from __future__ import print_function, division

from collections import defaultdict

from sympy import SYMPY_DEBUG

from sympy.core import (Basic, S, C, Add, Mul, Pow, Rational, Integer,
    Derivative, Wild, Symbol, sympify, expand, expand_mul, expand_func,
    Function, Equality, Dummy, Atom, count_ops, Expr, factor_terms,
    expand_multinomial, FunctionClass, expand_power_base, symbols, igcd,
    expand_power_exp, expand_log)
from sympy.core.add import _unevaluated_Add
from sympy.core.cache import cacheit
from sympy.core.compatibility import iterable, reduce, default_sort_key, ordered, xrange
from sympy.core.exprtools import Factors, gcd_terms
from sympy.core.numbers import Float, Number, I
from sympy.core.function import expand_log, count_ops
from sympy.core.mul import _keep_coeff, prod
from sympy.core.rules import Transform
from sympy.core.evaluate import global_evaluate
from sympy.functions import (
    gamma, exp, sqrt, log, root, exp_polar,
    sin, cos, tan, cot, sinh, cosh, tanh, coth, piecewise_fold, Piecewise)
from sympy.functions.elementary.exponential import ExpBase
from sympy.functions.elementary.integers import ceiling

from sympy.utilities.iterables import flatten, has_variety, sift

from sympy.simplify.cse_main import cse
from sympy.simplify.cse_opts import sub_pre, sub_post
from sympy.simplify.sqrtdenest import sqrtdenest
from sympy.ntheory.factor_ import multiplicity

from sympy.polys import (Poly, together, reduced, cancel, factor,
    ComputationFailed, lcm, gcd)

import mpmath


def _mexpand(expr):
    return expand_mul(expand_multinomial(expr))


def fraction(expr, exact=False):
    """Returns a pair with expression's numerator and denominator.
       If the given expression is not a fraction then this function
       will return the tuple (expr, 1).

       This function will not make any attempt to simplify nested
       fractions or to do any term rewriting at all.

       If only one of the numerator/denominator pair is needed then
       use numer(expr) or denom(expr) functions respectively.

       >>> from sympy import fraction, Rational, Symbol
       >>> from sympy.abc import x, y

       >>> fraction(x/y)
       (x, y)
       >>> fraction(x)
       (x, 1)

       >>> fraction(1/y**2)
       (1, y**2)

       >>> fraction(x*y/2)
       (x*y, 2)
       >>> fraction(Rational(1, 2))
       (1, 2)

       This function will also work fine with assumptions:

       >>> k = Symbol('k', negative=True)
       >>> fraction(x * y**k)
       (x, y**(-k))

       If we know nothing about sign of some exponent and 'exact'
       flag is unset, then structure this exponent's structure will
       be analyzed and pretty fraction will be returned:

       >>> from sympy import exp
       >>> fraction(2*x**(-y))
       (2, x**y)

       >>> fraction(exp(-x))
       (1, exp(x))

       >>> fraction(exp(-x), exact=True)
       (exp(-x), 1)

    """
    expr = sympify(expr)

    numer, denom = [], []

    for term in Mul.make_args(expr):
        if term.is_commutative and (term.is_Pow or term.func is exp):
            b, ex = term.as_base_exp()
            if ex.is_negative:
                if ex is S.NegativeOne:
                    denom.append(b)
                else:
                    denom.append(Pow(b, -ex))
            elif ex.is_positive:
                numer.append(term)
            elif not exact and ex.is_Mul:
                n, d = term.as_numer_denom()
                numer.append(n)
                denom.append(d)
            else:
                numer.append(term)
        elif term.is_Rational:
            n, d = term.as_numer_denom()
            numer.append(n)
            denom.append(d)
        else:
            numer.append(term)

    return Mul(*numer), Mul(*denom)


def numer(expr):
    return fraction(expr)[0]


def denom(expr):
    return fraction(expr)[1]


def fraction_expand(expr, **hints):
    return expr.expand(frac=True, **hints)


def numer_expand(expr, **hints):
    a, b = fraction(expr)
    return a.expand(numer=True, **hints) / b


def denom_expand(expr, **hints):
    a, b = fraction(expr)
    return a / b.expand(denom=True, **hints)

expand_numer = numer_expand
expand_denom = denom_expand
expand_fraction = fraction_expand


def collect(expr, syms, func=None, evaluate=None, exact=False, distribute_order_term=True):
    """
    Collect additive terms of an expression.

    This function collects additive terms of an expression with respect
    to a list of expression up to powers with rational exponents. By the
    term symbol here are meant arbitrary expressions, which can contain
    powers, products, sums etc. In other words symbol is a pattern which
    will be searched for in the expression's terms.

    The input expression is not expanded by :func:`collect`, so user is
    expected to provide an expression is an appropriate form. This makes
    :func:`collect` more predictable as there is no magic happening behind the
    scenes. However, it is important to note, that powers of products are
    converted to products of powers using the :func:`expand_power_base`
    function.

    There are two possible types of output. First, if ``evaluate`` flag is
    set, this function will return an expression with collected terms or
    else it will return a dictionary with expressions up to rational powers
    as keys and collected coefficients as values.

    Examples
    ========

    >>> from sympy import S, collect, expand, factor, Wild
    >>> from sympy.abc import a, b, c, x, y, z

    This function can collect symbolic coefficients in polynomials or
    rational expressions. It will manage to find all integer or rational
    powers of collection variable::

        >>> collect(a*x**2 + b*x**2 + a*x - b*x + c, x)
        c + x**2*(a + b) + x*(a - b)

    The same result can be achieved in dictionary form::

        >>> d = collect(a*x**2 + b*x**2 + a*x - b*x + c, x, evaluate=False)
        >>> d[x**2]
        a + b
        >>> d[x]
        a - b
        >>> d[S.One]
        c

    You can also work with multivariate polynomials. However, remember that
    this function is greedy so it will care only about a single symbol at time,
    in specification order::

        >>> collect(x**2 + y*x**2 + x*y + y + a*y, [x, y])
        x**2*(y + 1) + x*y + y*(a + 1)

    Also more complicated expressions can be used as patterns::

        >>> from sympy import sin, log
        >>> collect(a*sin(2*x) + b*sin(2*x), sin(2*x))
        (a + b)*sin(2*x)

        >>> collect(a*x*log(x) + b*(x*log(x)), x*log(x))
        x*(a + b)*log(x)

    You can use wildcards in the pattern::

        >>> w = Wild('w1')
        >>> collect(a*x**y - b*x**y, w**y)
        x**y*(a - b)

    It is also possible to work with symbolic powers, although it has more
    complicated behavior, because in this case power's base and symbolic part
    of the exponent are treated as a single symbol::

        >>> collect(a*x**c + b*x**c, x)
        a*x**c + b*x**c
        >>> collect(a*x**c + b*x**c, x**c)
        x**c*(a + b)

    However if you incorporate rationals to the exponents, then you will get
    well known behavior::

        >>> collect(a*x**(2*c) + b*x**(2*c), x**c)
        x**(2*c)*(a + b)

    Note also that all previously stated facts about :func:`collect` function
    apply to the exponential function, so you can get::

        >>> from sympy import exp
        >>> collect(a*exp(2*x) + b*exp(2*x), exp(x))
        (a + b)*exp(2*x)

    If you are interested only in collecting specific powers of some symbols
    then set ``exact`` flag in arguments::

        >>> collect(a*x**7 + b*x**7, x, exact=True)
        a*x**7 + b*x**7
        >>> collect(a*x**7 + b*x**7, x**7, exact=True)
        x**7*(a + b)

    You can also apply this function to differential equations, where
    derivatives of arbitrary order can be collected. Note that if you
    collect with respect to a function or a derivative of a function, all
    derivatives of that function will also be collected. Use
    ``exact=True`` to prevent this from happening::

        >>> from sympy import Derivative as D, collect, Function
        >>> f = Function('f') (x)

        >>> collect(a*D(f,x) + b*D(f,x), D(f,x))
        (a + b)*Derivative(f(x), x)

        >>> collect(a*D(D(f,x),x) + b*D(D(f,x),x), f)
        (a + b)*Derivative(f(x), x, x)

        >>> collect(a*D(D(f,x),x) + b*D(D(f,x),x), D(f,x), exact=True)
        a*Derivative(f(x), x, x) + b*Derivative(f(x), x, x)

        >>> collect(a*D(f,x) + b*D(f,x) + a*f + b*f, f)
        (a + b)*f(x) + (a + b)*Derivative(f(x), x)

    Or you can even match both derivative order and exponent at the same time::

        >>> collect(a*D(D(f,x),x)**2 + b*D(D(f,x),x)**2, D(f,x))
        (a + b)*Derivative(f(x), x, x)**2

    Finally, you can apply a function to each of the collected coefficients.
    For example you can factorize symbolic coefficients of polynomial::

        >>> f = expand((x + a + 1)**3)

        >>> collect(f, x, factor)
        x**3 + 3*x**2*(a + 1) + 3*x*(a + 1)**2 + (a + 1)**3

    .. note:: Arguments are expected to be in expanded form, so you might have
              to call :func:`expand` prior to calling this function.

    See Also
    ========
    collect_const, collect_sqrt, rcollect
    """
    if evaluate is None:
        evaluate = global_evaluate[0]

    def make_expression(terms):
        product = []

        for term, rat, sym, deriv in terms:
            if deriv is not None:
                var, order = deriv

                while order > 0:
                    term, order = Derivative(term, var), order - 1

            if sym is None:
                if rat is S.One:
                    product.append(term)
                else:
                    product.append(Pow(term, rat))
            else:
                product.append(Pow(term, rat*sym))

        return Mul(*product)

    def parse_derivative(deriv):
        # scan derivatives tower in the input expression and return
        # underlying function and maximal differentiation order
        expr, sym, order = deriv.expr, deriv.variables[0], 1

        for s in deriv.variables[1:]:
            if s == sym:
                order += 1
            else:
                raise NotImplementedError(
                    'Improve MV Derivative support in collect')

        while isinstance(expr, Derivative):
            s0 = expr.variables[0]

            for s in expr.variables:
                if s != s0:
                    raise NotImplementedError(
                        'Improve MV Derivative support in collect')

            if s0 == sym:
                expr, order = expr.expr, order + len(expr.variables)
            else:
                break

        return expr, (sym, Rational(order))

    def parse_term(expr):
        """Parses expression expr and outputs tuple (sexpr, rat_expo,
        sym_expo, deriv)
        where:
         - sexpr is the base expression
         - rat_expo is the rational exponent that sexpr is raised to
         - sym_expo is the symbolic exponent that sexpr is raised to
         - deriv contains the derivatives the the expression

         for example, the output of x would be (x, 1, None, None)
         the output of 2**x would be (2, 1, x, None)
        """
        rat_expo, sym_expo = S.One, None
        sexpr, deriv = expr, None

        if expr.is_Pow:
            if isinstance(expr.base, Derivative):
                sexpr, deriv = parse_derivative(expr.base)
            else:
                sexpr = expr.base

            if expr.exp.is_Number:
                rat_expo = expr.exp
            else:
                coeff, tail = expr.exp.as_coeff_Mul()

                if coeff.is_Number:
                    rat_expo, sym_expo = coeff, tail
                else:
                    sym_expo = expr.exp
        elif expr.func is C.exp:
            arg = expr.args[0]
            if arg.is_Rational:
                sexpr, rat_expo = S.Exp1, arg
            elif arg.is_Mul:
                coeff, tail = arg.as_coeff_Mul(rational=True)
                sexpr, rat_expo = C.exp(tail), coeff
        elif isinstance(expr, Derivative):
            sexpr, deriv = parse_derivative(expr)

        return sexpr, rat_expo, sym_expo, deriv

    def parse_expression(terms, pattern):
        """Parse terms searching for a pattern.
        terms is a list of tuples as returned by parse_terms;
        pattern is an expression treated as a product of factors
        """
        pattern = Mul.make_args(pattern)

        if len(terms) < len(pattern):
            # pattern is longer than matched product
            # so no chance for positive parsing result
            return None
        else:
            pattern = [parse_term(elem) for elem in pattern]

            terms = terms[:]  # need a copy
            elems, common_expo, has_deriv = [], None, False

            for elem, e_rat, e_sym, e_ord in pattern:

                if elem.is_Number and e_rat == 1 and e_sym is None:
                    # a constant is a match for everything
                    continue

                for j in range(len(terms)):
                    if terms[j] is None:
                        continue

                    term, t_rat, t_sym, t_ord = terms[j]

                    # keeping track of whether one of the terms had
                    # a derivative or not as this will require rebuilding
                    # the expression later
                    if t_ord is not None:
                        has_deriv = True

                    if (term.match(elem) is not None and
                            (t_sym == e_sym or t_sym is not None and
                            e_sym is not None and
                            t_sym.match(e_sym) is not None)):
                        if exact is False:
                            # we don't have to be exact so find common exponent
                            # for both expression's term and pattern's element
                            expo = t_rat / e_rat

                            if common_expo is None:
                                # first time
                                common_expo = expo
                            else:
                                # common exponent was negotiated before so
                                # there is no chance for a pattern match unless
                                # common and current exponents are equal
                                if common_expo != expo:
                                    common_expo = 1
                        else:
                            # we ought to be exact so all fields of
                            # interest must match in every details
                            if e_rat != t_rat or e_ord != t_ord:
                                continue

                        # found common term so remove it from the expression
                        # and try to match next element in the pattern
                        elems.append(terms[j])
                        terms[j] = None

                        break

                else:
                    # pattern element not found
                    return None

            return [_f for _f in terms if _f], elems, common_expo, has_deriv

    if evaluate:
        if expr.is_Mul:
            return expr.func(*[
                collect(term, syms, func, True, exact, distribute_order_term)
                for term in expr.args])
        elif expr.is_Pow:
            b = collect(
                expr.base, syms, func, True, exact, distribute_order_term)
            return Pow(b, expr.exp)

    if iterable(syms):
        syms = [expand_power_base(i, deep=False) for i in syms]
    else:
        syms = [expand_power_base(syms, deep=False)]

    expr = sympify(expr)
    order_term = None

    if distribute_order_term:
        order_term = expr.getO()

        if order_term is not None:
            if order_term.has(*syms):
                order_term = None
            else:
                expr = expr.removeO()

    summa = [expand_power_base(i, deep=False) for i in Add.make_args(expr)]

    collected, disliked = defaultdict(list), S.Zero
    for product in summa:
        terms = [parse_term(i) for i in Mul.make_args(product)]

        for symbol in syms:
            if SYMPY_DEBUG:
                print("DEBUG: parsing of expression %s with symbol %s " % (
                    str(terms), str(symbol))
                )

            result = parse_expression(terms, symbol)

            if SYMPY_DEBUG:
                print("DEBUG: returned %s" % str(result))

            if result is not None:
                terms, elems, common_expo, has_deriv = result

                # when there was derivative in current pattern we
                # will need to rebuild its expression from scratch
                if not has_deriv:
                    index = 1
                    for elem in elems:
                        e = elem[1]
                        if elem[2] is not None:
                            e *= elem[2]
                        index *= Pow(elem[0], e)
                else:
                    index = make_expression(elems)
                terms = expand_power_base(make_expression(terms), deep=False)
                index = expand_power_base(index, deep=False)
                collected[index].append(terms)
                break
        else:
            # none of the patterns matched
            disliked += product
    # add terms now for each key
    collected = dict([(k, Add(*v)) for k, v in collected.items()])

    if disliked is not S.Zero:
        collected[S.One] = disliked

    if order_term is not None:
        for key, val in collected.items():
            collected[key] = val + order_term

    if func is not None:
        collected = dict(
            [(key, func(val)) for key, val in collected.items()])

    if evaluate:
        return Add(*[key*val for key, val in collected.items()])
    else:
        return collected


def rcollect(expr, *vars):
    """
    Recursively collect sums in an expression.

    Examples
    ========

    >>> from sympy.simplify import rcollect
    >>> from sympy.abc import x, y

    >>> expr = (x**2*y + x*y + x + y)/(x + y)

    >>> rcollect(expr, y)
    (x + y*(x**2 + x + 1))/(x + y)

    See Also
    ========
    collect, collect_const, collect_sqrt
    """
    if expr.is_Atom or not expr.has(*vars):
        return expr
    else:
        expr = expr.__class__(*[rcollect(arg, *vars) for arg in expr.args])

        if expr.is_Add:
            return collect(expr, vars)
        else:
            return expr


def separatevars(expr, symbols=[], dict=False, force=False):
    """
    Separates variables in an expression, if possible.  By
    default, it separates with respect to all symbols in an
    expression and collects constant coefficients that are
    independent of symbols.

    If dict=True then the separated terms will be returned
    in a dictionary keyed to their corresponding symbols.
    By default, all symbols in the expression will appear as
    keys; if symbols are provided, then all those symbols will
    be used as keys, and any terms in the expression containing
    other symbols or non-symbols will be returned keyed to the
    string 'coeff'. (Passing None for symbols will return the
    expression in a dictionary keyed to 'coeff'.)

    If force=True, then bases of powers will be separated regardless
    of assumptions on the symbols involved.

    Notes
    =====
    The order of the factors is determined by Mul, so that the
    separated expressions may not necessarily be grouped together.

    Although factoring is necessary to separate variables in some
    expressions, it is not necessary in all cases, so one should not
    count on the returned factors being factored.

    Examples
    ========

    >>> from sympy.abc import x, y, z, alpha
    >>> from sympy import separatevars, sin
    >>> separatevars((x*y)**y)
    (x*y)**y
    >>> separatevars((x*y)**y, force=True)
    x**y*y**y

    >>> e = 2*x**2*z*sin(y)+2*z*x**2
    >>> separatevars(e)
    2*x**2*z*(sin(y) + 1)
    >>> separatevars(e, symbols=(x, y), dict=True)
    {'coeff': 2*z, x: x**2, y: sin(y) + 1}
    >>> separatevars(e, [x, y, alpha], dict=True)
    {'coeff': 2*z, alpha: 1, x: x**2, y: sin(y) + 1}

    If the expression is not really separable, or is only partially
    separable, separatevars will do the best it can to separate it
    by using factoring.

    >>> separatevars(x + x*y - 3*x**2)
    -x*(3*x - y - 1)

    If the expression is not separable then expr is returned unchanged
    or (if dict=True) then None is returned.

    >>> eq = 2*x + y*sin(x)
    >>> separatevars(eq) == eq
    True
    >>> separatevars(2*x + y*sin(x), symbols=(x, y), dict=True) == None
    True

    """
    expr = sympify(expr)
    if dict:
        return _separatevars_dict(_separatevars(expr, force), symbols)
    else:
        return _separatevars(expr, force)


def _separatevars(expr, force):
    if len(expr.free_symbols) == 1:
        return expr
    # don't destroy a Mul since much of the work may already be done
    if expr.is_Mul:
        args = list(expr.args)
        changed = False
        for i, a in enumerate(args):
            args[i] = separatevars(a, force)
            changed = changed or args[i] != a
        if changed:
            expr = expr.func(*args)
        return expr

    # get a Pow ready for expansion
    if expr.is_Pow:
        expr = Pow(separatevars(expr.base, force=force), expr.exp)

    # First try other expansion methods
    expr = expr.expand(mul=False, multinomial=False, force=force)

    _expr, reps = posify(expr) if force else (expr, {})
    expr = factor(_expr).subs(reps)

    if not expr.is_Add:
        return expr

    # Find any common coefficients to pull out
    args = list(expr.args)
    commonc = args[0].args_cnc(cset=True, warn=False)[0]
    for i in args[1:]:
        commonc &= i.args_cnc(cset=True, warn=False)[0]
    commonc = Mul(*commonc)
    commonc = commonc.as_coeff_Mul()[1]  # ignore constants
    commonc_set = commonc.args_cnc(cset=True, warn=False)[0]

    # remove them
    for i, a in enumerate(args):
        c, nc = a.args_cnc(cset=True, warn=False)
        c = c - commonc_set
        args[i] = Mul(*c)*Mul(*nc)
    nonsepar = Add(*args)

    if len(nonsepar.free_symbols) > 1:
        _expr = nonsepar
        _expr, reps = posify(_expr) if force else (_expr, {})
        _expr = (factor(_expr)).subs(reps)

        if not _expr.is_Add:
            nonsepar = _expr

    return commonc*nonsepar


def _separatevars_dict(expr, symbols):
    if symbols:
        if not all((t.is_Atom for t in symbols)):
            raise ValueError("symbols must be Atoms.")
        symbols = list(symbols)
    elif symbols is None:
        return {'coeff': expr}
    else:
        symbols = list(expr.free_symbols)
        if not symbols:
            return None

    ret = dict(((i, []) for i in symbols + ['coeff']))

    for i in Mul.make_args(expr):
        expsym = i.free_symbols
        intersection = set(symbols).intersection(expsym)
        if len(intersection) > 1:
            return None
        if len(intersection) == 0:
            # There are no symbols, so it is part of the coefficient
            ret['coeff'].append(i)
        else:
            ret[intersection.pop()].append(i)

    # rebuild
    for k, v in ret.items():
        ret[k] = Mul(*v)

    return ret


def ratsimp(expr):
    """
    Put an expression over a common denominator, cancel and reduce.

    Examples
    ========

    >>> from sympy import ratsimp
    >>> from sympy.abc import x, y
    >>> ratsimp(1/x + 1/y)
    (x + y)/(x*y)
    """

    f, g = cancel(expr).as_numer_denom()
    try:
        Q, r = reduced(f, [g], field=True, expand=False)
    except ComputationFailed:
        return f/g

    return Add(*Q) + cancel(r/g)


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.simplify 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
    ==========

    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.polys import parallel_poly_from_expr
    from sympy.polys.polyerrors import PolificationFailed, DomainError
    from sympy import solve, Monomial
    from sympy.polys.monomials import monomial_div
    from sympy.core.compatibility import combinations_with_replacement
    from sympy.utilities.misc import debug

    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(xrange(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):
        """
        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
        steps = 0

        maxdeg = a.total_degree() + b.total_degree()
        if quick:
            bound = maxdeg - 1
        else:
            bound = maxdeg
        while N + D <= bound:
            if (N, D) in tested:
                break
            tested.add((N, D))

            M1 = staircase(N)
            M2 = staircase(D)
            debug('%s / %s: %s, %s' % (N, D, M1, M2))

            Cs = symbols("c:%d" % len(M1), cls=Dummy)
            Ds = symbols("d:%d" % len(M2), cls=Dummy)
            ng = Cs + Ds

            c_hat = Poly(
                sum([Cs[i] * M1[i] for i in xrange(len(M1))]), opt.gens + ng)
            d_hat = Poly(
                sum([Ds[i] * M2[i] for i in xrange(len(M2))]), opt.gens + ng)

            r = reduced(a * d_hat - b * c_hat, G, opt.gens + ng,
                        order=opt.order, polys=True)[1]

            S = Poly(r, gens=opt.gens).coeffs()
            sol = solve(S, Cs + Ds, particular=True, quick=True)

            if sol and not all([s == 0 for s in sol.values()]):
                c = c_hat.subs(sol)
                d = d_hat.subs(sol)

                # The "free" variables occuring before as parameters
                # might still be in the substituted c, d, so set them
                # to the value chosen before:
                c = c.subs(dict(list(zip(Cs + Ds, [1] * (len(Cs) + len(Ds))))))
                d = d.subs(dict(list(zip(Cs + Ds, [1] * (len(Cs) + len(Ds))))))

                c = Poly(c, opt.gens)
                d = Poly(d, opt.gens)
                if d == 0:
                    raise ValueError('Ideal not prime?')

                allsol.append((c_hat, d_hat, S, Cs + Ds))
                if N + D != maxdeg:
                    allsol = [allsol[-1]]

                break

            steps += 1
            N += 1
            D += 1

        if steps > 0:
            c, d, allsol = _ratsimpmodprime(c, d, allsol, N, D - steps)
            c, d, allsol = _ratsimpmodprime(c, d, allsol, N - steps, D)

        return c, d, allsol

    # preprocessing. this improves performance a bit when deg(num)
    # and deg(denom) are large:
    num = reduced(num, G, opt.gens, order=opt.order)[1]
    denom = reduced(denom, G, opt.gens, order=opt.order)[1]

    if polynomial:
        return (num/denom).cancel()

    c, d, allsol = _ratsimpmodprime(
        Poly(num, opt.gens), Poly(denom, opt.gens), [])
    if not quick and allsol:
        debug('Looking for best minimal solution. Got: %s' % len(allsol))
        newsol = []
        for c_hat, d_hat, S, ng in allsol:
            sol = solve(S, ng, particular=True, quick=False)
            newsol.append((c_hat.subs(sol), d_hat.subs(sol)))
        c, d = min(newsol, key=lambda x: len(x[0].terms()) + len(x[1].terms()))

    if not domain.has_Field:
        cn, c = c.clear_denoms(convert=True)
        dn, d = d.clear_denoms(convert=True)
    r = Rational(cn, dn)

    return (c*r.q)/(d*r.p)


def trigsimp_groebner(expr, hints=[], quick=False, order="grlex",
                      polynomial=False):
    """
    Simplify trigonometric expressions using a groebner basis algorithm.

    This routine takes a fraction involving trigonometric or hyperbolic
    expressions, and tries to simplify it. The primary metric is the
    total degree. Some attempts are made to choose the simplest possible
    expression of the minimal degree, but this is non-rigorous, and also
    very slow (see the ``quick=True`` option).

    If ``polynomial`` is set to True, instead of simplifying numerator and
    denominator together, this function just brings numerator and denominator
    into a canonical form. This is much faster, but has potentially worse
    results. However, if the input is a polynomial, then the result is
    guaranteed to be an equivalent polynomial of minimal degree.

    The most important option is hints. Its entries can be any of the
    following:

    - a natural number
    - a function
    - an iterable of the form (func, var1, var2, ...)
    - anything else, interpreted as a generator

    A number is used to indicate that the search space should be increased.
    A function is used to indicate that said function is likely to occur in a
    simplified expression.
    An iterable is used indicate that func(var1 + var2 + ...) is likely to
    occur in a simplified .
    An additional generator also indicates that it is likely to occur.
    (See examples below).

    This routine carries out various computationally intensive algorithms.
    The option ``quick=True`` can be used to suppress one particularly slow
    step (at the expense of potentially more complicated results, but never at
    the expense of increased total degree).

    Examples
    ========

    >>> from sympy.abc import x, y
    >>> from sympy import sin, tan, cos, sinh, cosh, tanh
    >>> from sympy.simplify.simplify import trigsimp_groebner

    Suppose you want to simplify ``sin(x)*cos(x)``. Naively, nothing happens:

    >>> ex = sin(x)*cos(x)
    >>> trigsimp_groebner(ex)
    sin(x)*cos(x)

    This is because ``trigsimp_groebner`` only looks for a simplification
    involving just ``sin(x)`` and ``cos(x)``. You can tell it to also try
    ``2*x`` by passing ``hints=[2]``:

    >>> trigsimp_groebner(ex, hints=[2])
    sin(2*x)/2
    >>> trigsimp_groebner(sin(x)**2 - cos(x)**2, hints=[2])
    -cos(2*x)

    Increasing the search space this way can quickly become expensive. A much
    faster way is to give a specific expression that is likely to occur:

    >>> trigsimp_groebner(ex, hints=[sin(2*x)])
    sin(2*x)/2

    Hyperbolic expressions are similarly supported:

    >>> trigsimp_groebner(sinh(2*x)/sinh(x))
    2*cosh(x)

    Note how no hints had to be passed, since the expression already involved
    ``2*x``.

    The tangent function is also supported. You can either pass ``tan`` in the
    hints, to indicate that than should be tried whenever cosine or sine are,
    or you can pass a specific generator:

    >>> trigsimp_groebner(sin(x)/cos(x), hints=[tan])
    tan(x)
    >>> trigsimp_groebner(sinh(x)/cosh(x), hints=[tanh(x)])
    tanh(x)

    Finally, you can use the iterable form to suggest that angle sum formulae
    should be tried:

    >>> ex = (tan(x) + tan(y))/(1 - tan(x)*tan(y))
    >>> trigsimp_groebner(ex, hints=[(tan, x, y)])
    tan(x + y)
    """
    # TODO
    #  - preprocess by replacing everything by funcs we can handle
    # - optionally use cot instead of tan
    # - more intelligent hinting.
    #     For example, if the ideal is small, and we have sin(x), sin(y),
    #     add sin(x + y) automatically... ?
    # - algebraic numbers ...
    # - expressions of lowest degree are not distinguished properly
    #   e.g. 1 - sin(x)**2
    # - we could try to order the generators intelligently, so as to influence
    #   which monomials appear in the quotient basis

    # THEORY
    # ------
    # Ratsimpmodprime above can be used to "simplify" a rational function
    # modulo a prime ideal. "Simplify" mainly means finding an equivalent
    # expression of lower total degree.
    #
    # We intend to use this to simplify trigonometric functions. To do that,
    # we need to decide (a) which ring to use, and (b) modulo which ideal to
    # simplify. In practice, (a) means settling on a list of "generators"
    # a, b, c, ..., such that the fraction we want to simplify is a rational
    # function in a, b, c, ..., with coefficients in ZZ (integers).
    # (2) means that we have to decide what relations to impose on the
    # generators. There are two practical problems:
    #   (1) The ideal has to be *prime* (a technical term).
    #   (2) The relations have to be polynomials in the generators.
    #
    # We typically have two kinds of generators:
    # - trigonometric expressions, like sin(x), cos(5*x), etc
    # - "everything else", like gamma(x), pi, etc.
    #
    # Since this function is trigsimp, we will concentrate on what to do with
    # trigonometric expressions. We can also simplify hyperbolic expressions,
    # but the extensions should be clear.
    #
    # One crucial point is that all *other* generators really should behave
    # like indeterminates. In particular if (say) "I" is one of them, then
    # in fact I**2 + 1 = 0 and we may and will compute non-sensical
    # expressions. However, we can work with a dummy and add the relation
    # I**2 + 1 = 0 to our ideal, then substitute back in the end.
    #
    # Now regarding trigonometric generators. We split them into groups,
    # according to the argument of the trigonometric functions. We want to
    # organise this in such a way that most trigonometric identities apply in
    # the same group. For example, given sin(x), cos(2*x) and cos(y), we would
    # group as [sin(x), cos(2*x)] and [cos(y)].
    #
    # Our prime ideal will be built in three steps:
    # (1) For each group, compute a "geometrically prime" ideal of relations.
    #     Geometrically prime means that it generates a prime ideal in
    #     CC[gens], not just ZZ[gens].
    # (2) Take the union of all the generators of the ideals for all groups.
    #     By the geometric primality condition, this is still prime.
    # (3) Add further inter-group relations which preserve primality.
    #
    # Step (1) works as follows. We will isolate common factors in the
    # argument, so that all our generators are of the form sin(n*x), cos(n*x)
    # or tan(n*x), with n an integer. Suppose first there are no tan terms.
    # The ideal [sin(x)**2 + cos(x)**2 - 1] is geometrically prime, since
    # X**2 + Y**2 - 1 is irreducible over CC.
    # Now, if we have a generator sin(n*x), than we can, using trig identities,
    # express sin(n*x) as a polynomial in sin(x) and cos(x). We can add this
    # relation to the ideal, preserving geometric primality, since the quotient
    # ring is unchanged.
    # Thus we have treated all sin and cos terms.
    # For tan(n*x), we add a relation tan(n*x)*cos(n*x) - sin(n*x) = 0.
    # (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.

    from sympy.utilities.misc import debug
    from sympy import symbols
    from sympy.polys import parallel_poly_from_expr, groebner, ZZ
    from sympy.polys.polyerrors import PolificationFailed

    sin, cos, tan = C.sin, C.cos, C.tan
    sinh, cosh, tanh = C.sinh, C.cosh, C.tanh

    def parse_hints(hints):
        """Split hints into (n, funcs, iterables, gens)."""
        n = 1
        funcs, iterables, gens = [], [], []
        for e in hints:
            if isinstance(e, (int, 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.
        """
        gens = []
        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``.

        The meaning of ``hints`` is described in the main docstring.
        Return a new list of generators, and also the ideal we should
        work with.
        """
        # First parse the hints
        n, funcs, iterables, extragens = parse_hints(hints)
        debug('n=%s' % n, 'funcs:', funcs, 'iterables:',
              iterables, 'extragens:', extragens)

        # We just add the extragens to gens and analyse them as before
        gens = list(gens)
        gens.extend(extragens)

        # remove duplicates
        funcs = list(set(funcs))
        iterables = list(set(iterables))
        gens = list(set(gens))

        # all the functions we can do anything with
        allfuncs = set([sin, cos, tan, sinh, cosh, tanh])
        # sin(3*x) -> ((3, x), sin)
        trigterms = [(g.args[0].as_coeff_mul(), g.func) for g in gens
                     if g.func in allfuncs]
        # Our list of new generators - start with anything that we cannot
        # work with (i.e. is not a trigonometric term)
        freegens = [g for g in gens if g.func not in allfuncs]
        newgens = []
        trigdict = {}
        for (coeff, var), fn in trigterms:
            trigdict.setdefault(var, []).append((coeff, fn))
        res = [] # the ideal

        for key, val in trigdict.items():
            # We have now assembeled a dictionary. Its keys are common
            # arguments in trigonometric expressions, and values are lists of
            # pairs (fn, coeff). x0, (fn, coeff) in trigdict means that we
            # need to deal with fn(coeff*x0). We take the rational gcd of the
            # coeffs, call it ``gcd``. We then use x = x0/gcd as "base symbol",
            # all other arguments are integral multiples thereof.
            # We will build an ideal which works with sin(x), cos(x).
            # If hint tan is provided, also work with tan(x). Moreover, if
            # n > 1, also work with sin(k*x) for k <= n, and similarly for cos
            # (and tan if the hint is provided). Finally, any generators which
            # the ideal does not work with but we need to accomodate (either
            # because it was in expr or because it was provided as a hint)
            # we also build into the ideal.
            # This selection process is expressed in the list ``terms``.
            # build_ideal then generates the actual relations in our ideal,
            # from this list.
            fns = [x[1] for x in val]
            val = [x[0] for x in val]
            gcd = reduce(igcd, val)
            terms = [(fn, v/gcd) for (fn, v) in zip(fns, val)]
            fs = set(funcs + fns)
            for c, s, t in ([cos, sin, tan], [cosh, sinh, tanh]):
                if any(x in fs for x in (c, s, t)):
                    fs.add(c)
                    fs.add(s)
            for fn in fs:
                for k in range(1, n + 1):
                    terms.append((fn, k))
            extra = []
            for fn, v in terms:
                if fn == tan:
                    extra.append((sin, v))
                    extra.append((cos, v))
                if fn in [sin, cos] and tan in fs:
                    extra.append((tan, v))
                if fn == tanh:
                    extra.append((sinh, v))
                    extra.append((cosh, v))
                if fn in [sinh, cosh] and tanh in fs:
                    extra.append((tanh, v))
            terms.extend(extra)
            x = gcd*Mul(*key)
            r = build_ideal(x, terms)
            res.extend(r)
            newgens.extend(set(fn(v*x) for fn, v in terms))

        # Add generators for compound expressions from iterables
        for fn, args in iterables:
            if fn == tan:
                # Tan expressions are recovered from sin and cos.
                iterables.extend([(sin, args), (cos, args)])
            elif fn == tanh:
                # Tanh expressions are recovered from sihn and cosh.
                iterables.extend([(sinh, args), (cosh, args)])
            else:
                dummys = symbols('d:%i' % len(args), cls=Dummy)
                expr = fn( Add(*dummys)).expand(trig=True).subs(list(zip(dummys, args)))
                res.append(fn(Add(*args)) - expr)

        if myI in gens:
            res.append(myI**2 + 1)
            freegens.remove(myI)
            newgens.append(myI)

        return res, freegens, newgens

    myI = Dummy('I')
    expr = expr.subs(S.ImaginaryUnit, myI)
    subs = [(myI, S.ImaginaryUnit)]

    num, denom = cancel(expr).as_numer_denom()
    try:
        (pnum, pdenom), opt = parallel_poly_from_expr([num, denom])
    except PolificationFailed:
        return expr
    debug('initial gens:', opt.gens)
    ideal, freegens, gens = analyse_gens(opt.gens, hints)
    debug('ideal:', ideal)
    debug('new gens:', gens, " -- len", len(gens))
    debug('free gens:', freegens, " -- len", len(gens))
    # NOTE we force the domain to be ZZ to stop polys from injecting generators
    #      (which is usually a sign of a bug in the way we build the ideal)
    if not gens:
        return expr
    G = groebner(ideal, order=order, gens=gens, domain=ZZ)
    debug('groebner basis:', list(G), " -- len", len(G))

    # If our fraction is a polynomial in the free generators, simplify all
    # coefficients separately:
    if freegens and pdenom.has_only_gens(*set(gens).intersection(pdenom.gens)):
        num = Poly(num, gens=gens+freegens).eject(*gens)
        res = []
        for monom, coeff in num.terms():
            ourgens = set(parallel_poly_from_expr([coeff, denom])[1].gens)
            # We compute the transitive closure of all generators that can
            # be reached from our generators through relations in the ideal.
            changed = True
            while changed:
                changed = False
                for p in ideal:
                    p = Poly(p)
                    if not ourgens.issuperset(p.gens) and \
                       not p.has_only_gens(*set(p.gens).difference(ourgens)):
                        changed = True
                        ourgens.update(p.exclude().gens)
            # NOTE preserve order!
            realgens = [x for x in gens if x in ourgens]
            # The generators of the ideal have now been (implicitely) split
            # into two groups: those involving ourgens and those that don't.
            # Since we took the transitive closure above, these two groups
            # live in subgrings generated by a *disjoint* set of variables.
            # Any sensible groebner basis algorithm will preserve this disjoint
            # structure (i.e. the elements of the groebner basis can be split
            # similarly), and and the two subsets of the groebner basis then
            # form groebner bases by themselves. (For the smaller generating
            # sets, of course.)
            ourG = [g.as_expr() for g in G.polys if
                    g.has_only_gens(*ourgens.intersection(g.gens))]
            res.append(Mul(*[a**b for a, b in zip(freegens, monom)]) * \
                       ratsimpmodprime(coeff/denom, ourG, order=order,
                                       gens=realgens, quick=quick, domain=ZZ,
                                       polynomial=polynomial).subs(subs))
        return Add(*res)
        # NOTE The following is simpler and has less assumptions on the
        #      groebner basis algorithm. If the above turns out to be broken,
        #      use this.
        return Add(*[Mul(*[a**b for a, b in zip(freegens, monom)]) * \
                     ratsimpmodprime(coeff/denom, list(G), order=order,
                                     gens=gens, quick=quick, domain=ZZ)
                     for monom, coeff in num.terms()])
    else:
        return ratsimpmodprime(
            expr, list(G), order=order, gens=freegens+gens,
            quick=quick, domain=ZZ, polynomial=polynomial).subs(subs)


_trigs = (C.TrigonometricFunction, C.HyperbolicFunction)


def trigsimp(expr, **opts):
    """
    reduces expression by using known trig identities

    Notes
    =====

    method:
    - Determine the method to use. Valid choices are 'matching' (default),
    'groebner', 'combined', and 'fu'. If 'matching', simplify the
    expression recursively by targeting common patterns. If 'groebner', apply
    an experimental groebner basis algorithm. In this case further options
    are forwarded to ``trigsimp_groebner``, please refer to its docstring.
    If 'combined', first run the groebner basis algorithm with small
    default parameters, then run the 'matching' algorithm. 'fu' runs the
    collection of trigonometric transformations described by Fu, et al.
    (see the `fu` docstring).


    Examples
    ========

    >>> from sympy import trigsimp, sin, cos, log
    >>> from sympy.abc import x, y
    >>> e = 2*sin(x)**2 + 2*cos(x)**2
    >>> trigsimp(e)
    2

    Simplification occurs wherever trigonometric functions are located.

    >>> trigsimp(log(e))
    log(2)

    Using `method="groebner"` (or `"combined"`) might lead to greater
    simplification.

    The old trigsimp routine can be accessed as with method 'old'.

    >>> from sympy import coth, tanh
    >>> t = 3*tanh(x)**7 - 2/coth(x)**7
    >>> trigsimp(t, method='old') == t
    True
    >>> trigsimp(t)
    tanh(x)**7

    """
    from sympy.simplify.fu import fu

    expr = sympify(expr)

    try:
        return expr._eval_trigsimp(**opts)
    except AttributeError:
        pass

    old = opts.pop('old', False)
    if not old:
        opts.pop('deep', None)
        recursive = opts.pop('recursive', None)
        method = opts.pop('method', 'matching')
    else:
        method = 'old'

    def groebnersimp(ex, **opts):
        def traverse(e):
            if e.is_Atom:
                return e
            args = [traverse(x) for x in e.args]
            if e.is_Function or e.is_Pow:
                args = [trigsimp_groebner(x, **opts) for x in args]
            return e.func(*args)
        new = traverse(ex)
        if not isinstance(new, Expr):
            return new
        return trigsimp_groebner(new, **opts)

    trigsimpfunc = {
        'fu': (lambda x: fu(x, **opts)),
        'matching': (lambda x: futrig(x)),
        'groebner': (lambda x: groebnersimp(x, **opts)),
        'combined': (lambda x: futrig(groebnersimp(x,
                               polynomial=True, hints=[2, tan]))),
        'old': lambda x: trigsimp_old(x, **opts),
                   }[method]

    return trigsimpfunc(expr)


def collect_sqrt(expr, evaluate=None):
    """Return expr with terms having common square roots collected together.
    If ``evaluate`` is False a count indicating the number of sqrt-containing
    terms will be returned and, if non-zero, the terms of the Add will be
    returned, else the expression itself will be returned as a single term.
    If ``evaluate`` is True, the expression with any collected terms will be
    returned.

    Note: since I = sqrt(-1), it is collected, too.

    Examples
    ========

    >>> from sympy import sqrt
    >>> from sympy.simplify.simplify import collect_sqrt
    >>> from sympy.abc import a, b

    >>> r2, r3, r5 = [sqrt(i) for i in [2, 3, 5]]
    >>> collect_sqrt(a*r2 + b*r2)
    sqrt(2)*(a + b)
    >>> collect_sqrt(a*r2 + b*r2 + a*r3 + b*r3)
    sqrt(2)*(a + b) + sqrt(3)*(a + b)
    >>> collect_sqrt(a*r2 + b*r2 + a*r3 + b*r5)
    sqrt(3)*a + sqrt(5)*b + sqrt(2)*(a + b)

    If evaluate is False then the arguments will be sorted and
    returned as a list and a count of the number of sqrt-containing
    terms will be returned:

    >>> collect_sqrt(a*r2 + b*r2 + a*r3 + b*r5, evaluate=False)
    ((sqrt(3)*a, sqrt(5)*b, sqrt(2)*(a + b)), 3)
    >>> collect_sqrt(a*sqrt(2) + b, evaluate=False)
    ((b, sqrt(2)*a), 1)
    >>> collect_sqrt(a + b, evaluate=False)
    ((a + b,), 0)

    See Also
    ========
    collect, collect_const, rcollect
    """
    if evaluate is None:
        evaluate = global_evaluate[0]
    # this step will help to standardize any complex arguments
    # of sqrts
    coeff, expr = expr.as_content_primitive()
    vars = set()
    for a in Add.make_args(expr):
        for m in a.args_cnc()[0]:
            if m.is_number and (
                    m.is_Pow and m.exp.is_Rational and m.exp.q == 2 or
                    m is S.ImaginaryUnit):
                vars.add(m)

    # we only want radicals, so exclude Number handling; in this case
    # d will be evaluated
    d = collect_const(expr, *vars, Numbers=False)
    hit = expr != d

    if not evaluate:
        nrad = 0
        # make the evaluated args canonical
        args = list(ordered(Add.make_args(d)))
        for i, m in enumerate(args):
            c, nc = m.args_cnc()
            for ci in c:
                # XXX should this be restricted to ci.is_number as above?
                if ci.is_Pow and ci.exp.is_Rational and ci.exp.q == 2 or \
                        ci is S.ImaginaryUnit:
                    nrad += 1
                    break
            args[i] *= coeff
        if not (hit or nrad):
            args = [Add(*args)]
        return tuple(args), nrad

    return coeff*d


def collect_const(expr, *vars, **kwargs):
    """A non-greedy collection of terms with similar number coefficients in
    an Add expr. If ``vars`` is given then only those constants will be
    targeted. Although any Number can also be targeted, if this is not
    desired set ``Numbers=False`` and no Float or Rational will be collected.

    Examples
    ========

    >>> from sympy import sqrt
    >>> from sympy.abc import a, s, x, y, z
    >>> from sympy.simplify.simplify import collect_const
    >>> collect_const(sqrt(3) + sqrt(3)*(1 + sqrt(2)))
    sqrt(3)*(sqrt(2) + 2)
    >>> collect_const(sqrt(3)*s + sqrt(7)*s + sqrt(3) + sqrt(7))
    (sqrt(3) + sqrt(7))*(s + 1)
    >>> s = sqrt(2) + 2
    >>> collect_const(sqrt(3)*s + sqrt(3) + sqrt(7)*s + sqrt(7))
    (sqrt(2) + 3)*(sqrt(3) + sqrt(7))
    >>> collect_const(sqrt(3)*s + sqrt(3) + sqrt(7)*s + sqrt(7), sqrt(3))
    sqrt(7) + sqrt(3)*(sqrt(2) + 3) + sqrt(7)*(sqrt(2) + 2)

    The collection is sign-sensitive, giving higher precedence to the
    unsigned values:

    >>> collect_const(x - y - z)
    x - (y + z)
    >>> collect_const(-y - z)
    -(y + z)
    >>> collect_const(2*x - 2*y - 2*z, 2)
    2*(x - y - z)
    >>> collect_const(2*x - 2*y - 2*z, -2)
    2*x - 2*(y + z)

    See Also
    ========
    collect, collect_sqrt, rcollect
    """
    if not expr.is_Add:
        return expr

    recurse = False
    Numbers = kwargs.get('Numbers', True)

    if not vars:
        recurse = True
        vars = set()
        for a in expr.args:
            for m in Mul.make_args(a):
                if m.is_number:
                    vars.add(m)
    else:
        vars = sympify(vars)
    if not Numbers:
        vars = [v for v in vars if not v.is_Number]

    vars = list(ordered(vars))
    for v in vars:
        terms = defaultdict(list)
        Fv = Factors(v)
        for m in Add.make_args(expr):
            f = Factors(m)
            q, r = f.div(Fv)
            if r.is_one:
                # only accept this as a true factor if
                # it didn't change an exponent from an Integer
                # to a non-Integer, e.g. 2/sqrt(2) -> sqrt(2)
                # -- we aren't looking for this sort of change
                fwas = f.factors.copy()
                fnow = q.factors
                if not any(k in fwas and fwas[k].is_Integer and not
                        fnow[k].is_Integer for k in fnow):
                    terms[v].append(q.as_expr())
                    continue
            terms[S.One].append(m)

        args = []
        hit = False
        uneval = False
        for k in ordered(terms):
            v = terms[k]
            if k is S.One:
                args.extend(v)
                continue

            if len(v) > 1:
                v = Add(*v)
                hit = True
                if recurse and v != expr:
                    vars.append(v)
            else:
                v = v[0]

            # be careful not to let uneval become True unless
            # it must be because it's going to be more expensive
            # to rebuild the expression as an unevaluated one
            if Numbers and k.is_Number and v.is_Add:
                args.append(_keep_coeff(k, v, sign=True))
                uneval = True
            else:
                args.append(k*v)

        if hit:
            if uneval:
                expr = _unevaluated_Add(*args)
            else:
                expr = Add(*args)
            if not expr.is_Add:
                break

    return expr


def _split_gcd(*a):
    """
    split the list of integers ``a`` into a list of integers, ``a1`` having
    ``g = gcd(a1)``, and a list ``a2`` whose elements are not divisible by
    ``g``.  Returns ``g, a1, a2``

    Examples
    ========

    >>> from sympy.simplify.simplify import _split_gcd
    >>> _split_gcd(55, 35, 22, 14, 77, 10)
    (5, [55, 35, 10], [22, 14, 77])
    """
    g = a[0]
    b1 = [g]
    b2 = []
    for x in a[1:]:
        g1 = gcd(g, x)
        if g1 == 1:
            b2.append(x)
        else:
            g = g1
            b1.append(x)
    return g, b1, b2

def _is_sum_surds(p):
    args = p.args if p.is_Add else [p]
    for y in args:
        if not ((y**2).is_Rational and y.is_real):
            return False
    return True

def _nthroot_solve(p, n, prec):
    """
     helper function for ``nthroot``
     It denests ``p**Rational(1, n)`` using its minimal polynomial
    """
    from sympy.polys.numberfields import _minimal_polynomial_sq
    from sympy.solvers import solve
    while n % 2 == 0:
        p = sqrtdenest(sqrt(p))
        n = n // 2
    if n == 1:
        return p
    pn = p**Rational(1, n)
    x = Symbol('x')
    f = _minimal_polynomial_sq(p, n, x)
    if f is None:
        return None
    sols = solve(f, x)
    for sol in sols:
        if abs(sol - pn).n() < 1./10**prec:
            sol = sqrtdenest(sol)
            if _mexpand(sol**n) == p:
                return sol

def nthroot(expr, n, max_len=4, prec=15):
    """
    compute a real nth-root of a sum of surds

    Parameters
    ==========

    expr : sum of surds
    n : integer
    max_len : maximum number of surds passed as constants to ``nsimplify``

    Algorithm
    =========

    First ``nsimplify`` is used to get a candidate root; if it is not a
    root the minimal polynomial is computed; the answer is one of its
    roots.

    Examples
    ========

    >>> from sympy.simplify.simplify import nthroot
    >>> from sympy import Rational, sqrt
    >>> nthroot(90 + 34*sqrt(7), 3)
    sqrt(7) + 3

    """
    from sympy.simplify.sqrtdenest import sqrt_depth, is_algebraic
    expr = sympify(expr)
    n = sympify(n)
    p = expr**Rational(1, n)
    if not n.is_integer:
        return p
    if not _is_sum_surds(expr):
        return p
    surds = []
    coeff_muls = [x.as_coeff_Mul() for x in expr.args]
    for x, y in coeff_muls:
        if not x.is_rational:
            return p
        if y is S.One:
            continue
        if not (y.is_Pow and y.exp == S.Half and y.base.is_integer):
            return p
        surds.append(y)
    surds.sort()
    surds = surds[:max_len]
    if expr < 0 and n % 2 == 1:
        p = (-expr)**Rational(1, n)
        a = nsimplify(p, constants=surds)
        res = a if _mexpand(a**n) == _mexpand(-expr) else p
        return -res
    a = nsimplify(p, constants=surds)
    if _mexpand(a) is not _mexpand(p) and _mexpand(a**n) == _mexpand(expr):
        return _mexpand(a)
    expr = _nthroot_solve(expr, n, prec)
    if expr is None:
        return p
    return expr


def split_surds(expr):
    """
    split an expression with terms whose squares are rationals
    into a sum of terms whose surds squared have gcd equal to g
    and a sum of terms with surds squared prime with g

    Examples
    ========

    >>> from sympy import sqrt
    >>> from sympy.simplify.simplify import split_surds
    >>> split_surds(3*sqrt(3) + sqrt(5)/7 + sqrt(6) + sqrt(10) + sqrt(15))
    (3, sqrt(2) + sqrt(5) + 3, sqrt(5)/7 + sqrt(10))
    """
    args = sorted(expr.args, key=default_sort_key)
    coeff_muls = [x.as_coeff_Mul() for x in args]
    surds = [x[1]**2 for x in coeff_muls if x[1].is_Pow]
    surds.sort(key=default_sort_key)
    g, b1, b2 = _split_gcd(*surds)
    g2 = g
    if not b2 and len(b1) >= 2:
        b1n = [x/g for x in b1]
        b1n = [x for x in b1n if x != 1]
        # only a common factor has been factored; split again
        g1, b1n, b2 = _split_gcd(*b1n)
        g2 = g*g1
    a1v, a2v = [], []
    for c, s in coeff_muls:
        if s.is_Pow and s.exp == S.Half:
            s1 = s.base
            if s1 in b1:
                a1v.append(c*sqrt(s1/g2))
            else:
                a2v.append(c*s)
        else:
            a2v.append(c*s)
    a = Add(*a1v)
    b = Add(*a2v)
    return g2, a, b


def rad_rationalize(num, den):
    """
    Rationalize num/den by removing square roots in the denominator;
    num and den are sum of terms whose squares are rationals

    Examples
    ========

    >>> from sympy import sqrt
    >>> from sympy.simplify.simplify import rad_rationalize
    >>> rad_rationalize(sqrt(3), 1 + sqrt(2)/3)
    (-sqrt(3) + sqrt(6)/3, -7/9)
    """
    if not den.is_Add:
        return num, den
    g, a, b = split_surds(den)
    a = a*sqrt(g)
    num = _mexpand((a - b)*num)
    den = _mexpand(a**2 - b**2)
    return rad_rationalize(num, den)


def radsimp(expr, symbolic=True, max_terms=4):
    """
    Rationalize the denominator by removing square roots.

    Note: the expression returned from radsimp must be used with caution
    since if the denominator contains symbols, it will be possible to make
    substitutions that violate the assumptions of the simplification process:
    that for a denominator matching a + b*sqrt(c), a != +/-b*sqrt(c). (If
    there are no symbols, this assumptions is made valid by collecting terms
    of sqrt(c) so the match variable ``a`` does not contain ``sqrt(c)``.) If
    you do not want the simplification to occur for symbolic denominators, set
    ``symbolic`` to False.

    If there are more than ``max_terms`` radical terms then the expression is
    returned unchanged.

    Examples
    ========

    >>> from sympy import radsimp, sqrt, Symbol, denom, pprint, I
    >>> from sympy import factor_terms, fraction, signsimp
    >>> from sympy.simplify.simplify import collect_sqrt
    >>> from sympy.abc import a, b, c

    >>> radsimp(1/(I + 1))
    (1 - I)/2
    >>> radsimp(1/(2 + sqrt(2)))
    (-sqrt(2) + 2)/2
    >>> x,y = map(Symbol, 'xy')
    >>> e = ((2 + 2*sqrt(2))*x + (2 + sqrt(8))*y)/(2 + sqrt(2))
    >>> radsimp(e)
    sqrt(2)*(x + y)

    No simplification beyond removal of the gcd is done. One might
    want to polish the result a little, however, by collecting
    square root terms:

    >>> r2 = sqrt(2)
    >>> r5 = sqrt(5)
    >>> ans = radsimp(1/(y*r2 + x*r2 + a*r5 + b*r5)); pprint(ans)
        ___       ___       ___       ___
      \/ 5 *a + \/ 5 *b - \/ 2 *x - \/ 2 *y
    ------------------------------------------
       2               2      2              2
    5*a  + 10*a*b + 5*b  - 2*x  - 4*x*y - 2*y

    >>> n, d = fraction(ans)
    >>> pprint(factor_terms(signsimp(collect_sqrt(n))/d, radical=True))
            ___             ___
          \/ 5 *(a + b) - \/ 2 *(x + y)
    ------------------------------------------
       2               2      2              2
    5*a  + 10*a*b + 5*b  - 2*x  - 4*x*y - 2*y

    If radicals in the denominator cannot be removed or there is no denominator,
    the original expression will be returned.

    >>> radsimp(sqrt(2)*x + sqrt(2))
    sqrt(2)*x + sqrt(2)

    Results with symbols will not always be valid for all substitutions:

    >>> eq = 1/(a + b*sqrt(c))
    >>> eq.subs(a, b*sqrt(c))
    1/(2*b*sqrt(c))
    >>> radsimp(eq).subs(a, b*sqrt(c))
    nan

    If symbolic=False, symbolic denominators will not be transformed (but
    numeric denominators will still be processed):

    >>> radsimp(eq, symbolic=False)
    1/(a + b*sqrt(c))

    """
    from sympy.core.mul import _unevaluated_Mul as _umul
    from sympy.core.exprtools import Factors

    syms = symbols("a:d A:D")
    def _num(rterms):
        # return the multiplier that will simplify the expression described
        # by rterms [(sqrt arg, coeff), ... ]
        a, b, c, d, A, B, C, D = syms
        if len(rterms) == 2:
            reps = dict(list(zip([A, a, B, b], [j for i in rterms for j in i])))
            return (
            sqrt(A)*a - sqrt(B)*b).xreplace(reps)
        if len(rterms) == 3:
            reps = dict(list(zip([A, a, B, b, C, c], [j for i in rterms for j in i])))
            return (
            (sqrt(A)*a + sqrt(B)*b - sqrt(C)*c)*(2*sqrt(A)*sqrt(B)*a*b - A*a**2 -
            B*b**2 + C*c**2)).xreplace(reps)
        elif len(rterms) == 4:
            reps = dict(list(zip([A, a, B, b, C, c, D, d], [j for i in rterms for j in i])))
            return ((sqrt(A)*a + sqrt(B)*b - sqrt(C)*c - sqrt(D)*d)*(2*sqrt(A)*sqrt(B)*a*b
                - A*a**2 - B*b**2 - 2*sqrt(C)*sqrt(D)*c*d + C*c**2 +
                D*d**2)*(-8*sqrt(A)*sqrt(B)*sqrt(C)*sqrt(D)*a*b*c*d + A**2*a**4 -
                2*A*B*a**2*b**2 - 2*A*C*a**2*c**2 - 2*A*D*a**2*d**2 + B**2*b**4 -
                2*B*C*b**2*c**2 - 2*B*D*b**2*d**2 + C**2*c**4 - 2*C*D*c**2*d**2 +
                D**2*d**4)).xreplace(reps)
        elif len(rterms) == 1:
            return sqrt(rterms[0][0])
        else:
            raise NotImplementedError

    def ispow2(d, log2=False):
        if not d.is_Pow:
            return False
        e = d.exp
        if e.is_Rational and e.q == 2 or symbolic and fraction(e)[1] == 2:
            return True
        if log2:
            q = 1
            if e.is_Rational:
                q = e.q
            elif symbolic:
                d = fraction(e)[1]
                if d.is_Integer:
                    q = d
            if q != 1 and log(q, 2).is_Integer:
                return True
        return False

    def handle(expr):
        # Handle first reduces to the case
        # expr = 1/d, where d is an add, or d is base**p/2.
        # We do this by recursively calling handle on each piece.
        n, d = fraction(expr)

        if expr.is_Atom or (d.is_Atom and n.is_Atom):
            return expr
        elif not n.is_Atom:
            n = n.func(*[handle(a) for a in n.args])
            return _umul(n, handle(1/d))
        elif n is not S.One:
            return _umul(n, handle(1/d))
        elif d.is_Mul:
            return _umul(*[handle(1/d) for d in d.args])

        # By this step, expr is 1/d, and d is not a mul.
        if not symbolic and d.free_symbols:
            return expr

        if ispow2(d):
            d2 = sqrtdenest(sqrt(d.base))**fraction(d.exp)[0]
            if d2 != d:
                return handle(1/d2)
        elif d.is_Pow and (d.exp.is_integer or d.base.is_positive):
            # (1/d**i) = (1/d)**i
            return handle(1/d.base)**d.exp

        if not (d.is_Add or ispow2(d)):
            return 1/d.func(*[handle(a) for a in d.args])

        # handle 1/d treating d as an Add (though it may not be)

        keep = True  # keep changes that are made

        # flatten it and collect radicals after checking for special
        # conditions
        d = _mexpand(d)

        # did it change?
        if d.is_Atom:
            return 1/d

        # is it a number that might be handled easily?
        if d.is_number:
            _d = nsimplify(d)
            if _d.is_Number and _d.equals(d):
                return 1/_d

        while True:
            # collect similar terms
            collected = defaultdict(list)
            for m in Add.make_args(d):  # d might have become non-Add
                p2 = []
                other = []
                for i in Mul.make_args(m):
                    if ispow2(i, log2=True):
                        p2.append(i.base if i.exp is S.Half else i.base**(2*i.exp))
                    elif i is S.ImaginaryUnit:
                        p2.append(S.NegativeOne)
                    else:
                        other.append(i)
                collected[tuple(ordered(p2))].append(Mul(*other))
            rterms = list(ordered(list(collected.items())))
            rterms = [(Mul(*i), Add(*j)) for i, j in rterms]
            nrad = len(rterms) - (1 if rterms[0][0] is S.One else 0)
            if nrad < 1:
                break
            elif nrad > max_terms:
                # there may have been invalid operations leading to this point
                # so don't keep changes, e.g. this expression is troublesome
                # in collecting terms so as not to raise the issue of 2834:
                # r = sqrt(sqrt(5) + 5)
                # eq = 1/(sqrt(5)*r + 2*sqrt(5)*sqrt(-sqrt(5) + 5) + 5*r)
                keep = False
                break
            if len(rterms) > 4:
                # in general, only 4 terms can be removed with repeated squaring
                # but other considerations can guide selection of radical terms
                # so that radicals are removed
                if all([x.is_Integer and (y**2).is_Rational for x, y in rterms]):
                    nd, d = rad_rationalize(S.One, Add._from_args(
                        [sqrt(x)*y for x, y in rterms]))
                    n *= nd
                else:
                    # is there anything else that might be attempted?
                    keep = False
                break

            num = powsimp(_num(rterms))
            n *= num
            d *= num
            d = powdenest(_mexpand(d), force=symbolic)
            if d.is_Atom:
                break

        if not keep:
            return expr
        return _umul(n, 1/d)

    coeff, expr = expr.as_coeff_Add()
    expr = expr.normal()
    old = fraction(expr)
    n, d = fraction(handle(expr))
    if old != (n, d):
        if not d.is_Atom:
            was = (n, d)
            n = signsimp(n, evaluate=False)
            d = signsimp(d, evaluate=False)
            u = Factors(_umul(n, 1/d))
            u = _umul(*[k**v for k, v in u.factors.items()])
            n, d = fraction(u)
            if old == (n, d):
                n, d = was
        n = expand_mul(n)
        if d.is_Number or d.is_Add:
            n2, d2 = fraction(gcd_terms(_umul(n, 1/d)))
            if d2.is_Number or (d2.count_ops() <= d.count_ops()):
                n, d = [signsimp(i) for i in (n2, d2)]
                if n.is_Mul and n.args[0].is_Number:
                    n = n.func(*n.args)

    return coeff + _umul(n, 1/d)


def posify(eq):
    """Return eq (with generic symbols made positive) and a restore
    dictionary.

    Any symbol that has positive=None will be replaced with a positive dummy
    symbol having the same name. This replacement will allow more symbolic
    processing of expressions, especially those involving powers and
    logarithms.

    A dictionary that can be sent to subs to restore eq to its original
    symbols is also returned.

    >>> from sympy import posify, Symbol, log
    >>> from sympy.abc import x
    >>> posify(x + Symbol('p', positive=True) + Symbol('n', negative=True))
    (_x + n + p, {_x: x})

    >> log(1/x).expand() # should be log(1/x) but it comes back as -log(x)
    log(1/x)

    >>> log(posify(1/x)[0]).expand() # take [0] and ignore replacements
    -log(_x)
    >>> eq, rep = posify(1/x)
    >>> log(eq).expand().subs(rep)
    -log(x)
    >>> posify([x, 1 + x])
    ([_x, _x + 1], {_x: x})
    """
    eq = sympify(eq)
    if iterable(eq):
        f = type(eq)
        eq = list(eq)
        syms = set()
        for e in eq:
            syms = syms.union(e.atoms(C.Symbol))
        reps = {}
        for s in syms:
            reps.update(dict((v, k) for k, v in posify(s)[1].items()))
        for i, e in enumerate(eq):
            eq[i] = e.subs(reps)
        return f(eq), dict([(r, s) for s, r in reps.items()])

    reps = dict([(s, Dummy(s.name, positive=True))
                 for s in eq.free_symbols if s.is_positive is None])
    eq = eq.subs(reps)
    return eq, dict([(r, s) for s, r in reps.items()])


def _polarify(eq, lift, pause=False):
    from sympy import polar_lift, Integral
    if eq.is_polar:
        return eq
    if eq.is_number and not pause:
        return polar_lift(eq)
    if isinstance(eq, Symbol) and not pause and lift:
        return polar_lift(eq)
    elif eq.is_Atom:
        return eq
    elif eq.is_Add:
        r = eq.func(*[_polarify(arg, lift, pause=True) for arg in eq.args])
        if lift:
            return polar_lift(r)
        return r
    elif eq.is_Function:
        return eq.func(*[_polarify(arg, lift, pause=False) for arg in eq.args])
    elif isinstance(eq, Integral):
        # Don't lift the integration variable
        func = _polarify(eq.function, lift, pause=pause)
        limits = []
        for limit in eq.args[1:]:
            var = _polarify(limit[0], lift=False, pause=pause)
            rest = _polarify(limit[1:], lift=lift, pause=pause)
            limits.append((var,) + rest)
        return Integral(*((func,) + tuple(limits)))
    else:
        return eq.func(*[_polarify(arg, lift, pause=pause)
                         if isinstance(arg, Expr) else arg for arg in eq.args])


def polarify(eq, subs=True, lift=False):
    """
    Turn all numbers in eq into their polar equivalents (under the standard
    choice of argument).

    Note that no attempt is made to guess a formal convention of adding
    polar numbers, expressions like 1 + x will generally not be altered.

    Note also that this function does not promote exp(x) to exp_polar(x).

    If ``subs`` is True, all symbols which are not already polar will be
    substituted for polar dummies; in this case the function behaves much
    like posify.

    If ``lift`` is True, both addition statements and non-polar symbols are
    changed to their polar_lift()ed versions.
    Note that lift=True implies subs=False.

    >>> from sympy import polarify, sin, I
    >>> from sympy.abc import x, y
    >>> expr = (-x)**y
    >>> expr.expand()
    (-x)**y
    >>> polarify(expr)
    ((_x*exp_polar(I*pi))**_y, {_x: x, _y: y})
    >>> polarify(expr)[0].expand()
    _x**_y*exp_polar(_y*I*pi)
    >>> polarify(x, lift=True)
    polar_lift(x)
    >>> polarify(x*(1+y), lift=True)
    polar_lift(x)*polar_lift(y + 1)

    Adds are treated carefully:

    >>> polarify(1 + sin((1 + I)*x))
    (sin(_x*polar_lift(1 + I)) + 1, {_x: x})
    """
    if lift:
        subs = False
    eq = _polarify(sympify(eq), lift)
    if not subs:
        return eq
    reps = dict([(s, Dummy(s.name, polar=True)) for s in eq.free_symbols])
    eq = eq.subs(reps)
    return eq, dict([(r, s) for s, r in reps.items()])


def _unpolarify(eq, exponents_only, pause=False):
    from sympy import polar_lift, exp, principal_branch, pi

    if isinstance(eq, bool) or eq.is_Atom:
        return eq

    if not pause:
        if eq.func is exp_polar:
            return exp(_unpolarify(eq.exp, exponents_only))
        if eq.func is principal_branch and eq.args[1] == 2*pi:
            return _unpolarify(eq.args[0], exponents_only)
        if (
            eq.is_Add or eq.is_Mul or eq.is_Boolean or
            eq.is_Relational and (
                eq.rel_op in ('==', '!=') and 0 in eq.args or
                eq.rel_op not in ('==', '!='))
        ):
            return eq.func(*[_unpolarify(x, exponents_only) for x in eq.args])
        if eq.func is polar_lift:
            return _unpolarify(eq.args[0], exponents_only)

    if eq.is_Pow:
        expo = _unpolarify(eq.exp, exponents_only)
        base = _unpolarify(eq.base, exponents_only,
            not (expo.is_integer and not pause))
        return base**expo

    if eq.is_Function and getattr(eq.func, 'unbranched', False):
        return eq.func(*[_unpolarify(x, exponents_only, exponents_only)
            for x in eq.args])

    return eq.func(*[_unpolarify(x, exponents_only, True) for x in eq.args])


def unpolarify(eq, subs={}, exponents_only=False):
    """
    If p denotes the projection from the Riemann surface of the logarithm to
    the complex line, return a simplified version eq' of `eq` such that
    p(eq') == p(eq).
    Also apply the substitution subs in the end. (This is a convenience, since
    ``unpolarify``, in a certain sense, undoes polarify.)

    >>> from sympy import unpolarify, polar_lift, sin, I
    >>> unpolarify(polar_lift(I + 2))
    2 + I
    >>> unpolarify(sin(polar_lift(I + 7)))
    sin(7 + I)
    """
    from sympy import exp_polar, polar_lift
    if isinstance(eq, bool):
        return eq

    eq = sympify(eq)
    if subs != {}:
        return unpolarify(eq.subs(subs))
    changed = True
    pause = False
    if exponents_only:
        pause = True
    while changed:
        changed = False
        res = _unpolarify(eq, exponents_only, pause)
        if res != eq:
            changed = True
            eq = res
        if isinstance(res, bool):
            return res
    # Finally, replacing Exp(0) by 1 is always correct.
    # So is polar_lift(0) -> 0.
    return res.subs({exp_polar(0): 1, polar_lift(0): 0})


def _denest_pow(eq):
    """
    Denest powers.

    This is a helper function for powdenest that performs the actual
    transformation.
    """
    b, e = eq.as_base_exp()
    if b.is_Pow or isinstance(b.func, exp) and e != 1:
        new = b._eval_power(e)
        if new is not None:
            eq = new
            b, e = new.as_base_exp()

    # denest exp with log terms in exponent
    if b is S.Exp1 and e.is_Mul:
        logs = []
        other = []
        for ei in e.args:
            if any(ai.func is C.log for ai in Add.make_args(ei)):
                logs.append(ei)
            else:
                other.append(ei)
        logs = logcombine(Mul(*logs))
        return Pow(exp(logs), Mul(*other))

    _, be = b.as_base_exp()
    if be is S.One and not (b.is_Mul or
                            b.is_Rational and b.q != 1 or
                            b.is_positive):
        return eq

    # denest eq which is either pos**e or Pow**e or Mul**e or
    # Mul(b1**e1, b2**e2)

    # handle polar numbers specially
    polars, nonpolars = [], []
    for bb in Mul.make_args(b):
        if bb.is_polar:
            polars.append(bb.as_base_exp())
        else:
            nonpolars.append(bb)
    if len(polars) == 1 and not polars[0][0].is_Mul:
        return Pow(polars[0][0], polars[0][1]*e)*powdenest(Mul(*nonpolars)**e)
    elif polars:
        return Mul(*[powdenest(bb**(ee*e)) for (bb, ee) in polars]) \
            *powdenest(Mul(*nonpolars)**e)

    if b.is_Integer:
        # use log to see if there is a power here
        logb = expand_log(log(b))
        if logb.is_Mul:
            c, logb = logb.args
            e *= c
            base = logb.args[0]
            return Pow(base, e)

    # if b is not a Mul or any factor is an atom then there is nothing to do
    if not b.is_Mul or any(s.is_Atom for s in Mul.make_args(b)):
        return eq

    # let log handle the case of the base of the argument being a Mul, e.g.
    # sqrt(x**(2*i)*y**(6*i)) -> x**i*y**(3**i) if x and y are positive; we
    # will take the log, expand it, and then factor out the common powers that
    # now appear as coefficient. We do this manually since terms_gcd pulls out
    # fractions, terms_gcd(x+x*y/2) -> x*(y + 2)/2 and we don't want the 1/2;
    # gcd won't pull out numerators from a fraction: gcd(3*x, 9*x/2) -> x but
    # we want 3*x. Neither work with noncommutatives.

    def nc_gcd(aa, bb):
        a, b = [i.as_coeff_Mul() for i in [aa, bb]]
        c = gcd(a[0], b[0]).as_numer_denom()[0]
        g = Mul(*(a[1].args_cnc(cset=True)[0] & b[1].args_cnc(cset=True)[0]))
        return _keep_coeff(c, g)

    glogb = expand_log(log(b))
    if glogb.is_Add:
        args = glogb.args
        g = reduce(nc_gcd, args)
        if g != 1:
            cg, rg = g.as_coeff_Mul()
            glogb = _keep_coeff(cg, rg*Add(*[a/g for a in args]))

    # now put the log back together again
    if glogb.func is C.log or not glogb.is_Mul:
        if glogb.args[0].is_Pow or glogb.args[0].func is exp:
            glogb = _denest_pow(glogb.args[0])
            if (abs(glogb.exp) < 1) == True:
                return Pow(glogb.base, glogb.exp*e)
        return eq

    # the log(b) was a Mul so join any adds with logcombine
    add = []
    other = []
    for a in glogb.args:
        if a.is_Add:
            add.append(a)
        else:
            other.append(a)
    return Pow(exp(logcombine(Mul(*add))), e*Mul(*other))


def powdenest(eq, force=False, polar=False):
    r"""
    Collect exponents on powers as assumptions allow.

    Given ``(bb**be)**e``, this can be simplified as follows:
        * if ``bb`` is positive, or
        * ``e`` is an integer, or
        * ``|be| < 1`` then this simplifies to ``bb**(be*e)``

    Given a product of powers raised to a power, ``(bb1**be1 *
    bb2**be2...)**e``, simplification can be done as follows:

    - if e is positive, the gcd of all bei can be joined with e;
    - all non-negative bb can be separated from those that are negative
      and their gcd can be joined with e; autosimplification already
      handles this separation.
    - integer factors from powers that have integers in the denominator
      of the exponent can be removed from any term and the gcd of such
      integers can be joined with e

    Setting ``force`` to True will make symbols that are not explicitly
    negative behave as though they are positive, resulting in more
    denesting.

    Setting ``polar`` to True will do simplifications on the Riemann surface of
    the logarithm, also resulting in more denestings.

    When there are sums of logs in exp() then a product of powers may be
    obtained e.g. ``exp(3*(log(a) + 2*log(b)))`` - > ``a**3*b**6``.

    Examples
    ========

    >>> from sympy.abc import a, b, x, y, z
    >>> from sympy import Symbol, exp, log, sqrt, symbols, powdenest

    >>> powdenest((x**(2*a/3))**(3*x))
    (x**(2*a/3))**(3*x)
    >>> powdenest(exp(3*x*log(2)))
    2**(3*x)

    Assumptions may prevent expansion:

    >>> powdenest(sqrt(x**2))
    sqrt(x**2)

    >>> p = symbols('p', positive=True)
    >>> powdenest(sqrt(p**2))
    p

    No other expansion is done.

    >>> i, j = symbols('i,j', integer=True)
    >>> powdenest((x**x)**(i + j)) # -X-> (x**x)**i*(x**x)**j
    x**(x*(i + j))

    But exp() will be denested by moving all non-log terms outside of
    the function; this may result in the collapsing of the exp to a power
    with a different base:

    >>> powdenest(exp(3*y*log(x)))
    x**(3*y)
    >>> powdenest(exp(y*(log(a) + log(b))))
    (a*b)**y
    >>> powdenest(exp(3*(log(a) + log(b))))
    a**3*b**3

    If assumptions allow, symbols can also be moved to the outermost exponent:

    >>> i = Symbol('i', integer=True)
    >>> powdenest(((x**(2*i))**(3*y))**x)
    ((x**(2*i))**(3*y))**x
    >>> powdenest(((x**(2*i))**(3*y))**x, force=True)
    x**(6*i*x*y)

    >>> powdenest(((x**(2*a/3))**(3*y/i))**x)
    ((x**(2*a/3))**(3*y/i))**x
    >>> powdenest((x**(2*i)*y**(4*i))**z, force=True)
    (x*y**2)**(2*i*z)

    >>> n = Symbol('n', negative=True)

    >>> powdenest((x**i)**y, force=True)
    x**(i*y)
    >>> powdenest((n**i)**x, force=True)
    (n**i)**x

    """

    if force:
        eq, rep = posify(eq)
        return powdenest(eq, force=False).xreplace(rep)

    if polar:
        eq, rep = polarify(eq)
        return unpolarify(powdenest(unpolarify(eq, exponents_only=True)), rep)

    new = powsimp(sympify(eq))
    return new.xreplace(Transform(
        _denest_pow, filter=lambda m: m.is_Pow or m.func is exp))

_y = Dummy('y')


def powsimp(expr, deep=False, combine='all', force=False, measure=count_ops):
    """
    reduces expression by combining powers with similar bases and exponents.

    Notes
    =====

    If deep is True then powsimp() will also simplify arguments of
    functions. By default deep is set to False.

    If force is True then bases will be combined without checking for
    assumptions, e.g. sqrt(x)*sqrt(y) -> sqrt(x*y) which is not true
    if x and y are both negative.

    You can make powsimp() only combine bases or only combine exponents by
    changing combine='base' or combine='exp'.  By default, combine='all',
    which does both.  combine='base' will only combine::

         a   a          a                          2x      x
        x * y  =>  (x*y)   as well as things like 2   =>  4

    and combine='exp' will only combine
    ::

         a   b      (a + b)
        x * x  =>  x

    combine='exp' will strictly only combine exponents in the way that used
    to be automatic.  Also use deep=True if you need the old behavior.

    When combine='all', 'exp' is evaluated first.  Consider the first
    example below for when there could be an ambiguity relating to this.
    This is done so things like the second example can be completely
    combined.  If you want 'base' combined first, do something like
    powsimp(powsimp(expr, combine='base'), combine='exp').

    Examples
    ========

    >>> from sympy import powsimp, exp, log, symbols
    >>> from sympy.abc import x, y, z, n
    >>> powsimp(x**y*x**z*y**z, combine='all')
    x**(y + z)*y**z
    >>> powsimp(x**y*x**z*y**z, combine='exp')
    x**(y + z)*y**z
    >>> powsimp(x**y*x**z*y**z, combine='base', force=True)
    x**y*(x*y)**z

    >>> powsimp(x**z*x**y*n**z*n**y, combine='all', force=True)
    (n*x)**(y + z)
    >>> powsimp(x**z*x**y*n**z*n**y, combine='exp')
    n**(y + z)*x**(y + z)
    >>> powsimp(x**z*x**y*n**z*n**y, combine='base', force=True)
    (n*x)**y*(n*x)**z

    >>> x, y = symbols('x y', positive=True)
    >>> powsimp(log(exp(x)*exp(y)))
    log(exp(x)*exp(y))
    >>> powsimp(log(exp(x)*exp(y)), deep=True)
    x + y

    Radicals with Mul bases will be combined if combine='exp'

    >>> from sympy import sqrt, Mul
    >>> x, y = symbols('x y')

    Two radicals are automatically joined through Mul:
    >>> a=sqrt(x*sqrt(y))
    >>> a*a**3 == a**4
    True

    But if an integer power of that radical has been
    autoexpanded then Mul does not join the resulting factors:
    >>> a**4 # auto expands to a Mul, no longer a Pow
    x**2*y
    >>> _*a # so Mul doesn't combine them
    x**2*y*sqrt(x*sqrt(y))
    >>> powsimp(_) # but powsimp will
    (x*sqrt(y))**(5/2)
    >>> powsimp(x*y*a) # but won't when doing so would violate assumptions
    x*y*sqrt(x*sqrt(y))

    """

    def recurse(arg, **kwargs):
        _deep = kwargs.get('deep', deep)
        _combine = kwargs.get('combine', combine)
        _force = kwargs.get('force', force)
        _measure = kwargs.get('measure', measure)
        return powsimp(arg, _deep, _combine, _force, _measure)

    expr = sympify(expr)

    if not isinstance(expr, Basic) or expr.is_Atom or expr in (
            exp_polar(0), exp_polar(1)):
        return expr

    if deep or expr.is_Add or expr.is_Mul and _y not in expr.args:
        expr = expr.func(*[recurse(w) for w in expr.args])

    if expr.is_Pow:
        return recurse(expr*_y, deep=False)/_y

    if not expr.is_Mul:
        return expr

    # handle the Mul
    if combine in ('exp', 'all'):
        # Collect base/exp data, while maintaining order in the
        # non-commutative parts of the product
        c_powers = defaultdict(list)
        nc_part = []
        newexpr = []
        coeff = S.One
        for term in expr.args:
            if term.is_Rational:
                coeff *= term
                continue
            if term.is_Pow:
                term = _denest_pow(term)
            if term.is_commutative:
                b, e = term.as_base_exp()
                if deep:
                    b, e = [recurse(i) for i in [b, e]]
                if b.is_Pow or b.func is exp:
                    # don't let smthg like sqrt(x**a) split into x**a, 1/2
                    # or else it will be joined as x**(a/2) later
                    b, e = b**e, S.One
                c_powers[b].append(e)
            else:
                # This is the logic that combines exponents for equal,
                # but non-commutative bases: A**x*A**y == A**(x+y).
                if nc_part:
                    b1, e1 = nc_part[-1].as_base_exp()
                    b2, e2 = term.as_base_exp()
                    if (b1 == b2 and
                            e1.is_commutative and e2.is_commutative):
                        nc_part[-1] = Pow(b1, Add(e1, e2))
                        continue
                nc_part.append(term)

        # add up exponents of common bases
        for b, e in ordered(iter(c_powers.items())):
            # allow 2**x/4 -> 2**(x - 2); don't do this when b and e are
            # Numbers since autoevaluation will undo it, e.g.
            # 2**(1/3)/4 -> 2**(1/3 - 2) -> 2**(1/3)/4
            if (b and b.is_Number and not all(ei.is_Number for ei in e) and \
                    coeff is not S.One and
                    b not in (S.One, S.NegativeOne)):
                m = multiplicity(abs(b), abs(coeff))
                if m:
                    e.append(m)
                    coeff /= b**m
            c_powers[b] = Add(*e)
        if coeff is not S.One:
            if coeff in c_powers:
                c_powers[coeff] += S.One
            else:
                c_powers[coeff] = S.One

        # convert to plain dictionary
        c_powers = dict(c_powers)

        # check for base and inverted base pairs
        be = list(c_powers.items())
        skip = set()  # skip if we already saw them
        for b, e in be:
            if b in skip:
                continue
            bpos = b.is_positive or b.is_polar
            if bpos:
                binv = 1/b
                if b != binv and binv in c_powers:
                    if b.as_numer_denom()[0] is S.One:
                        c_powers.pop(b)
                        c_powers[binv] -= e
                    else:
                        skip.add(binv)
                        e = c_powers.pop(binv)
                        c_powers[b] -= e

        # check for base and negated base pairs
        be = list(c_powers.items())
        _n = S.NegativeOne
        for i, (b, e) in enumerate(be):
            if ((-b).is_Symbol or b.is_Add) and -b in c_powers:
                if (b.is_positive in (0, 1) or e.is_integer):
                    c_powers[-b] += c_powers.pop(b)
                    if _n in c_powers:
                        c_powers[_n] += e
                    else:
                        c_powers[_n] = e

        # filter c_powers and convert to a list
        c_powers = [(b, e) for b, e in c_powers.items() if e]

        # ==============================================================
        # check for Mul bases of Rational powers that can be combined with
        # separated bases, e.g. x*sqrt(x*y)*sqrt(x*sqrt(x*y)) ->
        # (x*sqrt(x*y))**(3/2)
        # ---------------- helper functions

        def ratq(x):
            '''Return Rational part of x's exponent as it appears in the bkey.
            '''
            return bkey(x)[0][1]

        def bkey(b, e=None):
            '''Return (b**s, c.q), c.p where e -> c*s. If e is not given then
            it will be taken by using as_base_exp() on the input b.
            e.g.
                x**3/2 -> (x, 2), 3
                x**y -> (x**y, 1), 1
                x**(2*y/3) -> (x**y, 3), 2
                exp(x/2) -> (exp(a), 2), 1

            '''
            if e is not None:  # coming from c_powers or from below
                if e.is_Integer:
                    return (b, S.One), e
                elif e.is_Rational:
                    return (b, Integer(e.q)), Integer(e.p)
                else:
                    c, m = e.as_coeff_Mul(rational=True)
                    if c is not S.One:
                        return (b**m, Integer(c.q)), Integer(c.p)
                    else:
                        return (b**e, S.One), S.One
            else:
                return bkey(*b.as_base_exp())

        def update(b):
            '''Decide what to do with base, b. If its exponent is now an
            integer multiple of the Rational denominator, then remove it
            and put the factors of its base in the common_b dictionary or
            update the existing bases if necessary. If it has been zeroed
            out, simply remove the base.
            '''
            newe, r = divmod(common_b[b], b[1])
            if not r:
                common_b.pop(b)
                if newe:
                    for m in Mul.make_args(b[0]**newe):
                        b, e = bkey(m)
                        if b not in common_b:
                            common_b[b] = 0
                        common_b[b] += e
                        if b[1] != 1:
                            bases.append(b)
        # ---------------- end of helper functions

        # assemble a dictionary of the factors having a Rational power
        common_b = {}
        done = []
        bases = []
        for b, e in c_powers:
            b, e = bkey(b, e)
            common_b[b] = e
            if b[1] != 1 and b[0].is_Mul:
                bases.append(b)
        bases.sort(key=default_sort_key)  # this makes tie-breaking canonical
        bases.sort(key=measure, reverse=True)  # handle longest first
        for base in bases:
            if base not in common_b:  # it may have been removed already
                continue
            b, exponent = base
            last = False  # True when no factor of base is a radical
            qlcm = 1  # the lcm of the radical denominators
            while True:
                bstart = b
                qstart = qlcm

                bb = []  # list of factors
                ee = []  # (factor's expo. and it's current value in common_b)
                for bi in Mul.make_args(b):
                    bib, bie = bkey(bi)
                    if bib not in common_b or common_b[bib] < bie:
                        ee = bb = []  # failed
                        break
                    ee.append([bie, common_b[bib]])
                    bb.append(bib)
                if ee:
                    # find the number of extractions possible
                    # e.g. [(1, 2), (2, 2)] -> min(2/1, 2/2) -> 1
                    min1 = ee[0][1]/ee[0][0]
                    for i in xrange(len(ee)):
                        rat = ee[i][1]/ee[i][0]
                        if rat < 1:
                            break
                        min1 = min(min1, rat)
                    else:
                        # update base factor counts
                        # e.g. if ee = [(2, 5), (3, 6)] then min1 = 2
                        # and the new base counts will be 5-2*2 and 6-2*3
                        for i in xrange(len(bb)):
                            common_b[bb[i]] -= min1*ee[i][0]
                            update(bb[i])
                        # update the count of the base
                        # e.g. x**2*y*sqrt(x*sqrt(y)) the count of x*sqrt(y)
                        # will increase by 4 to give bkey (x*sqrt(y), 2, 5)
                        common_b[base] += min1*qstart*exponent
                if (last  # no more radicals in base
                    or len(common_b) == 1  # nothing left to join with
                    or all(k[1] == 1 for k in common_b)  # no rad's in common_b
                        ):
                    break
                # see what we can exponentiate base by to remove any radicals
                # so we know what to search for
                # e.g. if base were x**(1/2)*y**(1/3) then we should
                # exponentiate by 6 and look for powers of x and y in the ratio
                # of 2 to 3
                qlcm = lcm([ratq(bi) for bi in Mul.make_args(bstart)])
                if qlcm == 1:
                    break  # we are done
                b = bstart**qlcm
                qlcm *= qstart
                if all(ratq(bi) == 1 for bi in Mul.make_args(b)):
                    last = True  # we are going to be done after this next pass
            # this base no longer can find anything to join with and
            # since it was longer than any other we are done with it
            b, q = base
            done.append((b, common_b.pop(base)*Rational(1, q)))

        # update c_powers and get ready to continue with powsimp
        c_powers = done
        # there may be terms still in common_b that were bases that were
        # identified as needing processing, so remove those, too
        for (b, q), e in common_b.items():
            if (b.is_Pow or b.func is exp) and \
                    q is not S.One and not b.exp.is_Rational:
                b, be = b.as_base_exp()
                b = b**(be/q)
            else:
                b = root(b, q)
            c_powers.append((b, e))
        check = len(c_powers)
        c_powers = dict(c_powers)
        assert len(c_powers) == check  # there should have been no duplicates
        # ==============================================================

        # rebuild the expression
        newexpr = expr.func(*(newexpr + [Pow(b, e) for b, e in c_powers.items()]))
        if combine == 'exp':
            return expr.func(newexpr, expr.func(*nc_part))
        else:
            return recurse(expr.func(*nc_part), combine='base') * \
                recurse(newexpr, combine='base')

    elif combine == 'base':

        # Build c_powers and nc_part.  These must both be lists not
        # dicts because exp's are not combined.
        c_powers = []
        nc_part = []
        for term in expr.args:
            if term.is_commutative:
                c_powers.append(list(term.as_base_exp()))
            else:
                # This is the logic that combines bases that are
                # different and non-commutative, but with equal and
                # commutative exponents: A**x*B**x == (A*B)**x.
                if nc_part:
                    b1, e1 = nc_part[-1].as_base_exp()
                    b2, e2 = term.as_base_exp()
                    if (e1 == e2 and e2.is_commutative):
                        nc_part[-1] = Pow(b1*b2, e1)
                        continue
                nc_part.append(term)

        # Pull out numerical coefficients from exponent if assumptions allow
        # e.g., 2**(2*x) => 4**x
        for i in xrange(len(c_powers)):
            b, e = c_powers[i]
            if not (b.is_nonnegative or e.is_integer or force or b.is_polar):
                continue
            exp_c, exp_t = e.as_coeff_Mul(rational=True)
            if exp_c is not S.One and exp_t is not S.One:
                c_powers[i] = [Pow(b, exp_c), exp_t]

        # Combine bases whenever they have the same exponent and
        # assumptions allow
        # first gather the potential bases under the common exponent
        c_exp = defaultdict(list)
        for b, e in c_powers:
            if deep:
                e = recurse(e)
            c_exp[e].append(b)
        del c_powers

        # Merge back in the results of the above to form a new product
        c_powers = defaultdict(list)
        for e in c_exp:
            bases = c_exp[e]

            # calculate the new base for e

            if len(bases) == 1:
                new_base = bases[0]
            elif e.is_integer or force:
                new_base = expr.func(*bases)
            else:
                # see which ones can be joined
                unk = []
                nonneg = []
                neg = []
                for bi in bases:
                    if bi.is_negative:
                        neg.append(bi)
                    elif bi.is_nonnegative:
                        nonneg.append(bi)
                    elif bi.is_polar:
                        nonneg.append(
                            bi)  # polar can be treated like non-negative
                    else:
                        unk.append(bi)
                if len(unk) == 1 and not neg or len(neg) == 1 and not unk:
                    # a single neg or a single unk can join the rest
                    nonneg.extend(unk + neg)
                    unk = neg = []
                elif neg:
                    # their negative signs cancel in groups of 2*q if we know
                    # that e = p/q else we have to treat them as unknown
                    israt = False
                    if e.is_Rational:
                        israt = True
                    else:
                        p, d = e.as_numer_denom()
                        if p.is_integer and d.is_integer:
                            israt = True
                    if israt:
                        neg = [-w for w in neg]
                        unk.extend([S.NegativeOne]*len(neg))
                    else:
                        unk.extend(neg)
                        neg = []
                    del israt

                # these shouldn't be joined
                for b in unk:
                    c_powers[b].append(e)
                # here is a new joined base
                new_base = expr.func(*(nonneg + neg))
                # if there are positive parts they will just get separated
                # again unless some change is made

                def _terms(e):
                    # return the number of terms of this expression
                    # when multiplied out -- assuming no joining of terms
                    if e.is_Add:
                        return sum([_terms(ai) for ai in e.args])
                    if e.is_Mul:
                        return prod([_terms(mi) for mi in e.args])
                    return 1
                xnew_base = expand_mul(new_base, deep=False)
                if len(Add.make_args(xnew_base)) < _terms(new_base):
                    new_base = factor_terms(xnew_base)

            c_powers[new_base].append(e)

        # break out the powers from c_powers now
        c_part = [Pow(b, ei) for b, e in c_powers.items() for ei in e]

        # we're done
        return expr.func(*(c_part + nc_part))

    else:
        raise ValueError("combine must be one of ('all', 'exp', 'base').")


def hypersimp(f, k):
    """Given combinatorial term f(k) simplify its consecutive term ratio
       i.e. f(k+1)/f(k).  The input term can be composed of functions and
       integer sequences which have equivalent representation in terms
       of gamma special function.

       The algorithm performs three basic steps:

       1. Rewrite all functions in terms of gamma, if possible.

       2. Rewrite all occurrences of gamma in terms of products
          of gamma and rising factorial with integer,  absolute
          constant exponent.

       3. Perform simplification of nested fractions, powers
          and if the resulting expression is a quotient of
          polynomials, reduce their total degree.

       If f(k) is hypergeometric then as result we arrive with a
       quotient of polynomials of minimal degree. Otherwise None
       is returned.

       For more information on the implemented algorithm refer to:

       1. W. Koepf, Algorithms for m-fold Hypergeometric Summation,
          Journal of Symbolic Computation (1995) 20, 399-417
    """
    f = sympify(f)

    g = f.subs(k, k + 1) / f

    g = g.rewrite(gamma)
    g = expand_func(g)
    g = powsimp(g, deep=True, combine='exp')

    if g.is_rational_function(k):
        return simplify(g, ratio=S.Infinity)
    else:
        return None


def hypersimilar(f, g, k):
    """Returns True if 'f' and 'g' are hyper-similar.

       Similarity in hypergeometric sense means that a quotient of
       f(k) and g(k) is a rational function in k.  This procedure
       is useful in solving recurrence relations.

       For more information see hypersimp().

    """
    f, g = list(map(sympify, (f, g)))

    h = (f/g).rewrite(gamma)
    h = h.expand(func=True, basic=False)

    return h.is_rational_function(k)

from sympy.utilities.timeutils import timethis

class _rf(Function):
    @classmethod
    def eval(cls, a, b):
        if b.is_Integer:
            if not b:
                return S.One

            n, result = int(b), S.One

            if n > 0:
                for i in xrange(n):
                    result *= a + i

                return result
            elif n < 0:
                for i in xrange(1, -n + 1):
                    result *= a - i

                return 1/result
        else:
            if b.is_Add:
                c, _b = b.as_coeff_Add()

                if c.is_Integer:
                    if c > 0:
                        return _rf(a, _b)*_rf(a + _b, c)
                    elif c < 0:
                        return _rf(a, _b)/_rf(a + _b + c, -c)

            if a.is_Add:
                c, _a = a.as_coeff_Add()

                if c.is_Integer:
                    if c > 0:
                        return _rf(_a, b)*_rf(_a + b, c)/_rf(_a, c)
                    elif c < 0:
                        return _rf(_a, b)*_rf(_a + c, -c)/_rf(_a + b + c, -c)



@timethis('combsimp')
def combsimp(expr):
    r"""
    Simplify combinatorial expressions.

    This function takes as input an expression containing factorials,
    binomials, Pochhammer symbol and other "combinatorial" functions,
    and tries to minimize the number of those functions and reduce
    the size of their arguments. The result is be given in terms of
    binomials and factorials.

    The algorithm works by rewriting all combinatorial functions as
    expressions involving rising factorials (Pochhammer symbols) and
    applies recurrence relations and other transformations applicable
    to rising factorials, to reduce their arguments, possibly letting
    the resulting rising factorial to cancel. Rising factorials with
    the second argument being an integer are expanded into polynomial
    forms and finally all other rising factorial are rewritten in terms
    more familiar functions. If the initial expression contained any
    combinatorial functions, the result is expressed using binomial
    coefficients and gamma functions. If the initial expression consisted
    of gamma functions alone, the result is expressed in terms of gamma
    functions.

    If the result is expressed using gamma functions, the following three
    additional steps are performed:

    1. Reduce the number of gammas by applying the reflection theorem
       gamma(x)*gamma(1-x) == pi/sin(pi*x).
    2. Reduce the number of gammas by applying the multiplication theorem
       gamma(x)*gamma(x+1/n)*...*gamma(x+(n-1)/n) == C*gamma(n*x).
    3. Reduce the number of prefactors by absorbing them into gammas, where
       possible.

    All transformation rules can be found (or was derived from) here:

    1. http://functions.wolfram.com/GammaBetaErf/Pochhammer/17/01/02/
    2. http://functions.wolfram.com/GammaBetaErf/Pochhammer/27/01/0005/

    Examples
    ========

    >>> from sympy.simplify import combsimp
    >>> from sympy import factorial, binomial
    >>> from sympy.abc import n, k

    >>> combsimp(factorial(n)/factorial(n - 3))
    n*(n - 2)*(n - 1)
    >>> combsimp(binomial(n+1, k+1)/binomial(n, k))
    (n + 1)/(k + 1)

    """
    factorial = C.factorial
    binomial = C.binomial
    gamma = C.gamma

    # as a rule of thumb, if the expression contained gammas initially, it
    # probably makes sense to retain them
    as_gamma = not expr.has(factorial, binomial)


    expr = expr.replace(binomial,
        lambda n, k: _rf((n - k + 1).expand(), k.expand())/_rf(1, k.expand()))
    expr = expr.replace(factorial,
        lambda n: _rf(1, n.expand()))
    expr = expr.rewrite(gamma)
    expr = expr.replace(gamma,
        lambda n: _rf(1, (n - 1).expand()))

    if as_gamma:
        expr = expr.replace(_rf,
            lambda a, b: gamma(a + b)/gamma(a))
    else:
        expr = expr.replace(_rf,
            lambda a, b: binomial(a + b - 1, b)*factorial(b))

    def rule(n, k):
        coeff, rewrite = S.One, False

        cn, _n = n.as_coeff_Add()

        if _n and cn.is_Integer and cn:
            coeff *= _rf(_n + 1, cn)/_rf(_n - k + 1, cn)
            rewrite = True
            n = _n

        # this sort of binomial has already been removed by
        # rising factorials but is left here in case the order
        # of rule application is changed
        if k.is_Add:
            ck, _k = k.as_coeff_Add()
            if _k and ck.is_Integer and ck:
                coeff *= _rf(n - ck - _k + 1, ck)/_rf(_k + 1, ck)
                rewrite = True
                k = _k

        if rewrite:
            return coeff*binomial(n, k)

    expr = expr.replace(binomial, rule)

    def rule_gamma(expr, level=0):
        """ Simplify products of gamma functions further. """

        if expr.is_Atom:
            return expr

        def gamma_rat(x):
            # helper to simplify ratios of gammas
            was = x.count(gamma)
            xx = x.replace(gamma, lambda n: _rf(1, (n - 1).expand()
                ).replace(_rf, lambda a, b: gamma(a + b)/gamma(a)))
            if xx.count(gamma) < was:
                x = xx
            return x

        def gamma_factor(x):
            # return True if there is a gamma factor in shallow args
            if x.func is gamma:
                return True
            if x.is_Add or x.is_Mul:
                return any(gamma_factor(xi) for xi in x.args)
            if x.is_Pow and (x.exp.is_integer or x.base.is_positive):
                return gamma_factor(x.base)
            return False

        # recursion step
        if level == 0:
            expr = expr.func(*[rule_gamma(x, level + 1) for x in expr.args])
            level += 1

        if not expr.is_Mul:
            return expr

        # non-commutative step
        if level == 1:
            args, nc = expr.args_cnc()
            if not args:
                return expr
            if nc:
                return rule_gamma(Mul._from_args(args), level + 1)*Mul._from_args(nc)
            level += 1

        # pure gamma handling, not factor absorbtion
        if level == 2:
            sifted = sift(expr.args, gamma_factor)
            gamma_ind = Mul(*sifted.pop(False, []))
            d = Mul(*sifted.pop(True, []))
            assert not sifted

            nd, dd = d.as_numer_denom()
            for ipass in range(2):
                args = list(ordered(Mul.make_args(nd)))
                for i, ni in enumerate(args):
                    if ni.is_Add:
                        ni, dd = Add(*[
                            rule_gamma(gamma_rat(a/dd), level + 1) for a in ni.args]
                            ).as_numer_denom()
                        args[i] = ni
                        if not dd.has(gamma):
                            break
                nd = Mul(*args)
                if ipass ==  0 and not gamma_factor(nd):
                    break
                nd, dd = dd, nd  # now process in reversed order
            expr = gamma_ind*nd/dd
            if not (expr.is_Mul and (gamma_factor(dd) or gamma_factor(nd))):
                return expr
            level += 1

        # iteration until constant
        if level == 3:
            while True:
                was = expr
                expr = rule_gamma(expr, 4)
                if expr == was:
                    return expr

        numer_gammas = []
        denom_gammas = []
        numer_others = []
        denom_others = []
        def explicate(p):
            if p is S.One:
                return None, []
            b, e = p.as_base_exp()
            if e.is_Integer:
                if b.func is gamma:
                    return True, [b.args[0]]*e
                else:
                    return False, [b]*e
            else:
                return False, [p]

        newargs = list(ordered(expr.args))
        while newargs:
            n, d = newargs.pop().as_numer_denom()
            isg, l = explicate(n)
            if isg:
                numer_gammas.extend(l)
            elif isg is False:
                numer_others.extend(l)
            isg, l = explicate(d)
            if isg:
                denom_gammas.extend(l)
            elif isg is False:
                denom_others.extend(l)

        # =========== level 2 work: pure gamma manipulation =========

        # Try to reduce the number of gamma factors by applying the
        # reflection formula gamma(x)*gamma(1-x) = pi/sin(pi*x)
        for gammas, numer, denom in [(
            numer_gammas, numer_others, denom_others),
                (denom_gammas, denom_others, numer_others)]:
            new = []
            while gammas:
                g1 = gammas.pop()
                if g1.is_integer:
                    new.append(g1)
                    continue
                for i, g2 in enumerate(gammas):
                    n = g1 + g2 - 1
                    if not n.is_Integer:
                        continue
                    numer.append(S.Pi)
                    denom.append(C.sin(S.Pi*g1))
                    gammas.pop(i)
                    if n > 0:
                        for k in range(n):
                            numer.append(1 - g1 + k)
                    elif n < 0:
                        for k in range(-n):
                            denom.append(-g1 - k)
                    break
                else:
                    new.append(g1)
            # /!\ updating IN PLACE
            gammas[:] = new

        # Try to reduce the number of gammas by using the duplication
        # theorem to cancel an upper and lower: gamma(2*s)/gamma(s) =
        # 2**(2*s + 1)/(4*sqrt(pi))*gamma(s + 1/2). Although this could
        # be done with higher argument ratios like gamma(3*x)/gamma(x),
        # this would not reduce the number of gammas as in this case.
        for ng, dg, no, do in [(numer_gammas, denom_gammas, numer_others,
                                denom_others),
                               (denom_gammas, numer_gammas, denom_others,
                                numer_others)]:

            while True:
                for x in ng:
                    for y in dg:
                        n = x - 2*y
                        if n.is_Integer:
                            break
                    else:
                        continue
                    break
                else:
                    break
                ng.remove(x)
                dg.remove(y)
                if n > 0:
                    for k in xrange(n):
                        no.append(2*y + k)
                elif n < 0:
                    for k in xrange(-n):
                        do.append(2*y - 1 - k)
                ng.append(y + S(1)/2)
                no.append(2**(2*y - 1))
                do.append(sqrt(S.Pi))

        # Try to reduce the number of gamma factors by applying the
        # multiplication theorem (used when n gammas with args differing
        # by 1/n mod 1 are encountered).
        #
        # run of 2 with args differing by 1/2
        #
        # >>> combsimp(gamma(x)*gamma(x+S.Half))
        # 2*sqrt(2)*2**(-2*x - 1/2)*sqrt(pi)*gamma(2*x)
        #
        # run of 3 args differing by 1/3 (mod 1)
        #
        # >>> combsimp(gamma(x)*gamma(x+S(1)/3)*gamma(x+S(2)/3))
        # 6*3**(-3*x - 1/2)*pi*gamma(3*x)
        # >>> combsimp(gamma(x)*gamma(x+S(1)/3)*gamma(x+S(5)/3))
        # 2*3**(-3*x - 1/2)*pi*(3*x + 2)*gamma(3*x)
        #
        def _run(coeffs):
            # find runs in coeffs such that the difference in terms (mod 1)
            # of t1, t2, ..., tn is 1/n
            from sympy.utilities.iterables import uniq
            u = list(uniq(coeffs))
            for i in range(len(u)):
                dj = ([((u[j] - u[i]) % 1, j) for j in range(i + 1, len(u))])
                for one, j in dj:
                    if one.p == 1 and one.q != 1:
                        n = one.q
                        got = [i]
                        get = list(range(1, n))
                        for d, j in dj:
                            m = n*d
                            if m.is_Integer and m in get:
                                get.remove(m)
                                got.append(j)
                                if not get:
                                    break
                        else:
                            continue
                        for i, j in enumerate(got):
                            c = u[j]
                            coeffs.remove(c)
                            got[i] = c
                        return one.q, got[0], got[1:]

        def _mult_thm(gammas, numer, denom):
            # pull off and analyze the leading coefficient from each gamma arg
            # looking for runs in those Rationals

            # expr -> coeff + resid -> rats[resid] = coeff
            rats = {}
            for g in gammas:
                c, resid = g.as_coeff_Add()
                rats.setdefault(resid, []).append(c)

            # look for runs in Rationals for each resid
            keys = sorted(rats, key=default_sort_key)
            for resid in keys:
                coeffs = list(sorted(rats[resid]))
                new = []
                while True:
                    run = _run(coeffs)
                    if run is None:
                        break

                    # process the sequence that was found:
                    # 1) convert all the gamma functions to have the right
                    #    argument (could be off by an integer)
                    # 2) append the factors corresponding to the theorem
                    # 3) append the new gamma function

                    n, ui, other = run

                    # (1)
                    for u in other:
                        con = resid + u - 1
                        for k in range(int(u - ui)):
                            numer.append(con - k)

                    con = n*(resid + ui)  # for (2) and (3)

                    # (2)
                    numer.append((2*S.Pi)**(S(n - 1)/2)*
                                 n**(S(1)/2 - con))
                    # (3)
                    new.append(con)

                # restore resid to coeffs
                rats[resid] = [resid + c for c in coeffs] + new

            # rebuild the gamma arguments
            g = []
            for resid in keys:
                g += rats[resid]
            # /!\ updating IN PLACE
            gammas[:] = g

        for l, numer, denom in [(numer_gammas, numer_others, denom_others),
                                (denom_gammas, denom_others, numer_others)]:
            _mult_thm(l, numer, denom)

        # =========== level >= 2 work: factor absorbtion =========

        if level >= 2:
            # Try to absorb factors into the gammas: x*gamma(x) -> gamma(x + 1)
            # and gamma(x)/(x - 1) -> gamma(x - 1)
            # This code (in particular repeated calls to find_fuzzy) can be very
            # slow.
            def find_fuzzy(l, x):
                if not l:
                    return
                S1, T1 = compute_ST(x)
                for y in l:
                    S2, T2 = inv[y]
                    if T1 != T2 or (not S1.intersection(S2) and
                                    (S1 != set() or S2 != set())):
                        continue
                    # XXX we want some simplification (e.g. cancel or
                    # simplify) but no matter what it's slow.
                    a = len(cancel(x/y).free_symbols)
                    b = len(x.free_symbols)
                    c = len(y.free_symbols)
                    # TODO is there a better heuristic?
                    if a == 0 and (b > 0 or c > 0):
                        return y

            # We thus try to avoid expensive calls by building the following
            # "invariants": For every factor or gamma function argument
            #   - the set of free symbols S
            #   - the set of functional components T
            # We will only try to absorb if T1==T2 and (S1 intersect S2 != emptyset
            # or S1 == S2 == emptyset)
            inv = {}

            def compute_ST(expr):
                from sympy import Function, Pow
                if expr in inv:
                    return inv[expr]
                return (expr.free_symbols, expr.atoms(Function).union(
                        set(e.exp for e in expr.atoms(Pow))))

            def update_ST(expr):
                inv[expr] = compute_ST(expr)
            for expr in numer_gammas + denom_gammas + numer_others + denom_others:
                update_ST(expr)

            for gammas, numer, denom in [(
                numer_gammas, numer_others, denom_others),
                    (denom_gammas, denom_others, numer_others)]:
                new = []
                while gammas:
                    g = gammas.pop()
                    cont = True
                    while cont:
                        cont = False
                        y = find_fuzzy(numer, g)
                        if y is not None:
                            numer.remove(y)
                            if y != g:
                                numer.append(y/g)
                                update_ST(y/g)
                            g += 1
                            cont = True
                        y = find_fuzzy(denom, g - 1)
                        if y is not None:
                            denom.remove(y)
                            if y != g - 1:
                                numer.append((g - 1)/y)
                                update_ST((g - 1)/y)
                            g -= 1
                            cont = True
                    new.append(g)
                # /!\ updating IN PLACE
                gammas[:] = new

        # =========== rebuild expr ==================================

        return C.Mul(*[gamma(g) for g in numer_gammas]) \
            / C.Mul(*[gamma(g) for g in denom_gammas]) \
            * C.Mul(*numer_others) / C.Mul(*denom_others)

    # (for some reason we cannot use Basic.replace in this case)
    was = factor(expr)
    expr = rule_gamma(was)
    if expr != was:
        expr = factor(expr)

    return expr


def signsimp(expr, evaluate=None):
    """Make all Add sub-expressions canonical wrt sign.

    If an Add subexpression, ``a``, can have a sign extracted,
    as determined by could_extract_minus_sign, it is replaced
    with Mul(-1, a, evaluate=False). This allows signs to be
    extracted from powers and products.

    Examples
    ========

    >>> from sympy import signsimp, exp, symbols
    >>> from sympy.abc import x, y
    >>> i = symbols('i', odd=True)
    >>> n = -1 + 1/x
    >>> n/x/(-n)**2 - 1/n/x
    (-1 + 1/x)/(x*(1 - 1/x)**2) - 1/(x*(-1 + 1/x))
    >>> signsimp(_)
    0
    >>> x*n + x*-n
    x*(-1 + 1/x) + x*(1 - 1/x)
    >>> signsimp(_)
    0

    Since powers automatically handle leading signs

    >>> (-2)**i
    -2**i

    signsimp can be used to put the base of a power with an integer
    exponent into canonical form:

    >>> n**i
    (-1 + 1/x)**i
    >>> signsimp(_)
    -(1 - 1/x)**i

    By default, signsimp doesn't leave behind any hollow simplification:
    if making an Add canonical wrt sign didn't change the expression, the
    original Add is restored. If this is not desired then the keyword
    ``evaluate`` can be set to False:

    >>> e = exp(y - x)
    >>> signsimp(e) == e
    True
    >>> signsimp(e, evaluate=False)
    exp(-(x - y))

    """
    if evaluate is None:
        evaluate = global_evaluate[0]
    expr = sympify(expr)
    if not isinstance(expr, Expr) or expr.is_Atom:
        return expr
    e = sub_post(sub_pre(expr))
    if not isinstance(e, Expr) or e.is_Atom:
        return e
    if e.is_Add:
        return e.func(*[signsimp(a) for a in e.args])
    if evaluate:
        e = e.xreplace(dict([(m, -(-m)) for m in e.atoms(Mul) if -(-m) != m]))
    return e


def simplify(expr, ratio=1.7, measure=count_ops, fu=False):
    """
    Simplifies the given expression.

    Simplification is not a well defined term and the exact strategies
    this function tries can change in the future versions of SymPy. If
    your algorithm relies on "simplification" (whatever it is), try to
    determine what you need exactly  -  is it powsimp()?, radsimp()?,
    together()?, logcombine()?, or something else? And use this particular
    function directly, because those are well defined and thus your algorithm
    will be robust.

    Nonetheless, especially for interactive use, or when you don't know
    anything about the structure of the expression, simplify() tries to apply
    intelligent heuristics to make the input expression "simpler".  For
    example:

    >>> from sympy import simplify, cos, sin
    >>> from sympy.abc import x, y
    >>> a = (x + x**2)/(x*sin(y)**2 + x*cos(y)**2)
    >>> a
    (x**2 + x)/(x*sin(y)**2 + x*cos(y)**2)
    >>> simplify(a)
    x + 1

    Note that we could have obtained the same result by using specific
    simplification functions:

    >>> from sympy import trigsimp, cancel
    >>> trigsimp(a)
    (x**2 + x)/x
    >>> cancel(_)
    x + 1

    In some cases, applying :func:`simplify` may actually result in some more
    complicated expression. The default ``ratio=1.7`` prevents more extreme
    cases: if (result length)/(input length) > ratio, then input is returned
    unmodified.  The ``measure`` parameter lets you specify the function used
    to determine how complex an expression is.  The function should take a
    single argument as an expression and return a number such that if
    expression ``a`` is more complex than expression ``b``, then
    ``measure(a) > measure(b)``.  The default measure function is
    :func:`count_ops`, which returns the total number of operations in the
    expression.

    For example, if ``ratio=1``, ``simplify`` output can't be longer
    than input.

    ::

        >>> from sympy import sqrt, simplify, count_ops, oo
        >>> root = 1/(sqrt(2)+3)

    Since ``simplify(root)`` would result in a slightly longer expression,
    root is returned unchanged instead::

       >>> simplify(root, ratio=1) == root
       True

    If ``ratio=oo``, simplify will be applied anyway::

        >>> count_ops(simplify(root, ratio=oo)) > count_ops(root)
        True

    Note that the shortest expression is not necessary the simplest, so
    setting ``ratio`` to 1 may not be a good idea.
    Heuristically, the default value ``ratio=1.7`` seems like a reasonable
    choice.

    You can easily define your own measure function based on what you feel
    should represent the "size" or "complexity" of the input expression.  Note
    that some choices, such as ``lambda expr: len(str(expr))`` may appear to be
    good metrics, but have other problems (in this case, the measure function
    may slow down simplify too much for very large expressions).  If you don't
    know what a good metric would be, the default, ``count_ops``, is a good
    one.

    For example:

    >>> from sympy import symbols, log
    >>> a, b = symbols('a b', positive=True)
    >>> g = log(a) + log(b) + log(a)*log(1/b)
    >>> h = simplify(g)
    >>> h
    log(a*b**(-log(a) + 1))
    >>> count_ops(g)
    8
    >>> count_ops(h)
    5

    So you can see that ``h`` is simpler than ``g`` using the count_ops metric.
    However, we may not like how ``simplify`` (in this case, using
    ``logcombine``) has created the ``b**(log(1/a) + 1)`` term.  A simple way
    to reduce this would be to give more weight to powers as operations in
    ``count_ops``.  We can do this by using the ``visual=True`` option:

    >>> print(count_ops(g, visual=True))
    2*ADD + DIV + 4*LOG + MUL
    >>> print(count_ops(h, visual=True))
    2*LOG + MUL + POW + SUB

    >>> from sympy import Symbol, S
    >>> def my_measure(expr):
    ...     POW = Symbol('POW')
    ...     # Discourage powers by giving POW a weight of 10
    ...     count = count_ops(expr, visual=True).subs(POW, 10)
    ...     # Every other operation gets a weight of 1 (the default)
    ...     count = count.replace(Symbol, type(S.One))
    ...     return count
    >>> my_measure(g)
    8
    >>> my_measure(h)
    14
    >>> 15./8 > 1.7 # 1.7 is the default ratio
    True
    >>> simplify(g, measure=my_measure)
    -log(a)*log(b) + log(a) + log(b)

    Note that because ``simplify()`` internally tries many different
    simplification strategies and then compares them using the measure
    function, we get a completely different result that is still different
    from the input expression by doing this.
    """
    from sympy.simplify.hyperexpand import hyperexpand
    from sympy.functions.special.bessel import BesselBase
    from sympy.vector import Vector

    expr = sympify(expr)

    try:
        return expr._eval_simplify(ratio=ratio, measure=measure)
    except AttributeError:
        pass

    original_expr = expr = signsimp(expr)

    from sympy.simplify.hyperexpand import hyperexpand
    from sympy.functions.special.bessel import BesselBase
    from sympy import Sum, Product

    if not isinstance(expr, Basic) or not expr.args:  # XXX: temporary hack
        return expr

    if not isinstance(expr, (Add, Mul, Pow, ExpBase)):
        return expr.func(*[simplify(x, ratio=ratio, measure=measure, fu=fu)
                         for x in expr.args])

    # TODO: Apply different strategies, considering expression pattern:
    # is it a purely rational function? Is there any trigonometric function?...
    # See also https://github.com/sympy/sympy/pull/185.

    def shorter(*choices):
        '''Return the choice that has the fewest ops. In case of a tie,
        the expression listed first is selected.'''
        if not has_variety(choices):
            return choices[0]
        return min(choices, key=measure)

    expr = bottom_up(expr, lambda w: w.normal())
    expr = Mul(*powsimp(expr).as_content_primitive())
    _e = cancel(expr)
    expr1 = shorter(_e, _mexpand(_e).cancel())  # issue 6829
    expr2 = shorter(together(expr, deep=True), together(expr1, deep=True))

    if ratio is S.Infinity:
        expr = expr2
    else:
        expr = shorter(expr2, expr1, expr)
    if not isinstance(expr, Basic):  # XXX: temporary hack
        return expr

    expr = factor_terms(expr, sign=False)

    # hyperexpand automatically only works on hypergeometric terms
    expr = hyperexpand(expr)

    expr = piecewise_fold(expr)

    if expr.has(BesselBase):
        expr = besselsimp(expr)

    if expr.has(C.TrigonometricFunction) and not fu or expr.has(
            C.HyperbolicFunction):
        expr = trigsimp(expr, deep=True)

    if expr.has(C.log):
        expr = shorter(expand_log(expr, deep=True), logcombine(expr))

    if expr.has(C.CombinatorialFunction, gamma):
        expr = combsimp(expr)

    if expr.has(Sum):
        expr = sum_simplify(expr)

    if expr.has(Product):
        expr = product_simplify(expr)

    short = shorter(powsimp(expr, combine='exp', deep=True), powsimp(expr), expr)
    short = shorter(short, factor_terms(short), expand_power_exp(expand_mul(short)))
    if short.has(C.TrigonometricFunction, C.HyperbolicFunction, C.ExpBase):
        short = exptrigsimp(short, simplify=False)

    # get rid of hollow 2-arg Mul factorization
    from sympy.core.rules import Transform
    hollow_mul = Transform(
        lambda x: Mul(*x.args),
        lambda x:
        x.is_Mul and
        len(x.args) == 2 and
        x.args[0].is_Number and
        x.args[1].is_Add and
        x.is_commutative)
    expr = short.xreplace(hollow_mul)

    numer, denom = expr.as_numer_denom()
    if denom.is_Add:
        n, d = fraction(radsimp(1/denom, symbolic=False, max_terms=1))
        if n is not S.One:
            expr = (numer*n).expand()/d

    if expr.could_extract_minus_sign():
        n, d = fraction(expr)
        if d != 0:
            expr = signsimp(-n/(-d))

    if measure(expr) > ratio*measure(original_expr):
        expr = original_expr

    return expr


def _real_to_rational(expr, tolerance=None):
    """
    Replace all reals in expr with rationals.

    >>> from sympy import nsimplify
    >>> from sympy.abc import x

    >>> nsimplify(.76 + .1*x**.5, rational=True)
    sqrt(x)/10 + 19/25

    """
    p = expr
    reps = {}
    reduce_num = None
    if tolerance is not None and tolerance < 1:
        reduce_num = ceiling(1/tolerance)
    for float in p.atoms(C.Float):
        key = float
        if reduce_num is not None:
            r = Rational(float).limit_denominator(reduce_num)
        elif (tolerance is not None and tolerance >= 1 and
                float.is_Integer is False):
            r = Rational(tolerance*round(float/tolerance)
                ).limit_denominator(int(tolerance))
        else:
            r = nsimplify(float, rational=False)
            # e.g. log(3).n() -> log(3) instead of a Rational
            if float and not r:
                r = Rational(float)
            elif not r.is_Rational:
                if float < 0:
                    float = -float
                    d = Pow(10, int((mpmath.log(float)/mpmath.log(10))))
                    r = -Rational(str(float/d))*d
                elif float > 0:
                    d = Pow(10, int((mpmath.log(float)/mpmath.log(10))))
                    r = Rational(str(float/d))*d
                else:
                    r = Integer(0)
        reps[key] = r
    return p.subs(reps, simultaneous=True)


def nsimplify(expr, constants=[], tolerance=None, full=False, rational=None):
    """
    Find a simple representation for a number or, if there are free symbols or
    if rational=True, then replace Floats with their Rational equivalents. If
    no change is made and rational is not False then Floats will at least be
    converted to Rationals.

    For numerical expressions, a simple formula that numerically matches the
    given numerical expression is sought (and the input should be possible
    to evalf to a precision of at least 30 digits).

    Optionally, a list of (rationally independent) constants to
    include in the formula may be given.

    A lower tolerance may be set to find less exact matches. If no tolerance
    is given then the least precise value will set the tolerance (e.g. Floats
    default to 15 digits of precision, so would be tolerance=10**-15).

    With full=True, a more extensive search is performed
    (this is useful to find simpler numbers when the tolerance
    is set low).

    Examples
    ========

    >>> from sympy import nsimplify, sqrt, GoldenRatio, exp, I, exp, pi
    >>> nsimplify(4/(1+sqrt(5)), [GoldenRatio])
    -2 + 2*GoldenRatio
    >>> nsimplify((1/(exp(3*pi*I/5)+1)))
    1/2 - I*sqrt(sqrt(5)/10 + 1/4)
    >>> nsimplify(I**I, [pi])
    exp(-pi/2)
    >>> nsimplify(pi, tolerance=0.01)
    22/7

    See Also
    ========
    sympy.core.function.nfloat

    """
    expr = sympify(expr)
    if rational or expr.free_symbols:
        return _real_to_rational(expr, tolerance)

    # SymPy's default tolerance for Rationals is 15; other numbers may have
    # lower tolerances set, so use them to pick the largest tolerance if None
    # was given
    if tolerance is None:
        tolerance = 10**-min([15] +
             [mpmath.libmp.libmpf.prec_to_dps(n._prec)
             for n in expr.atoms(Float)])
    # XXX should prec be set independent of tolerance or should it be computed
    # from tolerance?
    prec = 30
    bprec = int(prec*3.33)

    constants_dict = {}
    for constant in constants:
        constant = sympify(constant)
        v = constant.evalf(prec)
        if not v.is_Float:
            raise ValueError("constants must be real-valued")
        constants_dict[str(constant)] = v._to_mpmath(bprec)

    exprval = expr.evalf(prec, chop=True)
    re, im = exprval.as_real_imag()

    # safety check to make sure that this evaluated to a number
    if not (re.is_Number and im.is_Number):
        return expr

    def nsimplify_real(x):
        orig = mpmath.mp.dps
        xv = x._to_mpmath(bprec)
        try:
            # We'll be happy with low precision if a simple fraction
            if not (tolerance or full):
                mpmath.mp.dps = 15
                rat = mpmath.findpoly(xv, 1)
                if rat is not None:
                    return Rational(-int(rat[1]), int(rat[0]))
            mpmath.mp.dps = prec
            newexpr = mpmath.identify(xv, constants=constants_dict,
                tol=tolerance, full=full)
            if not newexpr:
                raise ValueError
            if full:
                newexpr = newexpr[0]
            expr = sympify(newexpr)
            if x and not expr:  # don't let x become 0
                raise ValueError
            if expr.is_finite is False and not xv in [mpmath.inf, mpmath.ninf]:
                raise ValueError
            return expr
        finally:
            # even though there are returns above, this is executed
            # before leaving
            mpmath.mp.dps = orig
    try:
        if re:
            re = nsimplify_real(re)
        if im:
            im = nsimplify_real(im)
    except ValueError:
        if rational is None:
            return _real_to_rational(expr)
        return expr

    rv = re + im*S.ImaginaryUnit
    # if there was a change or rational is explicitly not wanted
    # return the value, else return the Rational representation
    if rv != expr or rational is False:
        return rv
    return _real_to_rational(expr)


def logcombine(expr, force=False):
    """
    Takes logarithms and combines them using the following rules:

    - log(x) + log(y) == log(x*y) if both are not negative
    - a*log(x) == log(x**a) if x is positive and a is real

    If ``force`` is True then the assumptions above will be assumed to hold if
    there is no assumption already in place on a quantity. For example, if
    ``a`` is imaginary or the argument negative, force will not perform a
    combination but if ``a`` is a symbol with no assumptions the change will
    take place.

    Examples
    ========

    >>> from sympy import Symbol, symbols, log, logcombine, I
    >>> from sympy.abc import a, x, y, z
    >>> logcombine(a*log(x) + log(y) - log(z))
    a*log(x) + log(y) - log(z)
    >>> logcombine(a*log(x) + log(y) - log(z), force=True)
    log(x**a*y/z)
    >>> x,y,z = symbols('x,y,z', positive=True)
    >>> a = Symbol('a', real=True)
    >>> logcombine(a*log(x) + log(y) - log(z))
    log(x**a*y/z)

    The transformation is limited to factors and/or terms that
    contain logs, so the result depends on the initial state of
    expansion:

    >>> eq = (2 + 3*I)*log(x)
    >>> logcombine(eq, force=True) == eq
    True
    >>> logcombine(eq.expand(), force=True)
    log(x**2) + I*log(x**3)

    See Also
    ========
    posify: replace all symbols with symbols having positive assumptions

    """

    def f(rv):
        if not (rv.is_Add or rv.is_Mul):
            return rv

        def gooda(a):
            # bool to tell whether the leading ``a`` in ``a*log(x)``
            # could appear as log(x**a)
            return (a is not S.NegativeOne and  # -1 *could* go, but we disallow
                (a.is_real or force and a.is_real is not False))

        def goodlog(l):
            # bool to tell whether log ``l``'s argument can combine with others
            a = l.args[0]
            return a.is_positive or force and a.is_nonpositive is not False

        other = []
        logs = []
        log1 = defaultdict(list)
        for a in Add.make_args(rv):
            if a.func is log and goodlog(a):
                log1[()].append(([], a))
            elif not a.is_Mul:
                other.append(a)
            else:
                ot = []
                co = []
                lo = []
                for ai in a.args:
                    if ai.is_Rational and ai < 0:
                        ot.append(S.NegativeOne)
                        co.append(-ai)
                    elif ai.func is log and goodlog(ai):
                        lo.append(ai)
                    elif gooda(ai):
                        co.append(ai)
                    else:
                        ot.append(ai)
                if len(lo) > 1:
                    logs.append((ot, co, lo))
                elif lo:
                    log1[tuple(ot)].append((co, lo[0]))
                else:
                    other.append(a)

        # if there is only one log at each coefficient and none have
        # an exponent to place inside the log then there is nothing to do
        if not logs and all(len(log1[k]) == 1 and log1[k][0] == [] for k in log1):
            return rv

        # collapse multi-logs as far as possible in a canonical way
        # TODO: see if x*log(a)+x*log(a)*log(b) -> x*log(a)*(1+log(b))?
        # -- in this case, it's unambiguous, but if it were were a log(c) in
        # each term then it's arbitrary whether they are grouped by log(a) or
        # by log(c). So for now, just leave this alone; it's probably better to
        # let the user decide
        for o, e, l in logs:
            l = list(ordered(l))
            e = log(l.pop(0).args[0]**Mul(*e))
            while l:
                li = l.pop(0)
                e = log(li.args[0]**e)
            c, l = Mul(*o), e
            if l.func is log:  # it should be, but check to be sure
                log1[(c,)].append(([], l))
            else:
                other.append(c*l)

        # logs that have the same coefficient can multiply
        for k in list(log1.keys()):
            log1[Mul(*k)] = log(logcombine(Mul(*[
                l.args[0]**Mul(*c) for c, l in log1.pop(k)]),
                force=force))

        # logs that have oppositely signed coefficients can divide
        for k in ordered(list(log1.keys())):
            if not k in log1:  # already popped as -k
                continue
            if -k in log1:
                # figure out which has the minus sign; the one with
                # more op counts should be the one
                num, den = k, -k
                if num.count_ops() > den.count_ops():
                    num, den = den, num
                other.append(num*log(log1.pop(num).args[0]/log1.pop(den).args[0]))
            else:
                other.append(k*log1.pop(k))

        return Add(*other)

    return bottom_up(expr, f)


def bottom_up(rv, F, atoms=False, nonbasic=False):
    """Apply ``F`` to all expressions in an expression tree from the
    bottom up. If ``atoms`` is True, apply ``F`` even if there are no args;
    if ``nonbasic`` is True, try to apply ``F`` to non-Basic objects.
    """
    try:
        if rv.args:
            args = tuple([bottom_up(a, F, atoms, nonbasic)
                for a in rv.args])
            if args != rv.args:
                rv = rv.func(*args)
            rv = F(rv)
        elif atoms:
            rv = F(rv)
    except AttributeError:
        if nonbasic:
            try:
                rv = F(rv)
            except TypeError:
                pass

    return rv


def besselsimp(expr):
    """
    Simplify bessel-type functions.

    This routine tries to simplify bessel-type functions. Currently it only
    works on the Bessel J and I functions, however. It works by looking at all
    such functions in turn, and eliminating factors of "I" and "-1" (actually
    their polar equivalents) in front of the argument. Then, functions of
    half-integer order are rewritten using trigonometric functions and
    functions of integer order (> 1) are rewritten using functions
    of low order.  Finally, if the expression was changed, compute
    factorization of the result with factor().

    >>> from sympy import besselj, besseli, besselsimp, polar_lift, I, S
    >>> from sympy.abc import z, nu
    >>> besselsimp(besselj(nu, z*polar_lift(-1)))
    exp(I*pi*nu)*besselj(nu, z)
    >>> besselsimp(besseli(nu, z*polar_lift(-I)))
    exp(-I*pi*nu/2)*besselj(nu, z)
    >>> besselsimp(besseli(S(-1)/2, z))
    sqrt(2)*cosh(z)/(sqrt(pi)*sqrt(z))
    >>> besselsimp(z*besseli(0, z) + z*(besseli(2, z))/2 + besseli(1, z))
    3*z*besseli(0, z)/2
    """
    from sympy import besselj, bessely, besseli, besselk, jn, I, pi, Dummy
    # TODO
    # - better algorithm?
    # - simplify (cos(pi*b)*besselj(b,z) - besselj(-b,z))/sin(pi*b) ...
    # - use contiguity relations?

    def replacer(fro, to, factors):
        factors = set(factors)

        def repl(nu, z):
            if factors.intersection(Mul.make_args(z)):
                return to(nu, z)
            return fro(nu, z)
        return repl

    def torewrite(fro, to):
        def tofunc(nu, z):
            return fro(nu, z).rewrite(to)
        return tofunc

    def tominus(fro):
        def tofunc(nu, z):
            return exp(I*pi*nu)*fro(nu, exp_polar(-I*pi)*z)
        return tofunc

    orig_expr = expr

    ifactors = [I, exp_polar(I*pi/2), exp_polar(-I*pi/2)]
    expr = expr.replace(
        besselj, replacer(besselj,
        torewrite(besselj, besseli), ifactors))
    expr = expr.replace(
        besseli, replacer(besseli,
        torewrite(besseli, besselj), ifactors))

    minusfactors = [-1, exp_polar(I*pi)]
    expr = expr.replace(
        besselj, replacer(besselj, tominus(besselj), minusfactors))
    expr = expr.replace(
        besseli, replacer(besseli, tominus(besseli), minusfactors))

    z0 = Dummy('z')

    def expander(fro):
        def repl(nu, z):
            if (nu % 1) == S(1)/2:
                return exptrigsimp(trigsimp(unpolarify(
                        fro(nu, z0).rewrite(besselj).rewrite(jn).expand(
                            func=True)).subs(z0, z)))
            elif nu.is_Integer and nu > 1:
                return fro(nu, z).expand(func=True)
            return fro(nu, z)
        return repl

    expr = expr.replace(besselj, expander(besselj))
    expr = expr.replace(bessely, expander(bessely))
    expr = expr.replace(besseli, expander(besseli))
    expr = expr.replace(besselk, expander(besselk))

    if expr != orig_expr:
        expr = expr.factor()

    return expr

def exptrigsimp(expr, simplify=True):
    """
    Simplifies exponential / trigonometric / hyperbolic functions.
    When ``simplify`` is True (default) the expression obtained after the
    simplification step will be then be passed through simplify to
    precondition it so the final transformations will be applied.

    Examples
    ========

    >>> from sympy import exptrigsimp, exp, cosh, sinh
    >>> from sympy.abc import z

    >>> exptrigsimp(exp(z) + exp(-z))
    2*cosh(z)
    >>> exptrigsimp(cosh(z) - sinh(z))
    exp(-z)
    """
    from sympy.simplify.fu import hyper_as_trig, TR2i

    def exp_trig(e):
        # select the better of e, and e rewritten in terms of exp or trig
        # functions
        choices = [e]
        if e.has(*_trigs):
            choices.append(e.rewrite(exp))
        choices.append(e.rewrite(cos))
        return min(*choices, key=count_ops)
    newexpr = bottom_up(expr, exp_trig)

    if simplify:
        newexpr = newexpr.simplify()

    # conversion from exp to hyperbolic
    ex = newexpr.atoms(exp, S.Exp1)
    ex = [ei for ei in ex if 1/ei not in ex]
    ## sinh and cosh
    for ei in ex:
        e2 = ei**-2
        if e2 in ex:
            a = e2.args[0]/2 if not e2 is S.Exp1 else S.Half
            newexpr = newexpr.subs((e2 + 1)*ei, 2*cosh(a))
            newexpr = newexpr.subs((e2 - 1)*ei, 2*sinh(a))
    ## exp ratios to tan and tanh
    for ei in ex:
        n, d = ei - 1, ei + 1
        et = n/d
        etinv = d/n  # not 1/et or else recursion errors arise
        a = ei.args[0] if ei.func is exp else S.One
        if a.is_Mul or a is S.ImaginaryUnit:
            c = a.as_coefficient(I)
            if c:
                t = S.ImaginaryUnit*tan(c/2)
                newexpr = newexpr.subs(etinv, 1/t)
                newexpr = newexpr.subs(et, t)
                continue
        t = tanh(a/2)
        newexpr = newexpr.subs(etinv, 1/t)
        newexpr = newexpr.subs(et, t)

    # sin/cos and sinh/cosh ratios to tan and tanh, respectively
    if newexpr.has(C.HyperbolicFunction):
        e, f = hyper_as_trig(newexpr)
        newexpr = f(TR2i(e))
    if newexpr.has(C.TrigonometricFunction):
        newexpr = TR2i(newexpr)

    # can we ever generate an I where there was none previously?
    if not (newexpr.has(I) and not expr.has(I)):
        expr = newexpr
    return expr


def _is_Expr(e):
    """_eapply helper to tell whether ``e`` and all its args
    are Exprs."""
    if not isinstance(e, Expr):
        return False
    return all(_is_Expr(i) for i in e.args)


def _eapply(func, e, cond=None):
    """Apply ``func`` to ``e`` if all args are Exprs else only
    apply it to those args that *are* Exprs."""
    if not isinstance(e, Expr):
        return e
    if _is_Expr(e) or not e.args:
        return func(e)
    return e.func(*[
        _eapply(func, ei) if (cond is None or cond(ei)) else ei
        for ei in e.args])


def futrig(e, **kwargs):
    """Return simplified ``e`` using Fu-like transformations.
    This is not the "Fu" algorithm. This is called by default
    from ``trigsimp``. By default, hyperbolics subexpressions
    will be simplified, but this can be disabled by setting
    ``hyper=False``.

    Examples
    ========

    >>> from sympy import trigsimp, tan, sinh, tanh
    >>> from sympy.simplify.simplify import futrig
    >>> from sympy.abc import x
    >>> trigsimp(1/tan(x)**2)
    tan(x)**(-2)

    >>> futrig(sinh(x)/tanh(x))
    cosh(x)

    """
    from sympy.simplify.fu import hyper_as_trig

    e = sympify(e)

    if not isinstance(e, Basic):
        return e

    if not e.args:
        return e

    old = e
    e = bottom_up(e, lambda x: _futrig(x, **kwargs))

    if kwargs.pop('hyper', True) and e.has(C.HyperbolicFunction):
        e, f = hyper_as_trig(e)
        e = f(_futrig(e))

    if e != old and e.is_Mul and e.args[0].is_Rational:
        # redistribute leading coeff on 2-arg Add
        e = Mul(*e.as_coeff_Mul())
    return e


def _futrig(e, **kwargs):
    """Helper for futrig."""
    from sympy.strategies.tree import greedy
    from sympy.strategies.core import identity
    from sympy.simplify.fu import (
        TR1, TR2, TR3, TR2i, TR14, TR5, TR10, L, TR10i,
        TR8, TR6, TR15, TR16, TR111, TR5, TRmorrie, TR11, TR14, TR22,
        TR12)
    from sympy.core.compatibility import ordered, _nodes

    if not e.has(C.TrigonometricFunction):
        return e

    if e.is_Mul:
        coeff, e = e.as_independent(C.TrigonometricFunction)
    else:
        coeff = S.One

    Lops = lambda x: (L(x), x.count_ops(), _nodes(x), len(x.args), x.is_Add)
    trigs = lambda x: x.has(C.TrigonometricFunction)

    tree = [identity,
        (
        TR3,  # canonical angles
        TR1,  # sec-csc -> cos-sin
        TR12,  # expand tan of sum
        lambda x: _eapply(factor, x, trigs),
        TR2,  # tan-cot -> sin-cos
        [identity, lambda x: _eapply(_mexpand, x, trigs)],
        TR2i,  # sin-cos ratio -> tan
        lambda x: _eapply(lambda i: factor(i.normal()), x, trigs),
        TR14,  # factored identities
        TR5,  # sin-pow -> cos_pow
        TR10,  # sin-cos of sums -> sin-cos prod
        TR11, TR6, # reduce double angles and rewrite cos pows
        lambda x: _eapply(factor, x, trigs),
        TR14,  # factored powers of identities
        [identity, lambda x: _eapply(_mexpand, x, trigs)],
        TRmorrie,
        TR10i,  # sin-cos products > sin-cos of sums
        [identity, TR8],  # sin-cos products -> sin-cos of sums
        [identity, lambda x: TR2i(TR2(x))],  # tan -> sin-cos -> tan
        [
            lambda x: _eapply(expand_mul, TR5(x), trigs),
            lambda x: _eapply(
                expand_mul, TR15(x), trigs)], # pos/neg powers of sin
        [
            lambda x:  _eapply(expand_mul, TR6(x), trigs),
            lambda x:  _eapply(
                expand_mul, TR16(x), trigs)], # pos/neg powers of cos
        TR111,  # tan, sin, cos to neg power -> cot, csc, sec
        [identity, TR2i],  # sin-cos ratio to tan
        [identity, lambda x: _eapply(
            expand_mul, TR22(x), trigs)],  # tan-cot to sec-csc
        TR1, TR2, TR2i,
        [identity, lambda x: _eapply(
            factor_terms, TR12(x), trigs)],  # expand tan of sum
        )]
    e = greedy(tree, objective=Lops)(e)

    return coeff*e


def sum_simplify(s):
    """Main function for Sum simplification"""
    from sympy.concrete.summations import Sum

    terms = Add.make_args(s)
    s_t = [] # Sum Terms
    o_t = [] # Other Terms

    for term in terms:
        if isinstance(term, Mul):
            constant = 1
            other = 1
            s = 0
            n_sum_terms = 0
            for j in range(len(term.args)):
                if isinstance(term.args[j], Sum):
                    s = term.args[j]
                    n_sum_terms = n_sum_terms + 1
                elif term.args[j].is_number == True:
                    constant = constant * term.args[j]
                else:
                    other = other * term.args[j]
            if other == 1 and n_sum_terms == 1:
                # Insert the constant inside the Sum
                s_t.append(Sum(constant * s.function, *s.limits))
            elif other != 1 and n_sum_terms == 1:
                o_t.append(other * Sum(constant * s.function, *s.limits))
            else:
                o_t.append(term)
        elif isinstance(term, Sum):
            s_t.append(term)
        else:
            o_t.append(term)

    used = [False] * len(s_t)

    for method in range(2):
        for i, s_term1 in enumerate(s_t):
            if not used[i]:
                for j, s_term2 in enumerate(s_t):
                    if not used[j] and i != j:
                        if isinstance(sum_add(s_term1, s_term2, method), Sum):
                            s_t[i] = sum_add(s_term1, s_term2, method)
                            used[j] = True

    result = Add(*o_t)

    for i, s_term in enumerate(s_t):
        if not used[i]:
            result = Add(result, s_term)

    return result


def sum_add(self, other, method=0):
    """Helper function for Sum simplification"""
    from sympy.concrete.summations import Sum

    if type(self) == type(other):
        if method == 0:
            if self.limits == other.limits:
                return Sum(self.function + other.function, *self.limits)
        elif method == 1:
            if simplify(self.function - other.function) == 0:
                if len(self.limits) == len(other.limits) == 1:
                    i = self.limits[0][0]
                    x1 = self.limits[0][1]
                    y1 = self.limits[0][2]
                    j = other.limits[0][0]
                    x2 = other.limits[0][1]
                    y2 = other.limits[0][2]

                    if i == j:
                        if x2 == y1 + 1:
                            return Sum(self.function, (i, x1, y2))
                        elif x1 == y2 + 1:
                            return Sum(self.function, (i, x2, y1))

    return Add(self, other)


def product_simplify(s):
    """Main function for Product simplification"""
    from sympy.concrete.products import Product

    terms = Mul.make_args(s)
    p_t = [] # Product Terms
    o_t = [] # Other Terms

    for term in terms:
        if isinstance(term, Product):
            p_t.append(term)
        else:
            o_t.append(term)

    used = [False] * len(p_t)

    for method in range(2):
        for i, p_term1 in enumerate(p_t):
            if not used[i]:
                for j, p_term2 in enumerate(p_t):
                    if not used[j] and i != j:
                        if isinstance(product_mul(p_term1, p_term2, method), Product):
                            p_t[i] = product_mul(p_term1, p_term2, method)
                            used[j] = True

    result = Mul(*o_t)

    for i, p_term in enumerate(p_t):
        if not used[i]:
            result = Mul(result, p_term)

    return result


def product_mul(self, other, method=0):
    """Helper function for Product simplification"""
    from sympy.concrete.products import Product

    if type(self) == type(other):
        if method == 0:
            if self.limits == other.limits:
                return Product(self.function * other.function, *self.limits)
        elif method == 1:
            if simplify(self.function - other.function) == 0:
                if len(self.limits) == len(other.limits) == 1:
                    i = self.limits[0][0]
                    x1 = self.limits[0][1]
                    y1 = self.limits[0][2]
                    j = other.limits[0][0]
                    x2 = other.limits[0][1]
                    y2 = other.limits[0][2]

                    if i == j:
                        if x2 == y1 + 1:
                            return Product(self.function, (i, x1, y2))
                        elif x1 == y2 + 1:
                            return Product(self.function, (i, x2, y1))

    return Mul(self, other)

#-------------------- the old trigsimp routines ---------------------
_trigs = (C.TrigonometricFunction, C.HyperbolicFunction)


def trigsimp_old(expr, **opts):
    """
    reduces expression by using known trig identities

    Notes
    =====

    deep:
    - Apply trigsimp inside all objects with arguments

    recursive:
    - Use common subexpression elimination (cse()) and apply
    trigsimp recursively (this is quite expensive if the
    expression is large)

    method:
    - Determine the method to use. Valid choices are 'matching' (default),
    'groebner', 'combined', 'fu' and 'futrig'. If 'matching', simplify the
    expression recursively by pattern matching. If 'groebner', apply an
    experimental groebner basis algorithm. In this case further options
    are forwarded to ``trigsimp_groebner``, please refer to its docstring.
    If 'combined', first run the groebner basis algorithm with small
    default parameters, then run the 'matching' algorithm. 'fu' runs the
    collection of trigonometric transformations described by Fu, et al.
    (see the `fu` docstring) while `futrig` runs a subset of Fu-transforms
    that mimic the behavior of `trigsimp`.

    compare:
    - show input and output from `trigsimp` and `futrig` when different,
    but returns the `trigsimp` value.

    Examples
    ========

    >>> from sympy import trigsimp, sin, cos, log, cosh, sinh, tan, cot
    >>> from sympy.abc import x, y
    >>> e = 2*sin(x)**2 + 2*cos(x)**2
    >>> trigsimp(e, old=True)
    2
    >>> trigsimp(log(e), old=True)
    log(2*sin(x)**2 + 2*cos(x)**2)
    >>> trigsimp(log(e), deep=True, old=True)
    log(2)

    Using `method="groebner"` (or `"combined"`) can sometimes lead to a lot
    more simplification:

    >>> e = (-sin(x) + 1)/cos(x) + cos(x)/(-sin(x) + 1)
    >>> trigsimp(e, old=True)
    (-sin(x) + 1)/cos(x) - cos(x)/(sin(x) - 1)
    >>> trigsimp(e, method="groebner", old=True)
    2/cos(x)

    >>> trigsimp(1/cot(x)**2, compare=True, old=True)
          futrig: tan(x)**2
    cot(x)**(-2)

    """
    from sympy import tan
    from sympy.simplify.fu import fu

    old = expr
    first = opts.pop('first', True)
    if first:
        if not expr.has(*_trigs):
            return expr

        trigsyms = set.union(*[t.free_symbols for t in expr.atoms(*_trigs)])
        if len(trigsyms) > 1:
            d = separatevars(expr)
            if d.is_Mul:
                d = separatevars(d, dict=True) or d
            if isinstance(d, dict):
                expr = 1
                for k, v in d.items():
                    # remove hollow factoring
                    was = v
                    v = expand_mul(v)
                    opts['first'] = False
                    vnew = trigsimp(v, **opts)
                    if vnew == v:
                        vnew = was
                    expr *= vnew
                old = expr
            else:
                if d.is_Add:
                    for s in trigsyms:
                        r, e = expr.as_independent(s)
                        if r:
                            opts['first'] = False
                            expr = r + trigsimp(e, **opts)
                            if not expr.is_Add:
                                break
                    old = expr

    recursive = opts.pop('recursive', False)
    deep = opts.pop('deep', False)
    method = opts.pop('method', 'matching')

    def groebnersimp(ex, deep, **opts):
        def traverse(e):
            if e.is_Atom:
                return e
            args = [traverse(x) for x in e.args]
            if e.is_Function or e.is_Pow:
                args = [trigsimp_groebner(x, **opts) for x in args]
            return e.func(*args)
        if deep:
            ex = traverse(ex)
        return trigsimp_groebner(ex, **opts)

    trigsimpfunc = {
        'matching': (lambda x, d: _trigsimp(x, d)),
        'groebner': (lambda x, d: groebnersimp(x, d, **opts)),
        'combined': (lambda x, d: _trigsimp(groebnersimp(x,
                                       d, polynomial=True, hints=[2, tan]),
                                   d))
                   }[method]

    if recursive:
        w, g = cse(expr)
        g = trigsimpfunc(g[0], deep)

        for sub in reversed(w):
            g = g.subs(sub[0], sub[1])
            g = trigsimpfunc(g, deep)
        result = g
    else:
        result = trigsimpfunc(expr, deep)

    if opts.get('compare', False):
        f = futrig(old)
        if f != result:
            print('\tfutrig:', f)

    return result


def _dotrig(a, b):
    """Helper to tell whether ``a`` and ``b`` have the same sorts
    of symbols in them -- no need to test hyperbolic patterns against
    expressions that have no hyperbolics in them."""
    return a.func == b.func and (
        a.has(C.TrigonometricFunction) and b.has(C.TrigonometricFunction) or
        a.has(C.HyperbolicFunction) and b.has(C.HyperbolicFunction))


_trigpat = None
def _trigpats():
    global _trigpat
    a, b, c = symbols('a b c', cls=Wild)
    d = Wild('d', commutative=False)

    # for the simplifications like sinh/cosh -> tanh:
    # DO NOT REORDER THE FIRST 14 since these are assumed to be in this
    # order in _match_div_rewrite.
    matchers_division = (
        (a*sin(b)**c/cos(b)**c, a*tan(b)**c, sin(b), cos(b)),
        (a*tan(b)**c*cos(b)**c, a*sin(b)**c, sin(b), cos(b)),
        (a*cot(b)**c*sin(b)**c, a*cos(b)**c, sin(b), cos(b)),
        (a*tan(b)**c/sin(b)**c, a/cos(b)**c, sin(b), cos(b)),
        (a*cot(b)**c/cos(b)**c, a/sin(b)**c, sin(b), cos(b)),
        (a*cot(b)**c*tan(b)**c, a, sin(b), cos(b)),
        (a*(cos(b) + 1)**c*(cos(b) - 1)**c,
            a*(-sin(b)**2)**c, cos(b) + 1, cos(b) - 1),
        (a*(sin(b) + 1)**c*(sin(b) - 1)**c,
            a*(-cos(b)**2)**c, sin(b) + 1, sin(b) - 1),

        (a*sinh(b)**c/cosh(b)**c, a*tanh(b)**c, S.One, S.One),
        (a*tanh(b)**c*cosh(b)**c, a*sinh(b)**c, S.One, S.One),
        (a*coth(b)**c*sinh(b)**c, a*cosh(b)**c, S.One, S.One),
        (a*tanh(b)**c/sinh(b)**c, a/cosh(b)**c, S.One, S.One),
        (a*coth(b)**c/cosh(b)**c, a/sinh(b)**c, S.One, S.One),
        (a*coth(b)**c*tanh(b)**c, a, S.One, S.One),

        (c*(tanh(a) + tanh(b))/(1 + tanh(a)*tanh(b)),
            tanh(a + b)*c, S.One, S.One),
    )

    matchers_add = (
        (c*sin(a)*cos(b) + c*cos(a)*sin(b) + d, sin(a + b)*c + d),
        (c*cos(a)*cos(b) - c*sin(a)*sin(b) + d, cos(a + b)*c + d),
        (c*sin(a)*cos(b) - c*cos(a)*sin(b) + d, sin(a - b)*c + d),
        (c*cos(a)*cos(b) + c*sin(a)*sin(b) + d, cos(a - b)*c + d),
        (c*sinh(a)*cosh(b) + c*sinh(b)*cosh(a) + d, sinh(a + b)*c + d),
        (c*cosh(a)*cosh(b) + c*sinh(a)*sinh(b) + d, cosh(a + b)*c + d),
    )

    # for cos(x)**2 + sin(x)**2 -> 1
    matchers_identity = (
        (a*sin(b)**2, a - a*cos(b)**2),
        (a*tan(b)**2, a*(1/cos(b))**2 - a),
        (a*cot(b)**2, a*(1/sin(b))**2 - a),
        (a*sin(b + c), a*(sin(b)*cos(c) + sin(c)*cos(b))),
        (a*cos(b + c), a*(cos(b)*cos(c) - sin(b)*sin(c))),
        (a*tan(b + c), a*((tan(b) + tan(c))/(1 - tan(b)*tan(c)))),

        (a*sinh(b)**2, a*cosh(b)**2 - a),
        (a*tanh(b)**2, a - a*(1/cosh(b))**2),
        (a*coth(b)**2, a + a*(1/sinh(b))**2),
        (a*sinh(b + c), a*(sinh(b)*cosh(c) + sinh(c)*cosh(b))),
        (a*cosh(b + c), a*(cosh(b)*cosh(c) + sinh(b)*sinh(c))),
        (a*tanh(b + c), a*((tanh(b) + tanh(c))/(1 + tanh(b)*tanh(c)))),

    )

    # Reduce any lingering artifacts, such as sin(x)**2 changing
    # to 1-cos(x)**2 when sin(x)**2 was "simpler"
    artifacts = (
        (a - a*cos(b)**2 + c, a*sin(b)**2 + c, cos),
        (a - a*(1/cos(b))**2 + c, -a*tan(b)**2 + c, cos),
        (a - a*(1/sin(b))**2 + c, -a*cot(b)**2 + c, sin),

        (a - a*cosh(b)**2 + c, -a*sinh(b)**2 + c, cosh),
        (a - a*(1/cosh(b))**2 + c, a*tanh(b)**2 + c, cosh),
        (a + a*(1/sinh(b))**2 + c, a*coth(b)**2 + c, sinh),

        # same as above but with noncommutative prefactor
        (a*d - a*d*cos(b)**2 + c, a*d*sin(b)**2 + c, cos),
        (a*d - a*d*(1/cos(b))**2 + c, -a*d*tan(b)**2 + c, cos),
        (a*d - a*d*(1/sin(b))**2 + c, -a*d*cot(b)**2 + c, sin),

        (a*d - a*d*cosh(b)**2 + c, -a*d*sinh(b)**2 + c, cosh),
        (a*d - a*d*(1/cosh(b))**2 + c, a*d*tanh(b)**2 + c, cosh),
        (a*d + a*d*(1/sinh(b))**2 + c, a*d*coth(b)**2 + c, sinh),
    )

    _trigpat = (a, b, c, d, matchers_division, matchers_add,
        matchers_identity, artifacts)
    return _trigpat


def _replace_mul_fpowxgpow(expr, f, g, rexp, h, rexph):
    """Helper for _match_div_rewrite.

    Replace f(b_)**c_*g(b_)**(rexp(c_)) with h(b)**rexph(c) if f(b_)
    and g(b_) are both positive or if c_ is an integer.
    """
    # assert expr.is_Mul and expr.is_commutative and f != g
    fargs = defaultdict(int)
    gargs = defaultdict(int)
    args = []
    for x in expr.args:
        if x.is_Pow or x.func in (f, g):
            b, e = x.as_base_exp()
            if b.is_positive or e.is_integer:
                if b.func == f:
                    fargs[b.args[0]] += e
                    continue
                elif b.func == g:
                    gargs[b.args[0]] += e
                    continue
        args.append(x)
    common = set(fargs) & set(gargs)
    hit = False
    while common:
        key = common.pop()
        fe = fargs.pop(key)
        ge = gargs.pop(key)
        if fe == rexp(ge):
            args.append(h(key)**rexph(fe))
            hit = True
        else:
            fargs[key] = fe
            gargs[key] = ge
    if not hit:
        return expr
    while fargs:
        key, e = fargs.popitem()
        args.append(f(key)**e)
    while gargs:
        key, e = gargs.popitem()
        args.append(g(key)**e)
    return Mul(*args)


_idn = lambda x: x
_midn = lambda x: -x
_one = lambda x: S.One

def _match_div_rewrite(expr, i):
    """helper for __trigsimp"""
    if i == 0:
         expr = _replace_mul_fpowxgpow(expr, sin, cos,
            _midn, tan, _idn)
    elif i == 1:
         expr = _replace_mul_fpowxgpow(expr, tan, cos,
            _idn, sin, _idn)
    elif i == 2:
         expr = _replace_mul_fpowxgpow(expr, cot, sin,
            _idn, cos, _idn)
    elif i == 3:
         expr = _replace_mul_fpowxgpow(expr, tan, sin,
            _midn, cos, _midn)
    elif i == 4:
         expr = _replace_mul_fpowxgpow(expr, cot, cos,
            _midn, sin, _midn)
    elif i == 5:
         expr = _replace_mul_fpowxgpow(expr, cot, tan,
            _idn, _one, _idn)
    # i in (6, 7) is skipped
    elif i == 8:
         expr = _replace_mul_fpowxgpow(expr, sinh, cosh,
            _midn, tanh, _idn)
    elif i == 9:
         expr = _replace_mul_fpowxgpow(expr, tanh, cosh,
            _idn, sinh, _idn)
    elif i == 10:
        expr = _replace_mul_fpowxgpow(expr, coth, sinh,
            _idn, cosh, _idn)
    elif i == 11:
        expr = _replace_mul_fpowxgpow(expr, tanh, sinh,
            _midn, cosh, _midn)
    elif i == 12:
        expr = _replace_mul_fpowxgpow(expr, coth, cosh,
            _midn, sinh, _midn)
    elif i == 13:
        expr = _replace_mul_fpowxgpow(expr, coth, tanh,
            _idn, _one, _idn)
    else:
        return None
    return expr


def _trigsimp(expr, deep=False):
    # protect the cache from non-trig patterns; we only allow
    # trig patterns to enter the cache
    if expr.has(*_trigs):
        return __trigsimp(expr, deep)
    return expr


@cacheit
def __trigsimp(expr, deep=False):
    """recursive helper for trigsimp"""
    from sympy.simplify.fu import TR10i

    if _trigpat is None:
        _trigpats()
    a, b, c, d, matchers_division, matchers_add, \
    matchers_identity, artifacts = _trigpat

    if expr.is_Mul:
        # do some simplifications like sin/cos -> tan:
        if not expr.is_commutative:
            com, nc = expr.args_cnc()
            expr = _trigsimp(Mul._from_args(com), deep)*Mul._from_args(nc)
        else:
            for i, (pattern, simp, ok1, ok2) in enumerate(matchers_division):
                if not _dotrig(expr, pattern):
                    continue

                newexpr = _match_div_rewrite(expr, i)
                if newexpr is not None:
                    if newexpr != expr:
                        expr = newexpr
                        break
                    else:
                        continue

                # use SymPy matching instead
                res = expr.match(pattern)
                if res and res.get(c, 0):
                    if not res[c].is_integer:
                        ok = ok1.subs(res)
                        if not ok.is_positive:
                            continue
                        ok = ok2.subs(res)
                        if not ok.is_positive:
                            continue
                    # if "a" contains any of trig or hyperbolic funcs with
                    # argument "b" then skip the simplification
                    if any(w.args[0] == res[b] for w in res[a].atoms(
                            C.TrigonometricFunction, C.HyperbolicFunction)):
                        continue
                    # simplify and finish:
                    expr = simp.subs(res)
                    break  # process below

    if expr.is_Add:
        args = []
        for term in expr.args:
            if not term.is_commutative:
                com, nc = term.args_cnc()
                nc = Mul._from_args(nc)
                term = Mul._from_args(com)
            else:
                nc = S.One
            term = _trigsimp(term, deep)
            for pattern, result in matchers_identity:
                res = term.match(pattern)
                if res is not None:
                    term = result.subs(res)
                    break
            args.append(term*nc)
        if args != expr.args:
            expr = Add(*args)
            expr = min(expr, expand(expr), key=count_ops)
        if expr.is_Add:
            for pattern, result in matchers_add:
                if not _dotrig(expr, pattern):
                    continue
                expr = TR10i(expr)
                if expr.has(C.HyperbolicFunction):
                    res = expr.match(pattern)
                    # if "d" contains any trig or hyperbolic funcs with
                    # argument "a" or "b" then skip the simplification;
                    # this isn't perfect -- see tests
                    if res is None or not (a in res and b in res) or any(
                        w.args[0] in (res[a], res[b]) for w in res[d].atoms(
                            C.TrigonometricFunction, C.HyperbolicFunction)):
                        continue
                    expr = result.subs(res)
                    break

        # Reduce any lingering artifacts, such as sin(x)**2 changing
        # to 1 - cos(x)**2 when sin(x)**2 was "simpler"
        for pattern, result, ex in artifacts:
            if not _dotrig(expr, pattern):
                continue
            # Substitute a new wild that excludes some function(s)
            # to help influence a better match. This is because
            # sometimes, for example, 'a' would match sec(x)**2
            a_t = Wild('a', exclude=[ex])
            pattern = pattern.subs(a, a_t)
            result = result.subs(a, a_t)

            m = expr.match(pattern)
            was = None
            while m and was != expr:
                was = expr
                if m[a_t] == 0 or \
                        -m[a_t] in m[c].args or m[a_t] + m[c] == 0:
                    break
                if d in m and m[a_t]*m[d] + m[c] == 0:
                    break
                expr = result.subs(m)
                m = expr.match(pattern)
                m.setdefault(c, S.Zero)

    elif expr.is_Mul or expr.is_Pow or deep and expr.args:
        expr = expr.func(*[_trigsimp(a, deep) for a in expr.args])

    try:
        if not expr.has(*_trigs):
            raise TypeError
        e = expr.atoms(exp)
        new = expr.rewrite(exp, deep=deep)
        if new == e:
            raise TypeError
        fnew = factor(new)
        if fnew != new:
            new = sorted([new, factor(new)], key=count_ops)[0]
        # if all exp that were introduced disappeared then accept it
        if not (new.atoms(exp) - e):
            expr = new
    except TypeError:
        pass

    return expr
#------------------- end of old trigsimp routines --------------------
