def parse_hints(hints):
     """Split hints into (n, funcs, iterables, gens)."""
     n = 1
     funcs, iterables, gens = [], [], []
     for e in hints:
         if isinstance(e, (SYMPY_INTS, Integer)):
             n = e
         elif isinstance(e, FunctionClass):
             funcs.append(e)
         elif iterable(e):
             iterables.append((e[0], e[1:]))
             # XXX sin(x+2y)?
             # Note: we go through polys so e.g.
             # sin(-x) -> -sin(x) -> sin(x)
             gens.extend(parallel_poly_from_expr(
                 [e[0](x) for x in e[1:]] + [e[0](Add(*e[1:]))])[1].gens)
         else:
             gens.append(e)
     return n, funcs, iterables, gens

#.........这里部分代码省略.........
    # (This requires of course that we already have relations for cos(n*x) and
    # sin(n*x).) It is not obvious, but it seems that this preserves geometric
    # primality.
    # XXX A real proof would be nice. HELP!
    #     Sketch that <S**2 + C**2 - 1, C*T - S> is a prime ideal of
    #     CC[S, C, T]:
    #     - it suffices to show that the projective closure in CP**3 is
    #       irreducible
    #     - using the half-angle substitutions, we can express sin(x), tan(x),
    #       cos(x) as rational functions in tan(x/2)
    #     - from this, we get a rational map from CP**1 to our curve
    #     - this is a morphism, hence the curve is prime
    #
    # Step (2) is trivial.
    #
    # Step (3) works by adding selected relations of the form
    # sin(x + y) - sin(x)*cos(y) - sin(y)*cos(x), etc. Geometric primality is
    # preserved by the same argument as before.

    def parse_hints(hints):
        """Split hints into (n, funcs, iterables, gens)."""
        n = 1
        funcs, iterables, gens = [], [], []
        for e in hints:
            if isinstance(e, (SYMPY_INTS, Integer)):
                n = e
            elif isinstance(e, FunctionClass):
                funcs.append(e)
            elif iterable(e):
                iterables.append((e[0], e[1:]))
                # XXX sin(x+2y)?
                # Note: we go through polys so e.g.
                # sin(-x) -> -sin(x) -> sin(x)
                gens.extend(parallel_poly_from_expr(
                    [e[0](x) for x in e[1:]] + [e[0](Add(*e[1:]))])[1].gens)
            else:
                gens.append(e)
        return n, funcs, iterables, gens

    def build_ideal(x, terms):
        """
        Build generators for our ideal. Terms is an iterable with elements of
        the form (fn, coeff), indicating that we have a generator fn(coeff*x).

        If any of the terms is trigonometric, sin(x) and cos(x) are guaranteed
        to appear in terms. Similarly for hyperbolic functions. For tan(n*x),
        sin(n*x) and cos(n*x) are guaranteed.
        """
        I = []
        y = Dummy('y')
        for fn, coeff in terms:
            for c, s, t, rel in (
                    [cos, sin, tan, cos(x)**2 + sin(x)**2 - 1],
                    [cosh, sinh, tanh, cosh(x)**2 - sinh(x)**2 - 1]):
                if coeff == 1 and fn in [c, s]:
                    I.append(rel)
                elif fn == t:
                    I.append(t(coeff*x)*c(coeff*x) - s(coeff*x))
                elif fn in [c, s]:
                    cn = fn(coeff*y).expand(trig=True).subs(y, x)
                    I.append(fn(coeff*x) - cn)
        return list(set(I))

    def analyse_gens(gens, hints):
        """
        Analyse the generators ``gens``, using the hints ``hints``.

def reduce_rational_inequalities(exprs, gen, relational=True):
    """Reduce a system of rational inequalities with rational coefficients.

    Examples
    ========

    >>> from sympy import Poly, Symbol
    >>> from sympy.solvers.inequalities import reduce_rational_inequalities

    >>> x = Symbol('x', real=True)

    >>> reduce_rational_inequalities([[x**2 <= 0]], x)
    Eq(x, 0)

    >>> reduce_rational_inequalities([[x + 2 > 0]], x)
    And(-2 < x, x < oo)
    >>> reduce_rational_inequalities([[(x + 2, ">")]], x)
    And(-2 < x, x < oo)
    >>> reduce_rational_inequalities([[x + 2]], x)
    Eq(x, -2)
    """
    exact = True
    eqs = []
    solution = S.Reals if exprs else S.EmptySet
    for _exprs in exprs:
        _eqs = []

        for expr in _exprs:
            if isinstance(expr, tuple):
                expr, rel = expr
            else:
                if expr.is_Relational:
                    expr, rel = expr.lhs - expr.rhs, expr.rel_op
                else:
                    expr, rel = expr, '=='

            if expr is S.true:
                numer, denom, rel = S.Zero, S.One, '=='
            elif expr is S.false:
                numer, denom, rel = S.One, S.One, '=='
            else:
                numer, denom = expr.together().as_numer_denom()

            try:
                (numer, denom), opt = parallel_poly_from_expr(
                    (numer, denom), gen)
            except PolynomialError:
                raise PolynomialError(filldedent('''
                    only polynomials and
                    rational functions are supported in this context'''))

            if not opt.domain.is_Exact:
                numer, denom, exact = numer.to_exact(), denom.to_exact(), False

            domain = opt.domain.get_exact()

            if not (domain.is_ZZ or domain.is_QQ):
                expr = numer/denom
                expr = Relational(expr, 0, rel)
                solution &= solve_univariate_inequality(expr, gen, relational=False)
            else:
                _eqs.append(((numer, denom), rel))

        if _eqs:
            eqs.append(_eqs)

    if eqs:
        solution &= solve_rational_inequalities(eqs)

    if not exact:
        solution = solution.evalf()

    if relational:
        solution = solution.as_relational(gen)

    return solution

def reduce_rational_inequalities(exprs, gen, assume=True, relational=True):
    """Reduce a system of rational inequalities with rational coefficients.

    Examples
    ========

    >>> from sympy import Poly, Symbol
    >>> from sympy.solvers.inequalities import reduce_rational_inequalities

    >>> x = Symbol('x', real=True)

    >>> reduce_rational_inequalities([[x**2 <= 0]], x)
    x == 0

    >>> reduce_rational_inequalities([[x + 2 > 0]], x)
    And(-2 < x, x < oo)
    >>> reduce_rational_inequalities([[(x + 2, ">")]], x)
    And(-2 < x, x < oo)
    >>> reduce_rational_inequalities([[x + 2]], x)
    x == -2
    """
    exact = True
    eqs = []

    for _exprs in exprs:
        _eqs = []

        for expr in _exprs:
            if isinstance(expr, tuple):
                expr, rel = expr
            else:
                if expr.is_Relational:
                    expr, rel = expr.lhs - expr.rhs, expr.rel_op
                else:
                    expr, rel = expr, '=='

            try:
                (numer, denom), opt = parallel_poly_from_expr(
                    expr.together().as_numer_denom(), gen)
            except PolynomialError:
                raise PolynomialError("only polynomials and "
                    "rational functions are supported in this context")

            if not opt.domain.is_Exact:
                numer, denom, exact = numer.to_exact(), denom.to_exact(), False

            domain = opt.domain.get_exact()

            if not (domain.is_ZZ or domain.is_QQ):
                raise NotImplementedError(
                    "inequality solving is not supported over %s" % opt.domain)

            _eqs.append(((numer, denom), rel))

        eqs.append(_eqs)

    solution = solve_rational_inequalities(eqs)

    if not exact:
        solution = solution.evalf()

    if not relational:
        return solution

    real = ask(Q.real(gen), assumptions=assume)

    if not real:
        result = And(solution.as_relational(re(gen)), Eq(im(gen), 0))
    else:
        result = solution.as_relational(gen)

    return result

def gosper_normal(f, g, n, polys=True):
    r"""
    Compute the Gosper's normal form of ``f`` and ``g``.

    Given relatively prime univariate polynomials ``f`` and ``g``,
    rewrite their quotient to a normal form defined as follows:

    .. math::
        \frac{f(n)}{g(n)} = Z \cdot \frac{A(n) C(n+1)}{B(n) C(n)}

    where ``Z`` is an arbitrary constant and ``A``, ``B``, ``C`` are
    monic polynomials in ``n`` with the following properties:

    1. `\gcd(A(n), B(n+h)) = 1 \forall h \in \mathbb{N}`
    2. `\gcd(B(n), C(n+1)) = 1`
    3. `\gcd(A(n), C(n)) = 1`

    This normal form, or rational factorization in other words, is a
    crucial step in Gosper's algorithm and in solving of difference
    equations. It can be also used to decide if two hypergeometric
    terms are similar or not.

    This procedure will return a tuple containing elements of this
    factorization in the form ``(Z*A, B, C)``.

    **Examples**

    >>> from sympy.concrete.gosper import gosper_normal
    >>> from sympy.abc import n

    >>> gosper_normal(4*n+5, 2*(4*n+1)*(2*n+3), n, polys=False)
    (1/4, n + 3/2, n + 1/4)

    """
    (p, q), opt = parallel_poly_from_expr((f, g), n, field=True, extension=True)

    a, A = p.LC(), p.monic()
    b, B = q.LC(), q.monic()

    C, Z = A.one, a / b
    h = Dummy("h")

    D = Poly(n + h, n, h, domain=opt.domain)

    R = A.resultant(B.compose(D))
    roots = set(R.ground_roots().keys())

    for r in set(roots):
        if not r.is_Integer or r < 0:
            roots.remove(r)

    for i in sorted(roots):
        d = A.gcd(B.shift(+i))

        A = A.quo(d)
        B = B.quo(d.shift(-i))

        for j in xrange(1, i + 1):
            C *= d.shift(-j)

    A = A.mul_ground(Z)

    if not polys:
        A = A.as_expr()
        B = B.as_expr()
        C = C.as_expr()

    return A, B, C

def ratsimpmodprime(expr, G, *gens, **args):
    """
    Simplifies a rational expression ``expr`` modulo the prime ideal
    generated by ``G``.  ``G`` should be a Groebner basis of the
    ideal.

    >>> from sympy.simplify.ratsimp import ratsimpmodprime
    >>> from sympy.abc import x, y
    >>> eq = (x + y**5 + y)/(x - y)
    >>> ratsimpmodprime(eq, [x*y**5 - x - y], x, y, order='lex')
    (x**2 + x*y + x + y)/(x**2 - x*y)

    If ``polynomial`` is False, the algorithm computes a rational
    simplification which minimizes the sum of the total degrees of
    the numerator and the denominator.

    If ``polynomial`` is True, this function just brings numerator and
    denominator into a canonical form. This is much faster, but has
    potentially worse results.

    References
    ==========

    .. [1] M. Monagan, R. Pearce, Rational Simplification Modulo a Polynomial
    Ideal,
    http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.163.6984
    (specifically, the second algorithm)
    """
    from sympy import solve

    quick = args.pop('quick', True)
    polynomial = args.pop('polynomial', False)
    debug('ratsimpmodprime', expr)

    # usual preparation of polynomials:

    num, denom = cancel(expr).as_numer_denom()

    try:
        polys, opt = parallel_poly_from_expr([num, denom] + G, *gens, **args)
    except PolificationFailed:
        return expr

    domain = opt.domain

    if domain.has_assoc_Field:
        opt.domain = domain.get_field()
    else:
        raise DomainError(
            "can't compute rational simplification over %s" % domain)

    # compute only once
    leading_monomials = [g.LM(opt.order) for g in polys[2:]]
    tested = set()

    def staircase(n):
        """
        Compute all monomials with degree less than ``n`` that are
        not divisible by any element of ``leading_monomials``.
        """
        if n == 0:
            return [1]
        S = []
        for mi in combinations_with_replacement(range(len(opt.gens)), n):
            m = [0]*len(opt.gens)
            for i in mi:
                m[i] += 1
            if all([monomial_div(m, lmg) is None for lmg in
                    leading_monomials]):
                S.append(m)

        return [Monomial(s).as_expr(*opt.gens) for s in S] + staircase(n - 1)

    def _ratsimpmodprime(a, b, allsol, N=0, D=0):
        r"""
        Computes a rational simplification of ``a/b`` which minimizes
        the sum of the total degrees of the numerator and the denominator.

        The algorithm proceeds by looking at ``a * d - b * c`` modulo
        the ideal generated by ``G`` for some ``c`` and ``d`` with degree
        less than ``a`` and ``b`` respectively.
        The coefficients of ``c`` and ``d`` are indeterminates and thus
        the coefficients of the normalform of ``a * d - b * c`` are
        linear polynomials in these indeterminates.
        If these linear polynomials, considered as system of
        equations, have a nontrivial solution, then `\frac{a}{b}
        \equiv \frac{c}{d}` modulo the ideal generated by ``G``. So,
        by construction, the degree of ``c`` and ``d`` is less than
        the degree of ``a`` and ``b``, so a simpler representation
        has been found.
        After a simpler representation has been found, the algorithm
        tries to reduce the degree of the numerator and denominator
        and returns the result afterwards.

        As an extension, if quick=False, we look at all possible degrees such
        that the total degree is less than *or equal to* the best current
        solution. We retain a list of all solutions of minimal degree, and try
        to find the best one at the end.
        """
        c, d = a, b
#.........这里部分代码省略.........