2, rnd) => gmp.r_ui_pow_ui(rop, op1, op2, rnd),
                /** Set rop to op1 raised to op2, rounded in the direction rnd. */
                mpfr_ui_pow: (rop, op1, op2, rnd) => gmp.r_ui_pow(rop, op1, op2, rnd),
                /** Set rop to op1 raised to op2, rounded in the direction rnd. */
                mpfr_pow_z: (rop, op1, op2, rnd) => gmp.r_pow_z(rop, op1, op2, rnd),
                /** Set rop to the square root of op rounded in the direction rnd. */
                mpfr_sqrt: (rop, op, rnd) => gmp.r_sqrt(rop, op, rnd),
                /** Set rop to the square root of op rounded in the direction rnd. */
                mpfr_sqrt_ui: (rop, op, rnd) => gmp.r_sqrt_ui(rop, op, rnd),
                /** Set rop to the reciprocal square root of op rounded in the direction rnd. */
                mpfr_rec_sqrt: (rop, op, rnd) => gmp.r_rec_sqrt(rop, op, rnd),
                /** Set rop to op1 + op2 rounded in the direction rnd. */
                mpfr_add: (rop, op1, op2, rnd) => gmp.r_add(rop, op1, op2, rnd),
                /** Set rop to op1 - op2 rounded in the direction rnd. */
                mpfr_sub: (rop, op1, op2, rnd) => gmp.r_sub(rop, op1, op2, rnd),
                /** Set rop to op1 * op2 rounded in the direction rnd. */
                mpfr_mul: (rop, op1, op2, rnd) => gmp.r_mul(rop, op1, op2, rnd),
                /** Set rop to op1 / op2 rounded in the direction rnd. */
                mpfr_div: (rop, op1, op2, rnd) => gmp.r_div(rop, op1, op2, rnd),
                /** Set rop to op1 + op2 rounded in the direction rnd. */
                mpfr_add_ui: (rop, op1, op2, rnd) => gmp.r_add_ui(rop, op1, op2, rnd),
                /** Set rop to op1 - op2 rounded in the direction rnd. */
                mpfr_sub_ui: (rop, op1, op2, rnd) => gmp.r_sub_ui(rop, op1, op2, rnd),
                /** Set rop to op1 - op2 rounded in the direction rnd. */
                mpfr_ui_sub: (rop, op1, op2, rnd) => gmp.r_ui_sub(rop, op1, op2, rnd),
                /** Set rop to op1 * op2 rounded in the direction rnd. */
                mpfr_mul_ui: (rop, op1, op2, rnd) => gmp.r_mul_ui(rop, op1, op2, rnd),
                /** Set rop to op1 / op2 rounded in the direction rnd. */
                mpfr_div_ui: (rop, op1, op2, rnd) => gmp.r_div_ui(rop, op1, op2, rnd),
                /** Set rop to op1 / op2 rounded in the direction rnd. */
                mpfr_ui_div: (rop, op1, op2, rnd) => gmp.r_ui_div(rop, op1, op2, rnd),
                /** Set rop to op1 + op2 rounded in the direction rnd. */
                mpfr_add_si: (rop, op1, op2, rnd) => gmp.r_add_si(rop, op1, op2, rnd),
                /** Set rop to op1 - op2 rounded in the direction rnd. */
                mpfr_sub_si: (rop, op1, op2, rnd) => gmp.r_sub_si(rop, op1, op2, rnd),
                /** Set rop to op1 - op2 rounded in the direction rnd. */
                mpfr_si_sub: (rop, op1, op2, rnd) => gmp.r_si_sub(rop, op1, op2, rnd),
                /** Set rop to op1 * op2 rounded in the direction rnd. */
                mpfr_mul_si: (rop, op1, op2, rnd) => gmp.r_mul_si(rop, op1, op2, rnd),
                /** Set rop to op1 / op2 rounded in the direction rnd. */
                mpfr_div_si: (rop, op1, op2, rnd) => gmp.r_div_si(rop, op1, op2, rnd),
                /** Set rop to op1 / op2 rounded in the direction rnd. */
                mpfr_si_div: (rop, op1, op2, rnd) => gmp.r_si_div(rop, op1, op2, rnd),
                /** Set rop to op1 + op2 rounded in the direction rnd. */
                mpfr_add_d: (rop, op1, op2, rnd) => gmp.r_add_d(rop, op1, op2, rnd),
                /** Set rop to op1 - op2 rounded in the direction rnd. */
                mpfr_sub_d: (rop, op1, op2, rnd) => gmp.r_sub_d(rop, op1, op2, rnd),
                /** Set rop to op1 - op2 rounded in the direction rnd. */
                mpfr_d_sub: (rop, op1, op2, rnd) => gmp.r_d_sub(rop, op1, op2, rnd),
                /** Set rop to op1 * op2 rounded in the direction rnd. */
                mpfr_mul_d: (rop, op1, op2, rnd) => gmp.r_mul_d(rop, op1, op2, rnd),
                /** Set rop to op1 / op2 rounded in the direction rnd. */
                mpfr_div_d: (rop, op1, op2, rnd) => gmp.r_div_d(rop, op1, op2, rnd),
                /** Set rop to op1 / op2 rounded in the direction rnd. */
                mpfr_d_div: (rop, op1, op2, rnd) => gmp.r_d_div(rop, op1, op2, rnd),
                /** Set rop to the square of op rounded in the direction rnd. */
                mpfr_sqr: (rop, op, rnd) => gmp.r_sqr(rop, op, rnd),
                /** Set rop to the value of Pi rounded in the direction rnd. */
                mpfr_const_pi: (rop, rnd) => gmp.r_const_pi(rop, rnd),
                /** Set rop to the logarithm of 2 rounded in the direction rnd. */
                mpfr_const_log2: (rop, rnd) => gmp.r_const_log2(rop, rnd),
                /** Set rop to the value of Euler’s constant 0.577… rounded in the direction rnd. */
                mpfr_const_euler: (rop, rnd) => gmp.r_const_euler(rop, rnd),
                /** Set rop to the value of Catalan’s constant 0.915… rounded in the direction rnd. */
                mpfr_const_catalan: (rop, rnd) => gmp.r_const_catalan(rop, rnd),
                /** Set rop to the arithmetic-geometric mean of op1 and op2 rounded in the direction rnd. */
                mpfr_agm: (rop, op1, op2, rnd) => gmp.r_agm(rop, op1, op2, rnd),
                /** Set rop to the natural logarithm of op rounded in the direction rnd. */
                mpfr_log: (rop, op, rnd) => gmp.r_log(rop, op, rnd),
                /** Set rop to log2(op) rounded in the direction rnd. */
                mpfr_log2: (rop, op, rnd) => gmp.r_log2(rop, op, rnd),
                /** Set rop to log10(op) rounded in the direction rnd. */
                mpfr_log10: (rop, op, rnd) => gmp.r_log10(rop, op, rnd),
                /** Set rop to the logarithm of one plus op, rounded in the direction rnd. */
                mpfr_log1p: (rop, op, rnd) => gmp.r_log1p(rop, op, rnd),
                /** Set rop to the natural logarithm of op rounded in the direction rnd. */
                mpfr_log_ui: (rop, op, rnd) => gmp.r_log_ui(rop, op, rnd),
                /** Set rop to the exponential of op rounded in the direction rnd. */
                mpfr_exp: (rop, op, rnd) => gmp.r_exp(rop, op, rnd),
                /** Set rop to 2^op rounded in the direction rnd. */
                mpfr_exp2: (rop, op, rnd) => gmp.r_exp2(rop, op, rnd),
                /** Set rop to 10^op rounded in the direction rnd. */
                mpfr_exp10: (rop, op, rnd) => gmp.r_exp10(rop, op, rnd),
                /** Set rop to the e^op - 1, rounded in the direction rnd. */
                mpfr_expm1: (rop, op, rnd) => gmp.r_expm1(rop, op, rnd),
                /** Set rop to the exponential integral of op rounded in the direction rnd. */
                mpfr_eint: (rop, op, rnd) => gmp.r_eint(rop, op, rnd),
                /** Set rop to real part of the dilogarithm of op rounded in the direction rnd. */
                mpfr_li2: (rop, op, rnd) => gmp.r_li2(rop, op, rnd),
                /** Compare op1 and op2. */
                mpfr_cmp: (op1, op2) => gmp.r_cmp(op1, op2),
                /** Compare op1 and op2. */
                mpfr_cmp_d: (op1, op2) => gmp.r_cmp_d(op1, op2),
                /** Compare op1 and op2. */
                mpfr_cmp_ui: (op1, op2) => gmp.r_cmp_ui(op1, op2),
                /** Compare op1 and op2. */
                mpfr_cmp_si: (op1, op2) => gmp.r_cmp_si(op1, op2),
                /** Compare op1 and op2 * 2^e. */
                mpfr_cmp_ui_2exp: (op1, op2, e) => gmp.r_cmp_ui_2exp(op1, op2, e),
                /** Compare op1 and op2 * 2^e. */
                mpfr_cmp_si_2exp: (op1, op2, e) => gmp.r_cmp_si_2exp(op1, op2, e),
                /** Compare |op1| and |op2|. */
                mpfr_cmpabs: (op1, op2) => gmp.r_cmpabs(op1, op2),
                /** Compare |op1| and |op2|. */
                mpfr_cmpabs_ui: (op1, op2) => gmp.r_cmpabs_ui(op1, op2),
                /** Compute the relative difference between op1 and op2 and store the result in rop. */
                mpfr_reldiff: (rop, op1, op2, rnd) => { gmp.r_reldiff(rop, op1, op2, rnd); },
                /** Return non-zero if op1 and op2 are both non-zero ordinary numbers with the same exponent and the same first op3 bits. */
                mpfr_eq: (op1, op2, op3) => gmp.r_eq(op1, op2, op3),
                /** Return a positive value if op > 0, zero if op = 0, and a negative value if op < 0. */
                mpfr_sgn: (op) => gmp.r_sgn(op),
                /** Set rop to op1 * 2^op2 rounded in the direction rnd. */
                mpfr_mul_2exp: (rop, op1, op2, rnd) => gmp.r_mul_2exp(rop, op1, op2, rnd),
                /** Set rop to op1 divided by 2^op2 rounded in the direction rnd. */
                mpfr_div_2exp: (rop, op1, op2, rnd) => gmp.r_div_2exp(rop, op1, op2, rnd),
                /** Set rop to op1 * 2^op2 rounded in the direction rnd. */
                mpfr_mul_2ui: (rop, op1, op2, rnd) => gmp.r_mul_2ui(rop, op1, op2, rnd),
                /** Set rop to op1 divided by 2^op2 rounded in the direction rnd. */
                mpfr_div_2ui: (rop, op1, op2, rnd) => gmp.r_div_2ui(rop, op1, op2, rnd),
                /** Set rop to op1 * 2^op2 rounded in the direction rnd. */
                mpfr_mul_2si: (rop, op1, op2, rnd) => gmp.r_mul_2si(rop, op1, op2, rnd),
                /** Set rop to op1 divided by 2^op2 rounded in the direction rnd. */
                mpfr_div_2si: (rop, op1, op2, rnd) => gmp.r_div_2si(rop, op1, op2, rnd),
                /** Set rop to op rounded to the nearest representable integer in the given direction rnd. */
                mpfr_rint: (rop, op, rnd) => gmp.r_rint(rop, op, rnd),
                /** Set rop to op rounded to the nearest representable integer, rounding halfway cases with the even-rounding rule zero (like mpfr_rint(mpfr_t, mpfr_t, mpfr_rnd_t) with MPFR_RNDN). */
                mpfr_roundeven: (rop, op) => gmp.r_roundeven(rop, op),
                /** Set rop to op rounded to the nearest representable integer, rounding halfway cases away from zero (as in the roundTiesToAway mode of IEEE 754-2008). */
                mpfr_round: (rop, op) => gmp.r_round(rop, op),
                /** Set rop to op rounded to the next representable integer toward zero (like mpfr_rint(mpfr_t, mpfr_t, mpfr_rnd_t) with MPFR_RNDZ). */
                mpfr_trunc: (rop, op) => gmp.r_trunc(rop, op),
                /** Set rop to op rounded to the next higher or equal representable integer (like mpfr_rint(mpfr_t, mpfr_t, mpfr_rnd_t) with MPFR_RNDU). */
                mpfr_ceil: (rop, op) => gmp.r_ceil(rop, op),
                /** Set rop to op rounded to the next lower or equal representable integer. */
                mpfr_floor: (rop, op) => gmp.r_floor(rop, op),
                /** Set rop to op rounded to the nearest integer, rounding halfway cases to the nearest even integer. */
                mpfr_rint_roundeven: (rop, op, rnd) => gmp.r_rint_roundeven(rop, op, rnd),
                /** Set rop to op rounded to the nearest integer, rounding halfway cases away from zero. */
                mpfr_rint_round: (rop, op, rnd) => gmp.r_rint_round(rop, op, rnd),
                /** Set rop to op rounded to the next integer toward zero. */
                mpfr_rint_trunc: (rop, op, rnd) => gmp.r_rint_trunc(rop, op, rnd),
                /** Set rop to op rounded to the next higher or equal integer. */
                mpfr_rint_ceil: (rop, op, rnd) => gmp.r_rint_ceil(rop, op, rnd),
                /** Set rop to op rounded to the next lower or equal integer. */
                mpfr_rint_floor: (rop, op, rnd) => gmp.r_rint_floor(rop, op, rnd),
                /** Set rop to the fractional part of op, having the same sign as op, rounded in the direction rnd. */
                mpfr_frac: (rop, op, rnd) => gmp.r_frac(rop, op, rnd),
                /** Set simultaneously iop to the integral part of op and fop to the fractional part of op, rounded in the direction rnd with the corresponding precision of iop and fop. */
                mpfr_modf: (rop, fop, op, rnd) => gmp.r_modf(rop, fop, op, rnd),
                /** Set r to the value of x - n * y, rounded according to the direction rnd, where n is the integer quotient of x divided by y, rounded to the nearest integer (ties rounded to even). */
                mpfr_remquo: (r, q, x, y, rnd) => gmp.r_remquo(r, q, x, y, rnd),
                /** Set r to the value of x - n * y, rounded according to the direction rnd, where n is the integer quotient of x divided by y, rounded to the nearest integer (ties rounded to even). */
                mpfr_remainder: (rop, x, y, rnd) => gmp.r_remainder(rop, x, y, rnd),
                /** Set r to the value of x - n * y, rounded according to the direction rnd, where n is the integer quotient of x divided by y, rounded toward zero. */
                mpfr_fmod: (rop, x, y, rnd) => gmp.r_fmod(rop, x, y, rnd),
                /** Set r to the value of x - n * y, rounded according to the direction rnd, where n is the integer quotient of x divided by y, rounded toward zero. */
                mpfr_fmodquo: (rop, q, x, y, rnd) => gmp.r_fmodquo(rop, q, x, y, rnd),
                /** Return non-zero if op would fit in the C data type (32-bit) unsigned long when rounded to an integer in the direction rnd. */
                mpfr_fits_ulong_p: (op, rnd) => gmp.r_fits_ulong_p(op, rnd),
                /** Return non-zero if op would fit in the C data type (32-bit) long when rounded to an integer in the direction rnd. */
                mpfr_fits_slong_p: (op, rnd) => gmp.r_fits_slong_p(op, rnd),
                /** Return non-zero if op would fit in the C data type (32-bit) unsigned int when rounded to an integer in the direction rnd. */
                mpfr_fits_uint_p: (op, rnd) => gmp.r_fits_uint_p(op, rnd),
                /** Return non-zero if op would fit in the C data type (32-bit) int when rounded to an integer in the direction rnd. */
                mpfr_fits_sint_p: (op, rnd) => gmp.r_fits_sint_p(op, rnd),
                /** Return non-zero if op would fit in the C data type (16-bit) unsigned short when rounded to an integer in the direction rnd. */
                mpfr_fits_ushort_p: (op, rnd) => gmp.r_fits_ushort_p(op, rnd),
                /** Return non-zero if op would fit in the C data type (16-bit) short when rounded to an integer in the direction rnd. */
                mpfr_fits_sshort_p: (op, rnd) => gmp.r_fits_sshort_p(op, rnd),
                /** Return non-zero if op would fit in the C data type (32-bit) unsigned long when rounded to an integer in the direction rnd. */
                mpfr_fits_uintmax_p: (op, rnd) => gmp.r_fits_uintmax_p(op, rnd),
                /** Return non-zero if op would fit in the C data type (32-bit) long when rounded to an integer in the direction rnd. */
                mpfr_fits_intmax_p: (op, rnd) => gmp.r_fits_intmax_p(op, rnd),
                /** Swap the structures pointed to by x and y. */
                mpfr_swap: (x, y) => { gmp.r_swap(x, y); },
                /** Output op on stdout in some unspecified format, then a newline character. This function is mainly for debugging purpose. Thus invalid data may be supported. */
                mpfr_dump: (op) => { gmp.r_dump(op); },
                /** Return non-zero if op is NaN. Return zero otherwise. */
                mpfr_nan_p: (op) => gmp.r_nan_p(op),
                /** Return non-zero if op is an infinity. Return zero otherwise. */
                mpfr_inf_p: (op) => gmp.r_inf_p(op),
                /** Return non-zero if op is an ordinary number (i.e., neither NaN nor an infinity). Return zero otherwise. */
                mpfr_number_p: (op) => gmp.r_number_p(op),
                /** Return non-zero iff op is an integer. */
                mpfr_integer_p: (op) => gmp.r_integer_p(op),
                /** Return non-zero if op is zero. Return zero otherwise. */
                mpfr_zero_p: (op) => gmp.r_zero_p(op),
                /** Return non-zero if op is a regular number (i.e., neither NaN, nor an infinity nor zero). Return zero otherwise. */
                mpfr_regular_p: (op) => gmp.r_regular_p(op),
                /** Return non-zero if op1 > op2, and zero otherwise. */
                mpfr_greater_p: (op1, op2) => gmp.r_greater_p(op1, op2),
                /** Return non-zero if op1 ≥ op2, and zero otherwise. */
                mpfr_greaterequal_p: (op1, op2) => gmp.r_greaterequal_p(op1, op2),
                /** Return non-zero if op1 < op2, and zero otherwise. */
                mpfr_less_p: (op1, op2) => gmp.r_less_p(op1, op2),
                /** Return non-zero if op1 ≤ op2, and zero otherwise. */
                mpfr_lessequal_p: (op1, op2) => gmp.r_lessequal_p(op1, op2),
                /** Return non-zero if op1 < op2 or op1 > op2 (i.e., neither op1, nor op2 is NaN, and op1 ≠ op2), zero otherwise (i.e., op1 and/or op2 is NaN, or op1 = op2). */
                mpfr_lessgreater_p: (op1, op2) => gmp.r_lessgreater_p(op1, op2),
                /** Return non-zero if op1 = op2, and zero otherwise. */
                mpfr_equal_p: (op1, op2) => gmp.r_equal_p(op1, op2),
                /** Return non-zero if op1 or op2 is a NaN (i.e., they cannot be compared), zero otherwise. */
                mpfr_unordered_p: (op1, op2) => gmp.r_unordered_p(op1, op2),
                /** Set rop to the inverse hyperbolic tangent of op rounded in the direction rnd. */
                mpfr_atanh: (rop, op, rnd) => gmp.r_atanh(rop, op, rnd),
                /** Set rop to the inverse hyperbolic cosine of op rounded in the direction rnd. */
                mpfr_acosh: (rop, op, rnd) => gmp.r_acosh(rop, op, rnd),
                /** Set rop to the inverse hyperbolic sine of op rounded in the direction rnd. */
                mpfr_asinh: (rop, op, rnd) => gmp.r_asinh(rop, op, rnd),
                /** Set rop to the hyperbolic cosine of op rounded in the direction rnd. */
                mpfr_cosh: (rop, op, rnd) => gmp.r_cosh(rop, op, rnd),
                /** Set rop to the hyperbolic sine of op rounded in the direction rnd. */
                mpfr_sinh: (rop, op, rnd) => gmp.r_sinh(rop, op, rnd),
                /** Set rop to the hyperbolic tangent of op rounded in the direction rnd. */
                mpfr_tanh: (rop, op, rnd) => gmp.r_tanh(rop, op, rnd),
                /** Set simultaneously sop to the hyperbolic sine of op and cop to the hyperbolic cosine of op, rounded in the direction rnd with the corresponding precision of sop and cop, which must be different variables. */
                mpfr_sinh_cosh: (sop, cop, op, rnd) => gmp.r_sinh_cosh(sop, cop, op, rnd),
                /** Set rop to the hyperbolic secant of op rounded in the direction rnd. */
                mpfr_sech: (rop, op, rnd) => gmp.r_sech(rop, op, rnd),
                /** Set rop to the hyperbolic cosecant of op rounded in the direction rnd. */
                mpfr_csch: (rop, op, rnd) => gmp.r_csch(rop, op, rnd),
                /** Set rop to the hyperbolic cotangent of op rounded in the direction rnd. */
                mpfr_coth: (rop, op, rnd) => gmp.r_coth(rop, op, rnd),
                /** Set rop to the arc-cosine of op rounded in the direction rnd. */
                mpfr_acos: (rop, op, rnd) => gmp.r_acos(rop, op, rnd),
                /** Set rop to the arc-sine of op rounded in the direction rnd. */
                mpfr_asin: (rop, op, rnd) => gmp.r_asin(rop, op, rnd),
                /** Set rop to the arc-tangent of op rounded in the direction rnd. */
                mpfr_atan: (rop, op, rnd) => gmp.r_atan(rop, op, rnd),
                /** Set rop to the sine of op rounded in the direction rnd. */
                mpfr_sin: (rop, op, rnd) => gmp.r_sin(rop, op, rnd),
                /** Set simultaneously sop to the sine of op and cop to the cosine of op, rounded in the direction rnd with the corresponding precisions of sop and cop, which must be different variables. */
                mpfr_sin_cos: (sop, cop, op, rnd) => gmp.r_sin_cos(sop, cop, op, rnd),
                /** Set rop to the cosine of op rounded in the direction rnd. */
                mpfr_cos: (rop, op, rnd) => gmp.r_cos(rop, op, rnd),
                /** Set rop to the tangent of op rounded in the direction rnd. */
                mpfr_tan: (rop, op, rnd) => gmp.r_tan(rop, op, rnd),
                /** Set rop to the arc-tangent2 of y and x rounded in the direction rnd. */
                mpfr_atan2: (rop, y, x, rnd) => gmp.r_atan2(rop, y, x, rnd),
                /** Set rop to the secant of op rounded in the direction rnd. */
                mpfr_sec: (rop, op, rnd) => gmp.r_sec(rop, op, rnd),
                /** Set rop to the cosecant of op rounded in the direction rnd. */
                mpfr_csc: (rop, op, rnd) => gmp.r_csc(rop, op, rnd),
                /** Set rop to the cotangent of op rounded in the direction rnd. */
                mpfr_cot: (rop, op, rnd) => gmp.r_cot(rop, op, rnd),
                /** Set rop to the Euclidean norm of x and y, i.e., the square root of the sum of the squares of x and y rounded in the direction rnd. */
                mpfr_hypot: (rop, x, y, rnd) => gmp.r_hypot(rop, x, y, rnd),
                /** Set rop to the value of the error function on op rounded in the direction rnd. */
                mpfr_erf: (rop, op, rnd) => gmp.r_erf(rop, op, rnd),
                /** Set rop to the value of the complementary error function on op rounded in the direction rnd. */
                mpfr_erfc: (rop, op, rnd) => gmp.r_erfc(rop, op, rnd),
                /** Set rop to the cubic root of op rounded in the direction rnd. */
                mpfr_cbrt: (rop, op, rnd) => gmp.r_cbrt(rop, op, rnd),
                /** Set rop to the kth root of op rounded in the direction rnd. */
                mpfr_rootn_ui: (rop, op, k, rnd) => gmp.r_rootn_ui(rop, op, k, rnd),
                /** Set rop to the value of the Gamma function on op rounded in the direction rnd. */
                mpfr_gamma: (rop, op, rnd) => gmp.r_gamma(rop, op, rnd),
                /** Set rop to the value of the incomplete Gamma function on op and op2, rounded in the direction rnd. */
                mpfr_gamma_inc: (rop, op, op2, rnd) => gmp.r_gamma_inc(rop, op, op2, rnd),
                /** Set rop to the value of the Beta function at arguments op1 and op2, rounded in the direction rnd. */
                mpfr_beta: (rop, op1, op2, rnd) => gmp.r_beta(rop, op1, op2, rnd),
                /** Set rop to the value of the logarithm of the Gamma function on op rounded in the direction rnd. */
                mpfr_lngamma: (rop, op, rnd) => gmp.r_lngamma(rop, op, rnd),
                /** Set rop to the value of the logarithm of the absolute value of the Gamma function on op rounded in the direction rnd. */
                mpfr_lgamma: (rop, signp, op, rnd) => gmp.r_lgamma(rop, signp, op, rnd),
                /** Set rop to the value of the Digamma (sometimes also called Psi) function on op rounded in the direction rnd. */
                mpfr_digamma: (rop, op, rnd) => gmp.r_digamma(rop, op, rnd),
                /** Set rop to the value of the Riemann Zeta function on op rounded in the direction rnd. */
                mpfr_zeta: (rop, op, rnd) => gmp.r_zeta(rop, op, rnd),
                /** Set rop to the value of the Riemann Zeta function on op rounded in the direction rnd. */
                mpfr_zeta_ui: (rop, op, rnd) => gmp.r_zeta_ui(rop, op, rnd),
                /** Set rop to the factorial of op rounded in the direction rnd. */
                mpfr_fac_ui: (rop, op, rnd) => gmp.r_fac_ui(rop, op, rnd),
                /** Set rop to the value of the first kind Bessel function of order 0 on op rounded in the direction rnd. */
                mpfr_j0: (rop, op, rnd) => gmp.r_j0(rop, op, rnd),
                /** Set rop to the value of the first kind Bessel function of order 1 on op rounded in the direction rnd. */
                mpfr_j1: (rop, op, rnd) => gmp.r_j1(rop, op, rnd),
                /** Set rop to the value of the first kind Bessel function of order n on op rounded in the direction rnd. */
                mpfr_jn: (rop, n, op, rnd) => gmp.r_jn(rop, n, op, rnd),
                /** Set rop to the value of the first kind Bessel function of order 0 on op rounded in the direction rnd. */
                mpfr_y0: (rop, op, rnd) => gmp.r_y0(rop, op, rnd),
                /** Set rop to the value of the first kind Bessel function of order 1 on op rounded in the direction rnd. */
                mpfr_y1: (rop, op, rnd) => gmp.r_y1(rop, op, rnd),
                /** Set rop to the value of the first kind Bessel function of order n on op rounded in the direction rnd. */
                mpfr_yn: (rop, n, op, rnd) => gmp.r_yn(rop, n, op, rnd),
                /** Set rop to the value of the Airy function Ai on x rounded in the direction rnd. */
                mpfr_ai: (rop, x, rnd) => gmp.r_ai(rop, x, rnd),
                /** Set rop to the minimum of op1 and op2. */
                mpfr_min: (rop, op1, op2, rnd) => gmp.r_min(rop, op1, op2, rnd),
                /** Set rop to the maximum of op1 and op2. */
                mpfr_max: (rop, op1, op2, rnd) => gmp.r_max(rop, op1, op2, rnd),
                /** Set rop to the positive difference of op1 and op2, i.e., op1 - op2 rounded in the direction rnd if op1 > op2, +0 if op1 ≤ op2, and NaN if op1 or op2 is NaN. */
                mpfr_dim: (rop, op1, op2, rnd) => gmp.r_dim(rop, op1, op2, rnd),
                /** Set rop to op1 * op2 rounded in the direction rnd. */
                mpfr_mul_z: (rop, op1, op2, rnd) => gmp.r_mul_z(rop, op1, op2, rnd),
                /** Set rop to op1 / op2 rounded in the direction rnd. */
                mpfr_div_z: (rop, op1, op2, rnd) => gmp.r_div_z(rop, op1, op2, rnd),
                /** Set rop to op1 + op2 rounded in the direction rnd. */
                mpfr_add_z: (rop, op1, op2, rnd) => gmp.r_add_z(rop, op1, op2, rnd),
                /** Set rop to op1 - op2 rounded in the direction rnd. */
                mpfr_sub_z: (rop, op1, op2, rnd) => gmp.r_sub_z(rop, op1, op2, rnd),
                /** Set rop to op1 - op2 rounded in the direction rnd. */
                mpfr_z_sub: (rop, op1, op2, rnd) => gmp.r_z_sub(rop, op1, op2, rnd),
                /** Compare op1 and op2. */
                mpfr_cmp_z: (op1, op2) => gmp.r_cmp_z(op1, op2),
                /** Set rop to (op1 * op2) + op3 rounded in the direction rnd. */
                mpfr_fma: (rop, op1, op2, op3, rnd) => gmp.r_fma(rop, op1, op2, op3, rnd),
                /** Set rop to (op1 * op2) - op3 rounded in the direction rnd. */
                mpfr_fms: (rop, op1, op2, op3, rnd) => gmp.r_fms(rop, op1, op2, op3, rnd),
                /** Set rop to (op1 * op2) + (op3 * op4) rounded in the direction rnd. */
                mpfr_fmma: (rop, op1, op2, op3, op4, rnd) => gmp.r_fmma(rop, op1, op2, op3, op4, rnd),
                /** Set rop to (op1 * op2) - (op3 * op4) rounded in the direction rnd. */
                mpfr_fmms: (rop, op1, op2, op3, op4, rnd) => gmp.r_fmms(rop, op1, op2, op3, op4, rnd),
                /** Set rop to the sum of all elements of tab, whose size is n, correctly rounded in the direction rnd. */
                mpfr_sum: (rop, tab, n, rnd) => gmp.r_sum(rop, tab, n, rnd),
                /** Set rop to the dot product of elements of a by those of b, whose common size is n, correctly rounded in the direction rnd. */
                mpfr_dot: (rop, a, b, n, rnd) => gmp.r_dot(rop, a, b, n, rnd),
                /** Free all caches and pools used by MPFR internally. */
                mpfr_free_cache: () => { gmp.r_free_cache(); },
                /** Free various caches and pools used by MPFR internally, as specified by way, which is a set of flags */
                mpfr_free_cache2: (way) => { gmp.r_free_cache2(way); },
                /** Free the pools used by MPFR internally. */
                mpfr_free_pool: () => { gmp.r_free_pool(); },
                /** This function should be called before calling mp_set_memory_functions(allocate_function, reallocate_function, free_function). */
                mpfr_mp_memory_cleanup: () => gmp.r_mp_memory_cleanup(),
                /** This function rounds x emulating subnormal number arithmetic. */
                mpfr_subnormalize: (x, t, rnd) => gmp.r_subnormalize(x, t, rnd),
                /** Read a floating-point number from a string nptr in base base, rounded in the direction rnd. */
                mpfr_strtofr: (rop, nptr, endptr, base, rnd) => gmp.r_strtofr(rop, nptr, endptr, base, rnd),
                /** Return the needed size in bytes to store the significand of a floating-point number of precision prec. */
                mpfr_custom_get_size: (prec) => gmp.r_custom_get_size(prec),
                /** Initialize a significand of precision prec. */
                mpfr_custom_init: (significand, prec) => { gmp.r_custom_init(significand, prec); },
                /** Return a pointer to the significand used by a mpfr_t initialized with mpfr_custom_init_set. */
                mpfr_custom_get_significand: (x) => gmp.r_custom_get_significand(x),
                /** Return the exponent of x */
                mpfr_custom_get_exp: (x) => gmp.r_custom_get_exp(x),
                /** Inform MPFR that the significand of x has moved due to a garbage collect and update its new position to new_position. */
                mpfr_custom_move: (x, new_position) => { gmp.r_custom_move(x, new_position); },
                /** Perform a dummy initialization of a mpfr_t. */
                mpfr_custom_init_set: (x, kind, exp, prec, significand) => { gmp.r_custom_init_set(x, kind, exp, prec, significand); },
                /** Return the current kind of a mpfr_t as created by mpfr_custom_init_set. */
                mpfr_custom_get_kind: (x) => gmp.r_custom_get_kind(x),
                /** This function implements the totalOrder predicate from IEEE 754-2008, where -NaN < -Inf < negative finite numbers < -0 < +0 < positive finite numbers < +Inf < +NaN. It returns a non-zero value (true) when x is smaller than or equal to y for this order relation, and zero (false) otherwise */
                mpfr_total_order_p: (x, y) => gmp.r_total_order_p(x, y),
                /**************** Helper functions  ****************/
                /** Converts JS string into MPZ integer */
                mpz_set_string(mpz, input, base) {
                    const srcPtr = getStringPointer(input);
                    const res = gmp.z_set_str(mpz, srcPtr, base);
                    if (srcPtr !== strBuf) {
                        gmp.g_free(srcPtr);
                    }
                    return res;
                },
                /** Initializes new MPFR float from JS string */
                mpz_init_set_string(mpz, input, base) {
                    const srcPtr = getStringPointer(input);
                    const res = gmp.z_init_set_str(mpz, srcPtr, base);
                    if (srcPtr !== strBuf) {
                        gmp.g_free(srcPtr);
                    }
                    return res;
                },
                /** Converts MPZ int into JS string */
                mpz_to_string(x, base) {
                    let destPtr = 0;
                    if (gmp.z_sizeinbase(x, base) + 2 < PREALLOCATED_STR_SIZE) {
                        destPtr = strBuf;
                    }
                    const strPtr = gmp.z_get_str(destPtr, base, x);
                    const endPtr = this.mem.indexOf(0, strPtr);
                    const str = decoder.decode(this.mem.subarray(strPtr, endPtr));
                    if (destPtr !== strBuf) {
                        gmp.g_free(strPtr);
                    }
                    return str;
                },
                /** Converts JS string into MPQ rational */
                mpq_set_string(mpq, input, base) {
                    const srcPtr = getStringPointer(input);
                    const res = gmp.q_set_str(mpq, srcPtr, base);
                    if (srcPtr !== strBuf) {
                        gmp.g_free(srcPtr);
                    }
                    return res;
                },
                /** Converts MPQ rational into JS string */
                mpq_to_string(x, base) {
                    let destPtr = 0;
                    const requiredSize = gmp.z_sizeinbase(gmp.q_numref(x), base) + gmp.z_sizeinbase(gmp.q_denref(x), base) + 3;
                    if (requiredSize < PREALLOCATED_STR_SIZE) {
                        destPtr = strBuf;
                    }
                    const strPtr = gmp.q_get_str(destPtr, base, x);
                    const endPtr = this.mem.indexOf(0, strPtr);
                    const str = decoder.decode(this.mem.subarray(strPtr, endPtr));
                    if (destPtr !== strBuf) {
                        gmp.g_free(strPtr);
                    }
                    return str;
                },
                /** Converts JS string into MPFR float */
                mpfr_set_string(mpfr, input, base, rnd) {
                    const srcPtr = getStringPointer(input);
                    const res = gmp.r_set_str(mpfr, srcPtr, base, rnd);
                    if (srcPtr !== strBuf) {
                        gmp.g_free(srcPtr);
                    }
                    return res;
                },
                /** Initializes new MPFR float from JS string */
                mpfr_init_set_string(mpfr, input, base, rnd) {
                    const srcPtr = getStringPointer(input);
                    const res = gmp.r_init_set_str(mpfr, srcPtr, base, rnd);
                    if (srcPtr !== strBuf) {
                        gmp.g_free(srcPtr);
                    }
                    return res;
                },
                /** Converts MPFR float into JS string */
                mpfr_to_string(x, base, rnd) {
                    let destPtr = 0;
                    const n = gmp.r_get_str_ndigits(base, gmp.r_get_prec(x));
                    const requiredSize = Math.max(7, n + 2);
                    if (requiredSize < PREALLOCATED_STR_SIZE) {
                        destPtr = strBuf;
                    }
                    const strPtr = gmp.r_get_str(destPtr, mpfr_exp_t_ptr, base, n, x, rnd);
                    const endPtr = this.mem.indexOf(0, strPtr);
                    let str = decoder.decode(this.mem.subarray(strPtr, endPtr));
                    if (FLOAT_SPECIAL_VALUE_KEYS.includes(str)) {
                        str = FLOAT_SPECIAL_VALUES[str];
                    }
                    else {
                        // decimal point needs to be inserted
                        const pointPos = this.memView.getInt32(mpfr_exp_t_ptr, true);
                        str = insertDecimalPoint(str, pointPos);
                    }
                    if (destPtr !== strBuf) {
                        gmp.r_free_str(strPtr);
                    }
                    return str;
                }
            };
        });
    }

    exports.DivMode = void 0;
    (function (DivMode) {
        DivMode[DivMode["CEIL"] = 0] = "CEIL";
        DivMode[DivMode["FLOOR"] = 1] = "FLOOR";
        DivMode[DivMode["TRUNCATE"] = 2] = "TRUNCATE";
    })(exports.DivMode || (exports.DivMode = {}));
    const INVALID_PARAMETER_ERROR$1 = 'Invalid parameter!';
    function getIntegerContext(gmp, ctx) {
        const mpz_t_arr = [];
        const isInteger = (val) => ctx.intContext.isInteger(val);
        const isRational = (val) => ctx.rationalContext.isRational(val);
        const isFloat = (val) => ctx.floatContext.isFloat(val);
        const compare = (mpz_t, val) => {
            if (typeof val === 'number') {
                assertInt32(val);
                return gmp.mpz_cmp_si(mpz_t, val);
            }
            if (typeof val === 'string') {
                const i = IntegerFn(val);
                return gmp.mpz_cmp(mpz_t, i.mpz_t);
            }
            if (isInteger(val)) {
                return gmp.mpz_cmp(mpz_t, val.mpz_t);
            }
            if (isRational(val)) {
                return -gmp.mpq_cmp_z(val.mpq_t, mpz_t);
            }
            if (isFloat(val)) {
                return -gmp.mpfr_cmp_z(val.mpfr_t, mpz_t);
            }
            throw new Error(INVALID_PARAMETER_ERROR$1);
        };
        const IntPrototype = {
            mpz_t: 0,
            type: 'integer',
            /** Returns the sum of this number and the given one. */
            add(val) {
                if (typeof val === 'number') {
                    assertInt32(val);
                    const n = IntegerFn();
                    if (val < 0) {
                        gmp.mpz_sub_ui(n.mpz_t, this.mpz_t, -val);
                    }
                    else {
                        gmp.mpz_add_ui(n.mpz_t, this.mpz_t, val);
                    }
                    return n;
                }
                if (typeof val === 'string') {
                    const n = IntegerFn(val);
                    gmp.mpz_add(n.mpz_t, this.mpz_t, n.mpz_t);
                    return n;
                }
                if (isInteger(val)) {
                    const n = IntegerFn();
                    gmp.mpz_add(n.mpz_t, this.mpz_t, val.mpz_t);
                    return n;
                }
                if (isRational(val) || isFloat(val)) {
                    return val.add(this);
                }
                throw new Error(INVALID_PARAMETER_ERROR$1);
            },
            /** Returns the difference of this number and the given one. */
            sub(val) {
                if (typeof val === 'number') {
                    const n = IntegerFn();
                    assertInt32(val);
                    if (val < 0) {
                        gmp.mpz_add_ui(n.mpz_t, this.mpz_t, -val);
                    }
                    else {
                        gmp.mpz_sub_ui(n.mpz_t, this.mpz_t, val);
                    }
                    return n;
                }
                if (typeof val === 'string') {
                    const n = IntegerFn(val);
                    gmp.mpz_sub(n.mpz_t, this.mpz_t, n.mpz_t);
                    return n;
                }
                if (isInteger(val)) {
                    const n = IntegerFn();
                    gmp.mpz_sub(n.mpz_t, this.mpz_t, val.mpz_t);
                    return n;
                }
                if (isRational(val) || isFloat(val)) {
                    return val.neg().add(this);
                }
                throw new Error(INVALID_PARAMETER_ERROR$1);
            },
            /** Returns the product of this number and the given one. */
            mul(val) {
                if (typeof val === 'number') {
                    const n = IntegerFn();
                    assertInt32(val);
                    gmp.mpz_mul_si(n.mpz_t, this.mpz_t, val);
                    return n;
                }
                if (typeof val === 'string') {
                    const n = IntegerFn(val);
                    gmp.mpz_mul(n.mpz_t, this.mpz_t, n.mpz_t);
                    return n;
                }
                if (isInteger(val)) {
                    const n = IntegerFn();
                    gmp.mpz_mul(n.mpz_t, this.mpz_t, val.mpz_t);
                    return n;
                }
                if (isRational(val) || isFloat(val)) {
                    return val.mul(this);
                }
                throw new Error(INVALID_PARAMETER_ERROR$1);
            },
            /** Returns the number with inverted sign. */
            neg() {
                const n = IntegerFn();
                gmp.mpz_neg(n.mpz_t, this.mpz_t);
                return n;
            },
            /** Returns the absolute value of this number. */
            abs() {
                const n = IntegerFn();
                gmp.mpz_abs(n.mpz_t, this.mpz_t);
                return n;
            },
            /** Returns the result of the division of this number by the given one. */
            div(val, mode = exports.DivMode.CEIL) {
                if (typeof val === 'number') {
                    const n = IntegerFn(this);
                    assertInt32(val);
                    if (val < 0) {
                        gmp.mpz_neg(n.mpz_t, n.mpz_t);
                        val = -val;
                    }
                    if (mode === exports.DivMode.CEIL) {
                        gmp.mpz_cdiv_q_ui(n.mpz_t, n.mpz_t, val);
                    }
                    else if (mode === exports.DivMode.FLOOR) {
                        gmp.mpz_fdiv_q_ui(n.mpz_t, n.mpz_t, val);
                    }
                    else if (mode === exports.DivMode.TRUNCATE) {
                        gmp.mpz_tdiv_q_ui(n.mpz_t, n.mpz_t, val);
                    }
                    return n;
                }
                if (typeof val === 'string' || isInteger(val)) {
                    const n = IntegerFn(this);
                    const intVal = typeof val === 'string' ? IntegerFn(val) : val;
                    if (mode === exports.DivMode.CEIL) {
                        gmp.mpz_cdiv_q(n.mpz_t, this.mpz_t, intVal.mpz_t);
                    }
                    else if (mode === exports.DivMode.FLOOR) {
                        gmp.mpz_fdiv_q(n.mpz_t, this.mpz_t, intVal.mpz_t);
                    }
                    else if (mode === exports.DivMode.TRUNCATE) {
                        gmp.mpz_tdiv_q(n.mpz_t, this.mpz_t, intVal.mpz_t);
                    }
                    return n;
                }
                if (isRational(val)) {
                    return val.invert().mul(this);
                }
                if (isFloat(val)) {
                    return ctx.floatContext.Float(this).div(val);
                }
                throw new Error(INVALID_PARAMETER_ERROR$1);
            },
            /** Returns this number exponentiated to the given value. */
            pow(exp, mod) {
                if (typeof exp === 'number') {
                    const n = IntegerFn();
                    assertUint32(exp);
                    if (mod !== undefined) {
                        if (typeof mod === 'number') {
                            assertUint32(mod);
                            gmp.mpz_powm_ui(n.mpz_t, this.mpz_t, exp, IntegerFn(mod).mpz_t);
                        }
                        else {
                            gmp.mpz_powm_ui(n.mpz_t, this.mpz_t, exp, mod.mpz_t);
                        }
                    }
                    else {
                        gmp.mpz_pow_ui(n.mpz_t, this.mpz_t, exp);
                    }
                    return n;
                }
                if (isInteger(exp)) {
                    const n = IntegerFn();
                    if (mod !== undefined) {
                        if (typeof mod === 'number') {
                            assertUint32(mod);
                            gmp.mpz_powm(n.mpz_t, this.mpz_t, exp.mpz_t, IntegerFn(mod).mpz_t);
                        }
                        else {
                            gmp.mpz_powm(n.mpz_t, this.mpz_t, exp.mpz_t, mod.mpz_t);
                        }
                    }
                    else {
                        const expNum = exp.toNumber();
                        assertUint32(expNum);
                        gmp.mpz_pow_ui(n.mpz_t, this.mpz_t, expNum);
                    }
                    return n;
                }
                if (isRational(exp) && mod === undefined) {
                    const n = IntegerFn();
                    const numerator = exp.numerator().toNumber();
                    assertUint32(numerator);
                    const denominator = exp.denominator().toNumber();
                    assertUint32(denominator);
                    gmp.mpz_pow_ui(n.mpz_t, this.mpz_t, numerator);
                    gmp.mpz_root(n.mpz_t, n.mpz_t, denominator);
                    return n;
                }
                throw new Error(INVALID_PARAMETER_ERROR$1);
            },
            /** Returns the integer square root number, rounded down. */
            sqrt() {
                const n = IntegerFn();
                gmp.mpz_sqrt(n.mpz_t, this.mpz_t);
                return n;
            },
            /** Returns the truncated integer part of the nth root */
            nthRoot(nth) {
                const n = IntegerFn();
                assertUint32(nth);
                gmp.mpz_root(n.mpz_t, this.mpz_t, nth);
                return n;
            },
            /** Returns the factorial */
            factorial() {
                if (gmp.mpz_fits_ulong_p(this.mpz_t) === 0) {
                    throw new Error('Out of bounds!');
                }
                const n = IntegerFn();
                const value = gmp.mpz_get_ui(this.mpz_t);
                gmp.mpz_fac_ui(n.mpz_t, value);
                return n;
            },
            /** Returns the double factorial */
            doubleFactorial() {
                if (gmp.mpz_fits_ulong_p(this.mpz_t) === 0) {
                    throw new Error('Out of bounds!');
                }
                const n = IntegerFn();
                const value = gmp.mpz_get_ui(this.mpz_t);
                gmp.mpz_2fac_ui(n.mpz_t, value);
                return n;
            },
            /** Determines whether a number is prime using some trial divisions, then reps Miller-Rabin probabilistic primality tests. */
            isProbablyPrime(reps = 20) {
                assertUint32(reps);
                const ret = gmp.mpz_probab_prime_p(this.mpz_t, reps);
                if (ret === 0)
                    return false; // definitely non-prime
                if (ret === 1)
                    return 'probably-prime';
                if (ret === 2)
                    return true; // definitely prime
            },
            /** Identifies primes using a probabilistic algorithm; the chance of a composite passing will be extremely small. */
            nextPrime() {
                const n = IntegerFn();
                gmp.mpz_nextprime(n.mpz_t, this.mpz_t);
                return n;
            },
            /** Returns the greatest common divisor of this number and the given one. */
            gcd(val) {
                const n = IntegerFn();
                if (typeof val === 'number') {
                    assertUint32(val);
                    gmp.mpz_gcd_ui(n.mpz_t, this.mpz_t, val);
                    return n;
                }
                if (isInteger(val)) {
                    gmp.mpz_gcd(n.mpz_t, this.mpz_t, val.mpz_t);
                    return n;
                }
                throw new Error(INVALID_PARAMETER_ERROR$1);
            },
            /** Returns the least common multiple of this number and the given one. */
            lcm(val) {
                const n = IntegerFn();
                if (typeof val === 'number') {
                    assertUint32(val);
                    gmp.mpz_lcm_ui(n.mpz_t, this.mpz_t, val);
                    return n;
                }
                if (isInteger(val)) {
                    gmp.mpz_lcm(n.mpz_t, this.mpz_t, val.mpz_t);
                    return n;
                }
                throw new Error(INVALID_PARAMETER_ERROR$1);
            },
            /** Returns the one's complement. */
            complement1() {
                const n = IntegerFn();
                gmp.mpz_com(n.mpz_t, this.mpz_t);
                return n;
            },
            /** Returns the two's complement. */
            complement2() {
                const n = IntegerFn();
                gmp.mpz_com(n.mpz_t, this.mpz_t);
                gmp.mpz_add_ui(n.mpz_t, n.mpz_t, 1);
                return n;
            },
            /** Returns the integer bitwise-and combined with another integer. */
            and(val) {
                const n = IntegerFn();
                if (typeof val === 'number') {
                    assertUint32(val);
                    gmp.mpz_and(n.mpz_t, this.mpz_t, IntegerFn(val).mpz_t);
                    return n;
                }
                if (isInteger(val)) {
                    gmp.mpz_and(n.mpz_t, this.mpz_t, val.mpz_t);
                    return n;
                }
                throw new Error(INVALID_PARAMETER_ERROR$1);
            },
            /** Returns the integer bitwise-or combined with another integer. */
            or(val) {
                const n = IntegerFn();
                if (typeof val === 'number') {
                    assertUint32(val);
                    gmp.mpz_ior(n.mpz_t, this.mpz_t, IntegerFn(val).mpz_t);
                    return n;
                }
                if (isInteger(val)) {
                    gmp.mpz_ior(n.mpz_t, this.mpz_t, val.mpz_t);
                    return n;
                }
                throw new Error(INVALID_PARAMETER_ERROR$1);
            },
            /** Returns the integer bitwise-xor combined with another integer. */
            xor(val) {
                const n = IntegerFn();
                if (typeof val === 'number') {
                    assertUint32(val);
                    gmp.mpz_xor(n.mpz_t, this.mpz_t, IntegerFn(val).mpz_t);
                }
                else {
                    gmp.mpz_xor(n.mpz_t, this.mpz_t, val.mpz_t);
                }
                return n;
            },
            /** Returns the integer left shifted by a given number of bits. */
            shiftLeft(val) {
                assertUint32(val);
                const n = IntegerFn();
                gmp.mpz_mul_2exp(n.mpz_t, this.mpz_t, val);
                return n;
            },
            /** Returns the integer right shifted by a given number of bits. */
            shiftRight(val) {
                assertUint32(val);
                const n = IntegerFn();
                gmp.mpz_fdiv_q_2exp(n.mpz_t, this.mpz_t, val);
                return n;
            },
            /** Sets the value of bit i to 1. The least significant bit is number 0 */
            setBit(i) {
                const n = IntegerFn(this);
                assertUint32(i);
                gmp.mpz_setbit(n.mpz_t, i);
                return n;
            },
            /** Sets the value of multiple bits to 1. The least significant bit is number 0 */
            setBits(indices) {
                const n = IntegerFn(this);
                assertArray(indices);
                indices.forEach(i => {
                    assertUint32(i);
                    gmp.mpz_setbit(n.mpz_t, i);
                });
                return n;
            },
            /** Sets the value of bit i to 0. The least significant bit is number 0 */
            clearBit(index) {
                const n = IntegerFn(this);
                assertUint32(index);
                gmp.mpz_clrbit(n.mpz_t, index);
                return n;
            },
            /** Sets the value of multiple bits to 0. The least significant bit is number 0 */
            clearBits(indices) {
                const n = IntegerFn(this);
                assertArray(indices);
                indices.forEach(i => {
                    assertUint32(i);
                    gmp.mpz_clrbit(n.mpz_t, i);
                });
                return n;
            },
            /** Inverts the value of bit i. The least significant bit is number 0 */
            flipBit(index) {
                const n = IntegerFn(this);
                assertUint32(index);
                gmp.mpz_combit(n.mpz_t, index);
                return n;
            },
            /** Inverts the value of multiple bits. The least significant bit is number 0 */
            flipBits(indices) {
                const n = IntegerFn(this);
                assertArray(indices);
                indices.forEach(i => {
                    assertUint32(i);
                    gmp.mpz_combit(n.mpz_t, i);
                });
                return n;
            },
            /** Returns 0 or 1 based on the value of a bit at the provided index. The least significant bit is number 0 */
            getBit(index) {
                assertUint32(index);
                return gmp.mpz_tstbit(this.mpz_t, index);
            },
            // Returns the position of the most significant bit. The least significant bit is number 0.
            msbPosition() {
                return gmp.mpz_sizeinbase(this.mpz_t, 2) - 1;
            },
            /** Works similarly to JS Array.slice() but on bits. The least significant bit is number 0 */
            sliceBits(start, end) {
                if (start === undefined)
                    start = 0;
                assertInt32(start);
                const msb = gmp.mpz_sizeinbase(this.mpz_t, 2);
                if (start < 0)
                    start = msb + start;
                start = Math.max(0, start);
                if (end === undefined)
                    end = msb + 1;
                assertInt32(end);
                if (end < 0)
                    end = msb + end;
                end = Math.min(msb + 1, end);
                if (start >= end)
                    return IntegerFn(0);
                const n = IntegerFn(1);
                if (end < msb + 1) {
                    gmp.mpz_mul_2exp(n.mpz_t, n.mpz_t, end);
                    gmp.mpz_sub_ui(n.mpz_t, n.mpz_t, 1);
                    gmp.mpz_and(n.mpz_t, this.mpz_t, n.mpz_t);
                    gmp.mpz_fdiv_q_2exp(n.mpz_t, n.mpz_t, start);
                }
                else {
                    gmp.mpz_fdiv_q_2exp(n.mpz_t, this.mpz_t, start);
                }
                return n;
            },
            /** Creates new integer with the copy of binary representation of num to position offset. Optionally bitCount can be used to zero-pad the number to a specific number of bits. The least significant bit is number 0 */
            writeTo(num, offset = 0, bitCount) {
                assertUint32(offset);
                if (!isInteger(num))
                    throw new Error('Only Integers are supported');
                if (bitCount === undefined) {
                    bitCount = gmp.mpz_sizeinbase(num.mpz_t, 2);
                }
                assertUint32(bitCount);
                const aux = IntegerFn();
                const n = IntegerFn();
                gmp.mpz_fdiv_q_2exp(n.mpz_t, this.mpz_t, offset + bitCount);
                gmp.mpz_mul_2exp(n.mpz_t, n.mpz_t, bitCount);
                gmp.mpz_tdiv_r_2exp(aux.mpz_t, num.mpz_t, bitCount);
                gmp.mpz_ior(n.mpz_t, n.mpz_t, aux.mpz_t);
                gmp.mpz_tdiv_r_2exp(aux.mpz_t, this.mpz_t, offset);
                gmp.mpz_mul_2exp(n.mpz_t, n.mpz_t, offset);
                gmp.mpz_ior(n.mpz_t, n.mpz_t, aux.mpz_t);
                return n;
            },
            /** Returns true if the current number is equal to the provided number */
            isEqual(val) {
                return compare(this.mpz_t, val) === 0;
            },
            /** Returns true if the current number is less than the provided number */
            lessThan(val) {
                return compare(this.mpz_t, val) < 0;
            },
            /** Returns true if the current number is less than or equal to the provided number */
            lessOrEqual(val) {
                return compare(this.mpz_t, val) <= 0;
            },
            /** Returns true if the current number is greater than the provided number */
            greaterThan(val) {
                return compare(this.mpz_t, val) > 0;
            },
            /** Returns true if the current number is greater than or equal to the provided number */
            greaterOrEqual(val) {
                return compare(this.mpz_t, val) >= 0;
            },
            /** Returns the sign of the current value (-1 or 0 or 1) */
            sign() {
                return gmp.mpz_sgn(this.mpz_t);
            },
            /** Converts current value into a JavaScript number */
            toNumber() {
                if (gmp.mpz_fits_slong_p(this.mpz_t) === 0) {
                    return gmp.mpz_get_d(this.mpz_t);
                }
                return gmp.mpz_get_si(this.mpz_t);
            },
            /** Exports integer into an Uint8Array. Sign is ignored. */
            toBuffer(littleEndian = false) {
                const countPtr = gmp.malloc(4);
                const startptr = gmp.mpz_export(0, countPtr, littleEndian ? -1 : 1, 1, 1, 0, this.mpz_t);
                const size = gmp.memView.getUint32(countPtr, true);
                const endptr = startptr + size;
                const buf = gmp.mem.slice(startptr, endptr);
                gmp.free(startptr);
                gmp.free(countPtr);
                return buf;
            },
            /** Converts the number to string */
            toString(radix = 10) {
                if (!Number.isSafeInteger(radix) || radix < 2 || radix > 62) {
                    throw new Error('radix must have a value between 2 and 62');
                }
                return gmp.mpz_to_string(this.mpz_t, radix);
            },
            /** Converts the number to a rational number */
            toRational() {
                return ctx.rationalContext.Rational(this);
            },
            /** Converts the number to a floating-point number */
            toFloat() {
                return ctx.floatContext.Float(this);
            },
        };
        const IntegerFn = (num, radix = 10) => {
            const instance = Object.create(IntPrototype);
            instance.mpz_t = gmp.mpz_t();
            if (num === undefined) {
                gmp.mpz_init(instance.mpz_t);
            }
            else if (typeof num === 'string') {
                assertValidRadix(radix);
                const res = gmp.mpz_init_set_string(instance.mpz_t, num, radix);
                if (res !== 0) {
                    throw new Error('Invalid number provided!');
                }
            }
            else if (typeof num === 'number') {
                assertInt32(num);
                gmp.mpz_init_set_si(instance.mpz_t, num);
            }
            else if (isInteger(num)) {
                gmp.mpz_init_set(instance.mpz_t, num.mpz_t);
            }
            else if (ArrayBuffer.isView(num)) {
                if (!(num instanceof Uint8Array)) {
                    throw new Error('Only Uint8Array is supported!');
                }
                gmp.mpz_init(instance.mpz_t);
                const wasmBufPtr = gmp.malloc(num.length);
                gmp.mem.set(num, wasmBufPtr);
                gmp.mpz_import(instance.mpz_t, num.length, 1, 1, 1, 0, wasmBufPtr);
                gmp.free(wasmBufPtr);
            }
            else if (isRational(num)) {
                gmp.mpz_init(instance.mpz_t);
                const f = ctx.floatContext.Float(num);
                gmp.mpfr_get_z(instance.mpz_t, f.mpfr_t, 0);
            }
            else if (isFloat(num)) {
                gmp.mpz_init(instance.mpz_t);
                gmp.mpfr_get_z(instance.mpz_t, num.mpfr_t, num.rndMode);
            }
            else {
                gmp.mpz_t_free(instance.mpz_t);
                throw new Error('Invalid value for the Integer type!');
            }
            mpz_t_arr.push(instance.mpz_t);
            return instance;
        };
        return {
            Integer: IntegerFn,
            isInteger: (val) => IntPrototype.isPrototypeOf(val),
            destroy: () => {
                for (let i = mpz_t_arr.length - 1; i >= 0; i--) {
                    gmp.mpz_clear(mpz_t_arr[i]);
                    gmp.mpz_t_free(mpz_t_arr[i]);
                }
                mpz_t_arr.length = 0;
            }
        };
    }

    const INVALID_PARAMETER_ERROR = 'Invalid parameter!';
    function getRationalContext(gmp, ctx) {
        const mpq_t_arr = [];
        const isInteger = (val) => ctx.intContext.isInteger(val);
        const isRational = (val) => ctx.rationalContext.isRational(val);
        const isFloat = (val) => ctx.floatContext.isFloat(val);
        const compare = (mpq_t, val) => {
            if (typeof val === 'number') {
                assertInt32(val);
                return gmp.mpq_cmp_si(mpq_t, val, 1);
            }
            if (typeof val === 'string') {
                const r = RationalFn(val);
                return gmp.mpq_cmp(mpq_t, r.mpq_t);
            }
            if (isInteger(val)) {
                return gmp.mpq_cmp_z(mpq_t, val.mpz_t);
            }
            if (isRational(val)) {
                return gmp.mpq_cmp(mpq_t, val.mpq_t);
            }
            if (isFloat(val)) {
                return -gmp.mpfr_cmp_q(val.mpfr_t, mpq_t);
            }
            throw new Error(INVALID_PARAMETER_ERROR);
        };
        const RationalPrototype = {
            mpq_t: 0,
            type: 'rational',
            /** Returns the sum of this number and the given one. */
            add(val) {
                if (typeof val === 'number' || isInteger(val)) {
                    const n = RationalFn(0, 1);
                    gmp.mpq_add(n.mpq_t, this.mpq_t, RationalFn(val).mpq_t);
                    return n;
                }
                if (typeof val === 'string') {
                    const n = RationalFn(val);
                    gmp.mpq_add(n.mpq_t, this.mpq_t, n.mpq_t);
                    return n;
                }
                if (isRational(val)) {
                    const n = RationalFn(0, 1);
                    gmp.mpq_add(n.mpq_t, this.mpq_t, val.mpq_t);
                    return n;
                }
                if (isFloat(val)) {
                    return val.add(this);
                }
                throw new Error(INVALID_PARAMETER_ERROR);
            },
            /** Returns the difference of this number and the given one. */
            sub(val) {
                if (typeof val === 'number' || isInteger(val)) {
                    const n = RationalFn(0, 1);
                    gmp.mpq_sub(n.mpq_t, this.mpq_t, RationalFn(val).mpq_t);
                    return n;
                }
                if (typeof val === 'string') {
                    const n = RationalFn(val);
                    gmp.mpq_sub(n.mpq_t, this.mpq_t, n.mpq_t);
                    return n;
                }
                if (isRational(val)) {
                    const n = RationalFn(0, 1);
                    gmp.mpq_sub(n.mpq_t, this.mpq_t, val.mpq_t);
                    return n;
                }
                if (isFloat(val)) {
                    return val.neg().add(this);
                }
                throw new Error(INVALID_PARAMETER_ERROR);
            },
            /** Returns the product of this number and the given one. */
            mul(val) {
                if (typeof val === 'number' || isInteger(val)) {
                    const n = RationalFn(0, 1);
                    gmp.mpq_mul(n.mpq_t, this.mpq_t, RationalFn(val).mpq_t);
                    return n;
                }
                if (typeof val === 'string') {
                    const n = RationalFn(val);
                    gmp.mpq_mul(n.mpq_t, this.mpq_t, n.mpq_t);
                    return n;
                }
                if (isRational(val)) {
                    const n = RationalFn(0, 1);
                    gmp.mpq_mul(n.mpq_t, this.mpq_t, val.mpq_t);
                    return n;
                }
                if (isFloat(val)) {
                    return val.mul(this);
                }
                throw new Error(INVALID_PARAMETER_ERROR);
            },
            /** Returns the number with inverted sign. */
            neg() {
                const n = RationalFn(0, 1);
                gmp.mpq_neg(n.mpq_t, this.mpq_t);
                return n;
            },
            /** Returns the inverse of the number. */
            invert() {
                const n = RationalFn(0, 1);
                gmp.mpq_inv(n.mpq_t, this.mpq_t);
                return n;
            },
            /** Returns the absolute value of this number. */
            abs() {
                const n = RationalFn(0, 1);
                gmp.mpq_abs(n.mpq_t, this.mpq_t);
                return n;
            },
            /** Returns the result of the division of this number by the given one. */
            div(val) {
                if (typeof val === 'number' || isInteger(val)) {
                    const n = RationalFn(0, 1);
                    gmp.mpq_div(n.mpq_t, this.mpq_t, RationalFn(val).mpq_t);
                    return n;
                }
                if (typeof val === 'string') {
                    const n = RationalFn(val);
                    gmp.mpq_div(n.mpq_t, this.mpq_t, n.mpq_t);
                    return n;
                }
                if (isRational(val)) {
                    const n = RationalFn(0, 1);
                    gmp.mpq_div(n.mpq_t, this.mpq_t, val.mpq_t);
                    return n;
                }
                if (isFloat(val)) {
                    return ctx.floatContext.Float(this).div(val);
                }
                throw new Error(INVALID_PARAMETER_ERROR);
            },
            /** Returns true if the current number is equal to the provided number */
            isEqual(val) {
                if (typeof val === 'number' || isInteger(val)) {
                    return gmp.mpq_equal(this.mpq_t, RationalFn(val).mpq_t) !== 0;
                }
                if (typeof val === 'string') {
                    const n = RationalFn(val);
                    return gmp.mpq_equal(this.mpq_t, n.mpq_t) !== 0;
                }
                if (isRational(val)) {
                    return gmp.mpq_equal(this.mpq_t, val.mpq_t) !== 0;
                }
                if (isFloat(val)) {
                    return val.isEqual(this);
                }
                throw new Error(INVALID_PARAMETER_ERROR);
            },
            /** Returns true if the current number is less than the provided number */
            lessThan(val) {
                return compare(this.mpq_t, val) < 0;
            },
            /** Returns true if the current number is less than or equal to the provided number */
            lessOrEqual(val) {
                return compare(this.mpq_t, val) <= 0;
            },
            /** Returns true if the current number is greater than the provided number */
            greaterThan(val) {
                return compare(this.mpq_t, val) > 0;
            },
            /** Returns true if the current number is greater than or equal to the provided number */
            greaterOrEqual(val) {
                return compare(this.mpq_t, val) >= 0;
            },
            /** Returns the numerator of the number */
            numerator() {
                const n = ctx.intContext.Integer();
                gmp.mpq_get_num(n.mpz_t, this.mpq_t);
                return n;
            },
            /** Returns the denominator of the number */
            denominator() {
                const n = ctx.intContext.Integer();
                gmp.mpq_get_den(n.mpz_t, this.mpq_t);
                return n;
            },
            /** Returns the sign of the current value (-1 or 0 or 1) */
            sign() {
                return gmp.mpq_sgn(this.mpq_t);
            },
            /** Converts current value to a JavaScript number */
            toNumber() {
                return gmp.mpq_get_d(this.mpq_t);
            },
            /** Converts the number to string */
            toString(radix = 10) {
                if (!Number.isSafeInteger(radix) || radix < 2 || radix > 62) {
                    throw new Error('radix must have a value between 2 and 62');
                }
                return gmp.mpq_to_string(this.mpq_t, radix);
            },
            /** Converts the number to an integer */
            toInteger() {
                return ctx.intContext.Integer(this);
            },
            /** Converts the number to a floating-point number */
            toFloat() {
                return ctx.floatContext.Float(this);
            },
        };
        const parseParameters = (mpq_t, p1, p2) => {
            if (typeof p1 === 'number' && (p2 === undefined || typeof p2 === 'number')) {
                assertInt32(p1);
                if (p2 !== undefined) {
                    assertInt32(p2);
                    gmp.mpq_set_si(mpq_t, p1, Math.abs(p2));
                    if (p2 < 0) {
                        gmp.mpq_neg(mpq_t, mpq_t);
                    }
                }
                else {
                    gmp.mpq_set_si(mpq_t, p1, 1);
                }
                return;
            }
            if (isInteger(p1) && p2 === undefined) {
                gmp.mpq_set_z(mpq_t, p1.mpz_t);
                return;
            }
            if (isRational(p1) && p2 === undefined) {
                gmp.mpq_set(mpq_t, p1.mpq_t);
                return;
            }
            const finalString = p2 !== undefined ? `${p1.toString()}/${p2.toString()}` : p1.toString();
            const res = gmp.mpq_set_string(mpq_t, finalString, 10);
            if (res !== 0) {
                throw new Error('Invalid number provided!');
            }
        };
        const RationalFn = (p1, p2) => {
            const instance = Object.create(RationalPrototype);
            instance.mpq_t = gmp.mpq_t();
            gmp.mpq_init(instance.mpq_t);
            parseParameters(instance.mpq_t, p1, p2);
            gmp.mpq_canonicalize(instance.mpq_t);
            mpq_t_arr.push(instance.mpq_t);
            return instance;
        };
        return {
            Rational: RationalFn,
            isRational: (val) => RationalPrototype.isPrototypeOf(val),
            destroy: () => {
                for (let i = mpq_t_arr.length - 1; i >= 0; i--) {
                    gmp.mpq_clear(mpq_t_arr[i]);
                    gmp.mpq_t_free(mpq_t_arr[i]);
                }
                mpq_t_arr.length = 0;
            }
        };
    }

    exports.mpfr_rnd_t = void 0;
    (function (mpfr_rnd_t) {
        /** Round to nearest, with ties to even */
        mpfr_rnd_t[mpfr_rnd_t["MPFR_RNDN"] = 0] = "MPFR_RNDN";
        /** Round toward zero */
        mpfr_rnd_t[mpfr_rnd_t["MPFR_RNDZ"] = 1] = "MPFR_RNDZ";
        /** Round toward +Inf */
        mpfr_rnd_t[mpfr_rnd_t["MPFR_RNDU"] = 2] = "MPFR_RNDU";
        /** Round toward -Inf */
        mpfr_rnd_t[mpfr_rnd_t["MPFR_RNDD"] = 3] = "MPFR_RNDD";
        /** Round away from zero */
        mpfr_rnd_t[mpfr_rnd_t["MPFR_RNDA"] = 4] = "MPFR_RNDA";
        /** (Experimental) Faithful rounding */
        mpfr_rnd_t[mpfr_rnd_t["MPFR_RNDF"] = 5] = "MPFR_RNDF";
        /** (Experimental) Round to nearest, with ties away from zero (mpfr_round) */
        mpfr_rnd_t[mpfr_rnd_t["MPFR_RNDNA"] = -1] = "MPFR_RNDNA";
    })(exports.mpfr_rnd_t || (exports.mpfr_rnd_t = {}));
    exports.mpfr_flags = void 0;
    (function (mpfr_flags) {
        mpfr_flags[mpfr_flags["MPFR_FLAGS_UNDERFLOW"] = 1] = "MPFR_FLAGS_UNDERFLOW";
        mpfr_flags[mpfr_flags["MPFR_FLAGS_OVERFLOW"] = 2] = "MPFR_FLAGS_OVERFLOW";
        mpfr_flags[mpfr_flags["MPFR_FLAGS_NAN"] = 4] = "MPFR_FLAGS_NAN";
        mpfr_flags[mpfr_flags["MPFR_FLAGS_INEXACT"] = 8] = "MPFR_FLAGS_INEXACT";
        mpfr_flags[mpfr_flags["MPFR_FLAGS_ERANGE"] = 16] = "MPFR_FLAGS_ERANGE";
        mpfr_flags[mpfr_flags["MPFR_FLAGS_DIVBY0"] = 32] = "MPFR_FLAGS_DIVBY0";
        mpfr_flags[mpfr_flags["MPFR_FLAGS_ALL"] = 63] = "MPFR_FLAGS_ALL";
    })(exports.mpfr_flags || (exports.mpfr_flags = {}));
    exports.mpfr_free_cache_t = void 0;
    (function (mpfr_free_cache_t) {
        mpfr_free_cache_t[mpfr_free_cache_t["MPFR_FREE_LOCAL_CACHE"] = 1] = "MPFR_FREE_LOCAL_CACHE";
        mpfr_free_cache_t[mpfr_free_cache_t["MPFR_FREE_GLOBAL_CACHE"] = 2] = "MPFR_FREE_GLOBAL_CACHE"; /* 1 << 1 */
    })(exports.mpfr_free_cache_t || (exports.mpfr_free_cache_t = {}));

    function init() {
        return __awaiter(this, void 0, void 0, function* () {
            const binding = yield getGMPInterface();
            const createContext = (options) => {
                const ctx = {
                    intContext: null,
                    rationalContext: null,
                    floatContext: null,
                };
                ctx.intContext = getIntegerContext(binding, ctx);
                ctx.rationalContext = getRationalContext(binding, ctx);
                ctx.floatContext = getFloatContext(binding, ctx, options);
                return {
                    types: {
                        Integer: ctx.intContext.Integer,
                        Rational: ctx.rationalContext.Rational,
                        Float: ctx.floatContext.Float,
                        Pi: ctx.floatContext.Pi,
                        EulerConstant: ctx.floatContext.EulerConstant,
                        EulerNumber: ctx.floatContext.EulerNumber,
                        Log2: ctx.floatContext.Log2,
                        Catalan: ctx.floatContext.Catalan,
                    },
                    destroy: () => {
                        ctx.intContext.destroy();
                        ctx.rationalContext.destroy();
                        ctx.floatContext.destroy();
                    },
                };
            };
            return {
                binding,
                calculate: (fn, options = {}) => {
                    const context = createContext(options);
                    if (typeof fn !== 'function') {
                        throw new Error('calculate() requires a callback function');
                    }
                    const fnRes = fn(context.types);
                    const res = fnRes === null || fnRes === void 0 ? void 0 : fnRes.toString();
                    context.destroy();
                    return res;
                },
                getContext: (options = {}) => {
                    const context = createContext(options);
                    return Object.assign(Object.assign({}, context.types), { destroy: context.destroy });
                },
                /** Resets the WASM instance (clears all previously allocated objects) */
                reset: () => __awaiter(this, void 0, void 0, function* () {
                    return binding.reset();
                }),
            };
        });
    }
    const precisionToBits = (digits) => Math.ceil(digits * 3.3219281); // digits * log2(10)

    exports.init = init;
    exports.precisionToBits = precisionToBits;

    Object.defineProperty(exports, '__esModule', { value: true });

}));

}

export default gmpWasm