"""
This module contain solvers for all kinds of equations:

    - algebraic or transcendental, use solve()

    - recurrence, use rsolve()

    - differential, use dsolve()

    - nonlinear (numerically), use nsolve()
      (you will need a good starting point)

"""

from __future__ import print_function, division

from sympy.core.compatibility import (iterable, is_sequence, ordered,
    default_sort_key, range)
from sympy.core.sympify import sympify
from sympy.core import S, Add, Symbol, Equality, Dummy, Expr, Mul, Pow
from sympy.core.exprtools import factor_terms
from sympy.core.function import (expand_mul, expand_multinomial, expand_log,
                          Derivative, AppliedUndef, UndefinedFunction, nfloat,
                          Function, expand_power_exp, Lambda, _mexpand)
from sympy.integrals.integrals import Integral
from sympy.core.numbers import ilcm, Float
from sympy.core.relational import Relational, Ge
from sympy.core.logic import fuzzy_not
from sympy.logic.boolalg import And, Or, BooleanAtom
from sympy.core.basic import preorder_traversal

from sympy.functions import (log, exp, LambertW, cos, sin, tan, acos, asin, atan,
                             Abs, re, im, arg, sqrt, atan2)
from sympy.functions.elementary.trigonometric import (TrigonometricFunction,
                                                      HyperbolicFunction)
from sympy.simplify import (simplify, collect, powsimp, posify, powdenest,
                            nsimplify, denom, logcombine)
from sympy.simplify.sqrtdenest import sqrt_depth
from sympy.simplify.fu import TR1
from sympy.matrices import Matrix, zeros
from sympy.polys import roots, cancel, factor, Poly, together, degree
from sympy.polys.polyerrors import GeneratorsNeeded, PolynomialError
from sympy.functions.elementary.piecewise import piecewise_fold, Piecewise

from sympy.utilities.lambdify import lambdify
from sympy.utilities.misc import filldedent
from sympy.utilities.iterables import uniq, generate_bell, flatten

from mpmath import findroot

from sympy.solvers.polysys import solve_poly_system
from sympy.solvers.inequalities import reduce_inequalities

from types import GeneratorType
from collections import defaultdict
import warnings


def _ispow(e):
    """Return True if e is a Pow or is exp."""
    return isinstance(e, Expr) and (e.is_Pow or e.func is exp)


def _simple_dens(f, symbols):
    # when checking if a denominator is zero, we can just check the
    # base of powers with nonzero exponents since if the base is zero
    # the power will be zero, too. To keep it simple and fast, we
    # limit simplification to exponents that are Numbers
    dens = set()
    for d in denoms(f, symbols):
        if d.is_Pow and d.exp.is_Number:
            if d.exp.is_zero:
                continue  # foo**0 is never 0
            d = d.base
        dens.add(d)
    return dens


def denoms(eq, symbols=None):
    """Return (recursively) set of all denominators that appear in eq
    that contain any symbol in iterable ``symbols``; if ``symbols`` is
    None (default) then all denominators will be returned.

    Examples
    ========

    >>> from sympy.solvers.solvers import denoms
    >>> from sympy.abc import x, y, z
    >>> from sympy import sqrt

    >>> denoms(x/y)
    set([y])

    >>> denoms(x/(y*z))
    set([y, z])

    >>> denoms(3/x + y/z)
    set([x, z])

    >>> denoms(x/2 + y/z)
    set([2, z])
    """

    pot = preorder_traversal(eq)
    dens = set()
    for p in pot:
        den = denom(p)
        if den is S.One:
            continue
        for d in Mul.make_args(den):
            dens.add(d)
    if not symbols:
        return dens
    rv = []
    for d in dens:
        free = d.free_symbols
        if any(s in free for s in symbols):
            rv.append(d)
    return set(rv)


def checksol(f, symbol, sol=None, **flags):
    """Checks whether sol is a solution of equation f == 0.

    Input can be either a single symbol and corresponding value
    or a dictionary of symbols and values. When given as a dictionary
    and flag ``simplify=True``, the values in the dictionary will be
    simplified. ``f`` can be a single equation or an iterable of equations.
    A solution must satisfy all equations in ``f`` to be considered valid;
    if a solution does not satisfy any equation, False is returned; if one or
    more checks are inconclusive (and none are False) then None
    is returned.

    Examples
    ========

    >>> from sympy import symbols
    >>> from sympy.solvers import checksol
    >>> x, y = symbols('x,y')
    >>> checksol(x**4 - 1, x, 1)
    True
    >>> checksol(x**4 - 1, x, 0)
    False
    >>> checksol(x**2 + y**2 - 5**2, {x: 3, y: 4})
    True

    To check if an expression is zero using checksol, pass it
    as ``f`` and send an empty dictionary for ``symbol``:

    >>> checksol(x**2 + x - x*(x + 1), {})
    True

    None is returned if checksol() could not conclude.

    flags:
        'numerical=True (default)'
           do a fast numerical check if ``f`` has only one symbol.
        'minimal=True (default is False)'
           a very fast, minimal testing.
        'warn=True (default is False)'
           show a warning if checksol() could not conclude.
        'simplify=True (default)'
           simplify solution before substituting into function and
           simplify the function before trying specific simplifications
        'force=True (default is False)'
           make positive all symbols without assumptions regarding sign.

    """
    from sympy.physics.units import Unit

    minimal = flags.get('minimal', False)

    if sol is not None:
        sol = {symbol: sol}
    elif isinstance(symbol, dict):
        sol = symbol
    else:
        msg = 'Expecting (sym, val) or ({sym: val}, None) but got (%s, %s)'
        raise ValueError(msg % (symbol, sol))

    if iterable(f):
        if not f:
            raise ValueError('no functions to check')
        rv = True
        for fi in f:
            check = checksol(fi, sol, **flags)
            if check:
                continue
            if check is False:
                return False
            rv = None  # don't return, wait to see if there's a False
        return rv

    if isinstance(f, Poly):
        f = f.as_expr()
    elif isinstance(f, Equality):
        f = f.lhs - f.rhs


    if not f:
        return True

    if sol and not f.has(*list(sol.keys())):
        # if f(y) == 0, x=3 does not set f(y) to zero...nor does it not
        return None

    illegal = set([S.NaN,
               S.ComplexInfinity,
               S.Infinity,
               S.NegativeInfinity])
    if any(sympify(v).atoms() & illegal for k, v in sol.items()):
        return False

    was = f
    attempt = -1
    numerical = flags.get('numerical', True)
    while 1:
        attempt += 1
        if attempt == 0:
            val = f.subs(sol)
            if isinstance(val, Mul):
                val = val.as_independent(Unit)[0]
            if val.atoms() & illegal:
                return False
        elif attempt == 1:
            if val.free_symbols:
                if not val.is_constant(*list(sol.keys()), simplify=not minimal):
                    return False
                # there are free symbols -- simple expansion might work
                _, val = val.as_content_primitive()
                val = expand_mul(expand_multinomial(val))
        elif attempt == 2:
            if minimal:
                return
            if flags.get('simplify', True):
                for k in sol:
                    sol[k] = simplify(sol[k])
            # start over without the failed expanded form, possibly
            # with a simplified solution
            val = f.subs(sol)
            if flags.get('force', True):
                val, reps = posify(val)
                # expansion may work now, so try again and check
                exval = expand_mul(expand_multinomial(val))
                if exval.is_number or not exval.free_symbols:
                    # we can decide now
                    val = exval
        elif attempt == 3:
            val = powsimp(val)
        elif attempt == 4:
            val = cancel(val)
        elif attempt == 5:
            val = val.expand()
        elif attempt == 6:
            val = together(val)
        elif attempt == 7:
            val = powsimp(val)
        else:
            # if there are no radicals and no functions then this can't be
            # zero anymore -- can it?
            pot = preorder_traversal(expand_mul(val))
            seen = set()
            saw_pow_func = False
            for p in pot:
                if p in seen:
                    continue
                seen.add(p)
                if p.is_Pow and not p.exp.is_Integer:
                    saw_pow_func = True
                elif p.is_Function:
                    saw_pow_func = True
                elif isinstance(p, UndefinedFunction):
                    saw_pow_func = True
                if saw_pow_func:
                    break
            if saw_pow_func is False:
                return False
            if flags.get('force', True):
                # don't do a zero check with the positive assumptions in place
                val = val.subs(reps)
            nz = fuzzy_not(val.is_zero)
            if nz is not None:
                # issue 5673: nz may be True even when False
                # so these are just hacks to keep a false positive
                # from being returned

                # HACK 1: LambertW (issue 5673)
                if val.is_number and val.has(LambertW):
                    # don't eval this to verify solution since if we got here,
                    # numerical must be False
                    return None

                # add other HACKs here if necessary, otherwise we assume
                # the nz value is correct
                return not nz
            break

        if val == was:
            continue
        elif val.is_Rational:
            return val == 0
        if numerical and not val.free_symbols:
            return bool(abs(val.n(18).n(12, chop=True)) < 1e-9)
        was = val

    if flags.get('warn', False):
        warnings.warn("\n\tWarning: could not verify solution %s." % sol)
    # returns None if it can't conclude
    # TODO: improve solution testing


def check_assumptions(expr, **assumptions):
    """Checks whether expression `expr` satisfies all assumptions.

    `assumptions` is a dict of assumptions: {'assumption': True|False, ...}.

    Examples
    ========

       >>> from sympy import Symbol, pi, I, exp
       >>> from sympy.solvers.solvers import check_assumptions

       >>> check_assumptions(-5, integer=True)
       True
       >>> check_assumptions(pi, real=True, integer=False)
       True
       >>> check_assumptions(pi, real=True, negative=True)
       False
       >>> check_assumptions(exp(I*pi/7), real=False)
       True

       >>> x = Symbol('x', real=True, positive=True)
       >>> check_assumptions(2*x + 1, real=True, positive=True)
       True
       >>> check_assumptions(-2*x - 5, real=True, positive=True)
       False

       `None` is returned if check_assumptions() could not conclude.

       >>> check_assumptions(2*x - 1, real=True, positive=True)
       >>> z = Symbol('z')
       >>> check_assumptions(z, real=True)
    """
    expr = sympify(expr)

    result = True
    for key, expected in assumptions.items():
        if expected is None:
            continue
        test = getattr(expr, 'is_' + key, None)
        if test is expected:
            continue
        elif test is not None:
            return False
        result = None  # Can't conclude, unless an other test fails.
    return result


def solve(f, *symbols, **flags):
    """
    Algebraically solves equations and systems of equations.

    Currently supported are:
        - polynomial,
        - transcendental
        - piecewise combinations of the above
        - systems of linear and polynomial equations
        - sytems containing relational expressions.

    Input is formed as:

    * f
        - a single Expr or Poly that must be zero,
        - an Equality
        - a Relational expression or boolean
        - iterable of one or more of the above

    * symbols (object(s) to solve for) specified as
        - none given (other non-numeric objects will be used)
        - single symbol
        - denested list of symbols
          e.g. solve(f, x, y)
        - ordered iterable of symbols
          e.g. solve(f, [x, y])

    * flags
        'dict'=True (default is False)
            return list (perhaps empty) of solution mappings
        'set'=True (default is False)
            return list of symbols and set of tuple(s) of solution(s)
        'exclude=[] (default)'
            don't try to solve for any of the free symbols in exclude;
            if expressions are given, the free symbols in them will
            be extracted automatically.
        'check=True (default)'
            If False, don't do any testing of solutions. This can be
            useful if one wants to include solutions that make any
            denominator zero.
        'numerical=True (default)'
            do a fast numerical check if ``f`` has only one symbol.
        'minimal=True (default is False)'
            a very fast, minimal testing.
        'warn=True (default is False)'
            show a warning if checksol() could not conclude.
        'simplify=True (default)'
            simplify all but polynomials of order 3 or greater before
            returning them and (if check is not False) use the
            general simplify function on the solutions and the
            expression obtained when they are substituted into the
            function which should be zero
        'force=True (default is False)'
            make positive all symbols without assumptions regarding sign.
        'rational=True (default)'
            recast Floats as Rational; if this option is not used, the
            system containing floats may fail to solve because of issues
            with polys. If rational=None, Floats will be recast as
            rationals but the answer will be recast as Floats. If the
            flag is False then nothing will be done to the Floats.
        'manual=True (default is False)'
            do not use the polys/matrix method to solve a system of
            equations, solve them one at a time as you might "manually"
        'implicit=True (default is False)'
            allows solve to return a solution for a pattern in terms of
            other functions that contain that pattern; this is only
            needed if the pattern is inside of some invertible function
            like cos, exp, ....
        'particular=True (default is False)'
            instructs solve to try to find a particular solution to a linear
            system with as many zeros as possible; this is very expensive
        'quick=True (default is False)'
            when using particular=True, use a fast heuristic instead to find a
            solution with many zeros (instead of using the very slow method
            guaranteed to find the largest number of zeros possible)
        'cubics=True (default)'
            return explicit solutions when cubic expressions are encountered
        'quartics=True (default)'
            return explicit solutions when quartic expressions are encountered
        'quintics=True (default)'
            return explicit solutions (if possible) when quintic expressions
            are encountered

    Examples
    ========

    The output varies according to the input and can be seen by example::

        >>> from sympy import solve, Poly, Eq, Function, exp
        >>> from sympy.abc import x, y, z, a, b
        >>> f = Function('f')

    * boolean or univariate Relational

        >>> solve(x < 3)
        And(-oo < x, x < 3)

    * to always get a list of solution mappings, use flag dict=True

        >>> solve(x - 3, dict=True)
        [{x: 3}]
        >>> solve([x - 3, y - 1], dict=True)
        [{x: 3, y: 1}]

    * to get a list of symbols and set of solution(s) use flag set=True

        >>> solve([x**2 - 3, y - 1], set=True)
        ([x, y], set([(-sqrt(3), 1), (sqrt(3), 1)]))

    * single expression and single symbol that is in the expression

        >>> solve(x - y, x)
        [y]
        >>> solve(x - 3, x)
        [3]
        >>> solve(Eq(x, 3), x)
        [3]
        >>> solve(Poly(x - 3), x)
        [3]
        >>> solve(x**2 - y**2, x, set=True)
        ([x], set([(-y,), (y,)]))
        >>> solve(x**4 - 1, x, set=True)
        ([x], set([(-1,), (1,), (-I,), (I,)]))

    * single expression with no symbol that is in the expression

        >>> solve(3, x)
        []
        >>> solve(x - 3, y)
        []

    * single expression with no symbol given

          In this case, all free symbols will be selected as potential
          symbols to solve for. If the equation is univariate then a list
          of solutions is returned; otherwise -- as is the case when symbols are
          given as an iterable of length > 1 -- a list of mappings will be returned.

            >>> solve(x - 3)
            [3]
            >>> solve(x**2 - y**2)
            [{x: -y}, {x: y}]
            >>> solve(z**2*x**2 - z**2*y**2)
            [{x: -y}, {x: y}, {z: 0}]
            >>> solve(z**2*x - z**2*y**2)
            [{x: y**2}, {z: 0}]

    * when an object other than a Symbol is given as a symbol, it is
      isolated algebraically and an implicit solution may be obtained.
      This is mostly provided as a convenience to save one from replacing
      the object with a Symbol and solving for that Symbol. It will only
      work if the specified object can be replaced with a Symbol using the
      subs method.

          >>> solve(f(x) - x, f(x))
          [x]
          >>> solve(f(x).diff(x) - f(x) - x, f(x).diff(x))
          [x + f(x)]
          >>> solve(f(x).diff(x) - f(x) - x, f(x))
          [-x + Derivative(f(x), x)]
          >>> solve(x + exp(x)**2, exp(x), set=True)
          ([exp(x)], set([(-sqrt(-x),), (sqrt(-x),)]))

          >>> from sympy import Indexed, IndexedBase, Tuple, sqrt
          >>> A = IndexedBase('A')
          >>> eqs = Tuple(A[1] + A[2] - 3, A[1] - A[2] + 1)
          >>> solve(eqs, eqs.atoms(Indexed))
          {A[1]: 1, A[2]: 2}

        * To solve for a *symbol* implicitly, use 'implicit=True':

            >>> solve(x + exp(x), x)
            [-LambertW(1)]
            >>> solve(x + exp(x), x, implicit=True)
            [-exp(x)]

        * It is possible to solve for anything that can be targeted with
          subs:

            >>> solve(x + 2 + sqrt(3), x + 2)
            [-sqrt(3)]
            >>> solve((x + 2 + sqrt(3), x + 4 + y), y, x + 2)
            {y: -2 + sqrt(3), x + 2: -sqrt(3)}

        * Nothing heroic is done in this implicit solving so you may end up
          with a symbol still in the solution:

            >>> eqs = (x*y + 3*y + sqrt(3), x + 4 + y)
            >>> solve(eqs, y, x + 2)
            {y: -sqrt(3)/(x + 3), x + 2: (-2*x - 6 + sqrt(3))/(x + 3)}
            >>> solve(eqs, y*x, x)
            {x: -y - 4, x*y: -3*y - sqrt(3)}

        * if you attempt to solve for a number remember that the number
          you have obtained does not necessarily mean that the value is
          equivalent to the expression obtained:

            >>> solve(sqrt(2) - 1, 1)
            [sqrt(2)]
            >>> solve(x - y + 1, 1)  # /!\ -1 is targeted, too
            [x/(y - 1)]
            >>> [_.subs(z, -1) for _ in solve((x - y + 1).subs(-1, z), 1)]
            [-x + y]

        * To solve for a function within a derivative, use dsolve.

    * single expression and more than 1 symbol

        * when there is a linear solution

            >>> solve(x - y**2, x, y)
            [{x: y**2}]
            >>> solve(x**2 - y, x, y)
            [{y: x**2}]

        * when undetermined coefficients are identified

            * that are linear

                >>> solve((a + b)*x - b + 2, a, b)
                {a: -2, b: 2}

            * that are nonlinear

                >>> solve((a + b)*x - b**2 + 2, a, b, set=True)
                ([a, b], set([(-sqrt(2), sqrt(2)), (sqrt(2), -sqrt(2))]))

        * if there is no linear solution then the first successful
          attempt for a nonlinear solution will be returned

            >>> solve(x**2 - y**2, x, y)
            [{x: -y}, {x: y}]
            >>> solve(x**2 - y**2/exp(x), x, y)
            [{x: 2*LambertW(y/2)}]
            >>> solve(x**2 - y**2/exp(x), y, x)
            [{y: -x*sqrt(exp(x))}, {y: x*sqrt(exp(x))}]

    * iterable of one or more of the above

        * involving relationals or bools

            >>> solve([x < 3, x - 2])
            Eq(x, 2)
            >>> solve([x > 3, x - 2])
            False

        * when the system is linear

            * with a solution

                >>> solve([x - 3], x)
                {x: 3}
                >>> solve((x + 5*y - 2, -3*x + 6*y - 15), x, y)
                {x: -3, y: 1}
                >>> solve((x + 5*y - 2, -3*x + 6*y - 15), x, y, z)
                {x: -3, y: 1}
                >>> solve((x + 5*y - 2, -3*x + 6*y - z), z, x, y)
                {x: -5*y + 2, z: 21*y - 6}

            * without a solution

                >>> solve([x + 3, x - 3])
                []

        * when the system is not linear

            >>> solve([x**2 + y -2, y**2 - 4], x, y, set=True)
            ([x, y], set([(-2, -2), (0, 2), (2, -2)]))

        * if no symbols are given, all free symbols will be selected and a list
          of mappings returned

            >>> solve([x - 2, x**2 + y])
            [{x: 2, y: -4}]
            >>> solve([x - 2, x**2 + f(x)], set([f(x), x]))
            [{x: 2, f(x): -4}]

        * if any equation doesn't depend on the symbol(s) given it will be
          eliminated from the equation set and an answer may be given
          implicitly in terms of variables that were not of interest

            >>> solve([x - y, y - 3], x)
            {x: y}

    Notes
    =====

    solve() with check=True (default) will run through the symbol tags to
    elimate unwanted solutions.  If no assumptions are included all possible
    solutions will be returned.

        >>> from sympy import Symbol, solve
        >>> x = Symbol("x")
        >>> solve(x**2 - 1)
        [-1, 1]

    By using the positive tag only one solution will be returned:

        >>> pos = Symbol("pos", positive=True)
        >>> solve(pos**2 - 1)
        [1]


    Assumptions aren't checked when `solve()` input involves
    relationals or bools.

    When the solutions are checked, those that make any denominator zero
    are automatically excluded. If you do not want to exclude such solutions
    then use the check=False option:

        >>> from sympy import sin, limit
        >>> solve(sin(x)/x)  # 0 is excluded
        [pi]

    If check=False then a solution to the numerator being zero is found: x = 0.
    In this case, this is a spurious solution since sin(x)/x has the well known
    limit (without dicontinuity) of 1 at x = 0:

        >>> solve(sin(x)/x, check=False)
        [0, pi]

    In the following case, however, the limit exists and is equal to the the
    value of x = 0 that is excluded when check=True:

        >>> eq = x**2*(1/x - z**2/x)
        >>> solve(eq, x)
        []
        >>> solve(eq, x, check=False)
        [0]
        >>> limit(eq, x, 0, '-')
        0
        >>> limit(eq, x, 0, '+')
        0

    Disabling high-order, explicit solutions
    ----------------------------------------

    When solving polynomial expressions, one might not want explicit solutions
    (which can be quite long). If the expression is univariate, CRootOf
    instances will be returned instead:

        >>> solve(x**3 - x + 1)
        [-1/((-1/2 - sqrt(3)*I/2)*(3*sqrt(69)/2 + 27/2)**(1/3)) - (-1/2 -
        sqrt(3)*I/2)*(3*sqrt(69)/2 + 27/2)**(1/3)/3, -(-1/2 +
        sqrt(3)*I/2)*(3*sqrt(69)/2 + 27/2)**(1/3)/3 - 1/((-1/2 +
        sqrt(3)*I/2)*(3*sqrt(69)/2 + 27/2)**(1/3)), -(3*sqrt(69)/2 +
        27/2)**(1/3)/3 - 1/(3*sqrt(69)/2 + 27/2)**(1/3)]
        >>> solve(x**3 - x + 1, cubics=False)
        [CRootOf(x**3 - x + 1, 0),
         CRootOf(x**3 - x + 1, 1),
         CRootOf(x**3 - x + 1, 2)]

        If the expression is multivariate, no solution might be returned:

        >>> solve(x**3 - x + a, x, cubics=False)
        []

    Sometimes solutions will be obtained even when a flag is False because the
    expression could be factored. In the following example, the equation can
    be factored as the product of a linear and a quadratic factor so explicit
    solutions (which did not require solving a cubic expression) are obtained:

        >>> eq = x**3 + 3*x**2 + x - 1
        >>> solve(eq, cubics=False)
        [-1, -1 + sqrt(2), -sqrt(2) - 1]

    Solving equations involving radicals
    ------------------------------------

    Because of SymPy's use of the principle root (issue #8789), some solutions
    to radical equations will be missed unless check=False:

        >>> from sympy import root
        >>> eq = root(x**3 - 3*x**2, 3) + 1 - x
        >>> solve(eq)
        []
        >>> solve(eq, check=False)
        [1/3]

    In the above example there is only a single solution to the equation. Other
    expressions will yield spurious roots which must be checked manually;
    roots which give a negative argument to odd-powered radicals will also need
    special checking:

        >>> from sympy import real_root, S
        >>> eq = root(x, 3) - root(x, 5) + S(1)/7
        >>> solve(eq)  # this gives 2 solutions but misses a 3rd
        [CRootOf(7*_p**5 - 7*_p**3 + 1, 1)**15,
        CRootOf(7*_p**5 - 7*_p**3 + 1, 2)**15]
        >>> sol = solve(eq, check=False)
        >>> [abs(eq.subs(x,i).n(2)) for i in sol]
        [0.48, 0.e-110, 0.e-110, 0.052, 0.052]

        The first solution is negative so real_root must be used to see that
        it satisfies the expression:

        >>> abs(real_root(eq.subs(x, sol[0])).n(2))
        0.e-110

    If the roots of the equation are not real then more care will be necessary
    to find the roots, especially for higher order equations. Consider the
    following expression:

        >>> expr = root(x, 3) - root(x, 5)

    We will construct a known value for this expression at x = 3 by selecting
    the 1-th root for each radical:

        >>> expr1 = root(x, 3, 1) - root(x, 5, 1)
        >>> v = expr1.subs(x, -3)

    The solve function is unable to find any exact roots to this equation:

        >>> eq = Eq(expr, v); eq1 = Eq(expr1, v)
        >>> solve(eq, check=False), solve(eq1, check=False)
        ([], [])

    The function unrad, however, can be used to get a form of the equation for
    which numerical roots can be found:

        >>> from sympy.solvers.solvers import unrad
        >>> from sympy import nroots
        >>> e, (p, cov) = unrad(eq)
        >>> pvals = nroots(e)
        >>> inversion = solve(cov, x)[0]
        >>> xvals = [inversion.subs(p, i) for i in pvals]

    Although eq or eq1 could have been used to find xvals, the solution can
    only be verified with expr1:

        >>> z = expr - v
        >>> [xi.n(chop=1e-9) for xi in xvals if abs(z.subs(x, xi).n()) < 1e-9]
        []
        >>> z1 = expr1 - v
        >>> [xi.n(chop=1e-9) for xi in xvals if abs(z1.subs(x, xi).n()) < 1e-9]
        [-3.0]

    See Also
    ========

        - rsolve() for solving recurrence relationships
        - dsolve() for solving differential equations

    """
    # keeping track of how f was passed since if it is a list
    # a dictionary of results will be returned.
    ###########################################################################

    def _sympified_list(w):
        return list(map(sympify, w if iterable(w) else [w]))
    bare_f = not iterable(f)
    ordered_symbols = (symbols and
                       symbols[0] and
                       (isinstance(symbols[0], Symbol) or
                        is_sequence(symbols[0],
                        include=GeneratorType)
                       )
                      )
    f, symbols = (_sympified_list(w) for w in [f, symbols])

    implicit = flags.get('implicit', False)

    # preprocess equation(s)
    ###########################################################################
    for i, fi in enumerate(f):
        if isinstance(fi, Equality):
            if 'ImmutableMatrix' in [type(a).__name__ for a in fi.args]:
                f[i] = fi.lhs - fi.rhs
            else:
                f[i] = Add(fi.lhs, -fi.rhs, evaluate=False)
        elif isinstance(fi, Poly):
            f[i] = fi.as_expr()
        elif isinstance(fi, (bool, BooleanAtom)) or fi.is_Relational:
            return reduce_inequalities(f, symbols=symbols)

        # rewrite hyperbolics in terms of exp
        f[i] = f[i].replace(lambda w: isinstance(w, HyperbolicFunction),
                lambda w: w.rewrite(exp))

        # if we have a Matrix, we need to iterate over its elements again
        if f[i].is_Matrix:
            bare_f = False
            f.extend(list(f[i]))
            f[i] = S.Zero

        # if we can split it into real and imaginary parts then do so
        freei = f[i].free_symbols
        if freei and all(s.is_real or s.is_imaginary for s in freei):
            fr, fi = f[i].as_real_imag()
            # accept as long as new re, im, arg or atan2 are not introduced
            had = f[i].atoms(re, im, arg, atan2)
            if fr and fi and fr != fi and not any(
                    i.atoms(re, im, arg, atan2) - had for i in (fr, fi)):
                if bare_f:
                    bare_f = False
                f[i: i + 1] = [fr, fi]

    # preprocess symbol(s)
    ###########################################################################
    if not symbols:
        # get symbols from equations
        symbols = set().union(*[fi.free_symbols for fi in f])
        if len(symbols) < len(f):
            for fi in f:
                pot = preorder_traversal(fi)
                for p in pot:
                    if not (p.is_number or p.is_Add or p.is_Mul) or \
                            isinstance(p, AppliedUndef):
                        flags['dict'] = True  # better show symbols
                        symbols.add(p)
                        pot.skip()  # don't go any deeper
        symbols = list(symbols)
        # supply dummy symbols so solve(3) behaves like solve(3, x)
        for i in range(len(f) - len(symbols)):
            symbols.append(Dummy())

        ordered_symbols = False
    elif len(symbols) == 1 and iterable(symbols[0]):
        symbols = symbols[0]

    # remove symbols the user is not interested in
    exclude = flags.pop('exclude', set())
    if exclude:
        if isinstance(exclude, Expr):
            exclude = [exclude]
        exclude = set().union(*[e.free_symbols for e in sympify(exclude)])
    symbols = [s for s in symbols if s not in exclude]

    # real/imag handling -----------------------------
    w = Dummy('w')
    piece = Lambda(w, Piecewise((w, Ge(w, 0)), (-w, True)))
    for i, fi in enumerate(f):
        # Abs
        reps = []
        for a in fi.atoms(Abs):
            if not a.has(*symbols):
                continue
            if a.args[0].is_real is None:
                raise NotImplementedError('solving %s when the argument '
                    'is not real or imaginary.' % a)
            reps.append((a, piece(a.args[0]) if a.args[0].is_real else \
                piece(a.args[0]*S.ImaginaryUnit)))
        fi = fi.subs(reps)

        # arg
        _arg = [a for a in fi.atoms(arg) if a.has(*symbols)]
        fi = fi.xreplace(dict(list(zip(_arg,
            [atan(im(a.args[0])/re(a.args[0])) for a in _arg]))))

        # save changes
        f[i] = fi

    # see if re(s) or im(s) appear
    irf = []
    for s in symbols:
        if s.is_real or s.is_imaginary:
            continue  # neither re(x) nor im(x) will appear
        # if re(s) or im(s) appear, the auxiliary equation must be present
        if any(fi.has(re(s), im(s)) for fi in f):
            irf.append((s, re(s) + S.ImaginaryUnit*im(s)))
    if irf:
        for s, rhs in irf:
            for i, fi in enumerate(f):
                f[i] = fi.xreplace({s: rhs})
            f.append(s - rhs)
            symbols.extend([re(s), im(s)])
        if bare_f:
            bare_f = False
        flags['dict'] = True
    # end of real/imag handling  -----------------------------

    symbols = list(uniq(symbols))
    if not ordered_symbols:
        # we do this to make the results returned canonical in case f
        # contains a system of nonlinear equations; all other cases should
        # be unambiguous
        symbols = sorted(symbols, key=default_sort_key)

    # we can solve for non-symbol entities by replacing them with Dummy symbols
    symbols_new = []
    symbol_swapped = False
    for i, s in enumerate(symbols):
        if s.is_Symbol:
            s_new = s
        else:
            symbol_swapped = True
            s_new = Dummy('X%d' % i)
        symbols_new.append(s_new)

    if symbol_swapped:
        swap_sym = list(zip(symbols, symbols_new))
        f = [fi.subs(swap_sym) for fi in f]
        symbols = symbols_new
        swap_sym = dict([(v, k) for k, v in swap_sym])
    else:
        swap_sym = {}

    # this is needed in the next two events
    symset = set(symbols)

    # get rid of equations that have no symbols of interest; we don't
    # try to solve them because the user didn't ask and they might be
    # hard to solve; this means that solutions may be given in terms
    # of the eliminated equations e.g. solve((x-y, y-3), x) -> {x: y}
    newf = []
    for fi in f:
        # let the solver handle equations that..
        # - have no symbols but are expressions
        # - have symbols of interest
        # - have no symbols of interest but are constant
        # but when an expression is not constant and has no symbols of
        # interest, it can't change what we obtain for a solution from
        # the remaining equations so we don't include it; and if it's
        # zero it can be removed and if it's not zero, there is no
        # solution for the equation set as a whole
        #
        # The reason for doing this filtering is to allow an answer
        # to be obtained to queries like solve((x - y, y), x); without
        # this mod the return value is []
        ok = False
        if fi.has(*symset):
            ok = True
        else:
            free = fi.free_symbols
            if not free:
                if fi.is_Number:
                    if fi.is_zero:
                        continue
                    return []
                ok = True
            else:
                if fi.is_constant():
                    ok = True
        if ok:
            newf.append(fi)
    if not newf:
        return []
    f = newf
    del newf

    # mask off any Object that we aren't going to invert: Derivative,
    # Integral, etc... so that solving for anything that they contain will
    # give an implicit solution
    seen = set()
    non_inverts = set()
    for fi in f:
        pot = preorder_traversal(fi)
        for p in pot:
            if not isinstance(p, Expr) or isinstance(p, Piecewise):
                pass
            elif (isinstance(p, bool) or
                    not p.args or
                    p in symset or
                    p.is_Add or p.is_Mul or
                    p.is_Pow and not implicit or
                    p.is_Function and not implicit) and p.func not in (re, im):
                continue
            elif not p in seen:
                seen.add(p)
                if p.free_symbols & symset:
                    non_inverts.add(p)
                else:
                    continue
            pot.skip()
    del seen
    non_inverts = dict(list(zip(non_inverts, [Dummy() for d in non_inverts])))
    f = [fi.subs(non_inverts) for fi in f]

    non_inverts = [(v, k.subs(swap_sym)) for k, v in non_inverts.items()]

    # rationalize Floats
    floats = False
    if flags.get('rational', True) is not False:
        for i, fi in enumerate(f):
            if fi.has(Float):
                floats = True
                f[i] = nsimplify(fi, rational=True)

    # Any embedded piecewise functions need to be brought out to the
    # top level so that the appropriate strategy gets selected.
    # However, this is necessary only if one of the piecewise
    # functions depends on one of the symbols we are solving for.
    def _has_piecewise(e):
        if e.is_Piecewise:
            return e.has(*symbols)
        return any([_has_piecewise(a) for a in e.args])
    for i, fi in enumerate(f):
        if _has_piecewise(fi):
            f[i] = piecewise_fold(fi)

    #
    # try to get a solution
    ###########################################################################
    if bare_f:
        solution = _solve(f[0], *symbols, **flags)
    else:
        solution = _solve_system(f, symbols, **flags)

    #
    # postprocessing
    ###########################################################################
    # Restore masked-off objects
    if non_inverts:

        def _do_dict(solution):
            return dict([(k, v.subs(non_inverts)) for k, v in
                         solution.items()])
        for i in range(1):
            if type(solution) is dict:
                solution = _do_dict(solution)
                break
            elif solution and type(solution) is list:
                if type(solution[0]) is dict:
                    solution = [_do_dict(s) for s in solution]
                    break
                elif type(solution[0]) is tuple:
                    solution = [tuple([v.subs(non_inverts) for v in s]) for s
                                in solution]
                    break
                else:
                    solution = [v.subs(non_inverts) for v in solution]
                    break
            elif not solution:
                break
        else:
            raise NotImplementedError(filldedent('''
                            no handling of %s was implemented''' % solution))

    # Restore original "symbols" if a dictionary is returned.
    # This is not necessary for
    #   - the single univariate equation case
    #     since the symbol will have been removed from the solution;
    #   - the nonlinear poly_system since that only supports zero-dimensional
    #     systems and those results come back as a list
    #
    # ** unless there were Derivatives with the symbols, but those were handled
    #    above.
    if symbol_swapped:
        symbols = [swap_sym[k] for k in symbols]
        if type(solution) is dict:
            solution = dict([(swap_sym[k], v.subs(swap_sym))
                             for k, v in solution.items()])
        elif solution and type(solution) is list and type(solution[0]) is dict:
            for i, sol in enumerate(solution):
                solution[i] = dict([(swap_sym[k], v.subs(swap_sym))
                              for k, v in sol.items()])

    # undo the dictionary solutions returned when the system was only partially
    # solved with poly-system if all symbols are present
    if (
            not flags.get('dict', False) and
            solution and
            ordered_symbols and
            type(solution) is not dict and
            type(solution[0]) is dict and
            all(s in solution[0] for s in symbols)
    ):
        solution = [tuple([r[s].subs(r) for s in symbols]) for r in solution]

    # Get assumptions about symbols, to filter solutions.
    # Note that if assumptions about a solution can't be verified, it is still
    # returned.
    check = flags.get('check', True)

    # restore floats
    if floats and solution and flags.get('rational', None) is None:
        solution = nfloat(solution, exponent=False)

    if check and solution:  # assumption checking

        warn = flags.get('warn', False)
        got_None = []  # solutions for which one or more symbols gave None
        no_False = []  # solutions for which no symbols gave False
        if type(solution) is tuple:
            # this has already been checked and is in as_set form
            return solution
        elif type(solution) is list:
            if type(solution[0]) is tuple:
                for sol in solution:
                    for symb, val in zip(symbols, sol):
                        test = check_assumptions(val, **symb.assumptions0)
                        if test is False:
                            break
                        if test is None:
                            got_None.append(sol)
                    else:
                        no_False.append(sol)
            elif type(solution[0]) is dict:
                for sol in solution:
                    a_None = False
                    for symb, val in sol.items():
                        test = check_assumptions(val, **symb.assumptions0)
                        if test:
                            continue
                        if test is False:
                            break
                        a_None = True
                    else:
                        no_False.append(sol)
                        if a_None:
                            got_None.append(sol)
            else:  # list of expressions
                for sol in solution:
                    test = check_assumptions(sol, **symbols[0].assumptions0)
                    if test is False:
                        continue
                    no_False.append(sol)
                    if test is None:
                        got_None.append(sol)

        elif type(solution) is dict:
            a_None = False
            for symb, val in solution.items():
                test = check_assumptions(val, **symb.assumptions0)
                if test:
                    continue
                if test is False:
                    no_False = None
                    break
                a_None = True
            else:
                no_False = solution
                if a_None:
                    got_None.append(solution)

        elif isinstance(solution, (Relational, And, Or)):
            if len(symbols) != 1:
                raise ValueError("Length should be 1")
            if warn and symbols[0].assumptions0:
                warnings.warn(filldedent("""
                    \tWarning: assumptions about variable '%s' are
                    not handled currently.""" % symbols[0]))
            # TODO: check also variable assumptions for inequalities

        else:
            raise TypeError('Unrecognized solution')  # improve the checker

        solution = no_False
        if warn and got_None:
            warnings.warn(filldedent("""
                \tWarning: assumptions concerning following solution(s)
                can't be checked:""" + '\n\t' +
                ', '.join(str(s) for s in got_None)))

    #
    # done
    ###########################################################################

    as_dict = flags.get('dict', False)
    as_set = flags.get('set', False)

    if not as_set and isinstance(solution, list):
        # Make sure that a list of solutions is ordered in a canonical way.
        solution.sort(key=default_sort_key)

    if not as_dict and not as_set:
        return solution or []

    # return a list of mappings or []
    if not solution:
        solution = []
    else:
        if isinstance(solution, dict):
            solution = [solution]
        elif iterable(solution[0]):
            solution = [dict(list(zip(symbols, s))) for s in solution]
        elif isinstance(solution[0], dict):
            pass
        else:
            if len(symbols) != 1:
                raise ValueError("Length should be 1")
            solution = [{symbols[0]: s} for s in solution]
    if as_dict:
        return solution
    assert as_set
    if not solution:
        return [], set()
    k = list(ordered(solution[0].keys()))
    return k, set([tuple([s[ki] for ki in k]) for s in solution])


def _solve(f, *symbols, **flags):
    """Return a checked solution for f in terms of one or more of the
    symbols. A list should be returned except for the case when a linear
    undetermined-coefficients equation is encountered (in which case
    a dictionary is returned).

    If no method is implemented to solve the equation, a NotImplementedError
    will be raised. In the case that conversion of an expression to a Poly
    gives None a ValueError will be raised."""

    not_impl_msg = "No algorithms are implemented to solve equation %s"

    if len(symbols) != 1:
        soln = None
        free = f.free_symbols
        ex = free - set(symbols)
        if len(ex) != 1:
            ind, dep = f.as_independent(*symbols)
            ex = ind.free_symbols & dep.free_symbols
        if len(ex) == 1:
            ex = ex.pop()
            try:
                # soln may come back as dict, list of dicts or tuples, or
                # tuple of symbol list and set of solution tuples
                soln = solve_undetermined_coeffs(f, symbols, ex, **flags)
            except NotImplementedError:
                pass
        if soln:
            if flags.get('simplify', True):
                if type(soln) is dict:
                    for k in soln:
                        soln[k] = simplify(soln[k])
                elif type(soln) is list:
                    if type(soln[0]) is dict:
                        for d in soln:
                            for k in d:
                                d[k] = simplify(d[k])
                    elif type(soln[0]) is tuple:
                        soln = [tuple(simplify(i) for i in j) for j in soln]
                    else:
                        raise TypeError('unrecognized args in list')
                elif type(soln) is tuple:
                    sym, sols = soln
                    soln = sym, set([tuple(simplify(i) for i in j) for j in sols])
                else:
                    raise TypeError('unrecognized solution type')
            return soln
        # find first successful solution
        failed = []
        got_s = set([])
        result = []
        for s in symbols:
            xi, v = solve_linear(f, symbols=[s])
            if xi == s:
                # no need to check but we should simplify if desired
                if flags.get('simplify', True):
                    v = simplify(v)
                vfree = v.free_symbols
                if got_s and any([ss in vfree for ss in got_s]):
                    # sol depends on previously solved symbols: discard it
                    continue
                got_s.add(xi)
                result.append({xi: v})
            elif xi:  # there might be a non-linear solution if xi is not 0
                failed.append(s)
        if not failed:
            return result
        for s in failed:
            try:
                soln = _solve(f, s, **flags)
                for sol in soln:
                    if got_s and any([ss in sol.free_symbols for ss in got_s]):
                        # sol depends on previously solved symbols: discard it
                        continue
                    got_s.add(s)
                    result.append({s: sol})
            except NotImplementedError:
                continue
        if got_s:
            return result
        else:
            raise NotImplementedError(not_impl_msg % f)
    symbol = symbols[0]

    # /!\ capture this flag then set it to False so that no checking in
    # recursive calls will be done; only the final answer is checked
    checkdens = check = flags.pop('check', True)
    flags['check'] = False

    # build up solutions if f is a Mul
    if f.is_Mul:
        result = set()
        for m in f.args:
            soln = _solve(m, symbol, **flags)
            result.update(set(soln))
        result = list(result)
        if check:
            # all solutions have been checked but now we must
            # check that the solutions do not set denominators
            # in any factor to zero
            dens = _simple_dens(f, symbols)
            result = [s for s in result if
                all(not checksol(den, {symbol: s}, **flags) for den in
                dens)]
        # set flags for quick exit at end
        check = False
        flags['simplify'] = False

    elif f.is_Piecewise:
        result = set()
        for n, (expr, cond) in enumerate(f.args):
            candidates = _solve(piecewise_fold(expr), symbol, **flags)
            for candidate in candidates:
                if candidate in result:
                    continue
                try:
                    v = (cond == True) or cond.subs(symbol, candidate)
                except:
                    v = False
                if v != False:
                    # Only include solutions that do not match the condition
                    # of any previous pieces.
                    matches_other_piece = False
                    for other_n, (other_expr, other_cond) in enumerate(f.args):
                        if other_n == n:
                            break
                        if other_cond == False:
                            continue
                        try:
                            if other_cond.subs(symbol, candidate) == True:
                                matches_other_piece = True
                                break
                        except:
                            pass
                    if not matches_other_piece:
                        v = v == True or v.doit()
                        if isinstance(v, Relational):
                            v = v.canonical
                        result.add(Piecewise(
                            (candidate, v),
                            (S.NaN, True)
                        ))
        check = False
    else:
        # first see if it really depends on symbol and whether there
        # is only a linear solution
        f_num, sol = solve_linear(f, symbols=symbols)
        if f_num is S.Zero:
            return []
        elif f_num.is_Symbol:
            # no need to check but simplify if desired
            if flags.get('simplify', True):
                sol = simplify(sol)
            return [sol]

        result = False  # no solution was obtained
        msg = ''  # there is no failure message

        # Poly is generally robust enough to convert anything to
        # a polynomial and tell us the different generators that it
        # contains, so we will inspect the generators identified by
        # polys to figure out what to do.

        # try to identify a single generator that will allow us to solve this
        # as a polynomial, followed (perhaps) by a change of variables if the
        # generator is not a symbol

        try:
            poly = Poly(f_num)
            if poly is None:
                raise ValueError('could not convert %s to Poly' % f_num)
        except GeneratorsNeeded:
            simplified_f = simplify(f_num)
            if simplified_f != f_num:
                return _solve(simplified_f, symbol, **flags)
            raise ValueError('expression appears to be a constant')

        gens = [g for g in poly.gens if g.has(symbol)]

        def _as_base_q(x):
            """Return (b**e, q) for x = b**(p*e/q) where p/q is the leading
            Rational of the exponent of x, e.g. exp(-2*x/3) -> (exp(x), 3)
            """
            b, e = x.as_base_exp()
            if e.is_Rational:
                return b, e.q
            if not e.is_Mul:
                return x, 1
            c, ee = e.as_coeff_Mul()
            if c.is_Rational and c is not S.One:  # c could be a Float
                return b**ee, c.q
            return x, 1

        if len(gens) > 1:
            # If there is more than one generator, it could be that the
            # generators have the same base but different powers, e.g.
            #   >>> Poly(exp(x) + 1/exp(x))
            #   Poly(exp(-x) + exp(x), exp(-x), exp(x), domain='ZZ')
            #
            # If unrad was not disabled then there should be no rational
            # exponents appearing as in
            #   >>> Poly(sqrt(x) + sqrt(sqrt(x)))
            #   Poly(sqrt(x) + x**(1/4), sqrt(x), x**(1/4), domain='ZZ')

            bases, qs = list(zip(*[_as_base_q(g) for g in gens]))
            bases = set(bases)

            if len(bases) > 1 or not all(q == 1 for q in qs):
                funcs = set(b for b in bases if b.is_Function)

                trig = set([_ for _ in funcs if
                    isinstance(_, TrigonometricFunction)])
                other = funcs - trig
                if not other and len(funcs.intersection(trig)) > 1:
                    newf = TR1(f_num).rewrite(tan)
                    if newf != f_num:
                        result = _solve(newf, symbol, **flags)

                # just a simple case - see if replacement of single function
                # clears all symbol-dependent functions, e.g.
                # log(x) - log(log(x) - 1) - 3 can be solved even though it has
                # two generators.

                if result is False and funcs:
                    funcs = list(ordered(funcs))  # put shallowest function first
                    f1 = funcs[0]
                    t = Dummy('t')
                    # perform the substitution
                    ftry = f_num.subs(f1, t)

                    # if no Functions left, we can proceed with usual solve
                    if not ftry.has(symbol):
                        cv_sols = _solve(ftry, t, **flags)
                        cv_inv = _solve(t - f1, symbol, **flags)[0]
                        sols = list()
                        for sol in cv_sols:
                            sols.append(cv_inv.subs(t, sol))
                        result = list(ordered(sols))

                if result is False:
                    msg = 'multiple generators %s' % gens

            else:
                # e.g. case where gens are exp(x), exp(-x)
                u = bases.pop()
                t = Dummy('t')
                inv = _solve(u - t, symbol, **flags)
                if isinstance(u, (Pow, exp)):
                    # this will be resolved by factor in _tsolve but we might
                    # as well try a simple expansion here to get things in
                    # order so something like the following will work now without
                    # having to factor:
                    #
                    # >>> eq = (exp(I*(-x-2))+exp(I*(x+2)))
                    # >>> eq.subs(exp(x),y)  # fails
                    # exp(I*(-x - 2)) + exp(I*(x + 2))
                    # >>> eq.expand().subs(exp(x),y)  # works
                    # y**I*exp(2*I) + y**(-I)*exp(-2*I)
                    def _expand(p):
                        b, e = p.as_base_exp()
                        e = expand_mul(e)
                        return expand_power_exp(b**e)
                    ftry = f_num.replace(
                        lambda w: w.is_Pow or isinstance(w, exp),
                        _expand).subs(u, t)
                    if not ftry.has(symbol):
                        soln = _solve(ftry, t, **flags)
                        sols = list()
                        for sol in soln:
                            for i in inv:
                                sols.append(i.subs(t, sol))
                        result = list(ordered(sols))

        elif len(gens) == 1:

            # There is only one generator that we are interested in, but
            # there may have been more than one generator identified by
            # polys (e.g. for symbols other than the one we are interested
            # in) so recast the poly in terms of our generator of interest.
            # Also use composite=True with f_num since Poly won't update
            # poly as documented in issue 8810.

            poly = Poly(f_num, gens[0], composite=True)

            # if we aren't on the tsolve-pass, use roots
            if not flags.pop('tsolve', False):
                soln = None
                deg = poly.degree()
                flags['tsolve'] = True
                solvers = dict([(k, flags.get(k, True)) for k in
                    ('cubics', 'quartics', 'quintics')])
                soln = roots(poly, **solvers)
                if sum(soln.values()) < deg:
                    # e.g. roots(32*x**5 + 400*x**4 + 2032*x**3 +
                    #            5000*x**2 + 6250*x + 3189) -> {}
                    # so all_roots is used and RootOf instances are
                    # returned *unless* the system is multivariate
                    # or high-order EX domain.
                    try:
                        soln = poly.all_roots()
                    except NotImplementedError:
                        if not flags.get('incomplete', True):
                                raise NotImplementedError(
                                filldedent('''
    Neither high-order multivariate polynomials
    nor sorting of EX-domain polynomials is supported.
    If you want to see any results, pass keyword incomplete=True to
    solve; to see numerical values of roots
    for univariate expressions, use nroots.
    '''))
                        else:
                            pass
                else:
                    soln = list(soln.keys())

                if soln is not None:
                    u = poly.gen
                    if u != symbol:
                        try:
                            t = Dummy('t')
                            iv = _solve(u - t, symbol, **flags)
                            soln = list(ordered(set([i.subs(t, s) for i in iv for s in soln])))
                        except NotImplementedError:
                            # perhaps _tsolve can handle f_num
                            soln = None
                    else:
                        check = False  # only dens need to be checked
                    if soln is not None:
                        if len(soln) > 2:
                            # if the flag wasn't set then unset it since high-order
                            # results are quite long. Perhaps one could base this
                            # decision on a certain critical length of the
                            # roots. In addition, wester test M2 has an expression
                            # whose roots can be shown to be real with the
                            # unsimplified form of the solution whereas only one of
                            # the simplified forms appears to be real.
                            flags['simplify'] = flags.get('simplify', False)
                        result = soln

    # fallback if above fails
    # -----------------------
    if result is False:
        # try unrad
        if flags.pop('_unrad', True):
            try:
                u = unrad(f_num, symbol)
            except (ValueError, NotImplementedError):
                u = False
            if u:
                eq, cov = u
                if cov:
                    isym, ieq = cov
                    inv = _solve(ieq, symbol, **flags)[0]
                    rv = set([inv.subs(isym, xi) for xi in _solve(eq, isym, **flags)])
                else:
                    try:
                        rv = set(_solve(eq, symbol, **flags))
                    except NotImplementedError:
                        rv = None
                if rv is not None:
                    result = list(ordered(rv))
                    # if the flag wasn't set then unset it since unrad results
                    # can be quite long or of very high order
                    flags['simplify'] = flags.get('simplify', False)
            else:
                pass  # for coverage

    # try _tsolve
    if result is False:
        flags.pop('tsolve', None)  # allow tsolve to be used on next pass
        try:
            soln = _tsolve(f_num, symbol, **flags)
            if soln is not None:
                result = soln
        except PolynomialError:
            pass
    # ----------- end of fallback ----------------------------

    if result is False:
        raise NotImplementedError('\n'.join([msg, not_impl_msg % f]))

    if flags.get('simplify', True):
        result = list(map(simplify, result))
        # we just simplified the solution so we now set the flag to
        # False so the simplification doesn't happen again in checksol()
        flags['simplify'] = False

    if checkdens:
        # reject any result that makes any denom. affirmatively 0;
        # if in doubt, keep it
        dens = _simple_dens(f, symbols)
        result = [s for s in result if
                  all(not checksol(d, {symbol: s}, **flags)
                    for d in dens)]
    if check:
        # keep only results if the check is not False
        result = [r for r in result if
                  checksol(f_num, {symbol: r}, **flags) is not False]
    return result


def _solve_system(exprs, symbols, **flags):
    if not exprs:
        return []

    polys = []
    dens = set()
    failed = []
    result = False
    linear = False
    manual = flags.get('manual', False)
    checkdens = check = flags.get('check', True)

    for j, g in enumerate(exprs):
        dens.update(_simple_dens(g, symbols))
        i, d = _invert(g, *symbols)
        g = d - i
        g = g.as_numer_denom()[0]
        if manual:
            failed.append(g)
            continue

        poly = g.as_poly(*symbols, extension=True)

        if poly is not None:
            polys.append(poly)
        else:
            failed.append(g)

    if not polys:
        solved_syms = []
    else:
        if all(p.is_linear for p in polys):
            n, m = len(polys), len(symbols)
            matrix = zeros(n, m + 1)

            for i, poly in enumerate(polys):
                for monom, coeff in poly.terms():
                    try:
                        j = monom.index(1)
                        matrix[i, j] = coeff
                    except ValueError:
                        matrix[i, m] = -coeff

            # returns a dictionary ({symbols: values}) or None
            if flags.pop('particular', False):
                result = minsolve_linear_system(matrix, *symbols, **flags)
            else:
                result = solve_linear_system(matrix, *symbols, **flags)
            if failed:
                if result:
                    solved_syms = list(result.keys())
                else:
                    solved_syms = []
            else:
                linear = True

        else:
            if len(symbols) > len(polys):
                from sympy.utilities.iterables import subsets

                free = set().union(*[p.free_symbols for p in polys])
                free = list(ordered(free.intersection(symbols)))
                got_s = set()
                result = []
                for syms in subsets(free, len(polys)):
                    try:
                        # returns [] or list of tuples of solutions for syms
                        res = solve_poly_system(polys, *syms)
                        if res:
                            for r in res:
                                skip = False
                                for r1 in r:
                                    if got_s and any([ss in r1.free_symbols
                                           for ss in got_s]):
                                        # sol depends on previously
                                        # solved symbols: discard it
                                        skip = True
                                if not skip:
                                    got_s.update(syms)
                                    result.extend([dict(list(zip(syms, r)))])
                    except NotImplementedError:
                        pass
                if got_s:
                    solved_syms = list(got_s)
                else:
                    raise NotImplementedError('no valid subset found')
            else:
                try:
                    result = solve_poly_system(polys, *symbols)
                    solved_syms = symbols
                except NotImplementedError:
                    failed.extend([g.as_expr() for g in polys])
                    solved_syms = []
                if result:
                    # we don't know here if the symbols provided were given
                    # or not, so let solve resolve that. A list of dictionaries
                    # is going to always be returned from here.
                    #
                    result = [dict(list(zip(solved_syms, r))) for r in result]

    if result:
        if type(result) is dict:
            result = [result]
    else:
        result = [{}]

    if failed:
        # For each failed equation, see if we can solve for one of the
        # remaining symbols from that equation. If so, we update the
        # solution set and continue with the next failed equation,
        # repeating until we are done or we get an equation that can't
        # be solved.
        def _ok_syms(e, sort=False):
            rv = (e.free_symbols - solved_syms) & legal
            if sort:
                rv = list(rv)
                rv.sort(key=default_sort_key)
            return rv

        solved_syms = set(solved_syms)  # set of symbols we have solved for
        legal = set(symbols)  # what we are interested in

        # sort so equation with the fewest potential symbols is first
        for eq in ordered(failed, lambda _: len(_ok_syms(_))):
            u = Dummy()  # used in solution checking
            newresult = []
            bad_results = []
            got_s = set()
            hit = False
            for r in result:
                # update eq with everything that is known so far
                eq2 = eq.subs(r)
                # if check is True then we see if it satisfies this
                # equation, otherwise we just accept it
                if check and r:
                    b = checksol(u, u, eq2, minimal=True)
                    if b is not None:
                        # this solution is sufficient to know whether
                        # it is valid or not so we either accept or
                        # reject it, then continue
                        if b:
                            newresult.append(r)
                        else:
                            bad_results.append(r)
                        continue
                # search for a symbol amongst those available that
                # can be solved for
                ok_syms = _ok_syms(eq2, sort=True)
                if not ok_syms:
                    if r:
                        newresult.append(r)
                    break  # skip as it's independent of desired symbols
                for s in ok_syms:
                    try:
                        soln = _solve(eq2, s, **flags)
                    except NotImplementedError:
                        continue
                    # put each solution in r and append the now-expanded
                    # result in the new result list; use copy since the
                    # solution for s in being added in-place
                    for sol in soln:
                        if got_s and any([ss in sol.free_symbols for ss in got_s]):
                            # sol depends on previously solved symbols: discard it
                            continue
                        rnew = r.copy()
                        for k, v in r.items():
                            rnew[k] = v.subs(s, sol)
                        # and add this new solution
                        rnew[s] = sol
                        newresult.append(rnew)
                    hit = True
                    got_s.add(s)
                if not hit:
                    raise NotImplementedError('could not solve %s' % eq2)
            else:
                result = newresult
                for b in bad_results:
                    if b in result:
                        result.remove(b)

    default_simplify = bool(failed)  # rely on system-solvers to simplify
    if  flags.get('simplify', default_simplify):
        for r in result:
            for k in r:
                r[k] = simplify(r[k])
        flags['simplify'] = False  # don't need to do so in checksol now

    if checkdens:
        result = [r for r in result
            if not any(checksol(d, r, **flags) for d in dens)]

    if check and not linear:
        result = [r for r in result
            if not any(checksol(e, r, **flags) is False for e in exprs)]

    result = [r for r in result if r]
    if linear and result:
        result = result[0]
    return result


def solve_linear(lhs, rhs=0, symbols=[], exclude=[]):
    r""" Return a tuple derived from f = lhs - rhs that is one of
    the following:

        (0, 1) meaning that ``f`` is independent of the symbols in
        ``symbols`` that aren't in ``exclude``, e.g::

            >>> from sympy.solvers.solvers import solve_linear
            >>> from sympy.abc import x, y, z
            >>> from sympy import cos, sin
            >>> eq = y*cos(x)**2 + y*sin(x)**2 - y  # = y*(1 - 1) = 0
            >>> solve_linear(eq)
            (0, 1)
            >>> eq = cos(x)**2 + sin(x)**2  # = 1
            >>> solve_linear(eq)
            (0, 1)
            >>> solve_linear(x, exclude=[x])
            (0, 1)

        (0, 0) meaning that there is no solution to the equation
        amongst the symbols given.

            (If the first element of the tuple is not zero then
            the function is guaranteed to be dependent on a symbol
            in ``symbols``.)

        (symbol, solution) where symbol appears linearly in the
        numerator of ``f``, is in ``symbols`` (if given) and is
        not in ``exclude`` (if given). No simplification is done
        to ``f`` other than a ``mul=True`` expansion, so the
        solution will correspond strictly to a unique solution.

        ``(n, d)`` where ``n`` and ``d`` are the numerator and
        denominator of ``f`` when the numerator was not linear
        in any symbol of interest; ``n`` will never be a symbol
        unless a solution for that symbol was found (in which case
        the second element is the solution, not the denominator).


    Examples
    ========

    >>> from sympy.core.power import Pow
    >>> from sympy.polys.polytools import cancel

    The variable ``x`` appears as a linear variable in each of the
    following:

    >>> solve_linear(x + y**2)
    (x, -y**2)
    >>> solve_linear(1/x - y**2)
    (x, y**(-2))

    When not linear in x or y then the numerator and denominator are returned.

    >>> solve_linear(x**2/y**2 - 3)
    (x**2 - 3*y**2, y**2)

    If the numerator of the expression is a symbol then (0, 0) is
    returned if the solution for that symbol would have set any
    denominator to 0:

    >>> eq = 1/(1/x - 2)
    >>> eq.as_numer_denom()
    (x, -2*x + 1)
    >>> solve_linear(eq)
    (0, 0)

    But automatic rewriting may cause a symbol in the denominator to
    appear in the numerator so a solution will be returned:

    >>> (1/x)**-1
    x
    >>> solve_linear((1/x)**-1)
    (x, 0)

    Use an unevaluated expression to avoid this:

    >>> solve_linear(Pow(1/x, -1, evaluate=False))
    (0, 0)

    If ``x`` is allowed to cancel in the following expression, then it
    appears to be linear in ``x``, but this sort of cancellation is not
    done by ``solve_linear`` so the solution will always satisfy the
    original expression without causing a division by zero error.

    >>> eq = x**2*(1/x - z**2/x)
    >>> solve_linear(cancel(eq))
    (x, 0)
    >>> solve_linear(eq)
    (x**2*(-z**2 + 1), x)

    A list of symbols for which a solution is desired may be given:

    >>> solve_linear(x + y + z, symbols=[y])
    (y, -x - z)

    A list of symbols to ignore may also be given:

    >>> solve_linear(x + y + z, exclude=[x])
    (y, -x - z)

    (A solution for ``y`` is obtained because it is the first variable
    from the canonically sorted list of symbols that had a linear
    solution.)

    """
    if isinstance(lhs, Equality):
        if rhs:
            raise ValueError(filldedent('''
            If lhs is an Equality, rhs must be 0 but was %s''' % rhs))
        rhs = lhs.rhs
        lhs = lhs.lhs
    dens = None
    eq = lhs - rhs
    n, d = eq.as_numer_denom()
    if not n:
        return S.Zero, S.One

    free = n.free_symbols
    if not symbols:
        symbols = free
    else:
        bad = [s for s in symbols if not s.is_Symbol]
        if bad:
            if len(bad) == 1:
                bad = bad[0]
            if len(symbols) == 1:
                eg = 'solve(%s, %s)' % (eq, symbols[0])
            else:
                eg = 'solve(%s, *%s)' % (eq, list(symbols))
            raise ValueError(filldedent('''
                solve_linear only handles symbols, not %s. To isolate
                non-symbols use solve, e.g. >>> %s <<<.
                             ''' % (bad, eg)))
        symbols = free.intersection(symbols)
    symbols = symbols.difference(exclude)
    if not symbols:
        return S.Zero, S.One
    dfree = d.free_symbols

    # derivatives are easy to do but tricky to analyze to see if they
    # are going to disallow a linear solution, so for simplicity we
    # just evaluate the ones that have the symbols of interest
    derivs = defaultdict(list)
    for der in n.atoms(Derivative):
        csym = der.free_symbols & symbols
        for c in csym:
            derivs[c].append(der)

    all_zero = True
    for xi in sorted(symbols, key=default_sort_key):  # canonical order
        # if there are derivatives in this var, calculate them now
        if type(derivs[xi]) is list:
            derivs[xi] = dict([(der, der.doit()) for der in derivs[xi]])
        newn = n.subs(derivs[xi])
        dnewn_dxi = newn.diff(xi)
        # dnewn_dxi can be nonzero if it survives differentation by any
        # of its free symbols
        free = dnewn_dxi.free_symbols
        if dnewn_dxi and (not free or any(dnewn_dxi.diff(s) for s in free)):
            all_zero = False
            if dnewn_dxi is S.NaN:
                break
            if xi not in dnewn_dxi.free_symbols:
                vi = -(newn.subs(xi, 0))/dnewn_dxi
                if dens is None:
                    dens = _simple_dens(eq, symbols)
                if not any(checksol(di, {xi: vi}, minimal=True) is True
                          for di in dens):
                    # simplify any trivial integral
                    irep = [(i, i.doit()) for i in vi.atoms(Integral) if
                            i.function.is_number]
                    # do a slight bit of simplification
                    vi = expand_mul(vi.subs(irep))
                    return xi, vi
    if all_zero:
        return S.Zero, S.One
    if n.is_Symbol: # no solution for this symbol was found
        return S.Zero, S.Zero
    return n, d


def minsolve_linear_system(system, *symbols, **flags):
    r"""
    Find a particular solution to a linear system.

    In particular, try to find a solution with the minimal possible number
    of non-zero variables using a naive algorithm with exponential complexity.
    If ``quick=True``, a heuristic is used.
    """
    quick = flags.get('quick', False)
    # Check if there are any non-zero solutions at all
    s0 = solve_linear_system(system, *symbols, **flags)
    if not s0 or all(v == 0 for v in s0.values()):
        return s0
    if quick:
        # We just solve the system and try to heuristically find a nice
        # solution.
        s = solve_linear_system(system, *symbols)
        def update(determined, solution):
            delete = []
            for k, v in solution.items():
                solution[k] = v.subs(determined)
                if not solution[k].free_symbols:
                    delete.append(k)
                    determined[k] = solution[k]
            for k in delete:
                del solution[k]
        determined = {}
        update(determined, s)
        while s:
            # NOTE sort by default_sort_key to get deterministic result
            k = max((k for k in s.values()),
                    key=lambda x: (len(x.free_symbols), default_sort_key(x)))
            x = max(k.free_symbols, key=default_sort_key)
            if len(k.free_symbols) != 1:
                determined[x] = S(0)
            else:
                val = solve(k)[0]
                if val == 0 and all(v.subs(x, val) == 0 for v in s.values()):
                    determined[x] = S(1)
                else:
                    determined[x] = val
            update(determined, s)
        return determined
    else:
        # We try to select n variables which we want to be non-zero.
        # All others will be assumed zero. We try to solve the modified system.
        # If there is a non-trivial solution, just set the free variables to
        # one. If we do this for increasing n, trying all combinations of
        # variables, we will find an optimal solution.
        # We speed up slightly by starting at one less than the number of
        # variables the quick method manages.
        from itertools import combinations
        from sympy.utilities.misc import debug
        N = len(symbols)
        bestsol = minsolve_linear_system(system, *symbols, quick=True)
        n0 = len([x for x in bestsol.values() if x != 0])
        for n in range(n0 - 1, 1, -1):
            debug('minsolve: %s' % n)
            thissol = None
            for nonzeros in combinations(list(range(N)), n):
                subm = Matrix([system.col(i).T for i in nonzeros] + [system.col(-1).T]).T
                s = solve_linear_system(subm, *[symbols[i] for i in nonzeros])
                if s and not all(v == 0 for v in s.values()):
                    subs = [(symbols[v], S(1)) for v in nonzeros]
                    for k, v in s.items():
                        s[k] = v.subs(subs)
                    for sym in symbols:
                        if sym not in s:
                            if symbols.index(sym) in nonzeros:
                                s[sym] = S(1)
                            else:
                                s[sym] = S(0)
                    thissol = s
                    break
            if thissol is None:
                break
            bestsol = thissol
        return bestsol


def solve_linear_system(system, *symbols, **flags):
    r"""
    Solve system of N linear equations with M variables, which means
    both under- and overdetermined systems are supported. The possible
    number of solutions is zero, one or infinite. Respectively, this
    procedure will return None or a dictionary with solutions. In the
    case of underdetermined systems, all arbitrary parameters are skipped.
    This may cause a situation in which an empty dictionary is returned.
    In that case, all symbols can be assigned arbitrary values.

    Input to this functions is a Nx(M+1) matrix, which means it has
    to be in augmented form. If you prefer to enter N equations and M
    unknowns then use `solve(Neqs, *Msymbols)` instead. Note: a local
    copy of the matrix is made by this routine so the matrix that is
    passed will not be modified.

    The algorithm used here is fraction-free Gaussian elimination,
    which results, after elimination, in an upper-triangular matrix.
    Then solutions are found using back-substitution. This approach
    is more efficient and compact than the Gauss-Jordan method.

    >>> from sympy import Matrix, solve_linear_system
    >>> from sympy.abc import x, y

    Solve the following system::

           x + 4 y ==  2
        -2 x +   y == 14

    >>> system = Matrix(( (1, 4, 2), (-2, 1, 14)))
    >>> solve_linear_system(system, x, y)
    {x: -6, y: 2}

    A degenerate system returns an empty dictionary.

    >>> system = Matrix(( (0,0,0), (0,0,0) ))
    >>> solve_linear_system(system, x, y)
    {}

    """
    do_simplify = flags.get('simplify', True)

    if system.rows == system.cols - 1 == len(symbols):
        try:
            # well behaved n-equations and n-unknowns
            inv = inv_quick(system[:, :-1])
            rv = dict(zip(symbols, inv*system[:, -1]))
            if do_simplify:
                for k, v in rv.items():
                    rv[k] = simplify(v)
            if not all(i.is_zero for i in rv.values()):
                # non-trivial solution
                return rv
        except ValueError:
            pass

    matrix = system[:, :]
    syms = list(symbols)

    i, m = 0, matrix.cols - 1  # don't count augmentation

    while i < matrix.rows:
        if i == m:
            # an overdetermined system
            if any(matrix[i:, m]):
                return None   # no solutions
            else:
                # remove trailing rows
                matrix = matrix[:i, :]
                break

        if not matrix[i, i]:
            # there is no pivot in current column
            # so try to find one in other columns
            for k in range(i + 1, m):
                if matrix[i, k]:
                    break
            else:
                if matrix[i, m]:
                    # We need to know if this is always zero or not. We
                    # assume that if there are free symbols that it is not
                    # identically zero (or that there is more than one way
                    # to make this zero). Otherwise, if there are none, this
                    # is a constant and we assume that it does not simplify
                    # to zero XXX are there better (fast) ways to test this?
                    # The .equals(0) method could be used but that can be
                    # slow; numerical testing is prone to errors of scaling.
                    if not matrix[i, m].free_symbols:
                        return None  # no solution

                    # A row of zeros with a non-zero rhs can only be accepted
                    # if there is another equivalent row. Any such rows will
                    # be deleted.
                    nrows = matrix.rows
                    rowi = matrix.row(i)
                    ip = None
                    j = i + 1
                    while j < matrix.rows:
                        # do we need to see if the rhs of j
                        # is a constant multiple of i's rhs?
                        rowj = matrix.row(j)
                        if rowj == rowi:
                            matrix.row_del(j)
                        elif rowj[:-1] == rowi[:-1]:
                            if ip is None:
                                _, ip = rowi[-1].as_content_primitive()
                            _, jp = rowj[-1].as_content_primitive()
                            if not (simplify(jp - ip) or simplify(jp + ip)):
                                matrix.row_del(j)

                        j += 1

                    if nrows == matrix.rows:
                        # no solution
                        return None
                # zero row or was a linear combination of
                # other rows or was a row with a symbolic
                # expression that matched other rows, e.g. [0, 0, x - y]
                # so now we can safely skip it
                matrix.row_del(i)
                if not matrix:
                    # every choice of variable values is a solution
                    # so we return an empty dict instead of None
                    return dict()
                continue

            # we want to change the order of colums so
            # the order of variables must also change
            syms[i], syms[k] = syms[k], syms[i]
            matrix.col_swap(i, k)

        pivot_inv = S.One/matrix[i, i]

        # divide all elements in the current row by the pivot
        matrix.row_op(i, lambda x, _: x * pivot_inv)

        for k in range(i + 1, matrix.rows):
            if matrix[k, i]:
                coeff = matrix[k, i]

                # subtract from the current row the row containing
                # pivot and multiplied by extracted coefficient
                matrix.row_op(k, lambda x, j: simplify(x - matrix[i, j]*coeff))

        i += 1

    # if there weren't any problems, augmented matrix is now
    # in row-echelon form so we can check how many solutions
    # there are and extract them using back substitution

    if len(syms) == matrix.rows:
        # this system is Cramer equivalent so there is
        # exactly one solution to this system of equations
        k, solutions = i - 1, {}

        while k >= 0:
            content = matrix[k, m]

            # run back-substitution for variables
            for j in range(k + 1, m):
                content -= matrix[k, j]*solutions[syms[j]]

            if do_simplify:
                solutions[syms[k]] = simplify(content)
            else:
                solutions[syms[k]] = content

            k -= 1

        return solutions
    elif len(syms) > matrix.rows:
        # this system will have infinite number of solutions
        # dependent on exactly len(syms) - i parameters
        k, solutions = i - 1, {}

        while k >= 0:
            content = matrix[k, m]

            # run back-substitution for variables
            for j in range(k + 1, i):
                content -= matrix[k, j]*solutions[syms[j]]

            # run back-substitution for parameters
            for j in range(i, m):
                content -= matrix[k, j]*syms[j]

            if do_simplify:
                solutions[syms[k]] = simplify(content)
            else:
                solutions[syms[k]] = content

            k -= 1

        return solutions
    else:
        return []   # no solutions


def solve_undetermined_coeffs(equ, coeffs, sym, **flags):
    """Solve equation of a type p(x; a_1, ..., a_k) == q(x) where both
       p, q are univariate polynomials and f depends on k parameters.
       The result of this functions is a dictionary with symbolic
       values of those parameters with respect to coefficients in q.

       This functions accepts both Equations class instances and ordinary
       SymPy expressions. Specification of parameters and variable is
       obligatory for efficiency and simplicity reason.

       >>> from sympy import Eq
       >>> from sympy.abc import a, b, c, x
       >>> from sympy.solvers import solve_undetermined_coeffs

       >>> solve_undetermined_coeffs(Eq(2*a*x + a+b, x), [a, b], x)
       {a: 1/2, b: -1/2}

       >>> solve_undetermined_coeffs(Eq(a*c*x + a+b, x), [a, b], x)
       {a: 1/c, b: -1/c}

    """
    if isinstance(equ, Equality):
        # got equation, so move all the
        # terms to the left hand side
        equ = equ.lhs - equ.rhs

    equ = cancel(equ).as_numer_denom()[0]

    system = list(collect(equ.expand(), sym, evaluate=False).values())

    if not any(equ.has(sym) for equ in system):
        # consecutive powers in the input expressions have
        # been successfully collected, so solve remaining
        # system using Gaussian elimination algorithm
        return solve(system, *coeffs, **flags)
    else:
        return None  # no solutions


def solve_linear_system_LU(matrix, syms):
    """
    Solves the augmented matrix system using LUsolve and returns a dictionary
    in which solutions are keyed to the symbols of syms *as ordered*.

    The matrix must be invertible.

    Examples
    ========

    >>> from sympy import Matrix
    >>> from sympy.abc import x, y, z
    >>> from sympy.solvers.solvers import solve_linear_system_LU

    >>> solve_linear_system_LU(Matrix([
    ... [1, 2, 0, 1],
    ... [3, 2, 2, 1],
    ... [2, 0, 0, 1]]), [x, y, z])
    {x: 1/2, y: 1/4, z: -1/2}

    See Also
    ========

    sympy.matrices.LUsolve

    """
    if matrix.rows != matrix.cols - 1:
        raise ValueError("Rows should be equal to columns - 1")
    A = matrix[:matrix.rows, :matrix.rows]
    b = matrix[:, matrix.cols - 1:]
    soln = A.LUsolve(b)
    solutions = {}
    for i in range(soln.rows):
        solutions[syms[i]] = soln[i, 0]
    return solutions


def det_perm(M):
    """Return the det(``M``) by using permutations to select factors.
    For size larger than 8 the number of permutations becomes prohibitively
    large, or if there are no symbols in the matrix, it is better to use the
    standard determinant routines, e.g. `M.det()`.

    See Also
    ========
    det_minor
    det_quick
    """
    args = []
    s = True
    n = M.rows
    try:
        list = M._mat
    except AttributeError:
        list = flatten(M.tolist())
    for perm in generate_bell(n):
        fac = []
        idx = 0
        for j in perm:
            fac.append(list[idx + j])
            idx += n
        term = Mul(*fac) # disaster with unevaluated Mul -- takes forever for n=7
        args.append(term if s else -term)
        s = not s
    return Add(*args)


def det_minor(M):
    """Return the ``det(M)`` computed from minors without
    introducing new nesting in products.

    See Also
    ========
    det_perm
    det_quick
    """
    n = M.rows
    if n == 2:
        return M[0, 0]*M[1, 1] - M[1, 0]*M[0, 1]
    else:
        return sum([(1, -1)[i % 2]*Add(*[M[0, i]*d for d in
            Add.make_args(det_minor(M.minorMatrix(0, i)))])
            if M[0, i] else S.Zero for i in range(n)])


def det_quick(M, method=None):
    """Return ``det(M)`` assuming that either
    there are lots of zeros or the size of the matrix
    is small. If this assumption is not met, then the normal
    Matrix.det function will be used with method = ``method``.

    See Also
    ========
    det_minor
    det_perm
    """
    if any(i.has(Symbol) for i in M):
        if M.rows < 8 and all(i.has(Symbol) for i in M):
            return det_perm(M)
        return det_minor(M)
    else:
        return M.det(method=method) if method else M.det()


def inv_quick(M):
    """Return the inverse of ``M``, assuming that either
    there are lots of zeros or the size of the matrix
    is small.
    """
    from sympy.matrices import zeros
    if any(i.has(Symbol) for i in M):
        if all(i.has(Symbol) for i in M):
            det = lambda _: det_perm(_)
        else:
            det = lambda _: det_minor(_)
    else:
        return M.inv()
    n = M.rows
    d = det(M)
    if d is S.Zero:
        raise ValueError("Matrix det == 0; not invertible.")
    ret = zeros(n)
    s1 = -1
    for i in range(n):
        s = s1 = -s1
        for j in range(n):
            di = det(M.minorMatrix(i, j))
            ret[j, i] = s*di/d
            s = -s
    return ret


# these are functions that have multiple inverse values per period
multi_inverses = {
    sin: lambda x: (asin(x), S.Pi - asin(x)),
    cos: lambda x: (acos(x), 2*S.Pi - acos(x)),
}


def _tsolve(eq, sym, **flags):
    """
    Helper for _solve that solves a transcendental equation with respect
    to the given symbol. Various equations containing powers and logarithms,
    can be solved.

    There is currently no guarantee that all solutions will be returned or
    that a real solution will be favored over a complex one.

    Either a list of potential solutions will be returned or None will be
    returned (in the case that no method was known to get a solution
    for the equation). All other errors (like the inability to cast an
    expression as a Poly) are unhandled.

    Examples
    ========

    >>> from sympy import log
    >>> from sympy.solvers.solvers import _tsolve as tsolve
    >>> from sympy.abc import x

    >>> tsolve(3**(2*x + 5) - 4, x)
    [-5/2 + log(2)/log(3), (-5*log(3)/2 + log(2) + I*pi)/log(3)]

    >>> tsolve(log(x) + 2*x, x)
    [LambertW(2)/2]

    """
    if 'tsolve_saw' not in flags:
        flags['tsolve_saw'] = []
    if eq in flags['tsolve_saw']:
        return None
    else:
        flags['tsolve_saw'].append(eq)

    rhs, lhs = _invert(eq, sym)

    if lhs == sym:
        return [rhs]
    try:
        if lhs.is_Add:
            # it's time to try factoring; powdenest is used
            # to try get powers in standard form for better factoring
            f = factor(powdenest(lhs - rhs))
            if f.is_Mul:
                return _solve(f, sym, **flags)
            if rhs:
                f = logcombine(lhs, force=flags.get('force', True))
                if f.count(log) != lhs.count(log):
                    if f.func is log:
                        return _solve(f.args[0] - exp(rhs), sym, **flags)
                    return _tsolve(f - rhs, sym)

        elif lhs.is_Pow:
            if lhs.exp.is_Integer:
                if lhs - rhs != eq:
                    return _solve(lhs - rhs, sym, **flags)
            elif sym not in lhs.exp.free_symbols:
                return _solve(lhs.base - rhs**(1/lhs.exp), sym, **flags)
            elif not rhs and sym in lhs.exp.free_symbols:
                # f(x)**g(x) only has solutions where f(x) == 0 and g(x) != 0 at
                # the same place
                sol_base = _solve(lhs.base, sym, **flags)
                if not sol_base:
                    return sol_base  # no solutions to remove so return now
                return list(ordered(set(sol_base) - set(
                    _solve(lhs.exp, sym, **flags))))
            elif (rhs is not S.Zero and
                        lhs.base.is_positive and
                        lhs.exp.is_real):
                return _solve(lhs.exp*log(lhs.base) - log(rhs), sym, **flags)
            elif lhs.base == 0 and rhs == 1:
                return _solve(lhs.exp, sym, **flags)

        elif lhs.is_Mul and rhs.is_positive:
            llhs = expand_log(log(lhs))
            if llhs.is_Add:
                return _solve(llhs - log(rhs), sym, **flags)

        elif lhs.is_Function and len(lhs.args) == 1 and lhs.func in multi_inverses:
            # sin(x) = 1/3 -> x - asin(1/3) & x - (pi - asin(1/3))
            soln = []
            for i in multi_inverses[lhs.func](rhs):
                soln.extend(_solve(lhs.args[0] - i, sym, **flags))
            return list(ordered(soln))

        rewrite = lhs.rewrite(exp)
        if rewrite != lhs:
            return _solve(rewrite - rhs, sym, **flags)
    except NotImplementedError:
        pass

    # maybe it is a lambert pattern
    if flags.pop('bivariate', True):
        # lambert forms may need some help being recognized, e.g. changing
        # 2**(3*x) + x**3*log(2)**3 + 3*x**2*log(2)**2 + 3*x*log(2) + 1
        # to 2**(3*x) + (x*log(2) + 1)**3
        g = _filtered_gens(eq.as_poly(), sym)
        up_or_log = set()
        for gi in g:
            if gi.func is exp or gi.func is log:
                up_or_log.add(gi)
            elif gi.is_Pow:
                gisimp = powdenest(expand_power_exp(gi))
                if gisimp.is_Pow and sym in gisimp.exp.free_symbols:
                    up_or_log.add(gi)
        down = g.difference(up_or_log)
        eq_down = expand_log(expand_power_exp(eq)).subs(
            dict(list(zip(up_or_log, [0]*len(up_or_log)))))
        eq = expand_power_exp(factor(eq_down, deep=True) + (eq - eq_down))
        rhs, lhs = _invert(eq, sym)
        if lhs.has(sym):
            try:
                poly = lhs.as_poly()
                g = _filtered_gens(poly, sym)
                return _solve_lambert(lhs - rhs, sym, g)
            except NotImplementedError:
                # maybe it's a convoluted function
                if len(g) == 2:
                    try:
                        gpu = bivariate_type(lhs - rhs, *g)
                        if gpu is None:
                            raise NotImplementedError
                        g, p, u = gpu
                        flags['bivariate'] = False
                        inversion = _tsolve(g - u, sym, **flags)
                        if inversion:
                            sol = _solve(p, u, **flags)
                            return list(ordered(set([i.subs(u, s)
                                for i in inversion for s in sol])))
                    except NotImplementedError:
                        pass
                else:
                    pass

    if flags.pop('force', True):
        flags['force'] = False
        pos, reps = posify(lhs - rhs)
        for u, s in reps.items():
            if s == sym:
                break
        else:
            u = sym
        if pos.has(u):
            try:
                soln = _solve(pos, u, **flags)
                return list(ordered([s.subs(reps) for s in soln]))
            except NotImplementedError:
                pass
        else:
            pass  # here for coverage

    return  # here for coverage


# TODO: option for calculating J numerically


def nsolve(*args, **kwargs):
    r"""
    Solve a nonlinear equation system numerically::

        nsolve(f, [args,] x0, modules=['mpmath'], **kwargs)

    f is a vector function of symbolic expressions representing the system.
    args are the variables. If there is only one variable, this argument can
    be omitted.
    x0 is a starting vector close to a solution.

    Use the modules keyword to specify which modules should be used to
    evaluate the function and the Jacobian matrix. Make sure to use a module
    that supports matrices. For more information on the syntax, please see the
    docstring of lambdify.

    Overdetermined systems are supported.

    >>> from sympy import Symbol, nsolve
    >>> import sympy
    >>> import mpmath
    >>> mpmath.mp.dps = 15
    >>> x1 = Symbol('x1')
    >>> x2 = Symbol('x2')
    >>> f1 = 3 * x1**2 - 2 * x2**2 - 1
    >>> f2 = x1**2 - 2 * x1 + x2**2 + 2 * x2 - 8
    >>> print(nsolve((f1, f2), (x1, x2), (-1, 1)))
    [-1.19287309935246]
    [ 1.27844411169911]

    For one-dimensional functions the syntax is simplified:

    >>> from sympy import sin, nsolve
    >>> from sympy.abc import x
    >>> nsolve(sin(x), x, 2)
    3.14159265358979
    >>> nsolve(sin(x), 2)
    3.14159265358979

    mpmath.findroot is used, you can find there more extensive documentation,
    especially concerning keyword parameters and available solvers. Note,
    however, that this routine works only with the numerator of the function
    in the one-dimensional case, and for very steep functions near the root
    this may lead to a failure in the verification of the root. In this case
    you should use the flag `verify=False` and independently verify the
    solution.

    >>> from sympy import cos, cosh
    >>> from sympy.abc import i
    >>> f = cos(x)*cosh(x) - 1
    >>> nsolve(f, 3.14*100)
    Traceback (most recent call last):
    ...
    ValueError: Could not find root within given tolerance. (1.39267e+230 > 2.1684e-19)
    >>> ans = nsolve(f, 3.14*100, verify=False); ans
    312.588469032184
    >>> f.subs(x, ans).n(2)
    2.1e+121
    >>> (f/f.diff(x)).subs(x, ans).n(2)
    7.4e-15

    One might safely skip the verification if bounds of the root are known
    and a bisection method is used:

    >>> bounds = lambda i: (3.14*i, 3.14*(i + 1))
    >>> nsolve(f, bounds(100), solver='bisect', verify=False)
    315.730061685774
    """
    # there are several other SymPy functions that use method= so
    # guard against that here
    if 'method' in kwargs:
        raise ValueError(filldedent('''
            Keyword "method" should not be used in this context.  When using
            some mpmath solvers directly, the keyword "method" is
            used, but when using nsolve (and findroot) the keyword to use is
            "solver".'''))

    # interpret arguments
    if len(args) == 3:
        f = args[0]
        fargs = args[1]
        x0 = args[2]
    elif len(args) == 2:
        f = args[0]
        fargs = None
        x0 = args[1]
    elif len(args) < 2:
        raise TypeError('nsolve expected at least 2 arguments, got %i'
                        % len(args))
    else:
        raise TypeError('nsolve expected at most 3 arguments, got %i'
                        % len(args))
    modules = kwargs.get('modules', ['mpmath'])
    if iterable(f):
        f = list(f)
        for i, fi in enumerate(f):
            if isinstance(fi, Equality):
                f[i] = fi.lhs - fi.rhs
        f = Matrix(f).T
    if not isinstance(f, Matrix):
        # assume it's a sympy expression
        if isinstance(f, Equality):
            f = f.lhs - f.rhs
        f = f.evalf()
        syms = f.free_symbols
        if fargs is None:
            fargs = syms.copy().pop()
        if not (len(syms) == 1 and (fargs in syms or fargs[0] in syms)):
            raise ValueError(filldedent('''
                expected a one-dimensional and numerical function'''))

        # the function is much better behaved if there is no denominator
        f = f.as_numer_denom()[0]

        f = lambdify(fargs, f, modules)
        return findroot(f, x0, **kwargs)

    if len(fargs) > f.cols:
        raise NotImplementedError(filldedent('''
            need at least as many equations as variables'''))
    verbose = kwargs.get('verbose', False)
    if verbose:
        print('f(x):')
        print(f)
    # derive Jacobian
    J = f.jacobian(fargs)
    if verbose:
        print('J(x):')
        print(J)
    # create functions
    f = lambdify(fargs, f.T, modules)
    J = lambdify(fargs, J, modules)
    # solve the system numerically
    x = findroot(f, x0, J=J, **kwargs)
    return x


def _invert(eq, *symbols, **kwargs):
    """Return tuple (i, d) where ``i`` is independent of ``symbols`` and ``d``
    contains symbols. ``i`` and ``d`` are obtained after recursively using
    algebraic inversion until an uninvertible ``d`` remains. If there are no
    free symbols then ``d`` will be zero. Some (but not necessarily all)
    solutions to the expression ``i - d`` will be related to the solutions of
    the original expression.

    Examples
    ========

    >>> from sympy.solvers.solvers import _invert as invert
    >>> from sympy import sqrt, cos
    >>> from sympy.abc import x, y
    >>> invert(x - 3)
    (3, x)
    >>> invert(3)
    (3, 0)
    >>> invert(2*cos(x) - 1)
    (1/2, cos(x))
    >>> invert(sqrt(x) - 3)
    (3, sqrt(x))
    >>> invert(sqrt(x) + y, x)
    (-y, sqrt(x))
    >>> invert(sqrt(x) + y, y)
    (-sqrt(x), y)
    >>> invert(sqrt(x) + y, x, y)
    (0, sqrt(x) + y)

    If there is more than one symbol in a power's base and the exponent
    is not an Integer, then the principal root will be used for the
    inversion:

    >>> invert(sqrt(x + y) - 2)
    (4, x + y)
    >>> invert(sqrt(x + y) - 2)
    (4, x + y)

    If the exponent is an integer, setting ``integer_power`` to True
    will force the principal root to be selected:

    >>> invert(x**2 - 4, integer_power=True)
    (2, x)

    """
    eq = sympify(eq)
    free = eq.free_symbols
    if not symbols:
        symbols = free
    if not free & set(symbols):
        return eq, S.Zero

    dointpow = bool(kwargs.get('integer_power', False))

    lhs = eq
    rhs = S.Zero
    while True:
        was = lhs
        while True:
            indep, dep = lhs.as_independent(*symbols)

            # dep + indep == rhs
            if lhs.is_Add:
                # this indicates we have done it all
                if indep is S.Zero:
                    break

                lhs = dep
                rhs -= indep

            # dep * indep == rhs
            else:
                # this indicates we have done it all
                if indep is S.One:
                    break

                lhs = dep
                rhs /= indep

        # collect like-terms in symbols
        if lhs.is_Add:
            terms = {}
            for a in lhs.args:
                i, d = a.as_independent(*symbols)
                terms.setdefault(d, []).append(i)
            if any(len(v) > 1 for v in terms.values()):
                args = []
                for d, i in terms.items():
                    if len(i) > 1:
                        args.append(Add(*i)*d)
                    else:
                        args.append(i[0]*d)
                lhs = Add(*args)

        # if it's a two-term Add with rhs = 0 and two powers we can get the
        # dependent terms together, e.g. 3*f(x) + 2*g(x) -> f(x)/g(x) = -2/3
        if lhs.is_Add and not rhs and len(lhs.args) == 2 and \
                not lhs.is_polynomial(*symbols):
            a, b = ordered(lhs.args)
            ai, ad = a.as_independent(*symbols)
            bi, bd = b.as_independent(*symbols)
            if any(_ispow(i) for i in (ad, bd)):
                a_base, a_exp = ad.as_base_exp()
                b_base, b_exp = bd.as_base_exp()
                if a_base == b_base:
                    # a = -b
                    lhs = powsimp(powdenest(ad/bd))
                    rhs = -bi/ai
                else:
                    rat = ad/bd
                    _lhs = powsimp(ad/bd)
                    if _lhs != rat:
                        lhs = _lhs
                        rhs = -bi/ai
            if ai*bi is S.NegativeOne:
                if all(
                        isinstance(i, Function) for i in (ad, bd)) and \
                        ad.func == bd.func and len(ad.args) == len(bd.args):
                    if len(ad.args) == 1:
                        lhs = ad.args[0] - bd.args[0]
                    else:
                        # should be able to solve
                        # f(x, y) == f(2, 3) -> x == 2
                        # f(x, x + y) == f(2, 3) -> x == 2 or x == 3 - y
                        raise NotImplementedError('equal function with more than 1 argument')

        elif lhs.is_Mul and any(_ispow(a) for a in lhs.args):
            lhs = powsimp(powdenest(lhs))

        if lhs.is_Function:
            if hasattr(lhs, 'inverse') and len(lhs.args) == 1:
                #                    -1
                # f(x) = g  ->  x = f  (g)
                #
                # /!\ inverse should not be defined if there are multiple values
                # for the function -- these are handled in _tsolve
                #
                rhs = lhs.inverse()(rhs)
                lhs = lhs.args[0]
            elif lhs.func is atan2:
                y, x = lhs.args
                lhs = 2*atan(y/(sqrt(x**2 + y**2) + x))
        if rhs and lhs.is_Pow and lhs.exp.is_Integer and lhs.exp < 0:
            lhs = 1/lhs
            rhs = 1/rhs

        # base**a = b -> base = b**(1/a) if
        #    a is an Integer and dointpow=True (this gives real branch of root)
        #    a is not an Integer and the equation is multivariate and the
        #      base has more than 1 symbol in it
        # The rationale for this is that right now the multi-system solvers
        # doesn't try to resolve generators to see, for example, if the whole
        # system is written in terms of sqrt(x + y) so it will just fail, so we
        # do that step here.
        if lhs.is_Pow and (
            lhs.exp.is_Integer and dointpow or not lhs.exp.is_Integer and
                len(symbols) > 1 and len(lhs.base.free_symbols & set(symbols)) > 1):
            rhs = rhs**(1/lhs.exp)
            lhs = lhs.base

        if lhs == was:
            break
    return rhs, lhs


def unrad(eq, *syms, **flags):
    """ Remove radicals with symbolic arguments and return (eq, cov),
    None or raise an error:

    None is returned if there are no radicals to remove.

    NotImplementedError is raised if there are radicals and they cannot be
    removed or if the relationship between the original symbols and the
    change of variable needed to rewrite the system as a polynomial cannot
    be solved.

    Otherwise the tuple, ``(eq, cov)``, is returned where::

        ``eq``, ``cov``
            ``eq`` is an equation without radicals (in the symbol(s) of
            interest) whose solutions are a superset of the solutions to the
            original expression. ``eq`` might be re-written in terms of a new
            variable; the relationship to the original variables is given by
            ``cov`` which is a list containing ``v`` and ``v**p - b`` where
            ``p`` is the power needed to clear the radical and ``b`` is the
            radical now expressed as a polynomial in the symbols of interest.
            For example, for sqrt(2 - x) the tuple would be
            ``(c, c**2 - 2 + x)``. The solutions of ``eq`` will contain
            solutions to the original equation (if there are any).

    ``syms``
        an iterable of symbols which, if provided, will limit the focus of
        radical removal: only radicals with one or more of the symbols of
        interest will be cleared. All free symbols are used if ``syms`` is not
        set.

    ``flags`` are used internally for communication during recursive calls.
    Two options are also recognized::

        ``take``, when defined, is interpreted as a single-argument function
        that returns True if a given Pow should be handled.

    Radicals can be removed from an expression if::

        *   all bases of the radicals are the same; a change of variables is
            done in this case.
        *   if all radicals appear in one term of the expression
        *   there are only 4 terms with sqrt() factors or there are less than
            four terms having sqrt() factors
        *   there are only two terms with radicals

    Examples
    ========

    >>> from sympy.solvers.solvers import unrad
    >>> from sympy.abc import x
    >>> from sympy import sqrt, Rational, root, real_roots, solve

    >>> unrad(sqrt(x)*x**Rational(1, 3) + 2)
    (x**5 - 64, [])
    >>> unrad(sqrt(x) + root(x + 1, 3))
    (x**3 - x**2 - 2*x - 1, [])
    >>> eq = sqrt(x) + root(x, 3) - 2
    >>> unrad(eq)
    (_p**3 + _p**2 - 2, [_p, _p**6 - x])

    """
    _inv_error = 'cannot get an analytical solution for the inversion'

    uflags = dict(check=False, simplify=False)

    def _cov(p, e):
        if cov:
            # XXX - uncovered
            oldp, olde = cov
            if Poly(e, p).degree(p) in (1, 2):
                cov[:] = [p, olde.subs(oldp, _solve(e, p, **uflags)[0])]
            else:
                raise NotImplementedError
        else:
            cov[:] = [p, e]

    def _canonical(eq, cov):
        if cov:
            # change symbol to vanilla so no solutions are eliminated
            p, e = cov
            rep = {p: Dummy(p.name)}
            eq = eq.xreplace(rep)
            cov = [p.xreplace(rep), e.xreplace(rep)]

        # remove constants and powers of factors since these don't change
        # the location of the root; XXX should factor or factor_terms be used?
        eq = factor_terms(_mexpand(eq.as_numer_denom()[0], recursive=True), clear=True)
        if eq.is_Mul:
            args = []
            for f in eq.args:
                if f.is_number:
                    continue
                if f.is_Pow and _take(f, True):
                    args.append(f.base)
                else:
                    args.append(f)
            eq = Mul(*args)  # leave as Mul for more efficient solving

        # make the sign canonical
        free = eq.free_symbols
        if len(free) == 1:
            if eq.coeff(free.pop()**degree(eq)).could_extract_minus_sign():
                eq = -eq
        elif eq.could_extract_minus_sign():
            eq = -eq

        return eq, cov

    def _Q(pow):
        # return leading Rational of denominator of Pow's exponent
        c = pow.as_base_exp()[1].as_coeff_Mul()[0]
        if not c.is_Rational:
            return S.One
        return c.q

    # define the _take method that will determine whether a term is of interest
    def _take(d, take_int_pow):
        # return True if coefficient of any factor's exponent's den is not 1
        for pow in Mul.make_args(d):
            if not (pow.is_Symbol or pow.is_Pow):
                continue
            b, e = pow.as_base_exp()
            if not b.has(*syms):
                continue
            if not take_int_pow and _Q(pow) == 1:
                continue
            free = pow.free_symbols
            if free.intersection(syms):
                return True
        return False
    _take = flags.setdefault('_take', _take)

    cov, nwas, rpt = [flags.setdefault(k, v) for k, v in
        sorted(dict(cov=[], n=None, rpt=0).items())]

    # preconditioning
    eq = powdenest(factor_terms(eq, radical=True, clear=True))
    eq, d = eq.as_numer_denom()
    eq = _mexpand(eq, recursive=True)
    if eq.is_number:
        return

    syms = set(syms) or eq.free_symbols
    poly = eq.as_poly()
    gens = [g for g in poly.gens if _take(g, True)]
    if not gens:
        return

    # check for trivial case
    # - already a polynomial in integer powers
    if all(_Q(g) == 1 for g in gens):
        return
    # - an exponent has a symbol of interest (don't handle)
    if any(g.as_base_exp()[1].has(*syms) for g in gens):
        return

    def _rads_bases_lcm(poly):
        # if all the bases are the same or all the radicals are in one
        # term, `lcm` will be the lcm of the denominators of the
        # exponents of the radicals
        lcm = 1
        rads = set()
        bases = set()
        for g in poly.gens:
            if not _take(g, False):
                continue
            q = _Q(g)
            if q != 1:
                rads.add(g)
                lcm = ilcm(lcm, q)
                bases.add(g.base)
        return rads, bases, lcm
    rads, bases, lcm = _rads_bases_lcm(poly)

    if not rads:
        return

    covsym = Dummy('p', nonnegative=True)

    # only keep in syms symbols that actually appear in radicals;
    # and update gens
    newsyms = set()
    for r in rads:
        newsyms.update(syms & r.free_symbols)
    if newsyms != syms:
        syms = newsyms
        gens = [g for g in gens if g.free_symbols & syms]

    # get terms together that have common generators
    drad = dict(list(zip(rads, list(range(len(rads))))))
    rterms = {(): []}
    args = Add.make_args(poly.as_expr())
    for t in args:
        if _take(t, False):
            common = set(t.as_poly().gens).intersection(rads)
            key = tuple(sorted([drad[i] for i in common]))
        else:
            key = ()
        rterms.setdefault(key, []).append(t)
    others = Add(*rterms.pop(()))
    rterms = [Add(*rterms[k]) for k in rterms.keys()]

    # the output will depend on the order terms are processed, so
    # make it canonical quickly
    rterms = list(reversed(list(ordered(rterms))))

    ok = False  # we don't have a solution yet
    depth = sqrt_depth(eq)

    if len(rterms) == 1 and not (rterms[0].is_Add and lcm > 2):
        eq = rterms[0]**lcm - ((-others)**lcm)
        ok = True
    else:
        if len(rterms) == 1 and rterms[0].is_Add:
            rterms = list(rterms[0].args)
        if len(bases) == 1:
            b = bases.pop()
            if len(syms) > 1:
                free = b.free_symbols
                x = set([g for g in gens if g.is_Symbol]) & free
                if not x:
                    x = free
                x = ordered(x)
            else:
                x = syms
            x = list(x)[0]
            try:
                inv = _solve(covsym**lcm - b, x, **uflags)
                if not inv:
                    raise NotImplementedError
                eq = poly.as_expr().subs(b, covsym**lcm).subs(x, inv[0])
                _cov(covsym, covsym**lcm - b)
                return _canonical(eq, cov)
            except NotImplementedError:
                pass
        else:
            # no longer consider integer powers as generators
            gens = [g for g in gens if _Q(g) != 1]

        if len(rterms) == 2:
            if not others:
                eq = rterms[0]**lcm - (-rterms[1])**lcm
                ok = True
            elif not log(lcm, 2).is_Integer:
                # the lcm-is-power-of-two case is handled below
                r0, r1 = rterms
                if flags.get('_reverse', False):
                    r1, r0 = r0, r1
                i0 = _rads0, _bases0, lcm0 = _rads_bases_lcm(r0.as_poly())
                i1 = _rads1, _bases1, lcm1 = _rads_bases_lcm(r1.as_poly())
                for reverse in range(2):
                    if reverse:
                        i0, i1 = i1, i0
                        r0, r1 = r1, r0
                    _rads1, _, lcm1 = i1
                    _rads1 = Mul(*_rads1)
                    t1 = _rads1**lcm1
                    c = covsym**lcm1 - t1
                    for x in syms:
                        try:
                            sol = _solve(c, x, **uflags)
                            if not sol:
                                raise NotImplementedError
                            neweq = r0.subs(x, sol[0]) + covsym*r1/_rads1 + \
                                others
                            tmp = unrad(neweq, covsym)
                            if tmp:
                                eq, newcov = tmp
                                if newcov:
                                    newp, newc = newcov
                                    _cov(newp, c.subs(covsym,
                                        _solve(newc, covsym, **uflags)[0]))
                                else:
                                    _cov(covsym, c)
                            else:
                                eq = neweq
                                _cov(covsym, c)
                            ok = True
                            break
                        except NotImplementedError:
                            if reverse:
                                raise NotImplementedError(
                                    'no successful change of variable found')
                            else:
                                pass
                    if ok:
                        break
        elif len(rterms) == 3:
            # two cube roots and another with order less than 5
            # (so an analytical solution can be found) or a base
            # that matches one of the cube root bases
            info = [_rads_bases_lcm(i.as_poly()) for i in rterms]
            RAD = 0
            BASES = 1
            LCM = 2
            if info[0][LCM] != 3:
                info.append(info.pop(0))
                rterms.append(rterms.pop(0))
            elif info[1][LCM] != 3:
                info.append(info.pop(1))
                rterms.append(rterms.pop(1))
            if info[0][LCM] == info[1][LCM] == 3:
                if info[1][BASES] != info[2][BASES]:
                    info[0], info[1] = info[1], info[0]
                    rterms[0], rterms[1] = rterms[1], rterms[0]
                if info[1][BASES] == info[2][BASES]:
                    eq = rterms[0]**3 + (rterms[1] + rterms[2] + others)**3
                    ok = True
                elif info[2][LCM] < 5:
                    # a*root(A, 3) + b*root(B, 3) + others = c
                    a, b, c, d, A, B = [Dummy(i) for i in 'abcdAB']
                    # zz represents the unraded expression into which the
                    # specifics for this case are substituted
                    zz = (c - d)*(A**3*a**9 + 3*A**2*B*a**6*b**3 -
                        3*A**2*a**6*c**3 + 9*A**2*a**6*c**2*d - 9*A**2*a**6*c*d**2 +
                        3*A**2*a**6*d**3 + 3*A*B**2*a**3*b**6 + 21*A*B*a**3*b**3*c**3 -
                        63*A*B*a**3*b**3*c**2*d + 63*A*B*a**3*b**3*c*d**2 -
                        21*A*B*a**3*b**3*d**3 + 3*A*a**3*c**6 - 18*A*a**3*c**5*d +
                        45*A*a**3*c**4*d**2 - 60*A*a**3*c**3*d**3 + 45*A*a**3*c**2*d**4 -
                        18*A*a**3*c*d**5 + 3*A*a**3*d**6 + B**3*b**9 - 3*B**2*b**6*c**3 +
                        9*B**2*b**6*c**2*d - 9*B**2*b**6*c*d**2 + 3*B**2*b**6*d**3 +
                        3*B*b**3*c**6 - 18*B*b**3*c**5*d + 45*B*b**3*c**4*d**2 -
                        60*B*b**3*c**3*d**3 + 45*B*b**3*c**2*d**4 - 18*B*b**3*c*d**5 +
                        3*B*b**3*d**6 - c**9 + 9*c**8*d - 36*c**7*d**2 + 84*c**6*d**3 -
                        126*c**5*d**4 + 126*c**4*d**5 - 84*c**3*d**6 + 36*c**2*d**7 -
                        9*c*d**8 + d**9)
                    def _t(i):
                        b = Mul(*info[i][RAD])
                        return cancel(rterms[i]/b), Mul(*info[i][BASES])
                    aa, AA = _t(0)
                    bb, BB = _t(1)
                    cc = -rterms[2]
                    dd = others
                    eq = zz.xreplace(dict(zip(
                        (a, A, b, B, c, d),
                        (aa, AA, bb, BB, cc, dd))))
                    ok = True
        # handle power-of-2 cases
        if not ok:
            if log(lcm, 2).is_Integer and (not others and
                    len(rterms) == 4 or len(rterms) < 4):
                def _norm2(a, b):
                    return a**2 + b**2 + 2*a*b

                if len(rterms) == 4:
                    # (r0+r1)**2 - (r2+r3)**2
                    r0, r1, r2, r3 = rterms
                    eq = _norm2(r0, r1) - _norm2(r2, r3)
                    ok = True
                elif len(rterms) == 3:
                    # (r1+r2)**2 - (r0+others)**2
                    r0, r1, r2 = rterms
                    eq = _norm2(r1, r2) - _norm2(r0, others)
                    ok = True
                elif len(rterms) == 2:
                    # r0**2 - (r1+others)**2
                    r0, r1 = rterms
                    eq = r0**2 - _norm2(r1, others)
                    ok = True

    new_depth = sqrt_depth(eq) if ok else depth
    rpt += 1  # XXX how many repeats with others unchanging is enough?
    if not ok or (
                nwas is not None and len(rterms) == nwas and
                new_depth is not None and new_depth == depth and
                rpt > 3):
        raise NotImplementedError('Cannot remove all radicals')

    flags.update(dict(cov=cov, n=len(rterms), rpt=rpt))
    neq = unrad(eq, *syms, **flags)
    if neq:
        eq, cov = neq
    eq, cov = _canonical(eq, cov)
    return eq, cov

from sympy.solvers.bivariate import (
    bivariate_type, _solve_lambert, _filtered_gens)
