Add files using upload-large-folder tool

ckpts/universal/global_step120/zero/11.mlp.dense_4h_to_h.weight/exp_avg_sq.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b52e0c49f8ba36772094d243ff57ea468caffec36365d7965894ef18c69c09e1
+size 33555627

ckpts/universal/global_step120/zero/11.mlp.dense_4h_to_h.weight/fp32.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a2066afedd5ba39e417fe0ca4bb0dd4eec14af5b673c6093a15f42bb12b85bae
+size 33555533

ckpts/universal/global_step120/zero/14.input_layernorm.weight/exp_avg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3ffde4b05fd5f228b10401cc470fe1826e8c65dd62bb5db16c42cf5b1d8c6615
+size 9372

ckpts/universal/global_step120/zero/14.input_layernorm.weight/exp_avg_sq.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db31c7c8a727f102713d18886e43d4a6e9cb28ee5e6235788ad4025d077145fb
+size 9387

ckpts/universal/global_step120/zero/14.input_layernorm.weight/fp32.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:469ca80dcb5385265a7cdde3b0ff4f4eed282fd515c929dae464e5d7f0409334
+size 9293

ckpts/universal/global_step120/zero/20.post_attention_layernorm.weight/exp_avg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce67db68e1f415307ac462bf595a1c592cbfbba65c157a7155c25691e1e0e4c0
+size 9372

ckpts/universal/global_step120/zero/20.post_attention_layernorm.weight/exp_avg_sq.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:346bbe948843055fa9b2a5bcd21c2eec7f116ca48836e98badf19ab8687b3b39
+size 9387

ckpts/universal/global_step120/zero/20.post_attention_layernorm.weight/fp32.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e60f3e5e5b455ced5a141a0d56ab43bd10b814894897cb46b737830878dc5b35
+size 9293

ckpts/universal/global_step120/zero/24.mlp.dense_4h_to_h.weight/exp_avg_sq.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:116b57da1f77bc8a2490e1177fbaef43913dead5bdefa000fe87537aece53523
+size 33555627

ckpts/universal/global_step120/zero/24.mlp.dense_4h_to_h.weight/fp32.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd98c8354e01844182b9e134cb8f61c2ff74ab55ddcee3c04fd8ebf4c7d22a5d
+size 33555533

ckpts/universal/global_step120/zero/5.mlp.dense_4h_to_h.weight/exp_avg_sq.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3ca85bb2dc05ab5bdd9e72a9f3f6f4514fcd4d7f461185d659d09ae0e80e56f2
+size 33555627

venv/lib/python3.10/site-packages/sympy/core/add.py

@@ -0,0 +1,1287 @@
+from typing import Tuple as tTuple
+from collections import defaultdict
+from functools import cmp_to_key, reduce
+from operator import attrgetter
+from .basic import Basic
+from .parameters import global_parameters
+from .logic import _fuzzy_group, fuzzy_or, fuzzy_not
+from .singleton import S
+from .operations import AssocOp, AssocOpDispatcher
+from .cache import cacheit
+from .numbers import ilcm, igcd, equal_valued
+from .expr import Expr
+from .kind import UndefinedKind
+from sympy.utilities.iterables import is_sequence, sift
+
+# Key for sorting commutative args in canonical order
+_args_sortkey = cmp_to_key(Basic.compare)
+
+
+def _could_extract_minus_sign(expr):
+    # assume expr is Add-like
+    # We choose the one with less arguments with minus signs
+    negative_args = sum(1 for i in expr.args
+        if i.could_extract_minus_sign())
+    positive_args = len(expr.args) - negative_args
+    if positive_args > negative_args:
+        return False
+    elif positive_args < negative_args:
+        return True
+    # choose based on .sort_key() to prefer
+    # x - 1 instead of 1 - x and
+    # 3 - sqrt(2) instead of -3 + sqrt(2)
+    return bool(expr.sort_key() < (-expr).sort_key())
+
+
+def _addsort(args):
+    # in-place sorting of args
+    args.sort(key=_args_sortkey)
+
+
+def _unevaluated_Add(*args):
+    """Return a well-formed unevaluated Add: Numbers are collected and
+    put in slot 0 and args are sorted. Use this when args have changed
+    but you still want to return an unevaluated Add.
+
+    Examples
+    ========
+
+    >>> from sympy.core.add import _unevaluated_Add as uAdd
+    >>> from sympy import S, Add
+    >>> from sympy.abc import x, y
+    >>> a = uAdd(*[S(1.0), x, S(2)])
+    >>> a.args[0]
+    3.00000000000000
+    >>> a.args[1]
+    x
+
+    Beyond the Number being in slot 0, there is no other assurance of
+    order for the arguments since they are hash sorted. So, for testing
+    purposes, output produced by this in some other function can only
+    be tested against the output of this function or as one of several
+    options:
+
+    >>> opts = (Add(x, y, evaluate=False), Add(y, x, evaluate=False))
+    >>> a = uAdd(x, y)
+    >>> assert a in opts and a == uAdd(x, y)
+    >>> uAdd(x + 1, x + 2)
+    x + x + 3
+    """
+    args = list(args)
+    newargs = []
+    co = S.Zero
+    while args:
+        a = args.pop()
+        if a.is_Add:
+            # this will keep nesting from building up
+            # so that x + (x + 1) -> x + x + 1 (3 args)
+            args.extend(a.args)
+        elif a.is_Number:
+            co += a
+        else:
+            newargs.append(a)
+    _addsort(newargs)
+    if co:
+        newargs.insert(0, co)
+    return Add._from_args(newargs)
+
+
+class Add(Expr, AssocOp):
+    """
+    Expression representing addition operation for algebraic group.
+
+    .. deprecated:: 1.7
+
+       Using arguments that aren't subclasses of :class:`~.Expr` in core
+       operators (:class:`~.Mul`, :class:`~.Add`, and :class:`~.Pow`) is
+       deprecated. See :ref:`non-expr-args-deprecated` for details.
+
+    Every argument of ``Add()`` must be ``Expr``. Infix operator ``+``
+    on most scalar objects in SymPy calls this class.
+
+    Another use of ``Add()`` is to represent the structure of abstract
+    addition so that its arguments can be substituted to return different
+    class. Refer to examples section for this.
+
+    ``Add()`` evaluates the argument unless ``evaluate=False`` is passed.
+    The evaluation logic includes:
+
+    1. Flattening
+        ``Add(x, Add(y, z))`` -> ``Add(x, y, z)``
+
+    2. Identity removing
+        ``Add(x, 0, y)`` -> ``Add(x, y)``
+
+    3. Coefficient collecting by ``.as_coeff_Mul()``
+        ``Add(x, 2*x)`` -> ``Mul(3, x)``
+
+    4. Term sorting
+        ``Add(y, x, 2)`` -> ``Add(2, x, y)``
+
+    If no argument is passed, identity element 0 is returned. If single
+    element is passed, that element is returned.
+
+    Note that ``Add(*args)`` is more efficient than ``sum(args)`` because
+    it flattens the arguments. ``sum(a, b, c, ...)`` recursively adds the
+    arguments as ``a + (b + (c + ...))``, which has quadratic complexity.
+    On the other hand, ``Add(a, b, c, d)`` does not assume nested
+    structure, making the complexity linear.
+
+    Since addition is group operation, every argument should have the
+    same :obj:`sympy.core.kind.Kind()`.
+
+    Examples
+    ========
+
+    >>> from sympy import Add, I
+    >>> from sympy.abc import x, y
+    >>> Add(x, 1)
+    x + 1
+    >>> Add(x, x)
+    2*x
+    >>> 2*x**2 + 3*x + I*y + 2*y + 2*x/5 + 1.0*y + 1
+    2*x**2 + 17*x/5 + 3.0*y + I*y + 1
+
+    If ``evaluate=False`` is passed, result is not evaluated.
+
+    >>> Add(1, 2, evaluate=False)
+    1 + 2
+    >>> Add(x, x, evaluate=False)
+    x + x
+
+    ``Add()`` also represents the general structure of addition operation.
+
+    >>> from sympy import MatrixSymbol
+    >>> A,B = MatrixSymbol('A', 2,2), MatrixSymbol('B', 2,2)
+    >>> expr = Add(x,y).subs({x:A, y:B})
+    >>> expr
+    A + B
+    >>> type(expr)
+    <class 'sympy.matrices.expressions.matadd.MatAdd'>
+
+    Note that the printers do not display in args order.
+
+    >>> Add(x, 1)
+    x + 1
+    >>> Add(x, 1).args
+    (1, x)
+
+    See Also
+    ========
+
+    MatAdd
+
+    """
+
+    __slots__ = ()
+
+    args: tTuple[Expr, ...]
+
+    is_Add = True
+
+    _args_type = Expr
+
+    @classmethod
+    def flatten(cls, seq):
+        """
+        Takes the sequence "seq" of nested Adds and returns a flatten list.
+
+        Returns: (commutative_part, noncommutative_part, order_symbols)
+
+        Applies associativity, all terms are commutable with respect to
+        addition.
+
+        NB: the removal of 0 is already handled by AssocOp.__new__
+
+        See Also
+        ========
+
+        sympy.core.mul.Mul.flatten
+
+        """
+        from sympy.calculus.accumulationbounds import AccumBounds
+        from sympy.matrices.expressions import MatrixExpr
+        from sympy.tensor.tensor import TensExpr
+        rv = None
+        if len(seq) == 2:
+            a, b = seq
+            if b.is_Rational:
+                a, b = b, a
+            if a.is_Rational:
+                if b.is_Mul:
+                    rv = [a, b], [], None
+            if rv:
+                if all(s.is_commutative for s in rv[0]):
+                    return rv
+                return [], rv[0], None
+
+        terms = {}      # term -> coeff
+                        # e.g. x**2 -> 5   for ... + 5*x**2 + ...
+
+        coeff = S.Zero  # coefficient (Number or zoo) to always be in slot 0
+                        # e.g. 3 + ...
+        order_factors = []
+
+        extra = []
+
+        for o in seq:
+
+            # O(x)
+            if o.is_Order:
+                if o.expr.is_zero:
+                    continue
+                for o1 in order_factors:
+                    if o1.contains(o):
+                        o = None
+                        break
+                if o is None:
+                    continue
+                order_factors = [o] + [
+                    o1 for o1 in order_factors if not o.contains(o1)]
+                continue
+
+            # 3 or NaN
+            elif o.is_Number:
+                if (o is S.NaN or coeff is S.ComplexInfinity and
+                        o.is_finite is False) and not extra:
+                    # we know for sure the result will be nan
+                    return [S.NaN], [], None
+                if coeff.is_Number or isinstance(coeff, AccumBounds):
+                    coeff += o
+                    if coeff is S.NaN and not extra:
+                        # we know for sure the result will be nan
+                        return [S.NaN], [], None
+                continue
+
+            elif isinstance(o, AccumBounds):
+                coeff = o.__add__(coeff)
+                continue
+
+            elif isinstance(o, MatrixExpr):
+                # can't add 0 to Matrix so make sure coeff is not 0
+                extra.append(o)
+                continue
+
+            elif isinstance(o, TensExpr):
+                coeff = o.__add__(coeff) if coeff else o
+                continue
+
+            elif o is S.ComplexInfinity:
+                if coeff.is_finite is False and not extra:
+                    # we know for sure the result will be nan
+                    return [S.NaN], [], None
+                coeff = S.ComplexInfinity
+                continue
+
+            # Add([...])
+            elif o.is_Add:
+                # NB: here we assume Add is always commutative
+                seq.extend(o.args)  # TODO zerocopy?
+                continue
+
+            # Mul([...])
+            elif o.is_Mul:
+                c, s = o.as_coeff_Mul()
+
+            # check for unevaluated Pow, e.g. 2**3 or 2**(-1/2)
+            elif o.is_Pow:
+                b, e = o.as_base_exp()
+                if b.is_Number and (e.is_Integer or
+                                   (e.is_Rational and e.is_negative)):
+                    seq.append(b**e)
+                    continue
+                c, s = S.One, o
+
+            else:
+                # everything else
+                c = S.One
+                s = o
+
+            # now we have:
+            # o = c*s, where
+            #
+            # c is a Number
+            # s is an expression with number factor extracted
+            # let's collect terms with the same s, so e.g.
+            # 2*x**2 + 3*x**2  ->  5*x**2
+            if s in terms:
+                terms[s] += c
+                if terms[s] is S.NaN and not extra:
+                    # we know for sure the result will be nan
+                    return [S.NaN], [], None
+            else:
+                terms[s] = c
+
+        # now let's construct new args:
+        # [2*x**2, x**3, 7*x**4, pi, ...]
+        newseq = []
+        noncommutative = False
+        for s, c in terms.items():
+            # 0*s
+            if c.is_zero:
+                continue
+            # 1*s
+            elif c is S.One:
+                newseq.append(s)
+            # c*s
+            else:
+                if s.is_Mul:
+                    # Mul, already keeps its arguments in perfect order.
+                    # so we can simply put c in slot0 and go the fast way.
+                    cs = s._new_rawargs(*((c,) + s.args))
+                    newseq.append(cs)
+                elif s.is_Add:
+                    # we just re-create the unevaluated Mul
+                    newseq.append(Mul(c, s, evaluate=False))
+                else:
+                    # alternatively we have to call all Mul's machinery (slow)
+                    newseq.append(Mul(c, s))
+
+            noncommutative = noncommutative or not s.is_commutative
+
+        # oo, -oo
+        if coeff is S.Infinity:
+            newseq = [f for f in newseq if not (f.is_extended_nonnegative or f.is_real)]
+
+        elif coeff is S.NegativeInfinity:
+            newseq = [f for f in newseq if not (f.is_extended_nonpositive or f.is_real)]
+
+        if coeff is S.ComplexInfinity:
+            # zoo might be
+            #   infinite_real + finite_im
+            #   finite_real + infinite_im
+            #   infinite_real + infinite_im
+            # addition of a finite real or imaginary number won't be able to
+            # change the zoo nature; adding an infinite qualtity would result
+            # in a NaN condition if it had sign opposite of the infinite
+            # portion of zoo, e.g., infinite_real - infinite_real.
+            newseq = [c for c in newseq if not (c.is_finite and
+                                                c.is_extended_real is not None)]
+
+        # process O(x)
+        if order_factors:
+            newseq2 = []
+            for t in newseq:
+                for o in order_factors:
+                    # x + O(x) -> O(x)
+                    if o.contains(t):
+                        t = None
+                        break
+                # x + O(x**2) -> x + O(x**2)
+                if t is not None:
+                    newseq2.append(t)
+            newseq = newseq2 + order_factors
+            # 1 + O(1) -> O(1)
+            for o in order_factors:
+                if o.contains(coeff):
+                    coeff = S.Zero
+                    break
+
+        # order args canonically
+        _addsort(newseq)
+
+        # current code expects coeff to be first
+        if coeff is not S.Zero:
+            newseq.insert(0, coeff)
+
+        if extra:
+            newseq += extra
+            noncommutative = True
+
+        # we are done
+        if noncommutative:
+            return [], newseq, None
+        else:
+            return newseq, [], None
+
+    @classmethod
+    def class_key(cls):
+        return 3, 1, cls.__name__
+
+    @property
+    def kind(self):
+        k = attrgetter('kind')
+        kinds = map(k, self.args)
+        kinds = frozenset(kinds)
+        if len(kinds) != 1:
+            # Since addition is group operator, kind must be same.
+            # We know that this is unexpected signature, so return this.
+            result = UndefinedKind
+        else:
+            result, = kinds
+        return result
+
+    def could_extract_minus_sign(self):
+        return _could_extract_minus_sign(self)
+
+    @cacheit
+    def as_coeff_add(self, *deps):
+        """
+        Returns a tuple (coeff, args) where self is treated as an Add and coeff
+        is the Number term and args is a tuple of all other terms.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x
+        >>> (7 + 3*x).as_coeff_add()
+        (7, (3*x,))
+        >>> (7*x).as_coeff_add()
+        (0, (7*x,))
+        """
+        if deps:
+            l1, l2 = sift(self.args, lambda x: x.has_free(*deps), binary=True)
+            return self._new_rawargs(*l2), tuple(l1)
+        coeff, notrat = self.args[0].as_coeff_add()
+        if coeff is not S.Zero:
+            return coeff, notrat + self.args[1:]
+        return S.Zero, self.args
+
+    def as_coeff_Add(self, rational=False, deps=None):
+        """
+        Efficiently extract the coefficient of a summation.
+        """
+        coeff, args = self.args[0], self.args[1:]
+
+        if coeff.is_Number and not rational or coeff.is_Rational:
+            return coeff, self._new_rawargs(*args)
+        return S.Zero, self
+
+    # Note, we intentionally do not implement Add.as_coeff_mul().  Rather, we
+    # let Expr.as_coeff_mul() just always return (S.One, self) for an Add.  See
+    # issue 5524.
+
+    def _eval_power(self, e):
+        from .evalf import pure_complex
+        from .relational import is_eq
+        if len(self.args) == 2 and any(_.is_infinite for _ in self.args):
+            if e.is_zero is False and is_eq(e, S.One) is False:
+                # looking for literal a + I*b
+                a, b = self.args
+                if a.coeff(S.ImaginaryUnit):
+                    a, b = b, a
+                ico = b.coeff(S.ImaginaryUnit)
+                if ico and ico.is_extended_real and a.is_extended_real:
+                    if e.is_extended_negative:
+                        return S.Zero
+                    if e.is_extended_positive:
+                        return S.ComplexInfinity
+            return
+        if e.is_Rational and self.is_number:
+            ri = pure_complex(self)
+            if ri:
+                r, i = ri
+                if e.q == 2:
+                    from sympy.functions.elementary.miscellaneous import sqrt
+                    D = sqrt(r**2 + i**2)
+                    if D.is_Rational:
+                        from .exprtools import factor_terms
+                        from sympy.functions.elementary.complexes import sign
+                        from .function import expand_multinomial
+                        # (r, i, D) is a Pythagorean triple
+                        root = sqrt(factor_terms((D - r)/2))**e.p
+                        return root*expand_multinomial((
+                            # principle value
+                            (D + r)/abs(i) + sign(i)*S.ImaginaryUnit)**e.p)
+                elif e == -1:
+                    return _unevaluated_Mul(
+                        r - i*S.ImaginaryUnit,
+                        1/(r**2 + i**2))
+        elif e.is_Number and abs(e) != 1:
+            # handle the Float case: (2.0 + 4*x)**e -> 4**e*(0.5 + x)**e
+            c, m = zip(*[i.as_coeff_Mul() for i in self.args])
+            if any(i.is_Float for i in c):  # XXX should this always be done?
+                big = -1
+                for i in c:
+                    if abs(i) >= big:
+                        big = abs(i)
+                if big > 0 and not equal_valued(big, 1):
+                    from sympy.functions.elementary.complexes import sign
+                    bigs = (big, -big)
+                    c = [sign(i) if i in bigs else i/big for i in c]
+                    addpow = Add(*[c*m for c, m in zip(c, m)])**e
+                    return big**e*addpow
+
+    @cacheit
+    def _eval_derivative(self, s):
+        return self.func(*[a.diff(s) for a in self.args])
+
+    def _eval_nseries(self, x, n, logx, cdir=0):
+        terms = [t.nseries(x, n=n, logx=logx, cdir=cdir) for t in self.args]
+        return self.func(*terms)
+
+    def _matches_simple(self, expr, repl_dict):
+        # handle (w+3).matches('x+5') -> {w: x+2}
+        coeff, terms = self.as_coeff_add()
+        if len(terms) == 1:
+            return terms[0].matches(expr - coeff, repl_dict)
+        return
+
+    def matches(self, expr, repl_dict=None, old=False):
+        return self._matches_commutative(expr, repl_dict, old)
+
+    @staticmethod
+    def _combine_inverse(lhs, rhs):
+        """
+        Returns lhs - rhs, but treats oo like a symbol so oo - oo
+        returns 0, instead of a nan.
+        """
+        from sympy.simplify.simplify import signsimp
+        inf = (S.Infinity, S.NegativeInfinity)
+        if lhs.has(*inf) or rhs.has(*inf):
+            from .symbol import Dummy
+            oo = Dummy('oo')
+            reps = {
+                S.Infinity: oo,
+                S.NegativeInfinity: -oo}
+            ireps = {v: k for k, v in reps.items()}
+            eq = lhs.xreplace(reps) - rhs.xreplace(reps)
+            if eq.has(oo):
+                eq = eq.replace(
+                    lambda x: x.is_Pow and x.base is oo,
+                    lambda x: x.base)
+            rv = eq.xreplace(ireps)
+        else:
+            rv = lhs - rhs
+        srv = signsimp(rv)
+        return srv if srv.is_Number else rv
+
+    @cacheit
+    def as_two_terms(self):
+        """Return head and tail of self.
+
+        This is the most efficient way to get the head and tail of an
+        expression.
+
+        - if you want only the head, use self.args[0];
+        - if you want to process the arguments of the tail then use
+          self.as_coef_add() which gives the head and a tuple containing
+          the arguments of the tail when treated as an Add.
+        - if you want the coefficient when self is treated as a Mul
+          then use self.as_coeff_mul()[0]
+
+        >>> from sympy.abc import x, y
+        >>> (3*x - 2*y + 5).as_two_terms()
+        (5, 3*x - 2*y)
+        """
+        return self.args[0], self._new_rawargs(*self.args[1:])
+
+    def as_numer_denom(self):
+        """
+        Decomposes an expression to its numerator part and its
+        denominator part.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x, y, z
+        >>> (x*y/z).as_numer_denom()
+        (x*y, z)
+        >>> (x*(y + 1)/y**7).as_numer_denom()
+        (x*(y + 1), y**7)
+
+        See Also
+        ========
+
+        sympy.core.expr.Expr.as_numer_denom
+        """
+        # clear rational denominator
+        content, expr = self.primitive()
+        if not isinstance(expr, Add):
+            return Mul(content, expr, evaluate=False).as_numer_denom()
+        ncon, dcon = content.as_numer_denom()
+
+        # collect numerators and denominators of the terms
+        nd = defaultdict(list)
+        for f in expr.args:
+            ni, di = f.as_numer_denom()
+            nd[di].append(ni)
+
+        # check for quick exit
+        if len(nd) == 1:
+            d, n = nd.popitem()
+            return self.func(
+                *[_keep_coeff(ncon, ni) for ni in n]), _keep_coeff(dcon, d)
+
+        # sum up the terms having a common denominator
+        for d, n in nd.items():
+            if len(n) == 1:
+                nd[d] = n[0]
+            else:
+                nd[d] = self.func(*n)
+
+        # assemble single numerator and denominator
+        denoms, numers = [list(i) for i in zip(*iter(nd.items()))]
+        n, d = self.func(*[Mul(*(denoms[:i] + [numers[i]] + denoms[i + 1:]))
+                   for i in range(len(numers))]), Mul(*denoms)
+
+        return _keep_coeff(ncon, n), _keep_coeff(dcon, d)
+
+    def _eval_is_polynomial(self, syms):
+        return all(term._eval_is_polynomial(syms) for term in self.args)
+
+    def _eval_is_rational_function(self, syms):
+        return all(term._eval_is_rational_function(syms) for term in self.args)
+
+    def _eval_is_meromorphic(self, x, a):
+        return _fuzzy_group((arg.is_meromorphic(x, a) for arg in self.args),
+                            quick_exit=True)
+
+    def _eval_is_algebraic_expr(self, syms):
+        return all(term._eval_is_algebraic_expr(syms) for term in self.args)
+
+    # assumption methods
+    _eval_is_real = lambda self: _fuzzy_group(
+        (a.is_real for a in self.args), quick_exit=True)
+    _eval_is_extended_real = lambda self: _fuzzy_group(
+        (a.is_extended_real for a in self.args), quick_exit=True)
+    _eval_is_complex = lambda self: _fuzzy_group(
+        (a.is_complex for a in self.args), quick_exit=True)
+    _eval_is_antihermitian = lambda self: _fuzzy_group(
+        (a.is_antihermitian for a in self.args), quick_exit=True)
+    _eval_is_finite = lambda self: _fuzzy_group(
+        (a.is_finite for a in self.args), quick_exit=True)
+    _eval_is_hermitian = lambda self: _fuzzy_group(
+        (a.is_hermitian for a in self.args), quick_exit=True)
+    _eval_is_integer = lambda self: _fuzzy_group(
+        (a.is_integer for a in self.args), quick_exit=True)
+    _eval_is_rational = lambda self: _fuzzy_group(
+        (a.is_rational for a in self.args), quick_exit=True)
+    _eval_is_algebraic = lambda self: _fuzzy_group(
+        (a.is_algebraic for a in self.args), quick_exit=True)
+    _eval_is_commutative = lambda self: _fuzzy_group(
+        a.is_commutative for a in self.args)
+
+    def _eval_is_infinite(self):
+        sawinf = False
+        for a in self.args:
+            ainf = a.is_infinite
+            if ainf is None:
+                return None
+            elif ainf is True:
+                # infinite+infinite might not be infinite
+                if sawinf is True:
+                    return None
+                sawinf = True
+        return sawinf
+
+    def _eval_is_imaginary(self):
+        nz = []
+        im_I = []
+        for a in self.args:
+            if a.is_extended_real:
+                if a.is_zero:
+                    pass
+                elif a.is_zero is False:
+                    nz.append(a)
+                else:
+                    return
+            elif a.is_imaginary:
+                im_I.append(a*S.ImaginaryUnit)
+            elif a.is_Mul and S.ImaginaryUnit in a.args:
+                coeff, ai = a.as_coeff_mul(S.ImaginaryUnit)
+                if ai == (S.ImaginaryUnit,) and coeff.is_extended_real:
+                    im_I.append(-coeff)
+                else:
+                    return
+            else:
+                return
+        b = self.func(*nz)
+        if b != self:
+            if b.is_zero:
+                return fuzzy_not(self.func(*im_I).is_zero)
+            elif b.is_zero is False:
+                return False
+
+    def _eval_is_zero(self):
+        if self.is_commutative is False:
+            # issue 10528: there is no way to know if a nc symbol
+            # is zero or not
+            return
+        nz = []
+        z = 0
+        im_or_z = False
+        im = 0
+        for a in self.args:
+            if a.is_extended_real:
+                if a.is_zero:
+                    z += 1
+                elif a.is_zero is False:
+                    nz.append(a)
+                else:
+                    return
+            elif a.is_imaginary:
+                im += 1
+            elif a.is_Mul and S.ImaginaryUnit in a.args:
+                coeff, ai = a.as_coeff_mul(S.ImaginaryUnit)
+                if ai == (S.ImaginaryUnit,) and coeff.is_extended_real:
+                    im_or_z = True
+                else:
+                    return
+            else:
+                return
+        if z == len(self.args):
+            return True
+        if len(nz) in [0, len(self.args)]:
+            return None
+        b = self.func(*nz)
+        if b.is_zero:
+            if not im_or_z:
+                if im == 0:
+                    return True
+                elif im == 1:
+                    return False
+        if b.is_zero is False:
+            return False
+
+    def _eval_is_odd(self):
+        l = [f for f in self.args if not (f.is_even is True)]
+        if not l:
+            return False
+        if l[0].is_odd:
+            return self._new_rawargs(*l[1:]).is_even
+
+    def _eval_is_irrational(self):
+        for t in self.args:
+            a = t.is_irrational
+            if a:
+                others = list(self.args)
+                others.remove(t)
+                if all(x.is_rational is True for x in others):
+                    return True
+                return None
+            if a is None:
+                return
+        return False
+
+    def _all_nonneg_or_nonppos(self):
+        nn = np = 0
+        for a in self.args:
+            if a.is_nonnegative:
+                if np:
+                    return False
+                nn = 1
+            elif a.is_nonpositive:
+                if nn:
+                    return False
+                np = 1
+            else:
+                break
+        else:
+            return True
+
+    def _eval_is_extended_positive(self):
+        if self.is_number:
+            return super()._eval_is_extended_positive()
+        c, a = self.as_coeff_Add()
+        if not c.is_zero:
+            from .exprtools import _monotonic_sign
+            v = _monotonic_sign(a)
+            if v is not None:
+                s = v + c
+                if s != self and s.is_extended_positive and a.is_extended_nonnegative:
+                    return True
+                if len(self.free_symbols) == 1:
+                    v = _monotonic_sign(self)
+                    if v is not None and v != self and v.is_extended_positive:
+                        return True
+        pos = nonneg = nonpos = unknown_sign = False
+        saw_INF = set()
+        args = [a for a in self.args if not a.is_zero]
+        if not args:
+            return False
+        for a in args:
+            ispos = a.is_extended_positive
+            infinite = a.is_infinite
+            if infinite:
+                saw_INF.add(fuzzy_or((ispos, a.is_extended_nonnegative)))
+                if True in saw_INF and False in saw_INF:
+                    return
+            if ispos:
+                pos = True
+                continue
+            elif a.is_extended_nonnegative:
+                nonneg = True
+                continue
+            elif a.is_extended_nonpositive:
+                nonpos = True
+                continue
+
+            if infinite is None:
+                return
+            unknown_sign = True
+
+        if saw_INF:
+            if len(saw_INF) > 1:
+                return
+            return saw_INF.pop()
+        elif unknown_sign:
+            return
+        elif not nonpos and not nonneg and pos:
+            return True
+        elif not nonpos and pos:
+            return True
+        elif not pos and not nonneg:
+            return False
+
+    def _eval_is_extended_nonnegative(self):
+        if not self.is_number:
+            c, a = self.as_coeff_Add()
+            if not c.is_zero and a.is_extended_nonnegative:
+                from .exprtools import _monotonic_sign
+                v = _monotonic_sign(a)
+                if v is not None:
+                    s = v + c
+                    if s != self and s.is_extended_nonnegative:
+                        return True
+                    if len(self.free_symbols) == 1:
+                        v = _monotonic_sign(self)
+                        if v is not None and v != self and v.is_extended_nonnegative:
+                            return True
+
+    def _eval_is_extended_nonpositive(self):
+        if not self.is_number:
+            c, a = self.as_coeff_Add()
+            if not c.is_zero and a.is_extended_nonpositive:
+                from .exprtools import _monotonic_sign
+                v = _monotonic_sign(a)
+                if v is not None:
+                    s = v + c
+                    if s != self and s.is_extended_nonpositive:
+                        return True
+                    if len(self.free_symbols) == 1:
+                        v = _monotonic_sign(self)
+                        if v is not None and v != self and v.is_extended_nonpositive:
+                            return True
+
+    def _eval_is_extended_negative(self):
+        if self.is_number:
+            return super()._eval_is_extended_negative()
+        c, a = self.as_coeff_Add()
+        if not c.is_zero:
+            from .exprtools import _monotonic_sign
+            v = _monotonic_sign(a)
+            if v is not None:
+                s = v + c
+                if s != self and s.is_extended_negative and a.is_extended_nonpositive:
+                    return True
+                if len(self.free_symbols) == 1:
+                    v = _monotonic_sign(self)
+                    if v is not None and v != self and v.is_extended_negative:
+                        return True
+        neg = nonpos = nonneg = unknown_sign = False
+        saw_INF = set()
+        args = [a for a in self.args if not a.is_zero]
+        if not args:
+            return False
+        for a in args:
+            isneg = a.is_extended_negative
+            infinite = a.is_infinite
+            if infinite:
+                saw_INF.add(fuzzy_or((isneg, a.is_extended_nonpositive)))
+                if True in saw_INF and False in saw_INF:
+                    return
+            if isneg:
+                neg = True
+                continue
+            elif a.is_extended_nonpositive:
+                nonpos = True
+                continue
+            elif a.is_extended_nonnegative:
+                nonneg = True
+                continue
+
+            if infinite is None:
+                return
+            unknown_sign = True
+
+        if saw_INF:
+            if len(saw_INF) > 1:
+                return
+            return saw_INF.pop()
+        elif unknown_sign:
+            return
+        elif not nonneg and not nonpos and neg:
+            return True
+        elif not nonneg and neg:
+            return True
+        elif not neg and not nonpos:
+            return False
+
+    def _eval_subs(self, old, new):
+        if not old.is_Add:
+            if old is S.Infinity and -old in self.args:
+                # foo - oo is foo + (-oo) internally
+                return self.xreplace({-old: -new})
+            return None
+
+        coeff_self, terms_self = self.as_coeff_Add()
+        coeff_old, terms_old = old.as_coeff_Add()
+
+        if coeff_self.is_Rational and coeff_old.is_Rational:
+            if terms_self == terms_old:   # (2 + a).subs( 3 + a, y) -> -1 + y
+                return self.func(new, coeff_self, -coeff_old)
+            if terms_self == -terms_old:  # (2 + a).subs(-3 - a, y) -> -1 - y
+                return self.func(-new, coeff_self, coeff_old)
+
+        if coeff_self.is_Rational and coeff_old.is_Rational \
+                or coeff_self == coeff_old:
+            args_old, args_self = self.func.make_args(
+                terms_old), self.func.make_args(terms_self)
+            if len(args_old) < len(args_self):  # (a+b+c).subs(b+c,x) -> a+x
+                self_set = set(args_self)
+                old_set = set(args_old)
+
+                if old_set < self_set:
+                    ret_set = self_set - old_set
+                    return self.func(new, coeff_self, -coeff_old,
+                               *[s._subs(old, new) for s in ret_set])
+
+                args_old = self.func.make_args(
+                    -terms_old)     # (a+b+c+d).subs(-b-c,x) -> a-x+d
+                old_set = set(args_old)
+                if old_set < self_set:
+                    ret_set = self_set - old_set
+                    return self.func(-new, coeff_self, coeff_old,
+                               *[s._subs(old, new) for s in ret_set])
+
+    def removeO(self):
+        args = [a for a in self.args if not a.is_Order]
+        return self._new_rawargs(*args)
+
+    def getO(self):
+        args = [a for a in self.args if a.is_Order]
+        if args:
+            return self._new_rawargs(*args)
+
+    @cacheit
+    def extract_leading_order(self, symbols, point=None):
+        """
+        Returns the leading term and its order.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x
+        >>> (x + 1 + 1/x**5).extract_leading_order(x)
+        ((x**(-5), O(x**(-5))),)
+        >>> (1 + x).extract_leading_order(x)
+        ((1, O(1)),)
+        >>> (x + x**2).extract_leading_order(x)
+        ((x, O(x)),)
+
+        """
+        from sympy.series.order import Order
+        lst = []
+        symbols = list(symbols if is_sequence(symbols) else [symbols])
+        if not point:
+            point = [0]*len(symbols)
+        seq = [(f, Order(f, *zip(symbols, point))) for f in self.args]
+        for ef, of in seq:
+            for e, o in lst:
+                if o.contains(of) and o != of:
+                    of = None
+                    break
+            if of is None:
+                continue
+            new_lst = [(ef, of)]
+            for e, o in lst:
+                if of.contains(o) and o != of:
+                    continue
+                new_lst.append((e, o))
+            lst = new_lst
+        return tuple(lst)
+
+    def as_real_imag(self, deep=True, **hints):
+        """
+        Return a tuple representing a complex number.
+
+        Examples
+        ========
+
+        >>> from sympy import I
+        >>> (7 + 9*I).as_real_imag()
+        (7, 9)
+        >>> ((1 + I)/(1 - I)).as_real_imag()
+        (0, 1)
+        >>> ((1 + 2*I)*(1 + 3*I)).as_real_imag()
+        (-5, 5)
+        """
+        sargs = self.args
+        re_part, im_part = [], []
+        for term in sargs:
+            re, im = term.as_real_imag(deep=deep)
+            re_part.append(re)
+            im_part.append(im)
+        return (self.func(*re_part), self.func(*im_part))
+
+    def _eval_as_leading_term(self, x, logx=None, cdir=0):
+        from sympy.core.symbol import Dummy, Symbol
+        from sympy.series.order import Order
+        from sympy.functions.elementary.exponential import log
+        from sympy.functions.elementary.piecewise import Piecewise, piecewise_fold
+        from .function import expand_mul
+
+        o = self.getO()
+        if o is None:
+            o = Order(0)
+        old = self.removeO()
+
+        if old.has(Piecewise):
+            old = piecewise_fold(old)
+
+        # This expansion is the last part of expand_log. expand_log also calls
+        # expand_mul with factor=True, which would be more expensive
+        if any(isinstance(a, log) for a in self.args):
+            logflags = {"deep": True, "log": True, "mul": False, "power_exp": False,
+                "power_base": False, "multinomial": False, "basic": False, "force": False,
+                "factor": False}
+            old = old.expand(**logflags)
+        expr = expand_mul(old)
+
+        if not expr.is_Add:
+            return expr.as_leading_term(x, logx=logx, cdir=cdir)
+
+        infinite = [t for t in expr.args if t.is_infinite]
+
+        _logx = Dummy('logx') if logx is None else logx
+        leading_terms = [t.as_leading_term(x, logx=_logx, cdir=cdir) for t in expr.args]
+
+        min, new_expr = Order(0), 0
+
+        try:
+            for term in leading_terms:
+                order = Order(term, x)
+                if not min or order not in min:
+                    min = order
+                    new_expr = term
+                elif min in order:
+                    new_expr += term
+
+        except TypeError:
+            return expr
+
+        if logx is None:
+            new_expr = new_expr.subs(_logx, log(x))
+
+        is_zero = new_expr.is_zero
+        if is_zero is None:
+            new_expr = new_expr.trigsimp().cancel()
+            is_zero = new_expr.is_zero
+        if is_zero is True:
+            # simple leading term analysis gave us cancelled terms but we have to send
+            # back a term, so compute the leading term (via series)
+            try:
+                n0 = min.getn()
+            except NotImplementedError:
+                n0 = S.One
+            if n0.has(Symbol):
+                n0 = S.One
+            res = Order(1)
+            incr = S.One
+            while res.is_Order:
+                res = old._eval_nseries(x, n=n0+incr, logx=logx, cdir=cdir).cancel().powsimp().trigsimp()
+                incr *= 2
+            return res.as_leading_term(x, logx=logx, cdir=cdir)
+
+        elif new_expr is S.NaN:
+            return old.func._from_args(infinite) + o
+
+        else:
+            return new_expr
+
+    def _eval_adjoint(self):
+        return self.func(*[t.adjoint() for t in self.args])
+
+    def _eval_conjugate(self):
+        return self.func(*[t.conjugate() for t in self.args])
+
+    def _eval_transpose(self):
+        return self.func(*[t.transpose() for t in self.args])
+
+    def primitive(self):
+        """
+        Return ``(R, self/R)`` where ``R``` is the Rational GCD of ``self```.
+
+        ``R`` is collected only from the leading coefficient of each term.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x, y
+
+        >>> (2*x + 4*y).primitive()
+        (2, x + 2*y)
+
+        >>> (2*x/3 + 4*y/9).primitive()
+        (2/9, 3*x + 2*y)
+
+        >>> (2*x/3 + 4.2*y).primitive()
+        (1/3, 2*x + 12.6*y)
+
+        No subprocessing of term factors is performed:
+
+        >>> ((2 + 2*x)*x + 2).primitive()
+        (1, x*(2*x + 2) + 2)
+
+        Recursive processing can be done with the ``as_content_primitive()``
+        method:
+
+        >>> ((2 + 2*x)*x + 2).as_content_primitive()
+        (2, x*(x + 1) + 1)
+
+        See also: primitive() function in polytools.py
+
+        """
+
+        terms = []
+        inf = False
+        for a in self.args:
+            c, m = a.as_coeff_Mul()
+            if not c.is_Rational:
+                c = S.One
+                m = a
+            inf = inf or m is S.ComplexInfinity
+            terms.append((c.p, c.q, m))
+
+        if not inf:
+            ngcd = reduce(igcd, [t[0] for t in terms], 0)
+            dlcm = reduce(ilcm, [t[1] for t in terms], 1)
+        else:
+            ngcd = reduce(igcd, [t[0] for t in terms if t[1]], 0)
+            dlcm = reduce(ilcm, [t[1] for t in terms if t[1]], 1)
+
+        if ngcd == dlcm == 1:
+            return S.One, self
+        if not inf:
+            for i, (p, q, term) in enumerate(terms):
+                terms[i] = _keep_coeff(Rational((p//ngcd)*(dlcm//q)), term)
+        else:
+            for i, (p, q, term) in enumerate(terms):
+                if q:
+                    terms[i] = _keep_coeff(Rational((p//ngcd)*(dlcm//q)), term)
+                else:
+                    terms[i] = _keep_coeff(Rational(p, q), term)
+
+        # we don't need a complete re-flattening since no new terms will join
+        # so we just use the same sort as is used in Add.flatten. When the
+        # coefficient changes, the ordering of terms may change, e.g.
+        #     (3*x, 6*y) -> (2*y, x)
+        #
+        # We do need to make sure that term[0] stays in position 0, however.
+        #
+        if terms[0].is_Number or terms[0] is S.ComplexInfinity:
+            c = terms.pop(0)
+        else:
+            c = None
+        _addsort(terms)
+        if c:
+            terms.insert(0, c)
+        return Rational(ngcd, dlcm), self._new_rawargs(*terms)
+
+    def as_content_primitive(self, radical=False, clear=True):
+        """Return the tuple (R, self/R) where R is the positive Rational
+        extracted from self. If radical is True (default is False) then
+        common radicals will be removed and included as a factor of the
+        primitive expression.
+
+        Examples
+        ========
+
+        >>> from sympy import sqrt
+        >>> (3 + 3*sqrt(2)).as_content_primitive()
+        (3, 1 + sqrt(2))
+
+        Radical content can also be factored out of the primitive:
+
+        >>> (2*sqrt(2) + 4*sqrt(10)).as_content_primitive(radical=True)
+        (2, sqrt(2)*(1 + 2*sqrt(5)))
+
+        See docstring of Expr.as_content_primitive for more examples.
+        """
+        con, prim = self.func(*[_keep_coeff(*a.as_content_primitive(
+            radical=radical, clear=clear)) for a in self.args]).primitive()
+        if not clear and not con.is_Integer and prim.is_Add:
+            con, d = con.as_numer_denom()
+            _p = prim/d
+            if any(a.as_coeff_Mul()[0].is_Integer for a in _p.args):
+                prim = _p
+            else:
+                con /= d
+        if radical and prim.is_Add:
+            # look for common radicals that can be removed
+            args = prim.args
+            rads = []
+            common_q = None
+            for m in args:
+                term_rads = defaultdict(list)
+                for ai in Mul.make_args(m):
+                    if ai.is_Pow:
+                        b, e = ai.as_base_exp()
+                        if e.is_Rational and b.is_Integer:
+                            term_rads[e.q].append(abs(int(b))**e.p)
+                if not term_rads:
+                    break
+                if common_q is None:
+                    common_q = set(term_rads.keys())
+                else:
+                    common_q = common_q & set(term_rads.keys())
+                    if not common_q:
+                        break
+                rads.append(term_rads)
+            else:
+                # process rads
+                # keep only those in common_q
+                for r in rads:
+                    for q in list(r.keys()):
+                        if q not in common_q:
+                            r.pop(q)
+                    for q in r:
+                        r[q] = Mul(*r[q])
+                # find the gcd of bases for each q
+                G = []
+                for q in common_q:
+                    g = reduce(igcd, [r[q] for r in rads], 0)
+                    if g != 1:
+                        G.append(g**Rational(1, q))
+                if G:
+                    G = Mul(*G)
+                    args = [ai/G for ai in args]
+                    prim = G*prim.func(*args)
+
+        return con, prim
+
+    @property
+    def _sorted_args(self):
+        from .sorting import default_sort_key
+        return tuple(sorted(self.args, key=default_sort_key))
+
+    def _eval_difference_delta(self, n, step):
+        from sympy.series.limitseq import difference_delta as dd
+        return self.func(*[dd(a, n, step) for a in self.args])
+
+    @property
+    def _mpc_(self):
+        """
+        Convert self to an mpmath mpc if possible
+        """
+        from .numbers import Float
+        re_part, rest = self.as_coeff_Add()
+        im_part, imag_unit = rest.as_coeff_Mul()
+        if not imag_unit == S.ImaginaryUnit:
+            # ValueError may seem more reasonable but since it's a @property,
+            # we need to use AttributeError to keep from confusing things like
+            # hasattr.
+            raise AttributeError("Cannot convert Add to mpc. Must be of the form Number + Number*I")
+
+        return (Float(re_part)._mpf_, Float(im_part)._mpf_)
+
+    def __neg__(self):
+        if not global_parameters.distribute:
+            return super().__neg__()
+        return Mul(S.NegativeOne, self)
+
+add = AssocOpDispatcher('add')
+
+from .mul import Mul, _keep_coeff, _unevaluated_Mul
+from .numbers import Rational

venv/lib/python3.10/site-packages/sympy/core/alphabets.py

@@ -0,0 +1,4 @@
+greeks = ('alpha', 'beta', 'gamma', 'delta', 'epsilon', 'zeta',
+                    'eta', 'theta', 'iota', 'kappa', 'lambda', 'mu', 'nu',
+                    'xi', 'omicron', 'pi', 'rho', 'sigma', 'tau', 'upsilon',
+                    'phi', 'chi', 'psi', 'omega')

venv/lib/python3.10/site-packages/sympy/core/backend.py

@@ -0,0 +1,84 @@
+import os
+USE_SYMENGINE = os.getenv('USE_SYMENGINE', '0')
+USE_SYMENGINE = USE_SYMENGINE.lower() in ('1', 't', 'true')  # type: ignore
+
+if USE_SYMENGINE:
+    from symengine import (Symbol, Integer, sympify, S,
+        SympifyError, exp, log, gamma, sqrt, I, E, pi, Matrix,
+        sin, cos, tan, cot, csc, sec, asin, acos, atan, acot, acsc, asec,
+        sinh, cosh, tanh, coth, asinh, acosh, atanh, acoth,
+        lambdify, symarray, diff, zeros, eye, diag, ones,
+        expand, Function, symbols, var, Add, Mul, Derivative,
+        ImmutableMatrix, MatrixBase, Rational, Basic)
+    from symengine.lib.symengine_wrapper import gcd as igcd
+    from symengine import AppliedUndef
+else:
+    from sympy.core.add import Add
+    from sympy.core.basic import Basic
+    from sympy.core.function import (diff, Function, AppliedUndef,
+        expand, Derivative)
+    from sympy.core.mul import Mul
+    from sympy.core.numbers import igcd, pi, I, Integer, Rational, E
+    from sympy.core.singleton import S
+    from sympy.core.symbol import Symbol, var, symbols
+    from sympy.core.sympify import SympifyError, sympify
+    from sympy.functions.elementary.exponential import log, exp
+    from sympy.functions.elementary.hyperbolic import (coth, sinh,
+        acosh, acoth, tanh, asinh, atanh, cosh)
+    from sympy.functions.elementary.miscellaneous import sqrt
+    from sympy.functions.elementary.trigonometric import (csc,
+        asec, cos, atan, sec, acot, asin, tan, sin, cot, acsc, acos)
+    from sympy.functions.special.gamma_functions import gamma
+    from sympy.matrices.dense import (eye, zeros, diag, Matrix,
+        ones, symarray)
+    from sympy.matrices.immutable import ImmutableMatrix
+    from sympy.matrices.matrices import MatrixBase
+    from sympy.utilities.lambdify import lambdify
+
+
+#
+# XXX: Handling of immutable and mutable matrices in SymEngine is inconsistent
+# with SymPy's matrix classes in at least SymEngine version 0.7.0. Until that
+# is fixed the function below is needed for consistent behaviour when
+# attempting to simplify a matrix.
+#
+# Expected behaviour of a SymPy mutable/immutable matrix .simplify() method:
+#
+#   Matrix.simplify() : works in place, returns None
+#   ImmutableMatrix.simplify() : returns a simplified copy
+#
+# In SymEngine both mutable and immutable matrices simplify in place and return
+# None. This is inconsistent with the matrix being "immutable" and also the
+# returned None leads to problems in the mechanics module.
+#
+# The simplify function should not be used because simplify(M) sympifies the
+# matrix M and the SymEngine matrices all sympify to SymPy matrices. If we want
+# to work with SymEngine matrices then we need to use their .simplify() method
+# but that method does not work correctly with immutable matrices.
+#
+# The _simplify_matrix function can be removed when the SymEngine bug is fixed.
+# Since this should be a temporary problem we do not make this function part of
+# the public API.
+#
+#   SymEngine issue: https://github.com/symengine/symengine.py/issues/363
+#
+
+def _simplify_matrix(M):
+    """Return a simplified copy of the matrix M"""
+    assert isinstance(M, (Matrix, ImmutableMatrix))
+    Mnew = M.as_mutable() # makes a copy if mutable
+    Mnew.simplify()
+    if isinstance(M, ImmutableMatrix):
+        Mnew = Mnew.as_immutable()
+    return Mnew
+
+
+__all__ = [
+    'Symbol', 'Integer', 'sympify', 'S', 'SympifyError', 'exp', 'log',
+    'gamma', 'sqrt', 'I', 'E', 'pi', 'Matrix', 'sin', 'cos', 'tan', 'cot',
+    'csc', 'sec', 'asin', 'acos', 'atan', 'acot', 'acsc', 'asec', 'sinh',
+    'cosh', 'tanh', 'coth', 'asinh', 'acosh', 'atanh', 'acoth', 'lambdify',
+    'symarray', 'diff', 'zeros', 'eye', 'diag', 'ones', 'expand', 'Function',
+    'symbols', 'var', 'Add', 'Mul', 'Derivative', 'ImmutableMatrix',
+    'MatrixBase', 'Rational', 'Basic', 'igcd', 'AppliedUndef',
+]

venv/lib/python3.10/site-packages/sympy/core/core.py

@@ -0,0 +1,63 @@
+""" The core's core. """
+from __future__ import annotations
+
+
+# used for canonical ordering of symbolic sequences
+# via __cmp__ method:
+# FIXME this is *so* irrelevant and outdated!
+ordering_of_classes = [
+    # singleton numbers
+    'Zero', 'One', 'Half', 'Infinity', 'NaN', 'NegativeOne', 'NegativeInfinity',
+    # numbers
+    'Integer', 'Rational', 'Float',
+    # singleton symbols
+    'Exp1', 'Pi', 'ImaginaryUnit',
+    # symbols
+    'Symbol', 'Wild', 'Temporary',
+    # arithmetic operations
+    'Pow', 'Mul', 'Add',
+    # function values
+    'Derivative', 'Integral',
+    # defined singleton functions
+    'Abs', 'Sign', 'Sqrt',
+    'Floor', 'Ceiling',
+    'Re', 'Im', 'Arg',
+    'Conjugate',
+    'Exp', 'Log',
+    'Sin', 'Cos', 'Tan', 'Cot', 'ASin', 'ACos', 'ATan', 'ACot',
+    'Sinh', 'Cosh', 'Tanh', 'Coth', 'ASinh', 'ACosh', 'ATanh', 'ACoth',
+    'RisingFactorial', 'FallingFactorial',
+    'factorial', 'binomial',
+    'Gamma', 'LowerGamma', 'UpperGamma', 'PolyGamma',
+    'Erf',
+    # special polynomials
+    'Chebyshev', 'Chebyshev2',
+    # undefined functions
+    'Function', 'WildFunction',
+    # anonymous functions
+    'Lambda',
+    # Landau O symbol
+    'Order',
+    # relational operations
+    'Equality', 'Unequality', 'StrictGreaterThan', 'StrictLessThan',
+    'GreaterThan', 'LessThan',
+]
+
+
+class Registry:
+    """
+    Base class for registry objects.
+
+    Registries map a name to an object using attribute notation. Registry
+    classes behave singletonically: all their instances share the same state,
+    which is stored in the class object.
+
+    All subclasses should set `__slots__ = ()`.
+    """
+    __slots__ = ()
+
+    def __setattr__(self, name, obj):
+        setattr(self.__class__, name, obj)
+
+    def __delattr__(self, name):
+        delattr(self.__class__, name)

venv/lib/python3.10/site-packages/sympy/core/decorators.py

@@ -0,0 +1,238 @@
+"""
+SymPy core decorators.
+
+The purpose of this module is to expose decorators without any other
+dependencies, so that they can be easily imported anywhere in sympy/core.
+"""
+
+from functools import wraps
+from .sympify import SympifyError, sympify
+
+
+def _sympifyit(arg, retval=None):
+    """
+    decorator to smartly _sympify function arguments
+
+    Explanation
+    ===========
+
+    @_sympifyit('other', NotImplemented)
+    def add(self, other):
+        ...
+
+    In add, other can be thought of as already being a SymPy object.
+
+    If it is not, the code is likely to catch an exception, then other will
+    be explicitly _sympified, and the whole code restarted.
+
+    if _sympify(arg) fails, NotImplemented will be returned
+
+    See also
+    ========
+
+    __sympifyit
+    """
+    def deco(func):
+        return __sympifyit(func, arg, retval)
+
+    return deco
+
+
+def __sympifyit(func, arg, retval=None):
+    """Decorator to _sympify `arg` argument for function `func`.
+
+       Do not use directly -- use _sympifyit instead.
+    """
+
+    # we support f(a,b) only
+    if not func.__code__.co_argcount:
+        raise LookupError("func not found")
+    # only b is _sympified
+    assert func.__code__.co_varnames[1] == arg
+    if retval is None:
+        @wraps(func)
+        def __sympifyit_wrapper(a, b):
+            return func(a, sympify(b, strict=True))
+
+    else:
+        @wraps(func)
+        def __sympifyit_wrapper(a, b):
+            try:
+                # If an external class has _op_priority, it knows how to deal
+                # with SymPy objects. Otherwise, it must be converted.
+                if not hasattr(b, '_op_priority'):
+                    b = sympify(b, strict=True)
+                return func(a, b)
+            except SympifyError:
+                return retval
+
+    return __sympifyit_wrapper
+
+
+def call_highest_priority(method_name):
+    """A decorator for binary special methods to handle _op_priority.
+
+    Explanation
+    ===========
+
+    Binary special methods in Expr and its subclasses use a special attribute
+    '_op_priority' to determine whose special method will be called to
+    handle the operation. In general, the object having the highest value of
+    '_op_priority' will handle the operation. Expr and subclasses that define
+    custom binary special methods (__mul__, etc.) should decorate those
+    methods with this decorator to add the priority logic.
+
+    The ``method_name`` argument is the name of the method of the other class
+    that will be called.  Use this decorator in the following manner::
+
+        # Call other.__rmul__ if other._op_priority > self._op_priority
+        @call_highest_priority('__rmul__')
+        def __mul__(self, other):
+            ...
+
+        # Call other.__mul__ if other._op_priority > self._op_priority
+        @call_highest_priority('__mul__')
+        def __rmul__(self, other):
+        ...
+    """
+    def priority_decorator(func):
+        @wraps(func)
+        def binary_op_wrapper(self, other):
+            if hasattr(other, '_op_priority'):
+                if other._op_priority > self._op_priority:
+                    f = getattr(other, method_name, None)
+                    if f is not None:
+                        return f(self)
+            return func(self, other)
+        return binary_op_wrapper
+    return priority_decorator
+
+
+def sympify_method_args(cls):
+    '''Decorator for a class with methods that sympify arguments.
+
+    Explanation
+    ===========
+
+    The sympify_method_args decorator is to be used with the sympify_return
+    decorator for automatic sympification of method arguments. This is
+    intended for the common idiom of writing a class like :
+
+    Examples
+    ========
+
+    >>> from sympy import Basic, SympifyError, S
+    >>> from sympy.core.sympify import _sympify
+
+    >>> class MyTuple(Basic):
+    ...     def __add__(self, other):
+    ...         try:
+    ...             other = _sympify(other)
+    ...         except SympifyError:
+    ...             return NotImplemented
+    ...         if not isinstance(other, MyTuple):
+    ...             return NotImplemented
+    ...         return MyTuple(*(self.args + other.args))
+
+    >>> MyTuple(S(1), S(2)) + MyTuple(S(3), S(4))
+    MyTuple(1, 2, 3, 4)
+
+    In the above it is important that we return NotImplemented when other is
+    not sympifiable and also when the sympified result is not of the expected
+    type. This allows the MyTuple class to be used cooperatively with other
+    classes that overload __add__ and want to do something else in combination
+    with instance of Tuple.
+
+    Using this decorator the above can be written as
+
+    >>> from sympy.core.decorators import sympify_method_args, sympify_return
+
+    >>> @sympify_method_args
+    ... class MyTuple(Basic):
+    ...     @sympify_return([('other', 'MyTuple')], NotImplemented)
+    ...     def __add__(self, other):
+    ...          return MyTuple(*(self.args + other.args))
+
+    >>> MyTuple(S(1), S(2)) + MyTuple(S(3), S(4))
+    MyTuple(1, 2, 3, 4)
+
+    The idea here is that the decorators take care of the boiler-plate code
+    for making this happen in each method that potentially needs to accept
+    unsympified arguments. Then the body of e.g. the __add__ method can be
+    written without needing to worry about calling _sympify or checking the
+    type of the resulting object.
+
+    The parameters for sympify_return are a list of tuples of the form
+    (parameter_name, expected_type) and the value to return (e.g.
+    NotImplemented). The expected_type parameter can be a type e.g. Tuple or a
+    string 'Tuple'. Using a string is useful for specifying a Type within its
+    class body (as in the above example).
+
+    Notes: Currently sympify_return only works for methods that take a single
+    argument (not including self). Specifying an expected_type as a string
+    only works for the class in which the method is defined.
+    '''
+    # Extract the wrapped methods from each of the wrapper objects created by
+    # the sympify_return decorator. Doing this here allows us to provide the
+    # cls argument which is used for forward string referencing.
+    for attrname, obj in cls.__dict__.items():
+        if isinstance(obj, _SympifyWrapper):
+            setattr(cls, attrname, obj.make_wrapped(cls))
+    return cls
+
+
+def sympify_return(*args):
+    '''Function/method decorator to sympify arguments automatically
+
+    See the docstring of sympify_method_args for explanation.
+    '''
+    # Store a wrapper object for the decorated method
+    def wrapper(func):
+        return _SympifyWrapper(func, args)
+    return wrapper
+
+
+class _SympifyWrapper:
+    '''Internal class used by sympify_return and sympify_method_args'''
+
+    def __init__(self, func, args):
+        self.func = func
+        self.args = args
+
+    def make_wrapped(self, cls):
+        func = self.func
+        parameters, retval = self.args
+
+        # XXX: Handle more than one parameter?
+        [(parameter, expectedcls)] = parameters
+
+        # Handle forward references to the current class using strings
+        if expectedcls == cls.__name__:
+            expectedcls = cls
+
+        # Raise RuntimeError since this is a failure at import time and should
+        # not be recoverable.
+        nargs = func.__code__.co_argcount
+        # we support f(a, b) only
+        if nargs != 2:
+            raise RuntimeError('sympify_return can only be used with 2 argument functions')
+        # only b is _sympified
+        if func.__code__.co_varnames[1] != parameter:
+            raise RuntimeError('parameter name mismatch "%s" in %s' %
+                    (parameter, func.__name__))
+
+        @wraps(func)
+        def _func(self, other):
+            # XXX: The check for _op_priority here should be removed. It is
+            # needed to stop mutable matrices from being sympified to
+            # immutable matrices which breaks things in quantum...
+            if not hasattr(other, '_op_priority'):
+                try:
+                    other = sympify(other, strict=True)
+                except SympifyError:
+                    return retval
+            if not isinstance(other, expectedcls):
+                return retval
+            return func(self, other)
+
+        return _func

venv/lib/python3.10/site-packages/sympy/core/evalf.py

@@ -0,0 +1,1801 @@
+"""
+Adaptive numerical evaluation of SymPy expressions, using mpmath
+for mathematical functions.
+"""
+from __future__ import annotations
+from typing import Tuple as tTuple, Optional, Union as tUnion, Callable, List, Dict as tDict, Type, TYPE_CHECKING, \
+    Any, overload
+
+import math
+
+import mpmath.libmp as libmp
+from mpmath import (
+    make_mpc, make_mpf, mp, mpc, mpf, nsum, quadts, quadosc, workprec)
+from mpmath import inf as mpmath_inf
+from mpmath.libmp import (from_int, from_man_exp, from_rational, fhalf,
+        fnan, finf, fninf, fnone, fone, fzero, mpf_abs, mpf_add,
+        mpf_atan, mpf_atan2, mpf_cmp, mpf_cos, mpf_e, mpf_exp, mpf_log, mpf_lt,
+        mpf_mul, mpf_neg, mpf_pi, mpf_pow, mpf_pow_int, mpf_shift, mpf_sin,
+        mpf_sqrt, normalize, round_nearest, to_int, to_str)
+from mpmath.libmp import bitcount as mpmath_bitcount
+from mpmath.libmp.backend import MPZ
+from mpmath.libmp.libmpc import _infs_nan
+from mpmath.libmp.libmpf import dps_to_prec, prec_to_dps
+
+from .sympify import sympify
+from .singleton import S
+from sympy.external.gmpy import SYMPY_INTS
+from sympy.utilities.iterables import is_sequence
+from sympy.utilities.lambdify import lambdify
+from sympy.utilities.misc import as_int
+
+if TYPE_CHECKING:
+    from sympy.core.expr import Expr
+    from sympy.core.add import Add
+    from sympy.core.mul import Mul
+    from sympy.core.power import Pow
+    from sympy.core.symbol import Symbol
+    from sympy.integrals.integrals import Integral
+    from sympy.concrete.summations import Sum
+    from sympy.concrete.products import Product
+    from sympy.functions.elementary.exponential import exp, log
+    from sympy.functions.elementary.complexes import Abs, re, im
+    from sympy.functions.elementary.integers import ceiling, floor
+    from sympy.functions.elementary.trigonometric import atan
+    from .numbers import Float, Rational, Integer, AlgebraicNumber, Number
+
+LG10 = math.log(10, 2)
+rnd = round_nearest
+
+
+def bitcount(n):
+    """Return smallest integer, b, such that |n|/2**b < 1.
+    """
+    return mpmath_bitcount(abs(int(n)))
+
+# Used in a few places as placeholder values to denote exponents and
+# precision levels, e.g. of exact numbers. Must be careful to avoid
+# passing these to mpmath functions or returning them in final results.
+INF = float(mpmath_inf)
+MINUS_INF = float(-mpmath_inf)
+
+# ~= 100 digits. Real men set this to INF.
+DEFAULT_MAXPREC = 333
+
+
+class PrecisionExhausted(ArithmeticError):
+    pass
+
+#----------------------------------------------------------------------------#
+#                                                                            #
+#              Helper functions for arithmetic and complex parts             #
+#                                                                            #
+#----------------------------------------------------------------------------#
+
+"""
+An mpf value tuple is a tuple of integers (sign, man, exp, bc)
+representing a floating-point number: [1, -1][sign]*man*2**exp where
+sign is 0 or 1 and bc should correspond to the number of bits used to
+represent the mantissa (man) in binary notation, e.g.
+"""
+MPF_TUP = tTuple[int, int, int, int]  # mpf value tuple
+
+"""
+Explanation
+===========
+
+>>> from sympy.core.evalf import bitcount
+>>> sign, man, exp, bc = 0, 5, 1, 3
+>>> n = [1, -1][sign]*man*2**exp
+>>> n, bitcount(man)
+(10, 3)
+
+A temporary result is a tuple (re, im, re_acc, im_acc) where
+re and im are nonzero mpf value tuples representing approximate
+numbers, or None to denote exact zeros.
+
+re_acc, im_acc are integers denoting log2(e) where e is the estimated
+relative accuracy of the respective complex part, but may be anything
+if the corresponding complex part is None.
+
+"""
+TMP_RES = Any  # temporary result, should be some variant of
+# tUnion[tTuple[Optional[MPF_TUP], Optional[MPF_TUP],
+#               Optional[int], Optional[int]],
+#        'ComplexInfinity']
+# but mypy reports error because it doesn't know as we know
+# 1. re and re_acc are either both None or both MPF_TUP
+# 2. sometimes the result can't be zoo
+
+# type of the "options" parameter in internal evalf functions
+OPT_DICT = tDict[str, Any]
+
+
+def fastlog(x: Optional[MPF_TUP]) -> tUnion[int, Any]:
+    """Fast approximation of log2(x) for an mpf value tuple x.
+
+    Explanation
+    ===========
+
+    Calculated as exponent + width of mantissa. This is an
+    approximation for two reasons: 1) it gives the ceil(log2(abs(x)))
+    value and 2) it is too high by 1 in the case that x is an exact
+    power of 2. Although this is easy to remedy by testing to see if
+    the odd mpf mantissa is 1 (indicating that one was dealing with
+    an exact power of 2) that would decrease the speed and is not
+    necessary as this is only being used as an approximation for the
+    number of bits in x. The correct return value could be written as
+    "x[2] + (x[3] if x[1] != 1 else 0)".
+        Since mpf tuples always have an odd mantissa, no check is done
+    to see if the mantissa is a multiple of 2 (in which case the
+    result would be too large by 1).
+
+    Examples
+    ========
+
+    >>> from sympy import log
+    >>> from sympy.core.evalf import fastlog, bitcount
+    >>> s, m, e = 0, 5, 1
+    >>> bc = bitcount(m)
+    >>> n = [1, -1][s]*m*2**e
+    >>> n, (log(n)/log(2)).evalf(2), fastlog((s, m, e, bc))
+    (10, 3.3, 4)
+    """
+
+    if not x or x == fzero:
+        return MINUS_INF
+    return x[2] + x[3]
+
+
+def pure_complex(v: 'Expr', or_real=False) -> tuple['Number', 'Number'] | None:
+    """Return a and b if v matches a + I*b where b is not zero and
+    a and b are Numbers, else None. If `or_real` is True then 0 will
+    be returned for `b` if `v` is a real number.
+
+    Examples
+    ========
+
+    >>> from sympy.core.evalf import pure_complex
+    >>> from sympy import sqrt, I, S
+    >>> a, b, surd = S(2), S(3), sqrt(2)
+    >>> pure_complex(a)
+    >>> pure_complex(a, or_real=True)
+    (2, 0)
+    >>> pure_complex(surd)
+    >>> pure_complex(a + b*I)
+    (2, 3)
+    >>> pure_complex(I)
+    (0, 1)
+    """
+    h, t = v.as_coeff_Add()
+    if t:
+        c, i = t.as_coeff_Mul()
+        if i is S.ImaginaryUnit:
+            return h, c
+    elif or_real:
+        return h, S.Zero
+    return None
+
+
+# I don't know what this is, see function scaled_zero below
+SCALED_ZERO_TUP = tTuple[List[int], int, int, int]
+
+
+@overload
+def scaled_zero(mag: SCALED_ZERO_TUP, sign=1) -> MPF_TUP:
+    ...
+@overload
+def scaled_zero(mag: int, sign=1) -> tTuple[SCALED_ZERO_TUP, int]:
+    ...
+def scaled_zero(mag: tUnion[SCALED_ZERO_TUP, int], sign=1) -> \
+        tUnion[MPF_TUP, tTuple[SCALED_ZERO_TUP, int]]:
+    """Return an mpf representing a power of two with magnitude ``mag``
+    and -1 for precision. Or, if ``mag`` is a scaled_zero tuple, then just
+    remove the sign from within the list that it was initially wrapped
+    in.
+
+    Examples
+    ========
+
+    >>> from sympy.core.evalf import scaled_zero
+    >>> from sympy import Float
+    >>> z, p = scaled_zero(100)
+    >>> z, p
+    (([0], 1, 100, 1), -1)
+    >>> ok = scaled_zero(z)
+    >>> ok
+    (0, 1, 100, 1)
+    >>> Float(ok)
+    1.26765060022823e+30
+    >>> Float(ok, p)
+    0.e+30
+    >>> ok, p = scaled_zero(100, -1)
+    >>> Float(scaled_zero(ok), p)
+    -0.e+30
+    """
+    if isinstance(mag, tuple) and len(mag) == 4 and iszero(mag, scaled=True):
+        return (mag[0][0],) + mag[1:]
+    elif isinstance(mag, SYMPY_INTS):
+        if sign not in [-1, 1]:
+            raise ValueError('sign must be +/-1')
+        rv, p = mpf_shift(fone, mag), -1
+        s = 0 if sign == 1 else 1
+        rv = ([s],) + rv[1:]
+        return rv, p
+    else:
+        raise ValueError('scaled zero expects int or scaled_zero tuple.')
+
+
+def iszero(mpf: tUnion[MPF_TUP, SCALED_ZERO_TUP, None], scaled=False) -> Optional[bool]:
+    if not scaled:
+        return not mpf or not mpf[1] and not mpf[-1]
+    return mpf and isinstance(mpf[0], list) and mpf[1] == mpf[-1] == 1
+
+
+def complex_accuracy(result: TMP_RES) -> tUnion[int, Any]:
+    """
+    Returns relative accuracy of a complex number with given accuracies
+    for the real and imaginary parts. The relative accuracy is defined
+    in the complex norm sense as ||z|+|error|| / |z| where error
+    is equal to (real absolute error) + (imag absolute error)*i.
+
+    The full expression for the (logarithmic) error can be approximated
+    easily by using the max norm to approximate the complex norm.
+
+    In the worst case (re and im equal), this is wrong by a factor
+    sqrt(2), or by log2(sqrt(2)) = 0.5 bit.
+    """
+    if result is S.ComplexInfinity:
+        return INF
+    re, im, re_acc, im_acc = result
+    if not im:
+        if not re:
+            return INF
+        return re_acc
+    if not re:
+        return im_acc
+    re_size = fastlog(re)
+    im_size = fastlog(im)
+    absolute_error = max(re_size - re_acc, im_size - im_acc)
+    relative_error = absolute_error - max(re_size, im_size)
+    return -relative_error
+
+
+def get_abs(expr: 'Expr', prec: int, options: OPT_DICT) -> TMP_RES:
+    result = evalf(expr, prec + 2, options)
+    if result is S.ComplexInfinity:
+        return finf, None, prec, None
+    re, im, re_acc, im_acc = result
+    if not re:
+        re, re_acc, im, im_acc = im, im_acc, re, re_acc
+    if im:
+        if expr.is_number:
+            abs_expr, _, acc, _ = evalf(abs(N(expr, prec + 2)),
+                                        prec + 2, options)
+            return abs_expr, None, acc, None
+        else:
+            if 'subs' in options:
+                return libmp.mpc_abs((re, im), prec), None, re_acc, None
+            return abs(expr), None, prec, None
+    elif re:
+        return mpf_abs(re), None, re_acc, None
+    else:
+        return None, None, None, None
+
+
+def get_complex_part(expr: 'Expr', no: int, prec: int, options: OPT_DICT) -> TMP_RES:
+    """no = 0 for real part, no = 1 for imaginary part"""
+    workprec = prec
+    i = 0
+    while 1:
+        res = evalf(expr, workprec, options)
+        if res is S.ComplexInfinity:
+            return fnan, None, prec, None
+        value, accuracy = res[no::2]
+        # XXX is the last one correct? Consider re((1+I)**2).n()
+        if (not value) or accuracy >= prec or -value[2] > prec:
+            return value, None, accuracy, None
+        workprec += max(30, 2**i)
+        i += 1
+
+
+def evalf_abs(expr: 'Abs', prec: int, options: OPT_DICT) -> TMP_RES:
+    return get_abs(expr.args[0], prec, options)
+
+
+def evalf_re(expr: 're', prec: int, options: OPT_DICT) -> TMP_RES:
+    return get_complex_part(expr.args[0], 0, prec, options)
+
+
+def evalf_im(expr: 'im', prec: int, options: OPT_DICT) -> TMP_RES:
+    return get_complex_part(expr.args[0], 1, prec, options)
+
+
+def finalize_complex(re: MPF_TUP, im: MPF_TUP, prec: int) -> TMP_RES:
+    if re == fzero and im == fzero:
+        raise ValueError("got complex zero with unknown accuracy")
+    elif re == fzero:
+        return None, im, None, prec
+    elif im == fzero:
+        return re, None, prec, None
+
+    size_re = fastlog(re)
+    size_im = fastlog(im)
+    if size_re > size_im:
+        re_acc = prec
+        im_acc = prec + min(-(size_re - size_im), 0)
+    else:
+        im_acc = prec
+        re_acc = prec + min(-(size_im - size_re), 0)
+    return re, im, re_acc, im_acc
+
+
+def chop_parts(value: TMP_RES, prec: int) -> TMP_RES:
+    """
+    Chop off tiny real or complex parts.
+    """
+    if value is S.ComplexInfinity:
+        return value
+    re, im, re_acc, im_acc = value
+    # Method 1: chop based on absolute value
+    if re and re not in _infs_nan and (fastlog(re) < -prec + 4):
+        re, re_acc = None, None
+    if im and im not in _infs_nan and (fastlog(im) < -prec + 4):
+        im, im_acc = None, None
+    # Method 2: chop if inaccurate and relatively small
+    if re and im:
+        delta = fastlog(re) - fastlog(im)
+        if re_acc < 2 and (delta - re_acc <= -prec + 4):
+            re, re_acc = None, None
+        if im_acc < 2 and (delta - im_acc >= prec - 4):
+            im, im_acc = None, None
+    return re, im, re_acc, im_acc
+
+
+def check_target(expr: 'Expr', result: TMP_RES, prec: int):
+    a = complex_accuracy(result)
+    if a < prec:
+        raise PrecisionExhausted("Failed to distinguish the expression: \n\n%s\n\n"
+            "from zero. Try simplifying the input, using chop=True, or providing "
+            "a higher maxn for evalf" % (expr))
+
+
+def get_integer_part(expr: 'Expr', no: int, options: OPT_DICT, return_ints=False) -> \
+        tUnion[TMP_RES, tTuple[int, int]]:
+    """
+    With no = 1, computes ceiling(expr)
+    With no = -1, computes floor(expr)
+
+    Note: this function either gives the exact result or signals failure.
+    """
+    from sympy.functions.elementary.complexes import re, im
+    # The expression is likely less than 2^30 or so
+    assumed_size = 30
+    result = evalf(expr, assumed_size, options)
+    if result is S.ComplexInfinity:
+        raise ValueError("Cannot get integer part of Complex Infinity")
+    ire, iim, ire_acc, iim_acc = result
+
+    # We now know the size, so we can calculate how much extra precision
+    # (if any) is needed to get within the nearest integer
+    if ire and iim:
+        gap = max(fastlog(ire) - ire_acc, fastlog(iim) - iim_acc)
+    elif ire:
+        gap = fastlog(ire) - ire_acc
+    elif iim:
+        gap = fastlog(iim) - iim_acc
+    else:
+        # ... or maybe the expression was exactly zero
+        if return_ints:
+            return 0, 0
+        else:
+            return None, None, None, None
+
+    margin = 10
+
+    if gap >= -margin:
+        prec = margin + assumed_size + gap
+        ire, iim, ire_acc, iim_acc = evalf(
+            expr, prec, options)
+    else:
+        prec = assumed_size
+
+    # We can now easily find the nearest integer, but to find floor/ceil, we
+    # must also calculate whether the difference to the nearest integer is
+    # positive or negative (which may fail if very close).
+    def calc_part(re_im: 'Expr', nexpr: MPF_TUP):
+        from .add import Add
+        _, _, exponent, _ = nexpr
+        is_int = exponent == 0
+        nint = int(to_int(nexpr, rnd))
+        if is_int:
+            # make sure that we had enough precision to distinguish
+            # between nint and the re or im part (re_im) of expr that
+            # was passed to calc_part
+            ire, iim, ire_acc, iim_acc = evalf(
+                re_im - nint, 10, options)  # don't need much precision
+            assert not iim
+            size = -fastlog(ire) + 2  # -ve b/c ire is less than 1
+            if size > prec:
+                ire, iim, ire_acc, iim_acc = evalf(
+                    re_im, size, options)
+                assert not iim
+                nexpr = ire
+            nint = int(to_int(nexpr, rnd))
+            _, _, new_exp, _ = ire
+            is_int = new_exp == 0
+        if not is_int:
+            # if there are subs and they all contain integer re/im parts
+            # then we can (hopefully) safely substitute them into the
+            # expression
+            s = options.get('subs', False)
+            if s:
+                doit = True
+                # use strict=False with as_int because we take
+                # 2.0 == 2
+                for v in s.values():
+                    try:
+                        as_int(v, strict=False)
+                    except ValueError:
+                        try:
+                            [as_int(i, strict=False) for i in v.as_real_imag()]
+                            continue
+                        except (ValueError, AttributeError):
+                            doit = False
+                            break
+                if doit:
+                    re_im = re_im.subs(s)
+
+            re_im = Add(re_im, -nint, evaluate=False)
+            x, _, x_acc, _ = evalf(re_im, 10, options)
+            try:
+                check_target(re_im, (x, None, x_acc, None), 3)
+            except PrecisionExhausted:
+                if not re_im.equals(0):
+                    raise PrecisionExhausted
+                x = fzero
+            nint += int(no*(mpf_cmp(x or fzero, fzero) == no))
+        nint = from_int(nint)
+        return nint, INF
+
+    re_, im_, re_acc, im_acc = None, None, None, None
+
+    if ire:
+        re_, re_acc = calc_part(re(expr, evaluate=False), ire)
+    if iim:
+        im_, im_acc = calc_part(im(expr, evaluate=False), iim)
+
+    if return_ints:
+        return int(to_int(re_ or fzero)), int(to_int(im_ or fzero))
+    return re_, im_, re_acc, im_acc
+
+
+def evalf_ceiling(expr: 'ceiling', prec: int, options: OPT_DICT) -> TMP_RES:
+    return get_integer_part(expr.args[0], 1, options)
+
+
+def evalf_floor(expr: 'floor', prec: int, options: OPT_DICT) -> TMP_RES:
+    return get_integer_part(expr.args[0], -1, options)
+
+
+def evalf_float(expr: 'Float', prec: int, options: OPT_DICT) -> TMP_RES:
+    return expr._mpf_, None, prec, None
+
+
+def evalf_rational(expr: 'Rational', prec: int, options: OPT_DICT) -> TMP_RES:
+    return from_rational(expr.p, expr.q, prec), None, prec, None
+
+
+def evalf_integer(expr: 'Integer', prec: int, options: OPT_DICT) -> TMP_RES:
+    return from_int(expr.p, prec), None, prec, None
+
+#----------------------------------------------------------------------------#
+#                                                                            #
+#                            Arithmetic operations                           #
+#                                                                            #
+#----------------------------------------------------------------------------#
+
+
+def add_terms(terms: list, prec: int, target_prec: int) -> \
+        tTuple[tUnion[MPF_TUP, SCALED_ZERO_TUP, None], Optional[int]]:
+    """
+    Helper for evalf_add. Adds a list of (mpfval, accuracy) terms.
+
+    Returns
+    =======
+
+    - None, None if there are no non-zero terms;
+    - terms[0] if there is only 1 term;
+    - scaled_zero if the sum of the terms produces a zero by cancellation
+      e.g. mpfs representing 1 and -1 would produce a scaled zero which need
+      special handling since they are not actually zero and they are purposely
+      malformed to ensure that they cannot be used in anything but accuracy
+      calculations;
+    - a tuple that is scaled to target_prec that corresponds to the
+      sum of the terms.
+
+    The returned mpf tuple will be normalized to target_prec; the input
+    prec is used to define the working precision.
+
+    XXX explain why this is needed and why one cannot just loop using mpf_add
+    """
+
+    terms = [t for t in terms if not iszero(t[0])]
+    if not terms:
+        return None, None
+    elif len(terms) == 1:
+        return terms[0]
+
+    # see if any argument is NaN or oo and thus warrants a special return
+    special = []
+    from .numbers import Float
+    for t in terms:
+        arg = Float._new(t[0], 1)
+        if arg is S.NaN or arg.is_infinite:
+            special.append(arg)
+    if special:
+        from .add import Add
+        rv = evalf(Add(*special), prec + 4, {})
+        return rv[0], rv[2]
+
+    working_prec = 2*prec
+    sum_man, sum_exp = 0, 0
+    absolute_err: List[int] = []
+
+    for x, accuracy in terms:
+        sign, man, exp, bc = x
+        if sign:
+            man = -man
+        absolute_err.append(bc + exp - accuracy)
+        delta = exp - sum_exp
+        if exp >= sum_exp:
+            # x much larger than existing sum?
+            # first: quick test
+            if ((delta > working_prec) and
+                ((not sum_man) or
+                 delta - bitcount(abs(sum_man)) > working_prec)):
+                sum_man = man
+                sum_exp = exp
+            else:
+                sum_man += (man << delta)
+        else:
+            delta = -delta
+            # x much smaller than existing sum?
+            if delta - bc > working_prec:
+                if not sum_man:
+                    sum_man, sum_exp = man, exp
+            else:
+                sum_man = (sum_man << delta) + man
+                sum_exp = exp
+    absolute_error = max(absolute_err)
+    if not sum_man:
+        return scaled_zero(absolute_error)
+    if sum_man < 0:
+        sum_sign = 1
+        sum_man = -sum_man
+    else:
+        sum_sign = 0
+    sum_bc = bitcount(sum_man)
+    sum_accuracy = sum_exp + sum_bc - absolute_error
+    r = normalize(sum_sign, sum_man, sum_exp, sum_bc, target_prec,
+        rnd), sum_accuracy
+    return r
+
+
+def evalf_add(v: 'Add', prec: int, options: OPT_DICT) -> TMP_RES:
+    res = pure_complex(v)
+    if res:
+        h, c = res
+        re, _, re_acc, _ = evalf(h, prec, options)
+        im, _, im_acc, _ = evalf(c, prec, options)
+        return re, im, re_acc, im_acc
+
+    oldmaxprec = options.get('maxprec', DEFAULT_MAXPREC)
+
+    i = 0
+    target_prec = prec
+    while 1:
+        options['maxprec'] = min(oldmaxprec, 2*prec)
+
+        terms = [evalf(arg, prec + 10, options) for arg in v.args]
+        n = terms.count(S.ComplexInfinity)
+        if n >= 2:
+            return fnan, None, prec, None
+        re, re_acc = add_terms(
+            [a[0::2] for a in terms if isinstance(a, tuple) and a[0]], prec, target_prec)
+        im, im_acc = add_terms(
+            [a[1::2] for a in terms if isinstance(a, tuple) and a[1]], prec, target_prec)
+        if n == 1:
+            if re in (finf, fninf, fnan) or im in (finf, fninf, fnan):
+                return fnan, None, prec, None
+            return S.ComplexInfinity
+        acc = complex_accuracy((re, im, re_acc, im_acc))
+        if acc >= target_prec:
+            if options.get('verbose'):
+                print("ADD: wanted", target_prec, "accurate bits, got", re_acc, im_acc)
+            break
+        else:
+            if (prec - target_prec) > options['maxprec']:
+                break
+
+            prec = prec + max(10 + 2**i, target_prec - acc)
+            i += 1
+            if options.get('verbose'):
+                print("ADD: restarting with prec", prec)
+
+    options['maxprec'] = oldmaxprec
+    if iszero(re, scaled=True):
+        re = scaled_zero(re)
+    if iszero(im, scaled=True):
+        im = scaled_zero(im)
+    return re, im, re_acc, im_acc
+
+
+def evalf_mul(v: 'Mul', prec: int, options: OPT_DICT) -> TMP_RES:
+    res = pure_complex(v)
+    if res:
+        # the only pure complex that is a mul is h*I
+        _, h = res
+        im, _, im_acc, _ = evalf(h, prec, options)
+        return None, im, None, im_acc
+    args = list(v.args)
+
+    # see if any argument is NaN or oo and thus warrants a special return
+    has_zero = False
+    special = []
+    from .numbers import Float
+    for arg in args:
+        result = evalf(arg, prec, options)
+        if result is S.ComplexInfinity:
+            special.append(result)
+            continue
+        if result[0] is None:
+            if result[1] is None:
+                has_zero = True
+            continue
+        num = Float._new(result[0], 1)
+        if num is S.NaN:
+            return fnan, None, prec, None
+        if num.is_infinite:
+            special.append(num)
+    if special:
+        if has_zero:
+            return fnan, None, prec, None
+        from .mul import Mul
+        return evalf(Mul(*special), prec + 4, {})
+    if has_zero:
+        return None, None, None, None
+
+    # With guard digits, multiplication in the real case does not destroy
+    # accuracy. This is also true in the complex case when considering the
+    # total accuracy; however accuracy for the real or imaginary parts
+    # separately may be lower.
+    acc = prec
+
+    # XXX: big overestimate
+    working_prec = prec + len(args) + 5
+
+    # Empty product is 1
+    start = man, exp, bc = MPZ(1), 0, 1
+
+    # First, we multiply all pure real or pure imaginary numbers.
+    # direction tells us that the result should be multiplied by
+    # I**direction; all other numbers get put into complex_factors
+    # to be multiplied out after the first phase.
+    last = len(args)
+    direction = 0
+    args.append(S.One)
+    complex_factors = []
+
+    for i, arg in enumerate(args):
+        if i != last and pure_complex(arg):
+            args[-1] = (args[-1]*arg).expand()
+            continue
+        elif i == last and arg is S.One:
+            continue
+        re, im, re_acc, im_acc = evalf(arg, working_prec, options)
+        if re and im:
+            complex_factors.append((re, im, re_acc, im_acc))
+            continue
+        elif re:
+            (s, m, e, b), w_acc = re, re_acc
+        elif im:
+            (s, m, e, b), w_acc = im, im_acc
+            direction += 1
+        else:
+            return None, None, None, None
+        direction += 2*s
+        man *= m
+        exp += e
+        bc += b
+        while bc > 3*working_prec:
+            man >>= working_prec
+            exp += working_prec
+            bc -= working_prec
+        acc = min(acc, w_acc)
+    sign = (direction & 2) >> 1
+    if not complex_factors:
+        v = normalize(sign, man, exp, bitcount(man), prec, rnd)
+        # multiply by i
+        if direction & 1:
+            return None, v, None, acc
+        else:
+            return v, None, acc, None
+    else:
+        # initialize with the first term
+        if (man, exp, bc) != start:
+            # there was a real part; give it an imaginary part
+            re, im = (sign, man, exp, bitcount(man)), (0, MPZ(0), 0, 0)
+            i0 = 0
+        else:
+            # there is no real part to start (other than the starting 1)
+            wre, wim, wre_acc, wim_acc = complex_factors[0]
+            acc = min(acc,
+                      complex_accuracy((wre, wim, wre_acc, wim_acc)))
+            re = wre
+            im = wim
+            i0 = 1
+
+        for wre, wim, wre_acc, wim_acc in complex_factors[i0:]:
+            # acc is the overall accuracy of the product; we aren't
+            # computing exact accuracies of the product.
+            acc = min(acc,
+                      complex_accuracy((wre, wim, wre_acc, wim_acc)))
+
+            use_prec = working_prec
+            A = mpf_mul(re, wre, use_prec)
+            B = mpf_mul(mpf_neg(im), wim, use_prec)
+            C = mpf_mul(re, wim, use_prec)
+            D = mpf_mul(im, wre, use_prec)
+            re = mpf_add(A, B, use_prec)
+            im = mpf_add(C, D, use_prec)
+        if options.get('verbose'):
+            print("MUL: wanted", prec, "accurate bits, got", acc)
+        # multiply by I
+        if direction & 1:
+            re, im = mpf_neg(im), re
+        return re, im, acc, acc
+
+
+def evalf_pow(v: 'Pow', prec: int, options) -> TMP_RES:
+
+    target_prec = prec
+    base, exp = v.args
+
+    # We handle x**n separately. This has two purposes: 1) it is much
+    # faster, because we avoid calling evalf on the exponent, and 2) it
+    # allows better handling of real/imaginary parts that are exactly zero
+    if exp.is_Integer:
+        p: int = exp.p  # type: ignore
+        # Exact
+        if not p:
+            return fone, None, prec, None
+        # Exponentiation by p magnifies relative error by |p|, so the
+        # base must be evaluated with increased precision if p is large
+        prec += int(math.log(abs(p), 2))
+        result = evalf(base, prec + 5, options)
+        if result is S.ComplexInfinity:
+            if p < 0:
+                return None, None, None, None
+            return result
+        re, im, re_acc, im_acc = result
+        # Real to integer power
+        if re and not im:
+            return mpf_pow_int(re, p, target_prec), None, target_prec, None
+        # (x*I)**n = I**n * x**n
+        if im and not re:
+            z = mpf_pow_int(im, p, target_prec)
+            case = p % 4
+            if case == 0:
+                return z, None, target_prec, None
+            if case == 1:
+                return None, z, None, target_prec
+            if case == 2:
+                return mpf_neg(z), None, target_prec, None
+            if case == 3:
+                return None, mpf_neg(z), None, target_prec
+        # Zero raised to an integer power
+        if not re:
+            if p < 0:
+                return S.ComplexInfinity
+            return None, None, None, None
+        # General complex number to arbitrary integer power
+        re, im = libmp.mpc_pow_int((re, im), p, prec)
+        # Assumes full accuracy in input
+        return finalize_complex(re, im, target_prec)
+
+    result = evalf(base, prec + 5, options)
+    if result is S.ComplexInfinity:
+        if exp.is_Rational:
+            if exp < 0:
+                return None, None, None, None
+            return result
+        raise NotImplementedError
+
+    # Pure square root
+    if exp is S.Half:
+        xre, xim, _, _ = result
+        # General complex square root
+        if xim:
+            re, im = libmp.mpc_sqrt((xre or fzero, xim), prec)
+            return finalize_complex(re, im, prec)
+        if not xre:
+            return None, None, None, None
+        # Square root of a negative real number
+        if mpf_lt(xre, fzero):
+            return None, mpf_sqrt(mpf_neg(xre), prec), None, prec
+        # Positive square root
+        return mpf_sqrt(xre, prec), None, prec, None
+
+    # We first evaluate the exponent to find its magnitude
+    # This determines the working precision that must be used
+    prec += 10
+    result = evalf(exp, prec, options)
+    if result is S.ComplexInfinity:
+        return fnan, None, prec, None
+    yre, yim, _, _ = result
+    # Special cases: x**0
+    if not (yre or yim):
+        return fone, None, prec, None
+
+    ysize = fastlog(yre)
+    # Restart if too big
+    # XXX: prec + ysize might exceed maxprec
+    if ysize > 5:
+        prec += ysize
+        yre, yim, _, _ = evalf(exp, prec, options)
+
+    # Pure exponential function; no need to evalf the base
+    if base is S.Exp1:
+        if yim:
+            re, im = libmp.mpc_exp((yre or fzero, yim), prec)
+            return finalize_complex(re, im, target_prec)
+        return mpf_exp(yre, target_prec), None, target_prec, None
+
+    xre, xim, _, _ = evalf(base, prec + 5, options)
+    # 0**y
+    if not (xre or xim):
+        if yim:
+            return fnan, None, prec, None
+        if yre[0] == 1:  # y < 0
+            return S.ComplexInfinity
+        return None, None, None, None
+
+    # (real ** complex) or (complex ** complex)
+    if yim:
+        re, im = libmp.mpc_pow(
+            (xre or fzero, xim or fzero), (yre or fzero, yim),
+            target_prec)
+        return finalize_complex(re, im, target_prec)
+    # complex ** real
+    if xim:
+        re, im = libmp.mpc_pow_mpf((xre or fzero, xim), yre, target_prec)
+        return finalize_complex(re, im, target_prec)
+    # negative ** real
+    elif mpf_lt(xre, fzero):
+        re, im = libmp.mpc_pow_mpf((xre, fzero), yre, target_prec)
+        return finalize_complex(re, im, target_prec)
+    # positive ** real
+    else:
+        return mpf_pow(xre, yre, target_prec), None, target_prec, None
+
+
+#----------------------------------------------------------------------------#
+#                                                                            #
+#                            Special functions                               #
+#                                                                            #
+#----------------------------------------------------------------------------#
+
+
+def evalf_exp(expr: 'exp', prec: int, options: OPT_DICT) -> TMP_RES:
+    from .power import Pow
+    return evalf_pow(Pow(S.Exp1, expr.exp, evaluate=False), prec, options)
+
+
+def evalf_trig(v: 'Expr', prec: int, options: OPT_DICT) -> TMP_RES:
+    """
+    This function handles sin and cos of complex arguments.
+
+    TODO: should also handle tan of complex arguments.
+    """
+    from sympy.functions.elementary.trigonometric import cos, sin
+    if isinstance(v, cos):
+        func = mpf_cos
+    elif isinstance(v, sin):
+        func = mpf_sin
+    else:
+        raise NotImplementedError
+    arg = v.args[0]
+    # 20 extra bits is possibly overkill. It does make the need
+    # to restart very unlikely
+    xprec = prec + 20
+    re, im, re_acc, im_acc = evalf(arg, xprec, options)
+    if im:
+        if 'subs' in options:
+            v = v.subs(options['subs'])
+        return evalf(v._eval_evalf(prec), prec, options)
+    if not re:
+        if isinstance(v, cos):
+            return fone, None, prec, None
+        elif isinstance(v, sin):
+            return None, None, None, None
+        else:
+            raise NotImplementedError
+    # For trigonometric functions, we are interested in the
+    # fixed-point (absolute) accuracy of the argument.
+    xsize = fastlog(re)
+    # Magnitude <= 1.0. OK to compute directly, because there is no
+    # danger of hitting the first root of cos (with sin, magnitude
+    # <= 2.0 would actually be ok)
+    if xsize < 1:
+        return func(re, prec, rnd), None, prec, None
+    # Very large
+    if xsize >= 10:
+        xprec = prec + xsize
+        re, im, re_acc, im_acc = evalf(arg, xprec, options)
+    # Need to repeat in case the argument is very close to a
+    # multiple of pi (or pi/2), hitting close to a root
+    while 1:
+        y = func(re, prec, rnd)
+        ysize = fastlog(y)
+        gap = -ysize
+        accuracy = (xprec - xsize) - gap
+        if accuracy < prec:
+            if options.get('verbose'):
+                print("SIN/COS", accuracy, "wanted", prec, "gap", gap)
+                print(to_str(y, 10))
+            if xprec > options.get('maxprec', DEFAULT_MAXPREC):
+                return y, None, accuracy, None
+            xprec += gap
+            re, im, re_acc, im_acc = evalf(arg, xprec, options)
+            continue
+        else:
+            return y, None, prec, None
+
+
+def evalf_log(expr: 'log', prec: int, options: OPT_DICT) -> TMP_RES:
+    if len(expr.args)>1:
+        expr = expr.doit()
+        return evalf(expr, prec, options)
+    arg = expr.args[0]
+    workprec = prec + 10
+    result = evalf(arg, workprec, options)
+    if result is S.ComplexInfinity:
+        return result
+    xre, xim, xacc, _ = result
+
+    # evalf can return NoneTypes if chop=True
+    # issue 18516, 19623
+    if xre is xim is None:
+        # Dear reviewer, I do not know what -inf is;
+        # it looks to be (1, 0, -789, -3)
+        # but I'm not sure in general,
+        # so we just let mpmath figure
+        # it out by taking log of 0 directly.
+        # It would be better to return -inf instead.
+        xre = fzero
+
+    if xim:
+        from sympy.functions.elementary.complexes import Abs
+        from sympy.functions.elementary.exponential import log
+
+        # XXX: use get_abs etc instead
+        re = evalf_log(
+            log(Abs(arg, evaluate=False), evaluate=False), prec, options)
+        im = mpf_atan2(xim, xre or fzero, prec)
+        return re[0], im, re[2], prec
+
+    imaginary_term = (mpf_cmp(xre, fzero) < 0)
+
+    re = mpf_log(mpf_abs(xre), prec, rnd)
+    size = fastlog(re)
+    if prec - size > workprec and re != fzero:
+        from .add import Add
+        # We actually need to compute 1+x accurately, not x
+        add = Add(S.NegativeOne, arg, evaluate=False)
+        xre, xim, _, _ = evalf_add(add, prec, options)
+        prec2 = workprec - fastlog(xre)
+        # xre is now x - 1 so we add 1 back here to calculate x
+        re = mpf_log(mpf_abs(mpf_add(xre, fone, prec2)), prec, rnd)
+
+    re_acc = prec
+
+    if imaginary_term:
+        return re, mpf_pi(prec), re_acc, prec
+    else:
+        return re, None, re_acc, None
+
+
+def evalf_atan(v: 'atan', prec: int, options: OPT_DICT) -> TMP_RES:
+    arg = v.args[0]
+    xre, xim, reacc, imacc = evalf(arg, prec + 5, options)
+    if xre is xim is None:
+        return (None,)*4
+    if xim:
+        raise NotImplementedError
+    return mpf_atan(xre, prec, rnd), None, prec, None
+
+
+def evalf_subs(prec: int, subs: dict) -> dict:
+    """ Change all Float entries in `subs` to have precision prec. """
+    newsubs = {}
+    for a, b in subs.items():
+        b = S(b)
+        if b.is_Float:
+            b = b._eval_evalf(prec)
+        newsubs[a] = b
+    return newsubs
+
+
+def evalf_piecewise(expr: 'Expr', prec: int, options: OPT_DICT) -> TMP_RES:
+    from .numbers import Float, Integer
+    if 'subs' in options:
+        expr = expr.subs(evalf_subs(prec, options['subs']))
+        newopts = options.copy()
+        del newopts['subs']
+        if hasattr(expr, 'func'):
+            return evalf(expr, prec, newopts)
+        if isinstance(expr, float):
+            return evalf(Float(expr), prec, newopts)
+        if isinstance(expr, int):
+            return evalf(Integer(expr), prec, newopts)
+
+    # We still have undefined symbols
+    raise NotImplementedError
+
+
+def evalf_alg_num(a: 'AlgebraicNumber', prec: int, options: OPT_DICT) -> TMP_RES:
+    return evalf(a.to_root(), prec, options)
+
+#----------------------------------------------------------------------------#
+#                                                                            #
+#                            High-level operations                           #
+#                                                                            #
+#----------------------------------------------------------------------------#
+
+
+def as_mpmath(x: Any, prec: int, options: OPT_DICT) -> tUnion[mpc, mpf]:
+    from .numbers import Infinity, NegativeInfinity, Zero
+    x = sympify(x)
+    if isinstance(x, Zero) or x == 0.0:
+        return mpf(0)
+    if isinstance(x, Infinity):
+        return mpf('inf')
+    if isinstance(x, NegativeInfinity):
+        return mpf('-inf')
+    # XXX
+    result = evalf(x, prec, options)
+    return quad_to_mpmath(result)
+
+
+def do_integral(expr: 'Integral', prec: int, options: OPT_DICT) -> TMP_RES:
+    func = expr.args[0]
+    x, xlow, xhigh = expr.args[1]
+    if xlow == xhigh:
+        xlow = xhigh = 0
+    elif x not in func.free_symbols:
+        # only the difference in limits matters in this case
+        # so if there is a symbol in common that will cancel
+        # out when taking the difference, then use that
+        # difference
+        if xhigh.free_symbols & xlow.free_symbols:
+            diff = xhigh - xlow
+            if diff.is_number:
+                xlow, xhigh = 0, diff
+
+    oldmaxprec = options.get('maxprec', DEFAULT_MAXPREC)
+    options['maxprec'] = min(oldmaxprec, 2*prec)
+
+    with workprec(prec + 5):
+        xlow = as_mpmath(xlow, prec + 15, options)
+        xhigh = as_mpmath(xhigh, prec + 15, options)
+
+        # Integration is like summation, and we can phone home from
+        # the integrand function to update accuracy summation style
+        # Note that this accuracy is inaccurate, since it fails
+        # to account for the variable quadrature weights,
+        # but it is better than nothing
+
+        from sympy.functions.elementary.trigonometric import cos, sin
+        from .symbol import Wild
+
+        have_part = [False, False]
+        max_real_term: tUnion[float, int] = MINUS_INF
+        max_imag_term: tUnion[float, int] = MINUS_INF
+
+        def f(t: 'Expr') -> tUnion[mpc, mpf]:
+            nonlocal max_real_term, max_imag_term
+            re, im, re_acc, im_acc = evalf(func, mp.prec, {'subs': {x: t}})
+
+            have_part[0] = re or have_part[0]
+            have_part[1] = im or have_part[1]
+
+            max_real_term = max(max_real_term, fastlog(re))
+            max_imag_term = max(max_imag_term, fastlog(im))
+
+            if im:
+                return mpc(re or fzero, im)
+            return mpf(re or fzero)
+
+        if options.get('quad') == 'osc':
+            A = Wild('A', exclude=[x])
+            B = Wild('B', exclude=[x])
+            D = Wild('D')
+            m = func.match(cos(A*x + B)*D)
+            if not m:
+                m = func.match(sin(A*x + B)*D)
+            if not m:
+                raise ValueError("An integrand of the form sin(A*x+B)*f(x) "
+                  "or cos(A*x+B)*f(x) is required for oscillatory quadrature")
+            period = as_mpmath(2*S.Pi/m[A], prec + 15, options)
+            result = quadosc(f, [xlow, xhigh], period=period)
+            # XXX: quadosc does not do error detection yet
+            quadrature_error = MINUS_INF
+        else:
+            result, quadrature_err = quadts(f, [xlow, xhigh], error=1)
+            quadrature_error = fastlog(quadrature_err._mpf_)
+
+    options['maxprec'] = oldmaxprec
+
+    if have_part[0]:
+        re: Optional[MPF_TUP] = result.real._mpf_
+        re_acc: Optional[int]
+        if re == fzero:
+            re_s, re_acc = scaled_zero(int(-max(prec, max_real_term, quadrature_error)))
+            re = scaled_zero(re_s)  # handled ok in evalf_integral
+        else:
+            re_acc = int(-max(max_real_term - fastlog(re) - prec, quadrature_error))
+    else:
+        re, re_acc = None, None
+
+    if have_part[1]:
+        im: Optional[MPF_TUP] = result.imag._mpf_
+        im_acc: Optional[int]
+        if im == fzero:
+            im_s, im_acc = scaled_zero(int(-max(prec, max_imag_term, quadrature_error)))
+            im = scaled_zero(im_s)  # handled ok in evalf_integral
+        else:
+            im_acc = int(-max(max_imag_term - fastlog(im) - prec, quadrature_error))
+    else:
+        im, im_acc = None, None
+
+    result = re, im, re_acc, im_acc
+    return result
+
+
+def evalf_integral(expr: 'Integral', prec: int, options: OPT_DICT) -> TMP_RES:
+    limits = expr.limits
+    if len(limits) != 1 or len(limits[0]) != 3:
+        raise NotImplementedError
+    workprec = prec
+    i = 0
+    maxprec = options.get('maxprec', INF)
+    while 1:
+        result = do_integral(expr, workprec, options)
+        accuracy = complex_accuracy(result)
+        if accuracy >= prec:  # achieved desired precision
+            break
+        if workprec >= maxprec:  # can't increase accuracy any more
+            break
+        if accuracy == -1:
+            # maybe the answer really is zero and maybe we just haven't increased
+            # the precision enough. So increase by doubling to not take too long
+            # to get to maxprec.
+            workprec *= 2
+        else:
+            workprec += max(prec, 2**i)
+        workprec = min(workprec, maxprec)
+        i += 1
+    return result
+
+
+def check_convergence(numer: 'Expr', denom: 'Expr', n: 'Symbol') -> tTuple[int, Any, Any]:
+    """
+    Returns
+    =======
+
+    (h, g, p) where
+    -- h is:
+        > 0 for convergence of rate 1/factorial(n)**h
+        < 0 for divergence of rate factorial(n)**(-h)
+        = 0 for geometric or polynomial convergence or divergence
+
+    -- abs(g) is:
+        > 1 for geometric convergence of rate 1/h**n
+        < 1 for geometric divergence of rate h**n
+        = 1 for polynomial convergence or divergence
+
+        (g < 0 indicates an alternating series)
+
+    -- p is:
+        > 1 for polynomial convergence of rate 1/n**h
+        <= 1 for polynomial divergence of rate n**(-h)
+
+    """
+    from sympy.polys.polytools import Poly
+    npol = Poly(numer, n)
+    dpol = Poly(denom, n)
+    p = npol.degree()
+    q = dpol.degree()
+    rate = q - p
+    if rate:
+        return rate, None, None
+    constant = dpol.LC() / npol.LC()
+    from .numbers import equal_valued
+    if not equal_valued(abs(constant), 1):
+        return rate, constant, None
+    if npol.degree() == dpol.degree() == 0:
+        return rate, constant, 0
+    pc = npol.all_coeffs()[1]
+    qc = dpol.all_coeffs()[1]
+    return rate, constant, (qc - pc)/dpol.LC()
+
+
+def hypsum(expr: 'Expr', n: 'Symbol', start: int, prec: int) -> mpf:
+    """
+    Sum a rapidly convergent infinite hypergeometric series with
+    given general term, e.g. e = hypsum(1/factorial(n), n). The
+    quotient between successive terms must be a quotient of integer
+    polynomials.
+    """
+    from .numbers import Float, equal_valued
+    from sympy.simplify.simplify import hypersimp
+
+    if prec == float('inf'):
+        raise NotImplementedError('does not support inf prec')
+
+    if start:
+        expr = expr.subs(n, n + start)
+    hs = hypersimp(expr, n)
+    if hs is None:
+        raise NotImplementedError("a hypergeometric series is required")
+    num, den = hs.as_numer_denom()
+
+    func1 = lambdify(n, num)
+    func2 = lambdify(n, den)
+
+    h, g, p = check_convergence(num, den, n)
+
+    if h < 0:
+        raise ValueError("Sum diverges like (n!)^%i" % (-h))
+
+    term = expr.subs(n, 0)
+    if not term.is_Rational:
+        raise NotImplementedError("Non rational term functionality is not implemented.")
+
+    # Direct summation if geometric or faster
+    if h > 0 or (h == 0 and abs(g) > 1):
+        term = (MPZ(term.p) << prec) // term.q
+        s = term
+        k = 1
+        while abs(term) > 5:
+            term *= MPZ(func1(k - 1))
+            term //= MPZ(func2(k - 1))
+            s += term
+            k += 1
+        return from_man_exp(s, -prec)
+    else:
+        alt = g < 0
+        if abs(g) < 1:
+            raise ValueError("Sum diverges like (%i)^n" % abs(1/g))
+        if p < 1 or (equal_valued(p, 1) and not alt):
+            raise ValueError("Sum diverges like n^%i" % (-p))
+        # We have polynomial convergence: use Richardson extrapolation
+        vold = None
+        ndig = prec_to_dps(prec)
+        while True:
+            # Need to use at least quad precision because a lot of cancellation
+            # might occur in the extrapolation process; we check the answer to
+            # make sure that the desired precision has been reached, too.
+            prec2 = 4*prec
+            term0 = (MPZ(term.p) << prec2) // term.q
+
+            def summand(k, _term=[term0]):
+                if k:
+                    k = int(k)
+                    _term[0] *= MPZ(func1(k - 1))
+                    _term[0] //= MPZ(func2(k - 1))
+                return make_mpf(from_man_exp(_term[0], -prec2))
+
+            with workprec(prec):
+                v = nsum(summand, [0, mpmath_inf], method='richardson')
+            vf = Float(v, ndig)
+            if vold is not None and vold == vf:
+                break
+            prec += prec  # double precision each time
+            vold = vf
+
+        return v._mpf_
+
+
+def evalf_prod(expr: 'Product', prec: int, options: OPT_DICT) -> TMP_RES:
+    if all((l[1] - l[2]).is_Integer for l in expr.limits):
+        result = evalf(expr.doit(), prec=prec, options=options)
+    else:
+        from sympy.concrete.summations import Sum
+        result = evalf(expr.rewrite(Sum), prec=prec, options=options)
+    return result
+
+
+def evalf_sum(expr: 'Sum', prec: int, options: OPT_DICT) -> TMP_RES:
+    from .numbers import Float
+    if 'subs' in options:
+        expr = expr.subs(options['subs'])
+    func = expr.function
+    limits = expr.limits
+    if len(limits) != 1 or len(limits[0]) != 3:
+        raise NotImplementedError
+    if func.is_zero:
+        return None, None, prec, None
+    prec2 = prec + 10
+    try:
+        n, a, b = limits[0]
+        if b is not S.Infinity or a is S.NegativeInfinity or a != int(a):
+            raise NotImplementedError
+        # Use fast hypergeometric summation if possible
+        v = hypsum(func, n, int(a), prec2)
+        delta = prec - fastlog(v)
+        if fastlog(v) < -10:
+            v = hypsum(func, n, int(a), delta)
+        return v, None, min(prec, delta), None
+    except NotImplementedError:
+        # Euler-Maclaurin summation for general series
+        eps = Float(2.0)**(-prec)
+        for i in range(1, 5):
+            m = n = 2**i * prec
+            s, err = expr.euler_maclaurin(m=m, n=n, eps=eps,
+                eval_integral=False)
+            err = err.evalf()
+            if err is S.NaN:
+                raise NotImplementedError
+            if err <= eps:
+                break
+        err = fastlog(evalf(abs(err), 20, options)[0])
+        re, im, re_acc, im_acc = evalf(s, prec2, options)
+        if re_acc is None:
+            re_acc = -err
+        if im_acc is None:
+            im_acc = -err
+        return re, im, re_acc, im_acc
+
+
+#----------------------------------------------------------------------------#
+#                                                                            #
+#                            Symbolic interface                              #
+#                                                                            #
+#----------------------------------------------------------------------------#
+
+def evalf_symbol(x: 'Expr', prec: int, options: OPT_DICT) -> TMP_RES:
+    val = options['subs'][x]
+    if isinstance(val, mpf):
+        if not val:
+            return None, None, None, None
+        return val._mpf_, None, prec, None
+    else:
+        if '_cache' not in options:
+            options['_cache'] = {}
+        cache = options['_cache']
+        cached, cached_prec = cache.get(x, (None, MINUS_INF))
+        if cached_prec >= prec:
+            return cached
+        v = evalf(sympify(val), prec, options)
+        cache[x] = (v, prec)
+        return v
+
+
+evalf_table: tDict[Type['Expr'], Callable[['Expr', int, OPT_DICT], TMP_RES]] = {}
+
+
+def _create_evalf_table():
+    global evalf_table
+    from sympy.concrete.products import Product
+    from sympy.concrete.summations import Sum
+    from .add import Add
+    from .mul import Mul
+    from .numbers import Exp1, Float, Half, ImaginaryUnit, Integer, NaN, NegativeOne, One, Pi, Rational, \
+        Zero, ComplexInfinity, AlgebraicNumber
+    from .power import Pow
+    from .symbol import Dummy, Symbol
+    from sympy.functions.elementary.complexes import Abs, im, re
+    from sympy.functions.elementary.exponential import exp, log
+    from sympy.functions.elementary.integers import ceiling, floor
+    from sympy.functions.elementary.piecewise import Piecewise
+    from sympy.functions.elementary.trigonometric import atan, cos, sin
+    from sympy.integrals.integrals import Integral
+    evalf_table = {
+        Symbol: evalf_symbol,
+        Dummy: evalf_symbol,
+        Float: evalf_float,
+        Rational: evalf_rational,
+        Integer: evalf_integer,
+        Zero: lambda x, prec, options: (None, None, prec, None),
+        One: lambda x, prec, options: (fone, None, prec, None),
+        Half: lambda x, prec, options: (fhalf, None, prec, None),
+        Pi: lambda x, prec, options: (mpf_pi(prec), None, prec, None),
+        Exp1: lambda x, prec, options: (mpf_e(prec), None, prec, None),
+        ImaginaryUnit: lambda x, prec, options: (None, fone, None, prec),
+        NegativeOne: lambda x, prec, options: (fnone, None, prec, None),
+        ComplexInfinity: lambda x, prec, options: S.ComplexInfinity,
+        NaN: lambda x, prec, options: (fnan, None, prec, None),
+
+        exp: evalf_exp,
+
+        cos: evalf_trig,
+        sin: evalf_trig,
+
+        Add: evalf_add,
+        Mul: evalf_mul,
+        Pow: evalf_pow,
+
+        log: evalf_log,
+        atan: evalf_atan,
+        Abs: evalf_abs,
+
+        re: evalf_re,
+        im: evalf_im,
+        floor: evalf_floor,
+        ceiling: evalf_ceiling,
+
+        Integral: evalf_integral,
+        Sum: evalf_sum,
+        Product: evalf_prod,
+        Piecewise: evalf_piecewise,
+
+        AlgebraicNumber: evalf_alg_num,
+    }
+
+
+def evalf(x: 'Expr', prec: int, options: OPT_DICT) -> TMP_RES:
+    """
+    Evaluate the ``Expr`` instance, ``x``
+    to a binary precision of ``prec``. This
+    function is supposed to be used internally.
+
+    Parameters
+    ==========
+
+    x : Expr
+        The formula to evaluate to a float.
+    prec : int
+        The binary precision that the output should have.
+    options : dict
+        A dictionary with the same entries as
+        ``EvalfMixin.evalf`` and in addition,
+        ``maxprec`` which is the maximum working precision.
+
+    Returns
+    =======
+
+    An optional tuple, ``(re, im, re_acc, im_acc)``
+    which are the real, imaginary, real accuracy
+    and imaginary accuracy respectively. ``re`` is
+    an mpf value tuple and so is ``im``. ``re_acc``
+    and ``im_acc`` are ints.
+
+    NB: all these return values can be ``None``.
+    If all values are ``None``, then that represents 0.
+    Note that 0 is also represented as ``fzero = (0, 0, 0, 0)``.
+    """
+    from sympy.functions.elementary.complexes import re as re_, im as im_
+    try:
+        rf = evalf_table[type(x)]
+        r = rf(x, prec, options)
+    except KeyError:
+        # Fall back to ordinary evalf if possible
+        if 'subs' in options:
+            x = x.subs(evalf_subs(prec, options['subs']))
+        xe = x._eval_evalf(prec)
+        if xe is None:
+            raise NotImplementedError
+        as_real_imag = getattr(xe, "as_real_imag", None)
+        if as_real_imag is None:
+            raise NotImplementedError # e.g. FiniteSet(-1.0, 1.0).evalf()
+        re, im = as_real_imag()
+        if re.has(re_) or im.has(im_):
+            raise NotImplementedError
+        if re == 0.0:
+            re = None
+            reprec = None
+        elif re.is_number:
+            re = re._to_mpmath(prec, allow_ints=False)._mpf_
+            reprec = prec
+        else:
+            raise NotImplementedError
+        if im == 0.0:
+            im = None
+            imprec = None
+        elif im.is_number:
+            im = im._to_mpmath(prec, allow_ints=False)._mpf_
+            imprec = prec
+        else:
+            raise NotImplementedError
+        r = re, im, reprec, imprec
+
+    if options.get("verbose"):
+        print("### input", x)
+        print("### output", to_str(r[0] or fzero, 50) if isinstance(r, tuple) else r)
+        print("### raw", r) # r[0], r[2]
+        print()
+    chop = options.get('chop', False)
+    if chop:
+        if chop is True:
+            chop_prec = prec
+        else:
+            # convert (approximately) from given tolerance;
+            # the formula here will will make 1e-i rounds to 0 for
+            # i in the range +/-27 while 2e-i will not be chopped
+            chop_prec = int(round(-3.321*math.log10(chop) + 2.5))
+            if chop_prec == 3:
+                chop_prec -= 1
+        r = chop_parts(r, chop_prec)
+    if options.get("strict"):
+        check_target(x, r, prec)
+    return r
+
+
+def quad_to_mpmath(q, ctx=None):
+    """Turn the quad returned by ``evalf`` into an ``mpf`` or ``mpc``. """
+    mpc = make_mpc if ctx is None else ctx.make_mpc
+    mpf = make_mpf if ctx is None else ctx.make_mpf
+    if q is S.ComplexInfinity:
+        raise NotImplementedError
+    re, im, _, _ = q
+    if im:
+        if not re:
+            re = fzero
+        return mpc((re, im))
+    elif re:
+        return mpf(re)
+    else:
+        return mpf(fzero)
+
+
+class EvalfMixin:
+    """Mixin class adding evalf capability."""
+
+    __slots__ = ()  # type: tTuple[str, ...]
+
+    def evalf(self, n=15, subs=None, maxn=100, chop=False, strict=False, quad=None, verbose=False):
+        """
+        Evaluate the given formula to an accuracy of *n* digits.
+
+        Parameters
+        ==========
+
+        subs : dict, optional
+            Substitute numerical values for symbols, e.g.
+            ``subs={x:3, y:1+pi}``. The substitutions must be given as a
+            dictionary.
+
+        maxn : int, optional
+            Allow a maximum temporary working precision of maxn digits.
+
+        chop : bool or number, optional
+            Specifies how to replace tiny real or imaginary parts in
+            subresults by exact zeros.
+
+            When ``True`` the chop value defaults to standard precision.
+
+            Otherwise the chop value is used to determine the
+            magnitude of "small" for purposes of chopping.
+
+            >>> from sympy import N
+            >>> x = 1e-4
+            >>> N(x, chop=True)
+            0.000100000000000000
+            >>> N(x, chop=1e-5)
+            0.000100000000000000
+            >>> N(x, chop=1e-4)
+            0
+
+        strict : bool, optional
+            Raise ``PrecisionExhausted`` if any subresult fails to
+            evaluate to full accuracy, given the available maxprec.
+
+        quad : str, optional
+            Choose algorithm for numerical quadrature. By default,
+            tanh-sinh quadrature is used. For oscillatory
+            integrals on an infinite interval, try ``quad='osc'``.
+
+        verbose : bool, optional
+            Print debug information.
+
+        Notes
+        =====
+
+        When Floats are naively substituted into an expression,
+        precision errors may adversely affect the result. For example,
+        adding 1e16 (a Float) to 1 will truncate to 1e16; if 1e16 is
+        then subtracted, the result will be 0.
+        That is exactly what happens in the following:
+
+        >>> from sympy.abc import x, y, z
+        >>> values = {x: 1e16, y: 1, z: 1e16}
+        >>> (x + y - z).subs(values)
+        0
+
+        Using the subs argument for evalf is the accurate way to
+        evaluate such an expression:
+
+        >>> (x + y - z).evalf(subs=values)
+        1.00000000000000
+        """
+        from .numbers import Float, Number
+        n = n if n is not None else 15
+
+        if subs and is_sequence(subs):
+            raise TypeError('subs must be given as a dictionary')
+
+        # for sake of sage that doesn't like evalf(1)
+        if n == 1 and isinstance(self, Number):
+            from .expr import _mag
+            rv = self.evalf(2, subs, maxn, chop, strict, quad, verbose)
+            m = _mag(rv)
+            rv = rv.round(1 - m)
+            return rv
+
+        if not evalf_table:
+            _create_evalf_table()
+        prec = dps_to_prec(n)
+        options = {'maxprec': max(prec, int(maxn*LG10)), 'chop': chop,
+               'strict': strict, 'verbose': verbose}
+        if subs is not None:
+            options['subs'] = subs
+        if quad is not None:
+            options['quad'] = quad
+        try:
+            result = evalf(self, prec + 4, options)
+        except NotImplementedError:
+            # Fall back to the ordinary evalf
+            if hasattr(self, 'subs') and subs is not None:  # issue 20291
+                v = self.subs(subs)._eval_evalf(prec)
+            else:
+                v = self._eval_evalf(prec)
+            if v is None:
+                return self
+            elif not v.is_number:
+                return v
+            try:
+                # If the result is numerical, normalize it
+                result = evalf(v, prec, options)
+            except NotImplementedError:
+                # Probably contains symbols or unknown functions
+                return v
+        if result is S.ComplexInfinity:
+            return result
+        re, im, re_acc, im_acc = result
+        if re is S.NaN or im is S.NaN:
+            return S.NaN
+        if re:
+            p = max(min(prec, re_acc), 1)
+            re = Float._new(re, p)
+        else:
+            re = S.Zero
+        if im:
+            p = max(min(prec, im_acc), 1)
+            im = Float._new(im, p)
+            return re + im*S.ImaginaryUnit
+        else:
+            return re
+
+    n = evalf
+
+    def _evalf(self, prec):
+        """Helper for evalf. Does the same thing but takes binary precision"""
+        r = self._eval_evalf(prec)
+        if r is None:
+            r = self
+        return r
+
+    def _eval_evalf(self, prec):
+        return
+
+    def _to_mpmath(self, prec, allow_ints=True):
+        # mpmath functions accept ints as input
+        errmsg = "cannot convert to mpmath number"
+        if allow_ints and self.is_Integer:
+            return self.p
+        if hasattr(self, '_as_mpf_val'):
+            return make_mpf(self._as_mpf_val(prec))
+        try:
+            result = evalf(self, prec, {})
+            return quad_to_mpmath(result)
+        except NotImplementedError:
+            v = self._eval_evalf(prec)
+            if v is None:
+                raise ValueError(errmsg)
+            if v.is_Float:
+                return make_mpf(v._mpf_)
+            # Number + Number*I is also fine
+            re, im = v.as_real_imag()
+            if allow_ints and re.is_Integer:
+                re = from_int(re.p)
+            elif re.is_Float:
+                re = re._mpf_
+            else:
+                raise ValueError(errmsg)
+            if allow_ints and im.is_Integer:
+                im = from_int(im.p)
+            elif im.is_Float:
+                im = im._mpf_
+            else:
+                raise ValueError(errmsg)
+            return make_mpc((re, im))
+
+
+def N(x, n=15, **options):
+    r"""
+    Calls x.evalf(n, \*\*options).
+
+    Explanations
+    ============
+
+    Both .n() and N() are equivalent to .evalf(); use the one that you like better.
+    See also the docstring of .evalf() for information on the options.
+
+    Examples
+    ========
+
+    >>> from sympy import Sum, oo, N
+    >>> from sympy.abc import k
+    >>> Sum(1/k**k, (k, 1, oo))
+    Sum(k**(-k), (k, 1, oo))
+    >>> N(_, 4)
+    1.291
+
+    """
+    # by using rational=True, any evaluation of a string
+    # will be done using exact values for the Floats
+    return sympify(x, rational=True).evalf(n, **options)
+
+
+def _evalf_with_bounded_error(x: 'Expr', eps: 'Optional[Expr]' = None,
+                              m: int = 0,
+                              options: Optional[OPT_DICT] = None) -> TMP_RES:
+    """
+    Evaluate *x* to within a bounded absolute error.
+
+    Parameters
+    ==========
+
+    x : Expr
+        The quantity to be evaluated.
+    eps : Expr, None, optional (default=None)
+        Positive real upper bound on the acceptable error.
+    m : int, optional (default=0)
+        If *eps* is None, then use 2**(-m) as the upper bound on the error.
+    options: OPT_DICT
+        As in the ``evalf`` function.
+
+    Returns
+    =======
+
+    A tuple ``(re, im, re_acc, im_acc)``, as returned by ``evalf``.
+
+    See Also
+    ========
+
+    evalf
+
+    """
+    if eps is not None:
+        if not (eps.is_Rational or eps.is_Float) or not eps > 0:
+            raise ValueError("eps must be positive")
+        r, _, _, _ = evalf(1/eps, 1, {})
+        m = fastlog(r)
+
+    c, d, _, _ = evalf(x, 1, {})
+    # Note: If x = a + b*I, then |a| <= 2|c| and |b| <= 2|d|, with equality
+    # only in the zero case.
+    # If a is non-zero, then |c| = 2**nc for some integer nc, and c has
+    # bitcount 1. Therefore 2**fastlog(c) = 2**(nc+1) = 2|c| is an upper bound
+    # on |a|. Likewise for b and d.
+    nr, ni = fastlog(c), fastlog(d)
+    n = max(nr, ni) + 1
+    # If x is 0, then n is MINUS_INF, and p will be 1. Otherwise,
+    # n - 1 bits get us past the integer parts of a and b, and +1 accounts for
+    # the factor of <= sqrt(2) that is |x|/max(|a|, |b|).
+    p = max(1, m + n + 1)
+
+    options = options or {}
+    return evalf(x, p, options)

venv/lib/python3.10/site-packages/sympy/core/mod.py

@@ -0,0 +1,238 @@
+from .add import Add
+from .exprtools import gcd_terms
+from .function import Function
+from .kind import NumberKind
+from .logic import fuzzy_and, fuzzy_not
+from .mul import Mul
+from .numbers import equal_valued
+from .singleton import S
+
+
+class Mod(Function):
+    """Represents a modulo operation on symbolic expressions.
+
+    Parameters
+    ==========
+
+    p : Expr
+        Dividend.
+
+    q : Expr
+        Divisor.
+
+    Notes
+    =====
+
+    The convention used is the same as Python's: the remainder always has the
+    same sign as the divisor.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x, y
+    >>> x**2 % y
+    Mod(x**2, y)
+    >>> _.subs({x: 5, y: 6})
+    1
+
+    """
+
+    kind = NumberKind
+
+    @classmethod
+    def eval(cls, p, q):
+        def number_eval(p, q):
+            """Try to return p % q if both are numbers or +/-p is known
+            to be less than or equal q.
+            """
+
+            if q.is_zero:
+                raise ZeroDivisionError("Modulo by zero")
+            if p is S.NaN or q is S.NaN or p.is_finite is False or q.is_finite is False:
+                return S.NaN
+            if p is S.Zero or p in (q, -q) or (p.is_integer and q == 1):
+                return S.Zero
+
+            if q.is_Number:
+                if p.is_Number:
+                    return p%q
+                if q == 2:
+                    if p.is_even:
+                        return S.Zero
+                    elif p.is_odd:
+                        return S.One
+
+            if hasattr(p, '_eval_Mod'):
+                rv = getattr(p, '_eval_Mod')(q)
+                if rv is not None:
+                    return rv
+
+            # by ratio
+            r = p/q
+            if r.is_integer:
+                return S.Zero
+            try:
+                d = int(r)
+            except TypeError:
+                pass
+            else:
+                if isinstance(d, int):
+                    rv = p - d*q
+                    if (rv*q < 0) == True:
+                        rv += q
+                    return rv
+
+            # by difference
+            # -2|q| < p < 2|q|
+            d = abs(p)
+            for _ in range(2):
+                d -= abs(q)
+                if d.is_negative:
+                    if q.is_positive:
+                        if p.is_positive:
+                            return d + q
+                        elif p.is_negative:
+                            return -d
+                    elif q.is_negative:
+                        if p.is_positive:
+                            return d
+                        elif p.is_negative:
+                            return -d + q
+                    break
+
+        rv = number_eval(p, q)
+        if rv is not None:
+            return rv
+
+        # denest
+        if isinstance(p, cls):
+            qinner = p.args[1]
+            if qinner % q == 0:
+                return cls(p.args[0], q)
+            elif (qinner*(q - qinner)).is_nonnegative:
+                # |qinner| < |q| and have same sign
+                return p
+        elif isinstance(-p, cls):
+            qinner = (-p).args[1]
+            if qinner % q == 0:
+                return cls(-(-p).args[0], q)
+            elif (qinner*(q + qinner)).is_nonpositive:
+                # |qinner| < |q| and have different sign
+                return p
+        elif isinstance(p, Add):
+            # separating into modulus and non modulus
+            both_l = non_mod_l, mod_l = [], []
+            for arg in p.args:
+                both_l[isinstance(arg, cls)].append(arg)
+            # if q same for all
+            if mod_l and all(inner.args[1] == q for inner in mod_l):
+                net = Add(*non_mod_l) + Add(*[i.args[0] for i in mod_l])
+                return cls(net, q)
+
+        elif isinstance(p, Mul):
+            # separating into modulus and non modulus
+            both_l = non_mod_l, mod_l = [], []
+            for arg in p.args:
+                both_l[isinstance(arg, cls)].append(arg)
+
+            if mod_l and all(inner.args[1] == q for inner in mod_l) and all(t.is_integer for t in p.args) and q.is_integer:
+                # finding distributive term
+                non_mod_l = [cls(x, q) for x in non_mod_l]
+                mod = []
+                non_mod = []
+                for j in non_mod_l:
+                    if isinstance(j, cls):
+                        mod.append(j.args[0])
+                    else:
+                        non_mod.append(j)
+                prod_mod = Mul(*mod)
+                prod_non_mod = Mul(*non_mod)
+                prod_mod1 = Mul(*[i.args[0] for i in mod_l])
+                net = prod_mod1*prod_mod
+                return prod_non_mod*cls(net, q)
+
+            if q.is_Integer and q is not S.One:
+                non_mod_l = [i % q if i.is_Integer and (i % q is not S.Zero) else i for
+                             i in non_mod_l]
+
+            p = Mul(*(non_mod_l + mod_l))
+
+        # XXX other possibilities?
+
+        from sympy.polys.polyerrors import PolynomialError
+        from sympy.polys.polytools import gcd
+
+        # extract gcd; any further simplification should be done by the user
+        try:
+            G = gcd(p, q)
+            if not equal_valued(G, 1):
+                p, q = [gcd_terms(i/G, clear=False, fraction=False)
+                        for i in (p, q)]
+        except PolynomialError:  # issue 21373
+            G = S.One
+        pwas, qwas = p, q
+
+        # simplify terms
+        # (x + y + 2) % x -> Mod(y + 2, x)
+        if p.is_Add:
+            args = []
+            for i in p.args:
+                a = cls(i, q)
+                if a.count(cls) > i.count(cls):
+                    args.append(i)
+                else:
+                    args.append(a)
+            if args != list(p.args):
+                p = Add(*args)
+
+        else:
+            # handle coefficients if they are not Rational
+            # since those are not handled by factor_terms
+            # e.g. Mod(.6*x, .3*y) -> 0.3*Mod(2*x, y)
+            cp, p = p.as_coeff_Mul()
+            cq, q = q.as_coeff_Mul()
+            ok = False
+            if not cp.is_Rational or not cq.is_Rational:
+                r = cp % cq
+                if equal_valued(r, 0):
+                    G *= cq
+                    p *= int(cp/cq)
+                    ok = True
+            if not ok:
+                p = cp*p
+                q = cq*q
+
+        # simple -1 extraction
+        if p.could_extract_minus_sign() and q.could_extract_minus_sign():
+            G, p, q = [-i for i in (G, p, q)]
+
+        # check again to see if p and q can now be handled as numbers
+        rv = number_eval(p, q)
+        if rv is not None:
+            return rv*G
+
+        # put 1.0 from G on inside
+        if G.is_Float and equal_valued(G, 1):
+            p *= G
+            return cls(p, q, evaluate=False)
+        elif G.is_Mul and G.args[0].is_Float and equal_valued(G.args[0], 1):
+            p = G.args[0]*p
+            G = Mul._from_args(G.args[1:])
+        return G*cls(p, q, evaluate=(p, q) != (pwas, qwas))
+
+    def _eval_is_integer(self):
+        p, q = self.args
+        if fuzzy_and([p.is_integer, q.is_integer, fuzzy_not(q.is_zero)]):
+            return True
+
+    def _eval_is_nonnegative(self):
+        if self.args[1].is_positive:
+            return True
+
+    def _eval_is_nonpositive(self):
+        if self.args[1].is_negative:
+            return True
+
+    def _eval_rewrite_as_floor(self, a, b, **kwargs):
+        from sympy.functions.elementary.integers import floor
+        return a - b*floor(a/b)

venv/lib/python3.10/site-packages/sympy/core/parameters.py

@@ -0,0 +1,161 @@
+"""Thread-safe global parameters"""
+
+from .cache import clear_cache
+from contextlib import contextmanager
+from threading import local
+
+class _global_parameters(local):
+    """
+    Thread-local global parameters.
+
+    Explanation
+    ===========
+
+    This class generates thread-local container for SymPy's global parameters.
+    Every global parameters must be passed as keyword argument when generating
+    its instance.
+    A variable, `global_parameters` is provided as default instance for this class.
+
+    WARNING! Although the global parameters are thread-local, SymPy's cache is not
+    by now.
+    This may lead to undesired result in multi-threading operations.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x
+    >>> from sympy.core.cache import clear_cache
+    >>> from sympy.core.parameters import global_parameters as gp
+
+    >>> gp.evaluate
+    True
+    >>> x+x
+    2*x
+
+    >>> log = []
+    >>> def f():
+    ...     clear_cache()
+    ...     gp.evaluate = False
+    ...     log.append(x+x)
+    ...     clear_cache()
+    >>> import threading
+    >>> thread = threading.Thread(target=f)
+    >>> thread.start()
+    >>> thread.join()
+
+    >>> print(log)
+    [x + x]
+
+    >>> gp.evaluate
+    True
+    >>> x+x
+    2*x
+
+    References
+    ==========
+
+    .. [1] https://docs.python.org/3/library/threading.html
+
+    """
+    def __init__(self, **kwargs):
+        self.__dict__.update(kwargs)
+
+    def __setattr__(self, name, value):
+        if getattr(self, name) != value:
+            clear_cache()
+        return super().__setattr__(name, value)
+
+global_parameters = _global_parameters(evaluate=True, distribute=True, exp_is_pow=False)
+
+@contextmanager
+def evaluate(x):
+    """ Control automatic evaluation
+
+    Explanation
+    ===========
+
+    This context manager controls whether or not all SymPy functions evaluate
+    by default.
+
+    Note that much of SymPy expects evaluated expressions.  This functionality
+    is experimental and is unlikely to function as intended on large
+    expressions.
+
+    Examples
+    ========
+
+    >>> from sympy import evaluate
+    >>> from sympy.abc import x
+    >>> print(x + x)
+    2*x
+    >>> with evaluate(False):
+    ...     print(x + x)
+    x + x
+    """
+
+    old = global_parameters.evaluate
+
+    try:
+        global_parameters.evaluate = x
+        yield
+    finally:
+        global_parameters.evaluate = old
+
+
+@contextmanager
+def distribute(x):
+    """ Control automatic distribution of Number over Add
+
+    Explanation
+    ===========
+
+    This context manager controls whether or not Mul distribute Number over
+    Add. Plan is to avoid distributing Number over Add in all of sympy. Once
+    that is done, this contextmanager will be removed.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x
+    >>> from sympy.core.parameters import distribute
+    >>> print(2*(x + 1))
+    2*x + 2
+    >>> with distribute(False):
+    ...     print(2*(x + 1))
+    2*(x + 1)
+    """
+
+    old = global_parameters.distribute
+
+    try:
+        global_parameters.distribute = x
+        yield
+    finally:
+        global_parameters.distribute = old
+
+
+@contextmanager
+def _exp_is_pow(x):
+    """
+    Control whether `e^x` should be represented as ``exp(x)`` or a ``Pow(E, x)``.
+
+    Examples
+    ========
+
+    >>> from sympy import exp
+    >>> from sympy.abc import x
+    >>> from sympy.core.parameters import _exp_is_pow
+    >>> with _exp_is_pow(True): print(type(exp(x)))
+    <class 'sympy.core.power.Pow'>
+    >>> with _exp_is_pow(False): print(type(exp(x)))
+    exp
+    """
+    old = global_parameters.exp_is_pow
+
+    clear_cache()
+    try:
+        global_parameters.exp_is_pow = x
+        yield
+    finally:
+        clear_cache()
+        global_parameters.exp_is_pow = old

venv/lib/python3.10/site-packages/sympy/core/power.py

@@ -0,0 +1,2004 @@
+from __future__ import annotations
+from typing import Callable
+from math import log as _log, sqrt as _sqrt
+from itertools import product
+
+from .sympify import _sympify
+from .cache import cacheit
+from .singleton import S
+from .expr import Expr
+from .evalf import PrecisionExhausted
+from .function import (expand_complex, expand_multinomial,
+    expand_mul, _mexpand, PoleError)
+from .logic import fuzzy_bool, fuzzy_not, fuzzy_and, fuzzy_or
+from .parameters import global_parameters
+from .relational import is_gt, is_lt
+from .kind import NumberKind, UndefinedKind
+from sympy.external.gmpy import HAS_GMPY, gmpy
+from sympy.utilities.iterables import sift
+from sympy.utilities.exceptions import sympy_deprecation_warning
+from sympy.utilities.misc import as_int
+from sympy.multipledispatch import Dispatcher
+
+from mpmath.libmp import sqrtrem as mpmath_sqrtrem
+
+
+
+def isqrt(n):
+    """Return the largest integer less than or equal to sqrt(n)."""
+    if n < 0:
+        raise ValueError("n must be nonnegative")
+    n = int(n)
+
+    # Fast path: with IEEE 754 binary64 floats and a correctly-rounded
+    # math.sqrt, int(math.sqrt(n)) works for any integer n satisfying 0 <= n <
+    # 4503599761588224 = 2**52 + 2**27. But Python doesn't guarantee either
+    # IEEE 754 format floats *or* correct rounding of math.sqrt, so check the
+    # answer and fall back to the slow method if necessary.
+    if n < 4503599761588224:
+        s = int(_sqrt(n))
+        if 0 <= n - s*s <= 2*s:
+            return s
+
+    return integer_nthroot(n, 2)[0]
+
+
+def integer_nthroot(y, n):
+    """
+    Return a tuple containing x = floor(y**(1/n))
+    and a boolean indicating whether the result is exact (that is,
+    whether x**n == y).
+
+    Examples
+    ========
+
+    >>> from sympy import integer_nthroot
+    >>> integer_nthroot(16, 2)
+    (4, True)
+    >>> integer_nthroot(26, 2)
+    (5, False)
+
+    To simply determine if a number is a perfect square, the is_square
+    function should be used:
+
+    >>> from sympy.ntheory.primetest import is_square
+    >>> is_square(26)
+    False
+
+    See Also
+    ========
+    sympy.ntheory.primetest.is_square
+    integer_log
+    """
+    y, n = as_int(y), as_int(n)
+    if y < 0:
+        raise ValueError("y must be nonnegative")
+    if n < 1:
+        raise ValueError("n must be positive")
+    if HAS_GMPY and n < 2**63:
+        # Currently it works only for n < 2**63, else it produces TypeError
+        # sympy issue: https://github.com/sympy/sympy/issues/18374
+        # gmpy2 issue: https://github.com/aleaxit/gmpy/issues/257
+        if HAS_GMPY >= 2:
+            x, t = gmpy.iroot(y, n)
+        else:
+            x, t = gmpy.root(y, n)
+        return as_int(x), bool(t)
+    return _integer_nthroot_python(y, n)
+
+def _integer_nthroot_python(y, n):
+    if y in (0, 1):
+        return y, True
+    if n == 1:
+        return y, True
+    if n == 2:
+        x, rem = mpmath_sqrtrem(y)
+        return int(x), not rem
+    if n >= y.bit_length():
+        return 1, False
+    # Get initial estimate for Newton's method. Care must be taken to
+    # avoid overflow
+    try:
+        guess = int(y**(1./n) + 0.5)
+    except OverflowError:
+        exp = _log(y, 2)/n
+        if exp > 53:
+            shift = int(exp - 53)
+            guess = int(2.0**(exp - shift) + 1) << shift
+        else:
+            guess = int(2.0**exp)
+    if guess > 2**50:
+        # Newton iteration
+        xprev, x = -1, guess
+        while 1:
+            t = x**(n - 1)
+            xprev, x = x, ((n - 1)*x + y//t)//n
+            if abs(x - xprev) < 2:
+                break
+    else:
+        x = guess
+    # Compensate
+    t = x**n
+    while t < y:
+        x += 1
+        t = x**n
+    while t > y:
+        x -= 1
+        t = x**n
+    return int(x), t == y  # int converts long to int if possible
+
+
+def integer_log(y, x):
+    r"""
+    Returns ``(e, bool)`` where e is the largest nonnegative integer
+    such that :math:`|y| \geq |x^e|` and ``bool`` is True if $y = x^e$.
+
+    Examples
+    ========
+
+    >>> from sympy import integer_log
+    >>> integer_log(125, 5)
+    (3, True)
+    >>> integer_log(17, 9)
+    (1, False)
+    >>> integer_log(4, -2)
+    (2, True)
+    >>> integer_log(-125,-5)
+    (3, True)
+
+    See Also
+    ========
+    integer_nthroot
+    sympy.ntheory.primetest.is_square
+    sympy.ntheory.factor_.multiplicity
+    sympy.ntheory.factor_.perfect_power
+    """
+    if x == 1:
+        raise ValueError('x cannot take value as 1')
+    if y == 0:
+        raise ValueError('y cannot take value as 0')
+
+    if x in (-2, 2):
+        x = int(x)
+        y = as_int(y)
+        e = y.bit_length() - 1
+        return e, x**e == y
+    if x < 0:
+        n, b = integer_log(y if y > 0 else -y, -x)
+        return n, b and bool(n % 2 if y < 0 else not n % 2)
+
+    x = as_int(x)
+    y = as_int(y)
+    r = e = 0
+    while y >= x:
+        d = x
+        m = 1
+        while y >= d:
+            y, rem = divmod(y, d)
+            r = r or rem
+            e += m
+            if y > d:
+                d *= d
+                m *= 2
+    return e, r == 0 and y == 1
+
+
+class Pow(Expr):
+    """
+    Defines the expression x**y as "x raised to a power y"
+
+    .. deprecated:: 1.7
+
+       Using arguments that aren't subclasses of :class:`~.Expr` in core
+       operators (:class:`~.Mul`, :class:`~.Add`, and :class:`~.Pow`) is
+       deprecated. See :ref:`non-expr-args-deprecated` for details.
+
+    Singleton definitions involving (0, 1, -1, oo, -oo, I, -I):
+
+    +--------------+---------+-----------------------------------------------+
+    | expr         | value   | reason                                        |
+    +==============+=========+===============================================+
+    | z**0         | 1       | Although arguments over 0**0 exist, see [2].  |
+    +--------------+---------+-----------------------------------------------+
+    | z**1         | z       |                                               |
+    +--------------+---------+-----------------------------------------------+
+    | (-oo)**(-1)  | 0       |                                               |
+    +--------------+---------+-----------------------------------------------+
+    | (-1)**-1     | -1      |                                               |
+    +--------------+---------+-----------------------------------------------+
+    | S.Zero**-1   | zoo     | This is not strictly true, as 0**-1 may be    |
+    |              |         | undefined, but is convenient in some contexts |
+    |              |         | where the base is assumed to be positive.     |
+    +--------------+---------+-----------------------------------------------+
+    | 1**-1        | 1       |                                               |
+    +--------------+---------+-----------------------------------------------+
+    | oo**-1       | 0       |                                               |
+    +--------------+---------+-----------------------------------------------+
+    | 0**oo        | 0       | Because for all complex numbers z near        |
+    |              |         | 0, z**oo -> 0.                                |
+    +--------------+---------+-----------------------------------------------+
+    | 0**-oo       | zoo     | This is not strictly true, as 0**oo may be    |
+    |              |         | oscillating between positive and negative     |
+    |              |         | values or rotating in the complex plane.      |
+    |              |         | It is convenient, however, when the base      |
+    |              |         | is positive.                                  |
+    +--------------+---------+-----------------------------------------------+
+    | 1**oo        | nan     | Because there are various cases where         |
+    | 1**-oo       |         | lim(x(t),t)=1, lim(y(t),t)=oo (or -oo),       |
+    |              |         | but lim( x(t)**y(t), t) != 1.  See [3].       |
+    +--------------+---------+-----------------------------------------------+
+    | b**zoo       | nan     | Because b**z has no limit as z -> zoo         |
+    +--------------+---------+-----------------------------------------------+
+    | (-1)**oo     | nan     | Because of oscillations in the limit.         |
+    | (-1)**(-oo)  |         |                                               |
+    +--------------+---------+-----------------------------------------------+
+    | oo**oo       | oo      |                                               |
+    +--------------+---------+-----------------------------------------------+
+    | oo**-oo      | 0       |                                               |
+    +--------------+---------+-----------------------------------------------+
+    | (-oo)**oo    | nan     |                                               |
+    | (-oo)**-oo   |         |                                               |
+    +--------------+---------+-----------------------------------------------+
+    | oo**I        | nan     | oo**e could probably be best thought of as    |
+    | (-oo)**I     |         | the limit of x**e for real x as x tends to    |
+    |              |         | oo. If e is I, then the limit does not exist  |
+    |              |         | and nan is used to indicate that.             |
+    +--------------+---------+-----------------------------------------------+
+    | oo**(1+I)    | zoo     | If the real part of e is positive, then the   |
+    | (-oo)**(1+I) |         | limit of abs(x**e) is oo. So the limit value  |
+    |              |         | is zoo.                                       |
+    +--------------+---------+-----------------------------------------------+
+    | oo**(-1+I)   | 0       | If the real part of e is negative, then the   |
+    | -oo**(-1+I)  |         | limit is 0.                                   |
+    +--------------+---------+-----------------------------------------------+
+
+    Because symbolic computations are more flexible than floating point
+    calculations and we prefer to never return an incorrect answer,
+    we choose not to conform to all IEEE 754 conventions.  This helps
+    us avoid extra test-case code in the calculation of limits.
+
+    See Also
+    ========
+
+    sympy.core.numbers.Infinity
+    sympy.core.numbers.NegativeInfinity
+    sympy.core.numbers.NaN
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Exponentiation
+    .. [2] https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero
+    .. [3] https://en.wikipedia.org/wiki/Indeterminate_forms
+
+    """
+    is_Pow = True
+
+    __slots__ = ('is_commutative',)
+
+    args: tuple[Expr, Expr]
+    _args: tuple[Expr, Expr]
+
+    @cacheit
+    def __new__(cls, b, e, evaluate=None):
+        if evaluate is None:
+            evaluate = global_parameters.evaluate
+
+        b = _sympify(b)
+        e = _sympify(e)
+
+        # XXX: This can be removed when non-Expr args are disallowed rather
+        # than deprecated.
+        from .relational import Relational
+        if isinstance(b, Relational) or isinstance(e, Relational):
+            raise TypeError('Relational cannot be used in Pow')
+
+        # XXX: This should raise TypeError once deprecation period is over:
+        for arg in [b, e]:
+            if not isinstance(arg, Expr):
+                sympy_deprecation_warning(
+                    f"""
+    Using non-Expr arguments in Pow is deprecated (in this case, one of the
+    arguments is of type {type(arg).__name__!r}).
+
+    If you really did intend to construct a power with this base, use the **
+    operator instead.""",
+                    deprecated_since_version="1.7",
+                    active_deprecations_target="non-expr-args-deprecated",
+                    stacklevel=4,
+                )
+
+        if evaluate:
+            if e is S.ComplexInfinity:
+                return S.NaN
+            if e is S.Infinity:
+                if is_gt(b, S.One):
+                    return S.Infinity
+                if is_gt(b, S.NegativeOne) and is_lt(b, S.One):
+                    return S.Zero
+                if is_lt(b, S.NegativeOne):
+                    if b.is_finite:
+                        return S.ComplexInfinity
+                    if b.is_finite is False:
+                        return S.NaN
+            if e is S.Zero:
+                return S.One
+            elif e is S.One:
+                return b
+            elif e == -1 and not b:
+                return S.ComplexInfinity
+            elif e.__class__.__name__ == "AccumulationBounds":
+                if b == S.Exp1:
+                    from sympy.calculus.accumulationbounds import AccumBounds
+                    return AccumBounds(Pow(b, e.min), Pow(b, e.max))
+            # autosimplification if base is a number and exp odd/even
+            # if base is Number then the base will end up positive; we
+            # do not do this with arbitrary expressions since symbolic
+            # cancellation might occur as in (x - 1)/(1 - x) -> -1. If
+            # we returned Piecewise((-1, Ne(x, 1))) for such cases then
+            # we could do this...but we don't
+            elif (e.is_Symbol and e.is_integer or e.is_Integer
+                    ) and (b.is_number and b.is_Mul or b.is_Number
+                    ) and b.could_extract_minus_sign():
+                if e.is_even:
+                    b = -b
+                elif e.is_odd:
+                    return -Pow(-b, e)
+            if S.NaN in (b, e):  # XXX S.NaN**x -> S.NaN under assumption that x != 0
+                return S.NaN
+            elif b is S.One:
+                if abs(e).is_infinite:
+                    return S.NaN
+                return S.One
+            else:
+                # recognize base as E
+                from sympy.functions.elementary.exponential import exp_polar
+                if not e.is_Atom and b is not S.Exp1 and not isinstance(b, exp_polar):
+                    from .exprtools import factor_terms
+                    from sympy.functions.elementary.exponential import log
+                    from sympy.simplify.radsimp import fraction
+                    c, ex = factor_terms(e, sign=False).as_coeff_Mul()
+                    num, den = fraction(ex)
+                    if isinstance(den, log) and den.args[0] == b:
+                        return S.Exp1**(c*num)
+                    elif den.is_Add:
+                        from sympy.functions.elementary.complexes import sign, im
+                        s = sign(im(b))
+                        if s.is_Number and s and den == \
+                                log(-factor_terms(b, sign=False)) + s*S.ImaginaryUnit*S.Pi:
+                            return S.Exp1**(c*num)
+
+                obj = b._eval_power(e)
+                if obj is not None:
+                    return obj
+        obj = Expr.__new__(cls, b, e)
+        obj = cls._exec_constructor_postprocessors(obj)
+        if not isinstance(obj, Pow):
+            return obj
+        obj.is_commutative = (b.is_commutative and e.is_commutative)
+        return obj
+
+    def inverse(self, argindex=1):
+        if self.base == S.Exp1:
+            from sympy.functions.elementary.exponential import log
+            return log
+        return None
+
+    @property
+    def base(self) -> Expr:
+        return self._args[0]
+
+    @property
+    def exp(self) -> Expr:
+        return self._args[1]
+
+    @property
+    def kind(self):
+        if self.exp.kind is NumberKind:
+            return self.base.kind
+        else:
+            return UndefinedKind
+
+    @classmethod
+    def class_key(cls):
+        return 3, 2, cls.__name__
+
+    def _eval_refine(self, assumptions):
+        from sympy.assumptions.ask import ask, Q
+        b, e = self.as_base_exp()
+        if ask(Q.integer(e), assumptions) and b.could_extract_minus_sign():
+            if ask(Q.even(e), assumptions):
+                return Pow(-b, e)
+            elif ask(Q.odd(e), assumptions):
+                return -Pow(-b, e)
+
+    def _eval_power(self, other):
+        b, e = self.as_base_exp()
+        if b is S.NaN:
+            return (b**e)**other  # let __new__ handle it
+
+        s = None
+        if other.is_integer:
+            s = 1
+        elif b.is_polar:  # e.g. exp_polar, besselj, var('p', polar=True)...
+            s = 1
+        elif e.is_extended_real is not None:
+            from sympy.functions.elementary.complexes import arg, im, re, sign
+            from sympy.functions.elementary.exponential import exp, log
+            from sympy.functions.elementary.integers import floor
+            # helper functions ===========================
+            def _half(e):
+                """Return True if the exponent has a literal 2 as the
+                denominator, else None."""
+                if getattr(e, 'q', None) == 2:
+                    return True
+                n, d = e.as_numer_denom()
+                if n.is_integer and d == 2:
+                    return True
+            def _n2(e):
+                """Return ``e`` evaluated to a Number with 2 significant
+                digits, else None."""
+                try:
+                    rv = e.evalf(2, strict=True)
+                    if rv.is_Number:
+                        return rv
+                except PrecisionExhausted:
+                    pass
+            # ===================================================
+            if e.is_extended_real:
+                # we need _half(other) with constant floor or
+                # floor(S.Half - e*arg(b)/2/pi) == 0
+
+
+                # handle -1 as special case
+                if e == -1:
+                    # floor arg. is 1/2 + arg(b)/2/pi
+                    if _half(other):
+                        if b.is_negative is True:
+                            return S.NegativeOne**other*Pow(-b, e*other)
+                        elif b.is_negative is False:  # XXX ok if im(b) != 0?
+                            return Pow(b, -other)
+                elif e.is_even:
+                    if b.is_extended_real:
+                        b = abs(b)
+                    if b.is_imaginary:
+                        b = abs(im(b))*S.ImaginaryUnit
+
+                if (abs(e) < 1) == True or e == 1:
+                    s = 1  # floor = 0
+                elif b.is_extended_nonnegative:
+                    s = 1  # floor = 0
+                elif re(b).is_extended_nonnegative and (abs(e) < 2) == True:
+                    s = 1  # floor = 0
+                elif _half(other):
+                    s = exp(2*S.Pi*S.ImaginaryUnit*other*floor(
+                        S.Half - e*arg(b)/(2*S.Pi)))
+                    if s.is_extended_real and _n2(sign(s) - s) == 0:
+                        s = sign(s)
+                    else:
+                        s = None
+            else:
+                # e.is_extended_real is False requires:
+                #     _half(other) with constant floor or
+                #     floor(S.Half - im(e*log(b))/2/pi) == 0
+                try:
+                    s = exp(2*S.ImaginaryUnit*S.Pi*other*
+                        floor(S.Half - im(e*log(b))/2/S.Pi))
+                    # be careful to test that s is -1 or 1 b/c sign(I) == I:
+                    # so check that s is real
+                    if s.is_extended_real and _n2(sign(s) - s) == 0:
+                        s = sign(s)
+                    else:
+                        s = None
+                except PrecisionExhausted:
+                    s = None
+
+        if s is not None:
+            return s*Pow(b, e*other)
+
+    def _eval_Mod(self, q):
+        r"""A dispatched function to compute `b^e \bmod q`, dispatched
+        by ``Mod``.
+
+        Notes
+        =====
+
+        Algorithms:
+
+        1. For unevaluated integer power, use built-in ``pow`` function
+        with 3 arguments, if powers are not too large wrt base.
+
+        2. For very large powers, use totient reduction if $e \ge \log(m)$.
+        Bound on m, is for safe factorization memory wise i.e. $m^{1/4}$.
+        For pollard-rho to be faster than built-in pow $\log(e) > m^{1/4}$
+        check is added.
+
+        3. For any unevaluated power found in `b` or `e`, the step 2
+        will be recursed down to the base and the exponent
+        such that the $b \bmod q$ becomes the new base and
+        $\phi(q) + e \bmod \phi(q)$ becomes the new exponent, and then
+        the computation for the reduced expression can be done.
+        """
+
+        base, exp = self.base, self.exp
+
+        if exp.is_integer and exp.is_positive:
+            if q.is_integer and base % q == 0:
+                return S.Zero
+
+            from sympy.ntheory.factor_ import totient
+
+            if base.is_Integer and exp.is_Integer and q.is_Integer:
+                b, e, m = int(base), int(exp), int(q)
+                mb = m.bit_length()
+                if mb <= 80 and e >= mb and e.bit_length()**4 >= m:
+                    phi = int(totient(m))
+                    return Integer(pow(b, phi + e%phi, m))
+                return Integer(pow(b, e, m))
+
+            from .mod import Mod
+
+            if isinstance(base, Pow) and base.is_integer and base.is_number:
+                base = Mod(base, q)
+                return Mod(Pow(base, exp, evaluate=False), q)
+
+            if isinstance(exp, Pow) and exp.is_integer and exp.is_number:
+                bit_length = int(q).bit_length()
+                # XXX Mod-Pow actually attempts to do a hanging evaluation
+                # if this dispatched function returns None.
+                # May need some fixes in the dispatcher itself.
+                if bit_length <= 80:
+                    phi = totient(q)
+                    exp = phi + Mod(exp, phi)
+                    return Mod(Pow(base, exp, evaluate=False), q)
+
+    def _eval_is_even(self):
+        if self.exp.is_integer and self.exp.is_positive:
+            return self.base.is_even
+
+    def _eval_is_negative(self):
+        ext_neg = Pow._eval_is_extended_negative(self)
+        if ext_neg is True:
+            return self.is_finite
+        return ext_neg
+
+    def _eval_is_extended_positive(self):
+        if self.base == self.exp:
+            if self.base.is_extended_nonnegative:
+                return True
+        elif self.base.is_positive:
+            if self.exp.is_real:
+                return True
+        elif self.base.is_extended_negative:
+            if self.exp.is_even:
+                return True
+            if self.exp.is_odd:
+                return False
+        elif self.base.is_zero:
+            if self.exp.is_extended_real:
+                return self.exp.is_zero
+        elif self.base.is_extended_nonpositive:
+            if self.exp.is_odd:
+                return False
+        elif self.base.is_imaginary:
+            if self.exp.is_integer:
+                m = self.exp % 4
+                if m.is_zero:
+                    return True
+                if m.is_integer and m.is_zero is False:
+                    return False
+            if self.exp.is_imaginary:
+                from sympy.functions.elementary.exponential import log
+                return log(self.base).is_imaginary
+
+    def _eval_is_extended_negative(self):
+        if self.exp is S.Half:
+            if self.base.is_complex or self.base.is_extended_real:
+                return False
+        if self.base.is_extended_negative:
+            if self.exp.is_odd and self.base.is_finite:
+                return True
+            if self.exp.is_even:
+                return False
+        elif self.base.is_extended_positive:
+            if self.exp.is_extended_real:
+                return False
+        elif self.base.is_zero:
+            if self.exp.is_extended_real:
+                return False
+        elif self.base.is_extended_nonnegative:
+            if self.exp.is_extended_nonnegative:
+                return False
+        elif self.base.is_extended_nonpositive:
+            if self.exp.is_even:
+                return False
+        elif self.base.is_extended_real:
+            if self.exp.is_even:
+                return False
+
+    def _eval_is_zero(self):
+        if self.base.is_zero:
+            if self.exp.is_extended_positive:
+                return True
+            elif self.exp.is_extended_nonpositive:
+                return False
+        elif self.base == S.Exp1:
+            return self.exp is S.NegativeInfinity
+        elif self.base.is_zero is False:
+            if self.base.is_finite and self.exp.is_finite:
+                return False
+            elif self.exp.is_negative:
+                return self.base.is_infinite
+            elif self.exp.is_nonnegative:
+                return False
+            elif self.exp.is_infinite and self.exp.is_extended_real:
+                if (1 - abs(self.base)).is_extended_positive:
+                    return self.exp.is_extended_positive
+                elif (1 - abs(self.base)).is_extended_negative:
+                    return self.exp.is_extended_negative
+        elif self.base.is_finite and self.exp.is_negative:
+            # when self.base.is_zero is None
+            return False
+
+    def _eval_is_integer(self):
+        b, e = self.args
+        if b.is_rational:
+            if b.is_integer is False and e.is_positive:
+                return False  # rat**nonneg
+        if b.is_integer and e.is_integer:
+            if b is S.NegativeOne:
+                return True
+            if e.is_nonnegative or e.is_positive:
+                return True
+        if b.is_integer and e.is_negative and (e.is_finite or e.is_integer):
+            if fuzzy_not((b - 1).is_zero) and fuzzy_not((b + 1).is_zero):
+                return False
+        if b.is_Number and e.is_Number:
+            check = self.func(*self.args)
+            return check.is_Integer
+        if e.is_negative and b.is_positive and (b - 1).is_positive:
+            return False
+        if e.is_negative and b.is_negative and (b + 1).is_negative:
+            return False
+
+    def _eval_is_extended_real(self):
+        if self.base is S.Exp1:
+            if self.exp.is_extended_real:
+                return True
+            elif self.exp.is_imaginary:
+                return (2*S.ImaginaryUnit*self.exp/S.Pi).is_even
+
+        from sympy.functions.elementary.exponential import log, exp
+        real_b = self.base.is_extended_real
+        if real_b is None:
+            if self.base.func == exp and self.base.exp.is_imaginary:
+                return self.exp.is_imaginary
+            if self.base.func == Pow and self.base.base is S.Exp1 and self.base.exp.is_imaginary:
+                return self.exp.is_imaginary
+            return
+        real_e = self.exp.is_extended_real
+        if real_e is None:
+            return
+        if real_b and real_e:
+            if self.base.is_extended_positive:
+                return True
+            elif self.base.is_extended_nonnegative and self.exp.is_extended_nonnegative:
+                return True
+            elif self.exp.is_integer and self.base.is_extended_nonzero:
+                return True
+            elif self.exp.is_integer and self.exp.is_nonnegative:
+                return True
+            elif self.base.is_extended_negative:
+                if self.exp.is_Rational:
+                    return False
+        if real_e and self.exp.is_extended_negative and self.base.is_zero is False:
+            return Pow(self.base, -self.exp).is_extended_real
+        im_b = self.base.is_imaginary
+        im_e = self.exp.is_imaginary
+        if im_b:
+            if self.exp.is_integer:
+                if self.exp.is_even:
+                    return True
+                elif self.exp.is_odd:
+                    return False
+            elif im_e and log(self.base).is_imaginary:
+                return True
+            elif self.exp.is_Add:
+                c, a = self.exp.as_coeff_Add()
+                if c and c.is_Integer:
+                    return Mul(
+                        self.base**c, self.base**a, evaluate=False).is_extended_real
+            elif self.base in (-S.ImaginaryUnit, S.ImaginaryUnit):
+                if (self.exp/2).is_integer is False:
+                    return False
+        if real_b and im_e:
+            if self.base is S.NegativeOne:
+                return True
+            c = self.exp.coeff(S.ImaginaryUnit)
+            if c:
+                if self.base.is_rational and c.is_rational:
+                    if self.base.is_nonzero and (self.base - 1).is_nonzero and c.is_nonzero:
+                        return False
+                ok = (c*log(self.base)/S.Pi).is_integer
+                if ok is not None:
+                    return ok
+
+        if real_b is False and real_e: # we already know it's not imag
+            from sympy.functions.elementary.complexes import arg
+            i = arg(self.base)*self.exp/S.Pi
+            if i.is_complex: # finite
+                return i.is_integer
+
+    def _eval_is_complex(self):
+
+        if self.base == S.Exp1:
+            return fuzzy_or([self.exp.is_complex, self.exp.is_extended_negative])
+
+        if all(a.is_complex for a in self.args) and self._eval_is_finite():
+            return True
+
+    def _eval_is_imaginary(self):
+        if self.base.is_commutative is False:
+            return False
+
+        if self.base.is_imaginary:
+            if self.exp.is_integer:
+                odd = self.exp.is_odd
+                if odd is not None:
+                    return odd
+                return
+
+        if self.base == S.Exp1:
+            f = 2 * self.exp / (S.Pi*S.ImaginaryUnit)
+            # exp(pi*integer) = 1 or -1, so not imaginary
+            if f.is_even:
+                return False
+            # exp(pi*integer + pi/2) = I or -I, so it is imaginary
+            if f.is_odd:
+                return True
+            return None
+
+        if self.exp.is_imaginary:
+            from sympy.functions.elementary.exponential import log
+            imlog = log(self.base).is_imaginary
+            if imlog is not None:
+                return False  # I**i -> real; (2*I)**i -> complex ==> not imaginary
+
+        if self.base.is_extended_real and self.exp.is_extended_real:
+            if self.base.is_positive:
+                return False
+            else:
+                rat = self.exp.is_rational
+                if not rat:
+                    return rat
+                if self.exp.is_integer:
+                    return False
+                else:
+                    half = (2*self.exp).is_integer
+                    if half:
+                        return self.base.is_negative
+                    return half
+
+        if self.base.is_extended_real is False:  # we already know it's not imag
+            from sympy.functions.elementary.complexes import arg
+            i = arg(self.base)*self.exp/S.Pi
+            isodd = (2*i).is_odd
+            if isodd is not None:
+                return isodd
+
+    def _eval_is_odd(self):
+        if self.exp.is_integer:
+            if self.exp.is_positive:
+                return self.base.is_odd
+            elif self.exp.is_nonnegative and self.base.is_odd:
+                return True
+            elif self.base is S.NegativeOne:
+                return True
+
+    def _eval_is_finite(self):
+        if self.exp.is_negative:
+            if self.base.is_zero:
+                return False
+            if self.base.is_infinite or self.base.is_nonzero:
+                return True
+        c1 = self.base.is_finite
+        if c1 is None:
+            return
+        c2 = self.exp.is_finite
+        if c2 is None:
+            return
+        if c1 and c2:
+            if self.exp.is_nonnegative or fuzzy_not(self.base.is_zero):
+                return True
+
+    def _eval_is_prime(self):
+        '''
+        An integer raised to the n(>=2)-th power cannot be a prime.
+        '''
+        if self.base.is_integer and self.exp.is_integer and (self.exp - 1).is_positive:
+            return False
+
+    def _eval_is_composite(self):
+        """
+        A power is composite if both base and exponent are greater than 1
+        """
+        if (self.base.is_integer and self.exp.is_integer and
+            ((self.base - 1).is_positive and (self.exp - 1).is_positive or
+            (self.base + 1).is_negative and self.exp.is_positive and self.exp.is_even)):
+            return True
+
+    def _eval_is_polar(self):
+        return self.base.is_polar
+
+    def _eval_subs(self, old, new):
+        from sympy.calculus.accumulationbounds import AccumBounds
+
+        if isinstance(self.exp, AccumBounds):
+            b = self.base.subs(old, new)
+            e = self.exp.subs(old, new)
+            if isinstance(e, AccumBounds):
+                return e.__rpow__(b)
+            return self.func(b, e)
+
+        from sympy.functions.elementary.exponential import exp, log
+
+        def _check(ct1, ct2, old):
+            """Return (bool, pow, remainder_pow) where, if bool is True, then the
+            exponent of Pow `old` will combine with `pow` so the substitution
+            is valid, otherwise bool will be False.
+
+            For noncommutative objects, `pow` will be an integer, and a factor
+            `Pow(old.base, remainder_pow)` needs to be included. If there is
+            no such factor, None is returned. For commutative objects,
+            remainder_pow is always None.
+
+            cti are the coefficient and terms of an exponent of self or old
+            In this _eval_subs routine a change like (b**(2*x)).subs(b**x, y)
+            will give y**2 since (b**x)**2 == b**(2*x); if that equality does
+            not hold then the substitution should not occur so `bool` will be
+            False.
+
+            """
+            coeff1, terms1 = ct1
+            coeff2, terms2 = ct2
+            if terms1 == terms2:
+                if old.is_commutative:
+                    # Allow fractional powers for commutative objects
+                    pow = coeff1/coeff2
+                    try:
+                        as_int(pow, strict=False)
+                        combines = True
+                    except ValueError:
+                        b, e = old.as_base_exp()
+                        # These conditions ensure that (b**e)**f == b**(e*f) for any f
+                        combines = b.is_positive and e.is_real or b.is_nonnegative and e.is_nonnegative
+
+                    return combines, pow, None
+                else:
+                    # With noncommutative symbols, substitute only integer powers
+                    if not isinstance(terms1, tuple):
+                        terms1 = (terms1,)
+                    if not all(term.is_integer for term in terms1):
+                        return False, None, None
+
+                    try:
+                        # Round pow toward zero
+                        pow, remainder = divmod(as_int(coeff1), as_int(coeff2))
+                        if pow < 0 and remainder != 0:
+                            pow += 1
+                            remainder -= as_int(coeff2)
+
+                        if remainder == 0:
+                            remainder_pow = None
+                        else:
+                            remainder_pow = Mul(remainder, *terms1)
+
+                        return True, pow, remainder_pow
+                    except ValueError:
+                        # Can't substitute
+                        pass
+
+            return False, None, None
+
+        if old == self.base or (old == exp and self.base == S.Exp1):
+            if new.is_Function and isinstance(new, Callable):
+                return new(self.exp._subs(old, new))
+            else:
+                return new**self.exp._subs(old, new)
+
+        # issue 10829: (4**x - 3*y + 2).subs(2**x, y) -> y**2 - 3*y + 2
+        if isinstance(old, self.func) and self.exp == old.exp:
+            l = log(self.base, old.base)
+            if l.is_Number:
+                return Pow(new, l)
+
+        if isinstance(old, self.func) and self.base == old.base:
+            if self.exp.is_Add is False:
+                ct1 = self.exp.as_independent(Symbol, as_Add=False)
+                ct2 = old.exp.as_independent(Symbol, as_Add=False)
+                ok, pow, remainder_pow = _check(ct1, ct2, old)
+                if ok:
+                    # issue 5180: (x**(6*y)).subs(x**(3*y),z)->z**2
+                    result = self.func(new, pow)
+                    if remainder_pow is not None:
+                        result = Mul(result, Pow(old.base, remainder_pow))
+                    return result
+            else:  # b**(6*x + a).subs(b**(3*x), y) -> y**2 * b**a
+                # exp(exp(x) + exp(x**2)).subs(exp(exp(x)), w) -> w * exp(exp(x**2))
+                oarg = old.exp
+                new_l = []
+                o_al = []
+                ct2 = oarg.as_coeff_mul()
+                for a in self.exp.args:
+                    newa = a._subs(old, new)
+                    ct1 = newa.as_coeff_mul()
+                    ok, pow, remainder_pow = _check(ct1, ct2, old)
+                    if ok:
+                        new_l.append(new**pow)
+                        if remainder_pow is not None:
+                            o_al.append(remainder_pow)
+                        continue
+                    elif not old.is_commutative and not newa.is_integer:
+                        # If any term in the exponent is non-integer,
+                        # we do not do any substitutions in the noncommutative case
+                        return
+                    o_al.append(newa)
+                if new_l:
+                    expo = Add(*o_al)
+                    new_l.append(Pow(self.base, expo, evaluate=False) if expo != 1 else self.base)
+                    return Mul(*new_l)
+
+        if (isinstance(old, exp) or (old.is_Pow and old.base is S.Exp1)) and self.exp.is_extended_real and self.base.is_positive:
+            ct1 = old.exp.as_independent(Symbol, as_Add=False)
+            ct2 = (self.exp*log(self.base)).as_independent(
+                Symbol, as_Add=False)
+            ok, pow, remainder_pow = _check(ct1, ct2, old)
+            if ok:
+                result = self.func(new, pow)  # (2**x).subs(exp(x*log(2)), z) -> z
+                if remainder_pow is not None:
+                    result = Mul(result, Pow(old.base, remainder_pow))
+                return result
+
+    def as_base_exp(self):
+        """Return base and exp of self.
+
+        Explanation
+        ===========
+
+        If base a Rational less than 1, then return 1/Rational, -exp.
+        If this extra processing is not needed, the base and exp
+        properties will give the raw arguments.
+
+        Examples
+        ========
+
+        >>> from sympy import Pow, S
+        >>> p = Pow(S.Half, 2, evaluate=False)
+        >>> p.as_base_exp()
+        (2, -2)
+        >>> p.args
+        (1/2, 2)
+        >>> p.base, p.exp
+        (1/2, 2)
+
+        """
+
+        b, e = self.args
+        if b.is_Rational and b.p < b.q and b.p > 0:
+            return 1/b, -e
+        return b, e
+
+    def _eval_adjoint(self):
+        from sympy.functions.elementary.complexes import adjoint
+        i, p = self.exp.is_integer, self.base.is_positive
+        if i:
+            return adjoint(self.base)**self.exp
+        if p:
+            return self.base**adjoint(self.exp)
+        if i is False and p is False:
+            expanded = expand_complex(self)
+            if expanded != self:
+                return adjoint(expanded)
+
+    def _eval_conjugate(self):
+        from sympy.functions.elementary.complexes import conjugate as c
+        i, p = self.exp.is_integer, self.base.is_positive
+        if i:
+            return c(self.base)**self.exp
+        if p:
+            return self.base**c(self.exp)
+        if i is False and p is False:
+            expanded = expand_complex(self)
+            if expanded != self:
+                return c(expanded)
+        if self.is_extended_real:
+            return self
+
+    def _eval_transpose(self):
+        from sympy.functions.elementary.complexes import transpose
+        if self.base == S.Exp1:
+            return self.func(S.Exp1, self.exp.transpose())
+        i, p = self.exp.is_integer, (self.base.is_complex or self.base.is_infinite)
+        if p:
+            return self.base**self.exp
+        if i:
+            return transpose(self.base)**self.exp
+        if i is False and p is False:
+            expanded = expand_complex(self)
+            if expanded != self:
+                return transpose(expanded)
+
+    def _eval_expand_power_exp(self, **hints):
+        """a**(n + m) -> a**n*a**m"""
+        b = self.base
+        e = self.exp
+        if b == S.Exp1:
+            from sympy.concrete.summations import Sum
+            if isinstance(e, Sum) and e.is_commutative:
+                from sympy.concrete.products import Product
+                return Product(self.func(b, e.function), *e.limits)
+        if e.is_Add and (hints.get('force', False) or
+                b.is_zero is False or e._all_nonneg_or_nonppos()):
+            if e.is_commutative:
+                return Mul(*[self.func(b, x) for x in e.args])
+            if b.is_commutative:
+                c, nc = sift(e.args, lambda x: x.is_commutative, binary=True)
+                if c:
+                    return Mul(*[self.func(b, x) for x in c]
+                        )*b**Add._from_args(nc)
+        return self
+
+    def _eval_expand_power_base(self, **hints):
+        """(a*b)**n -> a**n * b**n"""
+        force = hints.get('force', False)
+
+        b = self.base
+        e = self.exp
+        if not b.is_Mul:
+            return self
+
+        cargs, nc = b.args_cnc(split_1=False)
+
+        # expand each term - this is top-level-only
+        # expansion but we have to watch out for things
+        # that don't have an _eval_expand method
+        if nc:
+            nc = [i._eval_expand_power_base(**hints)
+                if hasattr(i, '_eval_expand_power_base') else i
+                for i in nc]
+
+            if e.is_Integer:
+                if e.is_positive:
+                    rv = Mul(*nc*e)
+                else:
+                    rv = Mul(*[i**-1 for i in nc[::-1]]*-e)
+                if cargs:
+                    rv *= Mul(*cargs)**e
+                return rv
+
+            if not cargs:
+                return self.func(Mul(*nc), e, evaluate=False)
+
+            nc = [Mul(*nc)]
+
+        # sift the commutative bases
+        other, maybe_real = sift(cargs, lambda x: x.is_extended_real is False,
+            binary=True)
+        def pred(x):
+            if x is S.ImaginaryUnit:
+                return S.ImaginaryUnit
+            polar = x.is_polar
+            if polar:
+                return True
+            if polar is None:
+                return fuzzy_bool(x.is_extended_nonnegative)
+        sifted = sift(maybe_real, pred)
+        nonneg = sifted[True]
+        other += sifted[None]
+        neg = sifted[False]
+        imag = sifted[S.ImaginaryUnit]
+        if imag:
+            I = S.ImaginaryUnit
+            i = len(imag) % 4
+            if i == 0:
+                pass
+            elif i == 1:
+                other.append(I)
+            elif i == 2:
+                if neg:
+                    nonn = -neg.pop()
+                    if nonn is not S.One:
+                        nonneg.append(nonn)
+                else:
+                    neg.append(S.NegativeOne)
+            else:
+                if neg:
+                    nonn = -neg.pop()
+                    if nonn is not S.One:
+                        nonneg.append(nonn)
+                else:
+                    neg.append(S.NegativeOne)
+                other.append(I)
+            del imag
+
+        # bring out the bases that can be separated from the base
+
+        if force or e.is_integer:
+            # treat all commutatives the same and put nc in other
+            cargs = nonneg + neg + other
+            other = nc
+        else:
+            # this is just like what is happening automatically, except
+            # that now we are doing it for an arbitrary exponent for which
+            # no automatic expansion is done
+
+            assert not e.is_Integer
+
+            # handle negatives by making them all positive and putting
+            # the residual -1 in other
+            if len(neg) > 1:
+                o = S.One
+                if not other and neg[0].is_Number:
+                    o *= neg.pop(0)
+                if len(neg) % 2:
+                    o = -o
+                for n in neg:
+                    nonneg.append(-n)
+                if o is not S.One:
+                    other.append(o)
+            elif neg and other:
+                if neg[0].is_Number and neg[0] is not S.NegativeOne:
+                    other.append(S.NegativeOne)
+                    nonneg.append(-neg[0])
+                else:
+                    other.extend(neg)
+            else:
+                other.extend(neg)
+            del neg
+
+            cargs = nonneg
+            other += nc
+
+        rv = S.One
+        if cargs:
+            if e.is_Rational:
+                npow, cargs = sift(cargs, lambda x: x.is_Pow and
+                    x.exp.is_Rational and x.base.is_number,
+                    binary=True)
+                rv = Mul(*[self.func(b.func(*b.args), e) for b in npow])
+            rv *= Mul(*[self.func(b, e, evaluate=False) for b in cargs])
+        if other:
+            rv *= self.func(Mul(*other), e, evaluate=False)
+        return rv
+
+    def _eval_expand_multinomial(self, **hints):
+        """(a + b + ..)**n -> a**n + n*a**(n-1)*b + .., n is nonzero integer"""
+
+        base, exp = self.args
+        result = self
+
+        if exp.is_Rational and exp.p > 0 and base.is_Add:
+            if not exp.is_Integer:
+                n = Integer(exp.p // exp.q)
+
+                if not n:
+                    return result
+                else:
+                    radical, result = self.func(base, exp - n), []
+
+                    expanded_base_n = self.func(base, n)
+                    if expanded_base_n.is_Pow:
+                        expanded_base_n = \
+                            expanded_base_n._eval_expand_multinomial()
+                    for term in Add.make_args(expanded_base_n):
+                        result.append(term*radical)
+
+                    return Add(*result)
+
+            n = int(exp)
+
+            if base.is_commutative:
+                order_terms, other_terms = [], []
+
+                for b in base.args:
+                    if b.is_Order:
+                        order_terms.append(b)
+                    else:
+                        other_terms.append(b)
+
+                if order_terms:
+                    # (f(x) + O(x^n))^m -> f(x)^m + m*f(x)^{m-1} *O(x^n)
+                    f = Add(*other_terms)
+                    o = Add(*order_terms)
+
+                    if n == 2:
+                        return expand_multinomial(f**n, deep=False) + n*f*o
+                    else:
+                        g = expand_multinomial(f**(n - 1), deep=False)
+                        return expand_mul(f*g, deep=False) + n*g*o
+
+                if base.is_number:
+                    # Efficiently expand expressions of the form (a + b*I)**n
+                    # where 'a' and 'b' are real numbers and 'n' is integer.
+                    a, b = base.as_real_imag()
+
+                    if a.is_Rational and b.is_Rational:
+                        if not a.is_Integer:
+                            if not b.is_Integer:
+                                k = self.func(a.q * b.q, n)
+                                a, b = a.p*b.q, a.q*b.p
+                            else:
+                                k = self.func(a.q, n)
+                                a, b = a.p, a.q*b
+                        elif not b.is_Integer:
+                            k = self.func(b.q, n)
+                            a, b = a*b.q, b.p
+                        else:
+                            k = 1
+
+                        a, b, c, d = int(a), int(b), 1, 0
+
+                        while n:
+                            if n & 1:
+                                c, d = a*c - b*d, b*c + a*d
+                                n -= 1
+                            a, b = a*a - b*b, 2*a*b
+                            n //= 2
+
+                        I = S.ImaginaryUnit
+
+                        if k == 1:
+                            return c + I*d
+                        else:
+                            return Integer(c)/k + I*d/k
+
+                p = other_terms
+                # (x + y)**3 -> x**3 + 3*x**2*y + 3*x*y**2 + y**3
+                # in this particular example:
+                # p = [x,y]; n = 3
+                # so now it's easy to get the correct result -- we get the
+                # coefficients first:
+                from sympy.ntheory.multinomial import multinomial_coefficients
+                from sympy.polys.polyutils import basic_from_dict
+                expansion_dict = multinomial_coefficients(len(p), n)
+                # in our example: {(3, 0): 1, (1, 2): 3, (0, 3): 1, (2, 1): 3}
+                # and now construct the expression.
+                return basic_from_dict(expansion_dict, *p)
+            else:
+                if n == 2:
+                    return Add(*[f*g for f in base.args for g in base.args])
+                else:
+                    multi = (base**(n - 1))._eval_expand_multinomial()
+                    if multi.is_Add:
+                        return Add(*[f*g for f in base.args
+                            for g in multi.args])
+                    else:
+                        # XXX can this ever happen if base was an Add?
+                        return Add(*[f*multi for f in base.args])
+        elif (exp.is_Rational and exp.p < 0 and base.is_Add and
+                abs(exp.p) > exp.q):
+            return 1 / self.func(base, -exp)._eval_expand_multinomial()
+        elif exp.is_Add and base.is_Number and (hints.get('force', False) or
+                base.is_zero is False or exp._all_nonneg_or_nonppos()):
+            #  a + b      a  b
+            #  n      --> n  n, where n, a, b are Numbers
+            # XXX should be in expand_power_exp?
+            coeff, tail = [], []
+            for term in exp.args:
+                if term.is_Number:
+                    coeff.append(self.func(base, term))
+                else:
+                    tail.append(term)
+            return Mul(*(coeff + [self.func(base, Add._from_args(tail))]))
+        else:
+            return result
+
+    def as_real_imag(self, deep=True, **hints):
+        if self.exp.is_Integer:
+            from sympy.polys.polytools import poly
+
+            exp = self.exp
+            re_e, im_e = self.base.as_real_imag(deep=deep)
+            if not im_e:
+                return self, S.Zero
+            a, b = symbols('a b', cls=Dummy)
+            if exp >= 0:
+                if re_e.is_Number and im_e.is_Number:
+                    # We can be more efficient in this case
+                    expr = expand_multinomial(self.base**exp)
+                    if expr != self:
+                        return expr.as_real_imag()
+
+                expr = poly(
+                    (a + b)**exp)  # a = re, b = im; expr = (a + b*I)**exp
+            else:
+                mag = re_e**2 + im_e**2
+                re_e, im_e = re_e/mag, -im_e/mag
+                if re_e.is_Number and im_e.is_Number:
+                    # We can be more efficient in this case
+                    expr = expand_multinomial((re_e + im_e*S.ImaginaryUnit)**-exp)
+                    if expr != self:
+                        return expr.as_real_imag()
+
+                expr = poly((a + b)**-exp)
+
+            # Terms with even b powers will be real
+            r = [i for i in expr.terms() if not i[0][1] % 2]
+            re_part = Add(*[cc*a**aa*b**bb for (aa, bb), cc in r])
+            # Terms with odd b powers will be imaginary
+            r = [i for i in expr.terms() if i[0][1] % 4 == 1]
+            im_part1 = Add(*[cc*a**aa*b**bb for (aa, bb), cc in r])
+            r = [i for i in expr.terms() if i[0][1] % 4 == 3]
+            im_part3 = Add(*[cc*a**aa*b**bb for (aa, bb), cc in r])
+
+            return (re_part.subs({a: re_e, b: S.ImaginaryUnit*im_e}),
+            im_part1.subs({a: re_e, b: im_e}) + im_part3.subs({a: re_e, b: -im_e}))
+
+        from sympy.functions.elementary.trigonometric import atan2, cos, sin
+
+        if self.exp.is_Rational:
+            re_e, im_e = self.base.as_real_imag(deep=deep)
+
+            if im_e.is_zero and self.exp is S.Half:
+                if re_e.is_extended_nonnegative:
+                    return self, S.Zero
+                if re_e.is_extended_nonpositive:
+                    return S.Zero, (-self.base)**self.exp
+
+            # XXX: This is not totally correct since for x**(p/q) with
+            #      x being imaginary there are actually q roots, but
+            #      only a single one is returned from here.
+            r = self.func(self.func(re_e, 2) + self.func(im_e, 2), S.Half)
+
+            t = atan2(im_e, re_e)
+
+            rp, tp = self.func(r, self.exp), t*self.exp
+
+            return rp*cos(tp), rp*sin(tp)
+        elif self.base is S.Exp1:
+            from sympy.functions.elementary.exponential import exp
+            re_e, im_e = self.exp.as_real_imag()
+            if deep:
+                re_e = re_e.expand(deep, **hints)
+                im_e = im_e.expand(deep, **hints)
+            c, s = cos(im_e), sin(im_e)
+            return exp(re_e)*c, exp(re_e)*s
+        else:
+            from sympy.functions.elementary.complexes import im, re
+            if deep:
+                hints['complex'] = False
+
+                expanded = self.expand(deep, **hints)
+                if hints.get('ignore') == expanded:
+                    return None
+                else:
+                    return (re(expanded), im(expanded))
+            else:
+                return re(self), im(self)
+
+    def _eval_derivative(self, s):
+        from sympy.functions.elementary.exponential import log
+        dbase = self.base.diff(s)
+        dexp = self.exp.diff(s)
+        return self * (dexp * log(self.base) + dbase * self.exp/self.base)
+
+    def _eval_evalf(self, prec):
+        base, exp = self.as_base_exp()
+        if base == S.Exp1:
+            # Use mpmath function associated to class "exp":
+            from sympy.functions.elementary.exponential import exp as exp_function
+            return exp_function(self.exp, evaluate=False)._eval_evalf(prec)
+        base = base._evalf(prec)
+        if not exp.is_Integer:
+            exp = exp._evalf(prec)
+        if exp.is_negative and base.is_number and base.is_extended_real is False:
+            base = base.conjugate() / (base * base.conjugate())._evalf(prec)
+            exp = -exp
+            return self.func(base, exp).expand()
+        return self.func(base, exp)
+
+    def _eval_is_polynomial(self, syms):
+        if self.exp.has(*syms):
+            return False
+
+        if self.base.has(*syms):
+            return bool(self.base._eval_is_polynomial(syms) and
+                self.exp.is_Integer and (self.exp >= 0))
+        else:
+            return True
+
+    def _eval_is_rational(self):
+        # The evaluation of self.func below can be very expensive in the case
+        # of integer**integer if the exponent is large.  We should try to exit
+        # before that if possible:
+        if (self.exp.is_integer and self.base.is_rational
+                and fuzzy_not(fuzzy_and([self.exp.is_negative, self.base.is_zero]))):
+            return True
+        p = self.func(*self.as_base_exp())  # in case it's unevaluated
+        if not p.is_Pow:
+            return p.is_rational
+        b, e = p.as_base_exp()
+        if e.is_Rational and b.is_Rational:
+            # we didn't check that e is not an Integer
+            # because Rational**Integer autosimplifies
+            return False
+        if e.is_integer:
+            if b.is_rational:
+                if fuzzy_not(b.is_zero) or e.is_nonnegative:
+                    return True
+                if b == e:  # always rational, even for 0**0
+                    return True
+            elif b.is_irrational:
+                return e.is_zero
+        if b is S.Exp1:
+            if e.is_rational and e.is_nonzero:
+                return False
+
+    def _eval_is_algebraic(self):
+        def _is_one(expr):
+            try:
+                return (expr - 1).is_zero
+            except ValueError:
+                # when the operation is not allowed
+                return False
+
+        if self.base.is_zero or _is_one(self.base):
+            return True
+        elif self.base is S.Exp1:
+            s = self.func(*self.args)
+            if s.func == self.func:
+                if self.exp.is_nonzero:
+                    if self.exp.is_algebraic:
+                        return False
+                    elif (self.exp/S.Pi).is_rational:
+                        return False
+                    elif (self.exp/(S.ImaginaryUnit*S.Pi)).is_rational:
+                        return True
+            else:
+                return s.is_algebraic
+        elif self.exp.is_rational:
+            if self.base.is_algebraic is False:
+                return self.exp.is_zero
+            if self.base.is_zero is False:
+                if self.exp.is_nonzero:
+                    return self.base.is_algebraic
+                elif self.base.is_algebraic:
+                    return True
+            if self.exp.is_positive:
+                return self.base.is_algebraic
+        elif self.base.is_algebraic and self.exp.is_algebraic:
+            if ((fuzzy_not(self.base.is_zero)
+                and fuzzy_not(_is_one(self.base)))
+                or self.base.is_integer is False
+                or self.base.is_irrational):
+                return self.exp.is_rational
+
+    def _eval_is_rational_function(self, syms):
+        if self.exp.has(*syms):
+            return False
+
+        if self.base.has(*syms):
+            return self.base._eval_is_rational_function(syms) and \
+                self.exp.is_Integer
+        else:
+            return True
+
+    def _eval_is_meromorphic(self, x, a):
+        # f**g is meromorphic if g is an integer and f is meromorphic.
+        # E**(log(f)*g) is meromorphic if log(f)*g is meromorphic
+        # and finite.
+        base_merom = self.base._eval_is_meromorphic(x, a)
+        exp_integer = self.exp.is_Integer
+        if exp_integer:
+            return base_merom
+
+        exp_merom = self.exp._eval_is_meromorphic(x, a)
+        if base_merom is False:
+            # f**g = E**(log(f)*g) may be meromorphic if the
+            # singularities of log(f) and g cancel each other,
+            # for example, if g = 1/log(f). Hence,
+            return False if exp_merom else None
+        elif base_merom is None:
+            return None
+
+        b = self.base.subs(x, a)
+        # b is extended complex as base is meromorphic.
+        # log(base) is finite and meromorphic when b != 0, zoo.
+        b_zero = b.is_zero
+        if b_zero:
+            log_defined = False
+        else:
+            log_defined = fuzzy_and((b.is_finite, fuzzy_not(b_zero)))
+
+        if log_defined is False: # zero or pole of base
+            return exp_integer  # False or None
+        elif log_defined is None:
+            return None
+
+        if not exp_merom:
+            return exp_merom  # False or None
+
+        return self.exp.subs(x, a).is_finite
+
+    def _eval_is_algebraic_expr(self, syms):
+        if self.exp.has(*syms):
+            return False
+
+        if self.base.has(*syms):
+            return self.base._eval_is_algebraic_expr(syms) and \
+                self.exp.is_Rational
+        else:
+            return True
+
+    def _eval_rewrite_as_exp(self, base, expo, **kwargs):
+        from sympy.functions.elementary.exponential import exp, log
+
+        if base.is_zero or base.has(exp) or expo.has(exp):
+            return base**expo
+
+        if base.has(Symbol):
+            # delay evaluation if expo is non symbolic
+            # (as exp(x*log(5)) automatically reduces to x**5)
+            if global_parameters.exp_is_pow:
+                return Pow(S.Exp1, log(base)*expo, evaluate=expo.has(Symbol))
+            else:
+                return exp(log(base)*expo, evaluate=expo.has(Symbol))
+
+        else:
+            from sympy.functions.elementary.complexes import arg, Abs
+            return exp((log(Abs(base)) + S.ImaginaryUnit*arg(base))*expo)
+
+    def as_numer_denom(self):
+        if not self.is_commutative:
+            return self, S.One
+        base, exp = self.as_base_exp()
+        n, d = base.as_numer_denom()
+        # this should be the same as ExpBase.as_numer_denom wrt
+        # exponent handling
+        neg_exp = exp.is_negative
+        if exp.is_Mul and not neg_exp and not exp.is_positive:
+            neg_exp = exp.could_extract_minus_sign()
+        int_exp = exp.is_integer
+        # the denominator cannot be separated from the numerator if
+        # its sign is unknown unless the exponent is an integer, e.g.
+        # sqrt(a/b) != sqrt(a)/sqrt(b) when a=1 and b=-1. But if the
+        # denominator is negative the numerator and denominator can
+        # be negated and the denominator (now positive) separated.
+        if not (d.is_extended_real or int_exp):
+            n = base
+            d = S.One
+        dnonpos = d.is_nonpositive
+        if dnonpos:
+            n, d = -n, -d
+        elif dnonpos is None and not int_exp:
+            n = base
+            d = S.One
+        if neg_exp:
+            n, d = d, n
+            exp = -exp
+        if exp.is_infinite:
+            if n is S.One and d is not S.One:
+                return n, self.func(d, exp)
+            if n is not S.One and d is S.One:
+                return self.func(n, exp), d
+        return self.func(n, exp), self.func(d, exp)
+
+    def matches(self, expr, repl_dict=None, old=False):
+        expr = _sympify(expr)
+        if repl_dict is None:
+            repl_dict = {}
+
+        # special case, pattern = 1 and expr.exp can match to 0
+        if expr is S.One:
+            d = self.exp.matches(S.Zero, repl_dict)
+            if d is not None:
+                return d
+
+        # make sure the expression to be matched is an Expr
+        if not isinstance(expr, Expr):
+            return None
+
+        b, e = expr.as_base_exp()
+
+        # special case number
+        sb, se = self.as_base_exp()
+        if sb.is_Symbol and se.is_Integer and expr:
+            if e.is_rational:
+                return sb.matches(b**(e/se), repl_dict)
+            return sb.matches(expr**(1/se), repl_dict)
+
+        d = repl_dict.copy()
+        d = self.base.matches(b, d)
+        if d is None:
+            return None
+
+        d = self.exp.xreplace(d).matches(e, d)
+        if d is None:
+            return Expr.matches(self, expr, repl_dict)
+        return d
+
+    def _eval_nseries(self, x, n, logx, cdir=0):
+        # NOTE! This function is an important part of the gruntz algorithm
+        #       for computing limits. It has to return a generalized power
+        #       series with coefficients in C(log, log(x)). In more detail:
+        # It has to return an expression
+        #     c_0*x**e_0 + c_1*x**e_1 + ... (finitely many terms)
+        # where e_i are numbers (not necessarily integers) and c_i are
+        # expressions involving only numbers, the log function, and log(x).
+        # The series expansion of b**e is computed as follows:
+        # 1) We express b as f*(1 + g) where f is the leading term of b.
+        #    g has order O(x**d) where d is strictly positive.
+        # 2) Then b**e = (f**e)*((1 + g)**e).
+        #    (1 + g)**e is computed using binomial series.
+        from sympy.functions.elementary.exponential import exp, log
+        from sympy.series.limits import limit
+        from sympy.series.order import Order
+        from sympy.core.sympify import sympify
+        if self.base is S.Exp1:
+            e_series = self.exp.nseries(x, n=n, logx=logx)
+            if e_series.is_Order:
+                return 1 + e_series
+            e0 = limit(e_series.removeO(), x, 0)
+            if e0 is S.NegativeInfinity:
+                return Order(x**n, x)
+            if e0 is S.Infinity:
+                return self
+            t = e_series - e0
+            exp_series = term = exp(e0)
+            # series of exp(e0 + t) in t
+            for i in range(1, n):
+                term *= t/i
+                term = term.nseries(x, n=n, logx=logx)
+                exp_series += term
+            exp_series += Order(t**n, x)
+            from sympy.simplify.powsimp import powsimp
+            return powsimp(exp_series, deep=True, combine='exp')
+        from sympy.simplify.powsimp import powdenest
+        from .numbers import _illegal
+        self = powdenest(self, force=True).trigsimp()
+        b, e = self.as_base_exp()
+
+        if e.has(*_illegal):
+            raise PoleError()
+
+        if e.has(x):
+            return exp(e*log(b))._eval_nseries(x, n=n, logx=logx, cdir=cdir)
+
+        if logx is not None and b.has(log):
+            from .symbol import Wild
+            c, ex = symbols('c, ex', cls=Wild, exclude=[x])
+            b = b.replace(log(c*x**ex), log(c) + ex*logx)
+            self = b**e
+
+        b = b.removeO()
+        try:
+            from sympy.functions.special.gamma_functions import polygamma
+            if b.has(polygamma, S.EulerGamma) and logx is not None:
+                raise ValueError()
+            _, m = b.leadterm(x)
+        except (ValueError, NotImplementedError, PoleError):
+            b = b._eval_nseries(x, n=max(2, n), logx=logx, cdir=cdir).removeO()
+            if b.has(S.NaN, S.ComplexInfinity):
+                raise NotImplementedError()
+            _, m = b.leadterm(x)
+
+        if e.has(log):
+            from sympy.simplify.simplify import logcombine
+            e = logcombine(e).cancel()
+
+        if not (m.is_zero or e.is_number and e.is_real):
+            if self == self._eval_as_leading_term(x, logx=logx, cdir=cdir):
+                res = exp(e*log(b))._eval_nseries(x, n=n, logx=logx, cdir=cdir)
+                if res == exp(e*log(b)):
+                    return self
+                return res
+
+        f = b.as_leading_term(x, logx=logx)
+        g = (b/f - S.One).cancel(expand=False)
+        if not m.is_number:
+            raise NotImplementedError()
+        maxpow = n - m*e
+        if maxpow.has(Symbol):
+            maxpow = sympify(n)
+
+        if maxpow.is_negative:
+            return Order(x**(m*e), x)
+
+        if g.is_zero:
+            r = f**e
+            if r != self:
+                r += Order(x**n, x)
+            return r
+
+        def coeff_exp(term, x):
+            coeff, exp = S.One, S.Zero
+            for factor in Mul.make_args(term):
+                if factor.has(x):
+                    base, exp = factor.as_base_exp()
+                    if base != x:
+                        try:
+                            return term.leadterm(x)
+                        except ValueError:
+                            return term, S.Zero
+                else:
+                    coeff *= factor
+            return coeff, exp
+
+        def mul(d1, d2):
+            res = {}
+            for e1, e2 in product(d1, d2):
+                ex = e1 + e2
+                if ex < maxpow:
+                    res[ex] = res.get(ex, S.Zero) + d1[e1]*d2[e2]
+            return res
+
+        try:
+            c, d = g.leadterm(x, logx=logx)
+        except (ValueError, NotImplementedError):
+            if limit(g/x**maxpow, x, 0) == 0:
+                # g has higher order zero
+                return f**e + e*f**e*g  # first term of binomial series
+            else:
+                raise NotImplementedError()
+        if c.is_Float and d == S.Zero:
+            # Convert floats like 0.5 to exact SymPy numbers like S.Half, to
+            # prevent rounding errors which can induce wrong values of d leading
+            # to a NotImplementedError being returned from the block below.
+            from sympy.simplify.simplify import nsimplify
+            _, d = nsimplify(g).leadterm(x, logx=logx)
+        if not d.is_positive:
+            g = g.simplify()
+            if g.is_zero:
+                return f**e
+            _, d = g.leadterm(x, logx=logx)
+            if not d.is_positive:
+                g = ((b - f)/f).expand()
+                _, d = g.leadterm(x, logx=logx)
+                if not d.is_positive:
+                    raise NotImplementedError()
+
+        from sympy.functions.elementary.integers import ceiling
+        gpoly = g._eval_nseries(x, n=ceiling(maxpow), logx=logx, cdir=cdir).removeO()
+        gterms = {}
+
+        for term in Add.make_args(gpoly):
+            co1, e1 = coeff_exp(term, x)
+            gterms[e1] = gterms.get(e1, S.Zero) + co1
+
+        k = S.One
+        terms = {S.Zero: S.One}
+        tk = gterms
+
+        from sympy.functions.combinatorial.factorials import factorial, ff
+
+        while (k*d - maxpow).is_negative:
+            coeff = ff(e, k)/factorial(k)
+            for ex in tk:
+                terms[ex] = terms.get(ex, S.Zero) + coeff*tk[ex]
+            tk = mul(tk, gterms)
+            k += S.One
+
+        from sympy.functions.elementary.complexes import im
+
+        if not e.is_integer and m.is_zero and f.is_negative:
+            ndir = (b - f).dir(x, cdir)
+            if im(ndir).is_negative:
+                inco, inex = coeff_exp(f**e*(-1)**(-2*e), x)
+            elif im(ndir).is_zero:
+                inco, inex = coeff_exp(exp(e*log(b)).as_leading_term(x, logx=logx, cdir=cdir), x)
+            else:
+                inco, inex = coeff_exp(f**e, x)
+        else:
+            inco, inex = coeff_exp(f**e, x)
+        res = S.Zero
+
+        for e1 in terms:
+            ex = e1 + inex
+            res += terms[e1]*inco*x**(ex)
+
+        if not (e.is_integer and e.is_positive and (e*d - n).is_nonpositive and
+                res == _mexpand(self)):
+            try:
+                res += Order(x**n, x)
+            except NotImplementedError:
+                return exp(e*log(b))._eval_nseries(x, n=n, logx=logx, cdir=cdir)
+        return res
+
+    def _eval_as_leading_term(self, x, logx=None, cdir=0):
+        from sympy.functions.elementary.exponential import exp, log
+        e = self.exp
+        b = self.base
+        if self.base is S.Exp1:
+            arg = e.as_leading_term(x, logx=logx)
+            arg0 = arg.subs(x, 0)
+            if arg0 is S.NaN:
+                arg0 = arg.limit(x, 0)
+            if arg0.is_infinite is False:
+                return S.Exp1**arg0
+            raise PoleError("Cannot expand %s around 0" % (self))
+        elif e.has(x):
+            lt = exp(e * log(b))
+            return lt.as_leading_term(x, logx=logx, cdir=cdir)
+        else:
+            from sympy.functions.elementary.complexes import im
+            try:
+                f = b.as_leading_term(x, logx=logx, cdir=cdir)
+            except PoleError:
+                return self
+            if not e.is_integer and f.is_negative and not f.has(x):
+                ndir = (b - f).dir(x, cdir)
+                if im(ndir).is_negative:
+                    # Normally, f**e would evaluate to exp(e*log(f)) but on branch cuts
+                    # an other value is expected through the following computation
+                    # exp(e*(log(f) - 2*pi*I)) == f**e*exp(-2*e*pi*I) == f**e*(-1)**(-2*e).
+                    return self.func(f, e) * (-1)**(-2*e)
+                elif im(ndir).is_zero:
+                    log_leadterm = log(b)._eval_as_leading_term(x, logx=logx, cdir=cdir)
+                    if log_leadterm.is_infinite is False:
+                        return exp(e*log_leadterm)
+            return self.func(f, e)
+
+    @cacheit
+    def _taylor_term(self, n, x, *previous_terms): # of (1 + x)**e
+        from sympy.functions.combinatorial.factorials import binomial
+        return binomial(self.exp, n) * self.func(x, n)
+
+    def taylor_term(self, n, x, *previous_terms):
+        if self.base is not S.Exp1:
+            return super().taylor_term(n, x, *previous_terms)
+        if n < 0:
+            return S.Zero
+        if n == 0:
+            return S.One
+        from .sympify import sympify
+        x = sympify(x)
+        if previous_terms:
+            p = previous_terms[-1]
+            if p is not None:
+                return p * x / n
+        from sympy.functions.combinatorial.factorials import factorial
+        return x**n/factorial(n)
+
+    def _eval_rewrite_as_sin(self, base, exp):
+        if self.base is S.Exp1:
+            from sympy.functions.elementary.trigonometric import sin
+            return sin(S.ImaginaryUnit*self.exp + S.Pi/2) - S.ImaginaryUnit*sin(S.ImaginaryUnit*self.exp)
+
+    def _eval_rewrite_as_cos(self, base, exp):
+        if self.base is S.Exp1:
+            from sympy.functions.elementary.trigonometric import cos
+            return cos(S.ImaginaryUnit*self.exp) + S.ImaginaryUnit*cos(S.ImaginaryUnit*self.exp + S.Pi/2)
+
+    def _eval_rewrite_as_tanh(self, base, exp):
+        if self.base is S.Exp1:
+            from sympy.functions.elementary.hyperbolic import tanh
+            return (1 + tanh(self.exp/2))/(1 - tanh(self.exp/2))
+
+    def _eval_rewrite_as_sqrt(self, base, exp, **kwargs):
+        from sympy.functions.elementary.trigonometric import sin, cos
+        if base is not S.Exp1:
+            return None
+        if exp.is_Mul:
+            coeff = exp.coeff(S.Pi * S.ImaginaryUnit)
+            if coeff and coeff.is_number:
+                cosine, sine = cos(S.Pi*coeff), sin(S.Pi*coeff)
+                if not isinstance(cosine, cos) and not isinstance (sine, sin):
+                    return cosine + S.ImaginaryUnit*sine
+
+    def as_content_primitive(self, radical=False, clear=True):
+        """Return the tuple (R, self/R) where R is the positive Rational
+        extracted from self.
+
+        Examples
+        ========
+
+        >>> from sympy import sqrt
+        >>> sqrt(4 + 4*sqrt(2)).as_content_primitive()
+        (2, sqrt(1 + sqrt(2)))
+        >>> sqrt(3 + 3*sqrt(2)).as_content_primitive()
+        (1, sqrt(3)*sqrt(1 + sqrt(2)))
+
+        >>> from sympy import expand_power_base, powsimp, Mul
+        >>> from sympy.abc import x, y
+
+        >>> ((2*x + 2)**2).as_content_primitive()
+        (4, (x + 1)**2)
+        >>> (4**((1 + y)/2)).as_content_primitive()
+        (2, 4**(y/2))
+        >>> (3**((1 + y)/2)).as_content_primitive()
+        (1, 3**((y + 1)/2))
+        >>> (3**((5 + y)/2)).as_content_primitive()
+        (9, 3**((y + 1)/2))
+        >>> eq = 3**(2 + 2*x)
+        >>> powsimp(eq) == eq
+        True
+        >>> eq.as_content_primitive()
+        (9, 3**(2*x))
+        >>> powsimp(Mul(*_))
+        3**(2*x + 2)
+
+        >>> eq = (2 + 2*x)**y
+        >>> s = expand_power_base(eq); s.is_Mul, s
+        (False, (2*x + 2)**y)
+        >>> eq.as_content_primitive()
+        (1, (2*(x + 1))**y)
+        >>> s = expand_power_base(_[1]); s.is_Mul, s
+        (True, 2**y*(x + 1)**y)
+
+        See docstring of Expr.as_content_primitive for more examples.
+        """
+
+        b, e = self.as_base_exp()
+        b = _keep_coeff(*b.as_content_primitive(radical=radical, clear=clear))
+        ce, pe = e.as_content_primitive(radical=radical, clear=clear)
+        if b.is_Rational:
+            #e
+            #= ce*pe
+            #= ce*(h + t)
+            #= ce*h + ce*t
+            #=> self
+            #= b**(ce*h)*b**(ce*t)
+            #= b**(cehp/cehq)*b**(ce*t)
+            #= b**(iceh + r/cehq)*b**(ce*t)
+            #= b**(iceh)*b**(r/cehq)*b**(ce*t)
+            #= b**(iceh)*b**(ce*t + r/cehq)
+            h, t = pe.as_coeff_Add()
+            if h.is_Rational and b != S.Zero:
+                ceh = ce*h
+                c = self.func(b, ceh)
+                r = S.Zero
+                if not c.is_Rational:
+                    iceh, r = divmod(ceh.p, ceh.q)
+                    c = self.func(b, iceh)
+                return c, self.func(b, _keep_coeff(ce, t + r/ce/ceh.q))
+        e = _keep_coeff(ce, pe)
+        # b**e = (h*t)**e = h**e*t**e = c*m*t**e
+        if e.is_Rational and b.is_Mul:
+            h, t = b.as_content_primitive(radical=radical, clear=clear)  # h is positive
+            c, m = self.func(h, e).as_coeff_Mul()  # so c is positive
+            m, me = m.as_base_exp()
+            if m is S.One or me == e:  # probably always true
+                # return the following, not return c, m*Pow(t, e)
+                # which would change Pow into Mul; we let SymPy
+                # decide what to do by using the unevaluated Mul, e.g
+                # should it stay as sqrt(2 + 2*sqrt(5)) or become
+                # sqrt(2)*sqrt(1 + sqrt(5))
+                return c, self.func(_keep_coeff(m, t), e)
+        return S.One, self.func(b, e)
+
+    def is_constant(self, *wrt, **flags):
+        expr = self
+        if flags.get('simplify', True):
+            expr = expr.simplify()
+        b, e = expr.as_base_exp()
+        bz = b.equals(0)
+        if bz:  # recalculate with assumptions in case it's unevaluated
+            new = b**e
+            if new != expr:
+                return new.is_constant()
+        econ = e.is_constant(*wrt)
+        bcon = b.is_constant(*wrt)
+        if bcon:
+            if econ:
+                return True
+            bz = b.equals(0)
+            if bz is False:
+                return False
+        elif bcon is None:
+            return None
+
+        return e.equals(0)
+
+    def _eval_difference_delta(self, n, step):
+        b, e = self.args
+        if e.has(n) and not b.has(n):
+            new_e = e.subs(n, n + step)
+            return (b**(new_e - e) - 1) * self
+
+power = Dispatcher('power')
+power.add((object, object), Pow)
+
+from .add import Add
+from .numbers import Integer
+from .mul import Mul, _keep_coeff
+from .symbol import Symbol, Dummy, symbols

venv/lib/python3.10/site-packages/sympy/core/relational.py

@@ -0,0 +1,1587 @@
+from __future__ import annotations
+
+from .basic import Atom, Basic
+from .sorting import ordered
+from .evalf import EvalfMixin
+from .function import AppliedUndef
+from .singleton import S
+from .sympify import _sympify, SympifyError
+from .parameters import global_parameters
+from .logic import fuzzy_bool, fuzzy_xor, fuzzy_and, fuzzy_not
+from sympy.logic.boolalg import Boolean, BooleanAtom
+from sympy.utilities.iterables import sift
+from sympy.utilities.misc import filldedent
+
+__all__ = (
+    'Rel', 'Eq', 'Ne', 'Lt', 'Le', 'Gt', 'Ge',
+    'Relational', 'Equality', 'Unequality', 'StrictLessThan', 'LessThan',
+    'StrictGreaterThan', 'GreaterThan',
+)
+
+from .expr import Expr
+from sympy.multipledispatch import dispatch
+from .containers import Tuple
+from .symbol import Symbol
+
+
+def _nontrivBool(side):
+    return isinstance(side, Boolean) and \
+           not isinstance(side, Atom)
+
+
+# Note, see issue 4986.  Ideally, we wouldn't want to subclass both Boolean
+# and Expr.
+# from .. import Expr
+
+
+def _canonical(cond):
+    # return a condition in which all relationals are canonical
+    reps = {r: r.canonical for r in cond.atoms(Relational)}
+    return cond.xreplace(reps)
+    # XXX: AttributeError was being caught here but it wasn't triggered by any of
+    # the tests so I've removed it...
+
+
+def _canonical_coeff(rel):
+    # return -2*x + 1 < 0 as x > 1/2
+    # XXX make this part of Relational.canonical?
+    rel = rel.canonical
+    if not rel.is_Relational or rel.rhs.is_Boolean:
+        return rel  # Eq(x, True)
+    b, l = rel.lhs.as_coeff_Add(rational=True)
+    m, lhs = l.as_coeff_Mul(rational=True)
+    rhs = (rel.rhs - b)/m
+    if m < 0:
+        return rel.reversed.func(lhs, rhs)
+    return rel.func(lhs, rhs)
+
+
+class Relational(Boolean, EvalfMixin):
+    """Base class for all relation types.
+
+    Explanation
+    ===========
+
+    Subclasses of Relational should generally be instantiated directly, but
+    Relational can be instantiated with a valid ``rop`` value to dispatch to
+    the appropriate subclass.
+
+    Parameters
+    ==========
+
+    rop : str or None
+        Indicates what subclass to instantiate.  Valid values can be found
+        in the keys of Relational.ValidRelationOperator.
+
+    Examples
+    ========
+
+    >>> from sympy import Rel
+    >>> from sympy.abc import x, y
+    >>> Rel(y, x + x**2, '==')
+    Eq(y, x**2 + x)
+
+    A relation's type can be defined upon creation using ``rop``.
+    The relation type of an existing expression can be obtained
+    using its ``rel_op`` property.
+    Here is a table of all the relation types, along with their
+    ``rop`` and ``rel_op`` values:
+
+    +---------------------+----------------------------+------------+
+    |Relation             |``rop``                     |``rel_op``  |
+    +=====================+============================+============+
+    |``Equality``         |``==`` or ``eq`` or ``None``|``==``      |
+    +---------------------+----------------------------+------------+
+    |``Unequality``       |``!=`` or ``ne``            |``!=``      |
+    +---------------------+----------------------------+------------+
+    |``GreaterThan``      |``>=`` or ``ge``            |``>=``      |
+    +---------------------+----------------------------+------------+
+    |``LessThan``         |``<=`` or ``le``            |``<=``      |
+    +---------------------+----------------------------+------------+
+    |``StrictGreaterThan``|``>`` or ``gt``             |``>``       |
+    +---------------------+----------------------------+------------+
+    |``StrictLessThan``   |``<`` or ``lt``             |``<``       |
+    +---------------------+----------------------------+------------+
+
+    For example, setting ``rop`` to ``==`` produces an
+    ``Equality`` relation, ``Eq()``.
+    So does setting ``rop`` to ``eq``, or leaving ``rop`` unspecified.
+    That is, the first three ``Rel()`` below all produce the same result.
+    Using a ``rop`` from a different row in the table produces a
+    different relation type.
+    For example, the fourth ``Rel()`` below using ``lt`` for ``rop``
+    produces a ``StrictLessThan`` inequality:
+
+    >>> from sympy import Rel
+    >>> from sympy.abc import x, y
+    >>> Rel(y, x + x**2, '==')
+        Eq(y, x**2 + x)
+    >>> Rel(y, x + x**2, 'eq')
+        Eq(y, x**2 + x)
+    >>> Rel(y, x + x**2)
+        Eq(y, x**2 + x)
+    >>> Rel(y, x + x**2, 'lt')
+        y < x**2 + x
+
+    To obtain the relation type of an existing expression,
+    get its ``rel_op`` property.
+    For example, ``rel_op`` is ``==`` for the ``Equality`` relation above,
+    and ``<`` for the strict less than inequality above:
+
+    >>> from sympy import Rel
+    >>> from sympy.abc import x, y
+    >>> my_equality = Rel(y, x + x**2, '==')
+    >>> my_equality.rel_op
+        '=='
+    >>> my_inequality = Rel(y, x + x**2, 'lt')
+    >>> my_inequality.rel_op
+        '<'
+
+    """
+    __slots__ = ()
+
+    ValidRelationOperator: dict[str | None, type[Relational]] = {}
+
+    is_Relational = True
+
+    # ValidRelationOperator - Defined below, because the necessary classes
+    #   have not yet been defined
+
+    def __new__(cls, lhs, rhs, rop=None, **assumptions):
+        # If called by a subclass, do nothing special and pass on to Basic.
+        if cls is not Relational:
+            return Basic.__new__(cls, lhs, rhs, **assumptions)
+
+        # XXX: Why do this? There should be a separate function to make a
+        # particular subclass of Relational from a string.
+        #
+        # If called directly with an operator, look up the subclass
+        # corresponding to that operator and delegate to it
+        cls = cls.ValidRelationOperator.get(rop, None)
+        if cls is None:
+            raise ValueError("Invalid relational operator symbol: %r" % rop)
+
+        if not issubclass(cls, (Eq, Ne)):
+            # validate that Booleans are not being used in a relational
+            # other than Eq/Ne;
+            # Note: Symbol is a subclass of Boolean but is considered
+            # acceptable here.
+            if any(map(_nontrivBool, (lhs, rhs))):
+                raise TypeError(filldedent('''
+                    A Boolean argument can only be used in
+                    Eq and Ne; all other relationals expect
+                    real expressions.
+                '''))
+
+        return cls(lhs, rhs, **assumptions)
+
+    @property
+    def lhs(self):
+        """The left-hand side of the relation."""
+        return self._args[0]
+
+    @property
+    def rhs(self):
+        """The right-hand side of the relation."""
+        return self._args[1]
+
+    @property
+    def reversed(self):
+        """Return the relationship with sides reversed.
+
+        Examples
+        ========
+
+        >>> from sympy import Eq
+        >>> from sympy.abc import x
+        >>> Eq(x, 1)
+        Eq(x, 1)
+        >>> _.reversed
+        Eq(1, x)
+        >>> x < 1
+        x < 1
+        >>> _.reversed
+        1 > x
+        """
+        ops = {Eq: Eq, Gt: Lt, Ge: Le, Lt: Gt, Le: Ge, Ne: Ne}
+        a, b = self.args
+        return Relational.__new__(ops.get(self.func, self.func), b, a)
+
+    @property
+    def reversedsign(self):
+        """Return the relationship with signs reversed.
+
+        Examples
+        ========
+
+        >>> from sympy import Eq
+        >>> from sympy.abc import x
+        >>> Eq(x, 1)
+        Eq(x, 1)
+        >>> _.reversedsign
+        Eq(-x, -1)
+        >>> x < 1
+        x < 1
+        >>> _.reversedsign
+        -x > -1
+        """
+        a, b = self.args
+        if not (isinstance(a, BooleanAtom) or isinstance(b, BooleanAtom)):
+            ops = {Eq: Eq, Gt: Lt, Ge: Le, Lt: Gt, Le: Ge, Ne: Ne}
+            return Relational.__new__(ops.get(self.func, self.func), -a, -b)
+        else:
+            return self
+
+    @property
+    def negated(self):
+        """Return the negated relationship.
+
+        Examples
+        ========
+
+        >>> from sympy import Eq
+        >>> from sympy.abc import x
+        >>> Eq(x, 1)
+        Eq(x, 1)
+        >>> _.negated
+        Ne(x, 1)
+        >>> x < 1
+        x < 1
+        >>> _.negated
+        x >= 1
+
+        Notes
+        =====
+
+        This works more or less identical to ``~``/``Not``. The difference is
+        that ``negated`` returns the relationship even if ``evaluate=False``.
+        Hence, this is useful in code when checking for e.g. negated relations
+        to existing ones as it will not be affected by the `evaluate` flag.
+
+        """
+        ops = {Eq: Ne, Ge: Lt, Gt: Le, Le: Gt, Lt: Ge, Ne: Eq}
+        # If there ever will be new Relational subclasses, the following line
+        # will work until it is properly sorted out
+        # return ops.get(self.func, lambda a, b, evaluate=False: ~(self.func(a,
+        #      b, evaluate=evaluate)))(*self.args, evaluate=False)
+        return Relational.__new__(ops.get(self.func), *self.args)
+
+    @property
+    def weak(self):
+        """return the non-strict version of the inequality or self
+
+        EXAMPLES
+        ========
+
+        >>> from sympy.abc import x
+        >>> (x < 1).weak
+        x <= 1
+        >>> _.weak
+        x <= 1
+        """
+        return self
+
+    @property
+    def strict(self):
+        """return the strict version of the inequality or self
+
+        EXAMPLES
+        ========
+
+        >>> from sympy.abc import x
+        >>> (x <= 1).strict
+        x < 1
+        >>> _.strict
+        x < 1
+        """
+        return self
+
+    def _eval_evalf(self, prec):
+        return self.func(*[s._evalf(prec) for s in self.args])
+
+    @property
+    def canonical(self):
+        """Return a canonical form of the relational by putting a
+        number on the rhs, canonically removing a sign or else
+        ordering the args canonically. No other simplification is
+        attempted.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x, y
+        >>> x < 2
+        x < 2
+        >>> _.reversed.canonical
+        x < 2
+        >>> (-y < x).canonical
+        x > -y
+        >>> (-y > x).canonical
+        x < -y
+        >>> (-y < -x).canonical
+        x < y
+
+        The canonicalization is recursively applied:
+
+        >>> from sympy import Eq
+        >>> Eq(x < y, y > x).canonical
+        True
+        """
+        args = tuple([i.canonical if isinstance(i, Relational) else i for i in self.args])
+        if args != self.args:
+            r = self.func(*args)
+            if not isinstance(r, Relational):
+                return r
+        else:
+            r = self
+        if r.rhs.is_number:
+            if r.rhs.is_Number and r.lhs.is_Number and r.lhs > r.rhs:
+                r = r.reversed
+        elif r.lhs.is_number:
+            r = r.reversed
+        elif tuple(ordered(args)) != args:
+            r = r.reversed
+
+        LHS_CEMS = getattr(r.lhs, 'could_extract_minus_sign', None)
+        RHS_CEMS = getattr(r.rhs, 'could_extract_minus_sign', None)
+
+        if isinstance(r.lhs, BooleanAtom) or isinstance(r.rhs, BooleanAtom):
+            return r
+
+        # Check if first value has negative sign
+        if LHS_CEMS and LHS_CEMS():
+            return r.reversedsign
+        elif not r.rhs.is_number and RHS_CEMS and RHS_CEMS():
+            # Right hand side has a minus, but not lhs.
+            # How does the expression with reversed signs behave?
+            # This is so that expressions of the type
+            # Eq(x, -y) and Eq(-x, y)
+            # have the same canonical representation
+            expr1, _ = ordered([r.lhs, -r.rhs])
+            if expr1 != r.lhs:
+                return r.reversed.reversedsign
+
+        return r
+
+    def equals(self, other, failing_expression=False):
+        """Return True if the sides of the relationship are mathematically
+        identical and the type of relationship is the same.
+        If failing_expression is True, return the expression whose truth value
+        was unknown."""
+        if isinstance(other, Relational):
+            if other in (self, self.reversed):
+                return True
+            a, b = self, other
+            if a.func in (Eq, Ne) or b.func in (Eq, Ne):
+                if a.func != b.func:
+                    return False
+                left, right = [i.equals(j,
+                                        failing_expression=failing_expression)
+                               for i, j in zip(a.args, b.args)]
+                if left is True:
+                    return right
+                if right is True:
+                    return left
+                lr, rl = [i.equals(j, failing_expression=failing_expression)
+                          for i, j in zip(a.args, b.reversed.args)]
+                if lr is True:
+                    return rl
+                if rl is True:
+                    return lr
+                e = (left, right, lr, rl)
+                if all(i is False for i in e):
+                    return False
+                for i in e:
+                    if i not in (True, False):
+                        return i
+            else:
+                if b.func != a.func:
+                    b = b.reversed
+                if a.func != b.func:
+                    return False
+                left = a.lhs.equals(b.lhs,
+                                    failing_expression=failing_expression)
+                if left is False:
+                    return False
+                right = a.rhs.equals(b.rhs,
+                                     failing_expression=failing_expression)
+                if right is False:
+                    return False
+                if left is True:
+                    return right
+                return left
+
+    def _eval_simplify(self, **kwargs):
+        from .add import Add
+        from .expr import Expr
+        r = self
+        r = r.func(*[i.simplify(**kwargs) for i in r.args])
+        if r.is_Relational:
+            if not isinstance(r.lhs, Expr) or not isinstance(r.rhs, Expr):
+                return r
+            dif = r.lhs - r.rhs
+            # replace dif with a valid Number that will
+            # allow a definitive comparison with 0
+            v = None
+            if dif.is_comparable:
+                v = dif.n(2)
+            elif dif.equals(0):  # XXX this is expensive
+                v = S.Zero
+            if v is not None:
+                r = r.func._eval_relation(v, S.Zero)
+            r = r.canonical
+            # If there is only one symbol in the expression,
+            # try to write it on a simplified form
+            free = list(filter(lambda x: x.is_real is not False, r.free_symbols))
+            if len(free) == 1:
+                try:
+                    from sympy.solvers.solveset import linear_coeffs
+                    x = free.pop()
+                    dif = r.lhs - r.rhs
+                    m, b = linear_coeffs(dif, x)
+                    if m.is_zero is False:
+                        if m.is_negative:
+                            # Dividing with a negative number, so change order of arguments
+                            # canonical will put the symbol back on the lhs later
+                            r = r.func(-b / m, x)
+                        else:
+                            r = r.func(x, -b / m)
+                    else:
+                        r = r.func(b, S.Zero)
+                except ValueError:
+                    # maybe not a linear function, try polynomial
+                    from sympy.polys.polyerrors import PolynomialError
+                    from sympy.polys.polytools import gcd, Poly, poly
+                    try:
+                        p = poly(dif, x)
+                        c = p.all_coeffs()
+                        constant = c[-1]
+                        c[-1] = 0
+                        scale = gcd(c)
+                        c = [ctmp / scale for ctmp in c]
+                        r = r.func(Poly.from_list(c, x).as_expr(), -constant / scale)
+                    except PolynomialError:
+                        pass
+            elif len(free) >= 2:
+                try:
+                    from sympy.solvers.solveset import linear_coeffs
+                    from sympy.polys.polytools import gcd
+                    free = list(ordered(free))
+                    dif = r.lhs - r.rhs
+                    m = linear_coeffs(dif, *free)
+                    constant = m[-1]
+                    del m[-1]
+                    scale = gcd(m)
+                    m = [mtmp / scale for mtmp in m]
+                    nzm = list(filter(lambda f: f[0] != 0, list(zip(m, free))))
+                    if scale.is_zero is False:
+                        if constant != 0:
+                            # lhs: expression, rhs: constant
+                            newexpr = Add(*[i * j for i, j in nzm])
+                            r = r.func(newexpr, -constant / scale)
+                        else:
+                            # keep first term on lhs
+                            lhsterm = nzm[0][0] * nzm[0][1]
+                            del nzm[0]
+                            newexpr = Add(*[i * j for i, j in nzm])
+                            r = r.func(lhsterm, -newexpr)
+
+                    else:
+                        r = r.func(constant, S.Zero)
+                except ValueError:
+                    pass
+        # Did we get a simplified result?
+        r = r.canonical
+        measure = kwargs['measure']
+        if measure(r) < kwargs['ratio'] * measure(self):
+            return r
+        else:
+            return self
+
+    def _eval_trigsimp(self, **opts):
+        from sympy.simplify.trigsimp import trigsimp
+        return self.func(trigsimp(self.lhs, **opts), trigsimp(self.rhs, **opts))
+
+    def expand(self, **kwargs):
+        args = (arg.expand(**kwargs) for arg in self.args)
+        return self.func(*args)
+
+    def __bool__(self):
+        raise TypeError("cannot determine truth value of Relational")
+
+    def _eval_as_set(self):
+        # self is univariate and periodicity(self, x) in (0, None)
+        from sympy.solvers.inequalities import solve_univariate_inequality
+        from sympy.sets.conditionset import ConditionSet
+        syms = self.free_symbols
+        assert len(syms) == 1
+        x = syms.pop()
+        try:
+            xset = solve_univariate_inequality(self, x, relational=False)
+        except NotImplementedError:
+            # solve_univariate_inequality raises NotImplementedError for
+            # unsolvable equations/inequalities.
+            xset = ConditionSet(x, self, S.Reals)
+        return xset
+
+    @property
+    def binary_symbols(self):
+        # override where necessary
+        return set()
+
+
+Rel = Relational
+
+
+class Equality(Relational):
+    """
+    An equal relation between two objects.
+
+    Explanation
+    ===========
+
+    Represents that two objects are equal.  If they can be easily shown
+    to be definitively equal (or unequal), this will reduce to True (or
+    False).  Otherwise, the relation is maintained as an unevaluated
+    Equality object.  Use the ``simplify`` function on this object for
+    more nontrivial evaluation of the equality relation.
+
+    As usual, the keyword argument ``evaluate=False`` can be used to
+    prevent any evaluation.
+
+    Examples
+    ========
+
+    >>> from sympy import Eq, simplify, exp, cos
+    >>> from sympy.abc import x, y
+    >>> Eq(y, x + x**2)
+    Eq(y, x**2 + x)
+    >>> Eq(2, 5)
+    False
+    >>> Eq(2, 5, evaluate=False)
+    Eq(2, 5)
+    >>> _.doit()
+    False
+    >>> Eq(exp(x), exp(x).rewrite(cos))
+    Eq(exp(x), sinh(x) + cosh(x))
+    >>> simplify(_)
+    True
+
+    See Also
+    ========
+
+    sympy.logic.boolalg.Equivalent : for representing equality between two
+        boolean expressions
+
+    Notes
+    =====
+
+    Python treats 1 and True (and 0 and False) as being equal; SymPy
+    does not. And integer will always compare as unequal to a Boolean:
+
+    >>> Eq(True, 1), True == 1
+    (False, True)
+
+    This class is not the same as the == operator.  The == operator tests
+    for exact structural equality between two expressions; this class
+    compares expressions mathematically.
+
+    If either object defines an ``_eval_Eq`` method, it can be used in place of
+    the default algorithm.  If ``lhs._eval_Eq(rhs)`` or ``rhs._eval_Eq(lhs)``
+    returns anything other than None, that return value will be substituted for
+    the Equality.  If None is returned by ``_eval_Eq``, an Equality object will
+    be created as usual.
+
+    Since this object is already an expression, it does not respond to
+    the method ``as_expr`` if one tries to create `x - y` from ``Eq(x, y)``.
+    This can be done with the ``rewrite(Add)`` method.
+
+    .. deprecated:: 1.5
+
+       ``Eq(expr)`` with a single argument is a shorthand for ``Eq(expr, 0)``,
+       but this behavior is deprecated and will be removed in a future version
+       of SymPy.
+
+    """
+    rel_op = '=='
+
+    __slots__ = ()
+
+    is_Equality = True
+
+    def __new__(cls, lhs, rhs, **options):
+        evaluate = options.pop('evaluate', global_parameters.evaluate)
+        lhs = _sympify(lhs)
+        rhs = _sympify(rhs)
+        if evaluate:
+            val = is_eq(lhs, rhs)
+            if val is None:
+                return cls(lhs, rhs, evaluate=False)
+            else:
+                return _sympify(val)
+
+        return Relational.__new__(cls, lhs, rhs)
+
+    @classmethod
+    def _eval_relation(cls, lhs, rhs):
+        return _sympify(lhs == rhs)
+
+    def _eval_rewrite_as_Add(self, *args, **kwargs):
+        """
+        return Eq(L, R) as L - R. To control the evaluation of
+        the result set pass `evaluate=True` to give L - R;
+        if `evaluate=None` then terms in L and R will not cancel
+        but they will be listed in canonical order; otherwise
+        non-canonical args will be returned. If one side is 0, the
+        non-zero side will be returned.
+
+        Examples
+        ========
+
+        >>> from sympy import Eq, Add
+        >>> from sympy.abc import b, x
+        >>> eq = Eq(x + b, x - b)
+        >>> eq.rewrite(Add)
+        2*b
+        >>> eq.rewrite(Add, evaluate=None).args
+        (b, b, x, -x)
+        >>> eq.rewrite(Add, evaluate=False).args
+        (b, x, b, -x)
+        """
+        from .add import _unevaluated_Add, Add
+        L, R = args
+        if L == 0:
+            return R
+        if R == 0:
+            return L
+        evaluate = kwargs.get('evaluate', True)
+        if evaluate:
+            # allow cancellation of args
+            return L - R
+        args = Add.make_args(L) + Add.make_args(-R)
+        if evaluate is None:
+            # no cancellation, but canonical
+            return _unevaluated_Add(*args)
+        # no cancellation, not canonical
+        return Add._from_args(args)
+
+    @property
+    def binary_symbols(self):
+        if S.true in self.args or S.false in self.args:
+            if self.lhs.is_Symbol:
+                return {self.lhs}
+            elif self.rhs.is_Symbol:
+                return {self.rhs}
+        return set()
+
+    def _eval_simplify(self, **kwargs):
+        # standard simplify
+        e = super()._eval_simplify(**kwargs)
+        if not isinstance(e, Equality):
+            return e
+        from .expr import Expr
+        if not isinstance(e.lhs, Expr) or not isinstance(e.rhs, Expr):
+            return e
+        free = self.free_symbols
+        if len(free) == 1:
+            try:
+                from .add import Add
+                from sympy.solvers.solveset import linear_coeffs
+                x = free.pop()
+                m, b = linear_coeffs(
+                    e.rewrite(Add, evaluate=False), x)
+                if m.is_zero is False:
+                    enew = e.func(x, -b / m)
+                else:
+                    enew = e.func(m * x, -b)
+                measure = kwargs['measure']
+                if measure(enew) <= kwargs['ratio'] * measure(e):
+                    e = enew
+            except ValueError:
+                pass
+        return e.canonical
+
+    def integrate(self, *args, **kwargs):
+        """See the integrate function in sympy.integrals"""
+        from sympy.integrals.integrals import integrate
+        return integrate(self, *args, **kwargs)
+
+    def as_poly(self, *gens, **kwargs):
+        '''Returns lhs-rhs as a Poly
+
+        Examples
+        ========
+
+        >>> from sympy import Eq
+        >>> from sympy.abc import x
+        >>> Eq(x**2, 1).as_poly(x)
+        Poly(x**2 - 1, x, domain='ZZ')
+        '''
+        return (self.lhs - self.rhs).as_poly(*gens, **kwargs)
+
+
+Eq = Equality
+
+
+class Unequality(Relational):
+    """An unequal relation between two objects.
+
+    Explanation
+    ===========
+
+    Represents that two objects are not equal.  If they can be shown to be
+    definitively equal, this will reduce to False; if definitively unequal,
+    this will reduce to True.  Otherwise, the relation is maintained as an
+    Unequality object.
+
+    Examples
+    ========
+
+    >>> from sympy import Ne
+    >>> from sympy.abc import x, y
+    >>> Ne(y, x+x**2)
+    Ne(y, x**2 + x)
+
+    See Also
+    ========
+    Equality
+
+    Notes
+    =====
+    This class is not the same as the != operator.  The != operator tests
+    for exact structural equality between two expressions; this class
+    compares expressions mathematically.
+
+    This class is effectively the inverse of Equality.  As such, it uses the
+    same algorithms, including any available `_eval_Eq` methods.
+
+    """
+    rel_op = '!='
+
+    __slots__ = ()
+
+    def __new__(cls, lhs, rhs, **options):
+        lhs = _sympify(lhs)
+        rhs = _sympify(rhs)
+        evaluate = options.pop('evaluate', global_parameters.evaluate)
+        if evaluate:
+            val = is_neq(lhs, rhs)
+            if val is None:
+                return cls(lhs, rhs, evaluate=False)
+            else:
+                return _sympify(val)
+
+        return Relational.__new__(cls, lhs, rhs, **options)
+
+    @classmethod
+    def _eval_relation(cls, lhs, rhs):
+        return _sympify(lhs != rhs)
+
+    @property
+    def binary_symbols(self):
+        if S.true in self.args or S.false in self.args:
+            if self.lhs.is_Symbol:
+                return {self.lhs}
+            elif self.rhs.is_Symbol:
+                return {self.rhs}
+        return set()
+
+    def _eval_simplify(self, **kwargs):
+        # simplify as an equality
+        eq = Equality(*self.args)._eval_simplify(**kwargs)
+        if isinstance(eq, Equality):
+            # send back Ne with the new args
+            return self.func(*eq.args)
+        return eq.negated  # result of Ne is the negated Eq
+
+
+Ne = Unequality
+
+
+class _Inequality(Relational):
+    """Internal base class for all *Than types.
+
+    Each subclass must implement _eval_relation to provide the method for
+    comparing two real numbers.
+
+    """
+    __slots__ = ()
+
+    def __new__(cls, lhs, rhs, **options):
+
+        try:
+            lhs = _sympify(lhs)
+            rhs = _sympify(rhs)
+        except SympifyError:
+            return NotImplemented
+
+        evaluate = options.pop('evaluate', global_parameters.evaluate)
+        if evaluate:
+            for me in (lhs, rhs):
+                if me.is_extended_real is False:
+                    raise TypeError("Invalid comparison of non-real %s" % me)
+                if me is S.NaN:
+                    raise TypeError("Invalid NaN comparison")
+            # First we invoke the appropriate inequality method of `lhs`
+            # (e.g., `lhs.__lt__`).  That method will try to reduce to
+            # boolean or raise an exception.  It may keep calling
+            # superclasses until it reaches `Expr` (e.g., `Expr.__lt__`).
+            # In some cases, `Expr` will just invoke us again (if neither it
+            # nor a subclass was able to reduce to boolean or raise an
+            # exception).  In that case, it must call us with
+            # `evaluate=False` to prevent infinite recursion.
+            return cls._eval_relation(lhs, rhs, **options)
+
+        # make a "non-evaluated" Expr for the inequality
+        return Relational.__new__(cls, lhs, rhs, **options)
+
+    @classmethod
+    def _eval_relation(cls, lhs, rhs, **options):
+        val = cls._eval_fuzzy_relation(lhs, rhs)
+        if val is None:
+            return cls(lhs, rhs, evaluate=False)
+        else:
+            return _sympify(val)
+
+
+class _Greater(_Inequality):
+    """Not intended for general use
+
+    _Greater is only used so that GreaterThan and StrictGreaterThan may
+    subclass it for the .gts and .lts properties.
+
+    """
+    __slots__ = ()
+
+    @property
+    def gts(self):
+        return self._args[0]
+
+    @property
+    def lts(self):
+        return self._args[1]
+
+
+class _Less(_Inequality):
+    """Not intended for general use.
+
+    _Less is only used so that LessThan and StrictLessThan may subclass it for
+    the .gts and .lts properties.
+
+    """
+    __slots__ = ()
+
+    @property
+    def gts(self):
+        return self._args[1]
+
+    @property
+    def lts(self):
+        return self._args[0]
+
+
+class GreaterThan(_Greater):
+    r"""Class representations of inequalities.
+
+    Explanation
+    ===========
+
+    The ``*Than`` classes represent inequal relationships, where the left-hand
+    side is generally bigger or smaller than the right-hand side.  For example,
+    the GreaterThan class represents an inequal relationship where the
+    left-hand side is at least as big as the right side, if not bigger.  In
+    mathematical notation:
+
+    lhs $\ge$ rhs
+
+    In total, there are four ``*Than`` classes, to represent the four
+    inequalities:
+
+    +-----------------+--------+
+    |Class Name       | Symbol |
+    +=================+========+
+    |GreaterThan      | ``>=`` |
+    +-----------------+--------+
+    |LessThan         | ``<=`` |
+    +-----------------+--------+
+    |StrictGreaterThan| ``>``  |
+    +-----------------+--------+
+    |StrictLessThan   | ``<``  |
+    +-----------------+--------+
+
+    All classes take two arguments, lhs and rhs.
+
+    +----------------------------+-----------------+
+    |Signature Example           | Math Equivalent |
+    +============================+=================+
+    |GreaterThan(lhs, rhs)       |   lhs $\ge$ rhs |
+    +----------------------------+-----------------+
+    |LessThan(lhs, rhs)          |   lhs $\le$ rhs |
+    +----------------------------+-----------------+
+    |StrictGreaterThan(lhs, rhs) |   lhs $>$ rhs   |
+    +----------------------------+-----------------+
+    |StrictLessThan(lhs, rhs)    |   lhs $<$ rhs   |
+    +----------------------------+-----------------+
+
+    In addition to the normal .lhs and .rhs of Relations, ``*Than`` inequality
+    objects also have the .lts and .gts properties, which represent the "less
+    than side" and "greater than side" of the operator.  Use of .lts and .gts
+    in an algorithm rather than .lhs and .rhs as an assumption of inequality
+    direction will make more explicit the intent of a certain section of code,
+    and will make it similarly more robust to client code changes:
+
+    >>> from sympy import GreaterThan, StrictGreaterThan
+    >>> from sympy import LessThan, StrictLessThan
+    >>> from sympy import And, Ge, Gt, Le, Lt, Rel, S
+    >>> from sympy.abc import x, y, z
+    >>> from sympy.core.relational import Relational
+
+    >>> e = GreaterThan(x, 1)
+    >>> e
+    x >= 1
+    >>> '%s >= %s is the same as %s <= %s' % (e.gts, e.lts, e.lts, e.gts)
+    'x >= 1 is the same as 1 <= x'
+
+    Examples
+    ========
+
+    One generally does not instantiate these classes directly, but uses various
+    convenience methods:
+
+    >>> for f in [Ge, Gt, Le, Lt]:  # convenience wrappers
+    ...     print(f(x, 2))
+    x >= 2
+    x > 2
+    x <= 2
+    x < 2
+
+    Another option is to use the Python inequality operators (``>=``, ``>``,
+    ``<=``, ``<``) directly.  Their main advantage over the ``Ge``, ``Gt``,
+    ``Le``, and ``Lt`` counterparts, is that one can write a more
+    "mathematical looking" statement rather than littering the math with
+    oddball function calls.  However there are certain (minor) caveats of
+    which to be aware (search for 'gotcha', below).
+
+    >>> x >= 2
+    x >= 2
+    >>> _ == Ge(x, 2)
+    True
+
+    However, it is also perfectly valid to instantiate a ``*Than`` class less
+    succinctly and less conveniently:
+
+    >>> Rel(x, 1, ">")
+    x > 1
+    >>> Relational(x, 1, ">")
+    x > 1
+
+    >>> StrictGreaterThan(x, 1)
+    x > 1
+    >>> GreaterThan(x, 1)
+    x >= 1
+    >>> LessThan(x, 1)
+    x <= 1
+    >>> StrictLessThan(x, 1)
+    x < 1
+
+    Notes
+    =====
+
+    There are a couple of "gotchas" to be aware of when using Python's
+    operators.
+
+    The first is that what your write is not always what you get:
+
+        >>> 1 < x
+        x > 1
+
+        Due to the order that Python parses a statement, it may
+        not immediately find two objects comparable.  When ``1 < x``
+        is evaluated, Python recognizes that the number 1 is a native
+        number and that x is *not*.  Because a native Python number does
+        not know how to compare itself with a SymPy object
+        Python will try the reflective operation, ``x > 1`` and that is the
+        form that gets evaluated, hence returned.
+
+        If the order of the statement is important (for visual output to
+        the console, perhaps), one can work around this annoyance in a
+        couple ways:
+
+        (1) "sympify" the literal before comparison
+
+        >>> S(1) < x
+        1 < x
+
+        (2) use one of the wrappers or less succinct methods described
+        above
+
+        >>> Lt(1, x)
+        1 < x
+        >>> Relational(1, x, "<")
+        1 < x
+
+    The second gotcha involves writing equality tests between relationals
+    when one or both sides of the test involve a literal relational:
+
+        >>> e = x < 1; e
+        x < 1
+        >>> e == e  # neither side is a literal
+        True
+        >>> e == x < 1  # expecting True, too
+        False
+        >>> e != x < 1  # expecting False
+        x < 1
+        >>> x < 1 != x < 1  # expecting False or the same thing as before
+        Traceback (most recent call last):
+        ...
+        TypeError: cannot determine truth value of Relational
+
+        The solution for this case is to wrap literal relationals in
+        parentheses:
+
+        >>> e == (x < 1)
+        True
+        >>> e != (x < 1)
+        False
+        >>> (x < 1) != (x < 1)
+        False
+
+    The third gotcha involves chained inequalities not involving
+    ``==`` or ``!=``. Occasionally, one may be tempted to write:
+
+        >>> e = x < y < z
+        Traceback (most recent call last):
+        ...
+        TypeError: symbolic boolean expression has no truth value.
+
+        Due to an implementation detail or decision of Python [1]_,
+        there is no way for SymPy to create a chained inequality with
+        that syntax so one must use And:
+
+        >>> e = And(x < y, y < z)
+        >>> type( e )
+        And
+        >>> e
+        (x < y) & (y < z)
+
+        Although this can also be done with the '&' operator, it cannot
+        be done with the 'and' operarator:
+
+        >>> (x < y) & (y < z)
+        (x < y) & (y < z)
+        >>> (x < y) and (y < z)
+        Traceback (most recent call last):
+        ...
+        TypeError: cannot determine truth value of Relational
+
+    .. [1] This implementation detail is that Python provides no reliable
+       method to determine that a chained inequality is being built.
+       Chained comparison operators are evaluated pairwise, using "and"
+       logic (see
+       https://docs.python.org/3/reference/expressions.html#not-in). This
+       is done in an efficient way, so that each object being compared
+       is only evaluated once and the comparison can short-circuit. For
+       example, ``1 > 2 > 3`` is evaluated by Python as ``(1 > 2) and (2
+       > 3)``. The ``and`` operator coerces each side into a bool,
+       returning the object itself when it short-circuits. The bool of
+       the --Than operators will raise TypeError on purpose, because
+       SymPy cannot determine the mathematical ordering of symbolic
+       expressions. Thus, if we were to compute ``x > y > z``, with
+       ``x``, ``y``, and ``z`` being Symbols, Python converts the
+       statement (roughly) into these steps:
+
+        (1) x > y > z
+        (2) (x > y) and (y > z)
+        (3) (GreaterThanObject) and (y > z)
+        (4) (GreaterThanObject.__bool__()) and (y > z)
+        (5) TypeError
+
+       Because of the ``and`` added at step 2, the statement gets turned into a
+       weak ternary statement, and the first object's ``__bool__`` method will
+       raise TypeError.  Thus, creating a chained inequality is not possible.
+
+           In Python, there is no way to override the ``and`` operator, or to
+           control how it short circuits, so it is impossible to make something
+           like ``x > y > z`` work.  There was a PEP to change this,
+           :pep:`335`, but it was officially closed in March, 2012.
+
+    """
+    __slots__ = ()
+
+    rel_op = '>='
+
+    @classmethod
+    def _eval_fuzzy_relation(cls, lhs, rhs):
+        return is_ge(lhs, rhs)
+
+    @property
+    def strict(self):
+        return Gt(*self.args)
+
+Ge = GreaterThan
+
+
+class LessThan(_Less):
+    __doc__ = GreaterThan.__doc__
+    __slots__ = ()
+
+    rel_op = '<='
+
+    @classmethod
+    def _eval_fuzzy_relation(cls, lhs, rhs):
+        return is_le(lhs, rhs)
+
+    @property
+    def strict(self):
+        return Lt(*self.args)
+
+Le = LessThan
+
+
+class StrictGreaterThan(_Greater):
+    __doc__ = GreaterThan.__doc__
+    __slots__ = ()
+
+    rel_op = '>'
+
+    @classmethod
+    def _eval_fuzzy_relation(cls, lhs, rhs):
+        return is_gt(lhs, rhs)
+
+    @property
+    def weak(self):
+        return Ge(*self.args)
+
+
+Gt = StrictGreaterThan
+
+
+class StrictLessThan(_Less):
+    __doc__ = GreaterThan.__doc__
+    __slots__ = ()
+
+    rel_op = '<'
+
+    @classmethod
+    def _eval_fuzzy_relation(cls, lhs, rhs):
+        return is_lt(lhs, rhs)
+
+    @property
+    def weak(self):
+        return Le(*self.args)
+
+Lt = StrictLessThan
+
+# A class-specific (not object-specific) data item used for a minor speedup.
+# It is defined here, rather than directly in the class, because the classes
+# that it references have not been defined until now (e.g. StrictLessThan).
+Relational.ValidRelationOperator = {
+    None: Equality,
+    '==': Equality,
+    'eq': Equality,
+    '!=': Unequality,
+    '<>': Unequality,
+    'ne': Unequality,
+    '>=': GreaterThan,
+    'ge': GreaterThan,
+    '<=': LessThan,
+    'le': LessThan,
+    '>': StrictGreaterThan,
+    'gt': StrictGreaterThan,
+    '<': StrictLessThan,
+    'lt': StrictLessThan,
+}
+
+
+def _n2(a, b):
+    """Return (a - b).evalf(2) if a and b are comparable, else None.
+    This should only be used when a and b are already sympified.
+    """
+    # /!\ it is very important (see issue 8245) not to
+    # use a re-evaluated number in the calculation of dif
+    if a.is_comparable and b.is_comparable:
+        dif = (a - b).evalf(2)
+        if dif.is_comparable:
+            return dif
+
+
+@dispatch(Expr, Expr)
+def _eval_is_ge(lhs, rhs):
+    return None
+
+
+@dispatch(Basic, Basic)
+def _eval_is_eq(lhs, rhs):
+    return None
+
+
+@dispatch(Tuple, Expr) # type: ignore
+def _eval_is_eq(lhs, rhs):  # noqa:F811
+    return False
+
+
+@dispatch(Tuple, AppliedUndef) # type: ignore
+def _eval_is_eq(lhs, rhs):  # noqa:F811
+    return None
+
+
+@dispatch(Tuple, Symbol) # type: ignore
+def _eval_is_eq(lhs, rhs):  # noqa:F811
+    return None
+
+
+@dispatch(Tuple, Tuple) # type: ignore
+def _eval_is_eq(lhs, rhs):  # noqa:F811
+    if len(lhs) != len(rhs):
+        return False
+
+    return fuzzy_and(fuzzy_bool(is_eq(s, o)) for s, o in zip(lhs, rhs))
+
+
+def is_lt(lhs, rhs, assumptions=None):
+    """Fuzzy bool for lhs is strictly less than rhs.
+
+    See the docstring for :func:`~.is_ge` for more.
+    """
+    return fuzzy_not(is_ge(lhs, rhs, assumptions))
+
+
+def is_gt(lhs, rhs, assumptions=None):
+    """Fuzzy bool for lhs is strictly greater than rhs.
+
+    See the docstring for :func:`~.is_ge` for more.
+    """
+    return fuzzy_not(is_le(lhs, rhs, assumptions))
+
+
+def is_le(lhs, rhs, assumptions=None):
+    """Fuzzy bool for lhs is less than or equal to rhs.
+
+    See the docstring for :func:`~.is_ge` for more.
+    """
+    return is_ge(rhs, lhs, assumptions)
+
+
+def is_ge(lhs, rhs, assumptions=None):
+    """
+    Fuzzy bool for *lhs* is greater than or equal to *rhs*.
+
+    Parameters
+    ==========
+
+    lhs : Expr
+        The left-hand side of the expression, must be sympified,
+        and an instance of expression. Throws an exception if
+        lhs is not an instance of expression.
+
+    rhs : Expr
+        The right-hand side of the expression, must be sympified
+        and an instance of expression. Throws an exception if
+        lhs is not an instance of expression.
+
+    assumptions: Boolean, optional
+        Assumptions taken to evaluate the inequality.
+
+    Returns
+    =======
+
+    ``True`` if *lhs* is greater than or equal to *rhs*, ``False`` if *lhs*
+    is less than *rhs*, and ``None`` if the comparison between *lhs* and
+    *rhs* is indeterminate.
+
+    Explanation
+    ===========
+
+    This function is intended to give a relatively fast determination and
+    deliberately does not attempt slow calculations that might help in
+    obtaining a determination of True or False in more difficult cases.
+
+    The four comparison functions ``is_le``, ``is_lt``, ``is_ge``, and ``is_gt`` are
+    each implemented in terms of ``is_ge`` in the following way:
+
+    is_ge(x, y) := is_ge(x, y)
+    is_le(x, y) := is_ge(y, x)
+    is_lt(x, y) := fuzzy_not(is_ge(x, y))
+    is_gt(x, y) := fuzzy_not(is_ge(y, x))
+
+    Therefore, supporting new type with this function will ensure behavior for
+    other three functions as well.
+
+    To maintain these equivalences in fuzzy logic it is important that in cases where
+    either x or y is non-real all comparisons will give None.
+
+    Examples
+    ========
+
+    >>> from sympy import S, Q
+    >>> from sympy.core.relational import is_ge, is_le, is_gt, is_lt
+    >>> from sympy.abc import x
+    >>> is_ge(S(2), S(0))
+    True
+    >>> is_ge(S(0), S(2))
+    False
+    >>> is_le(S(0), S(2))
+    True
+    >>> is_gt(S(0), S(2))
+    False
+    >>> is_lt(S(2), S(0))
+    False
+
+    Assumptions can be passed to evaluate the quality which is otherwise
+    indeterminate.
+
+    >>> print(is_ge(x, S(0)))
+    None
+    >>> is_ge(x, S(0), assumptions=Q.positive(x))
+    True
+
+    New types can be supported by dispatching to ``_eval_is_ge``.
+
+    >>> from sympy import Expr, sympify
+    >>> from sympy.multipledispatch import dispatch
+    >>> class MyExpr(Expr):
+    ...     def __new__(cls, arg):
+    ...         return super().__new__(cls, sympify(arg))
+    ...     @property
+    ...     def value(self):
+    ...         return self.args[0]
+    >>> @dispatch(MyExpr, MyExpr)
+    ... def _eval_is_ge(a, b):
+    ...     return is_ge(a.value, b.value)
+    >>> a = MyExpr(1)
+    >>> b = MyExpr(2)
+    >>> is_ge(b, a)
+    True
+    >>> is_le(a, b)
+    True
+    """
+    from sympy.assumptions.wrapper import AssumptionsWrapper, is_extended_nonnegative
+
+    if not (isinstance(lhs, Expr) and isinstance(rhs, Expr)):
+        raise TypeError("Can only compare inequalities with Expr")
+
+    retval = _eval_is_ge(lhs, rhs)
+
+    if retval is not None:
+        return retval
+    else:
+        n2 = _n2(lhs, rhs)
+        if n2 is not None:
+            # use float comparison for infinity.
+            # otherwise get stuck in infinite recursion
+            if n2 in (S.Infinity, S.NegativeInfinity):
+                n2 = float(n2)
+            return n2 >= 0
+
+        _lhs = AssumptionsWrapper(lhs, assumptions)
+        _rhs = AssumptionsWrapper(rhs, assumptions)
+        if _lhs.is_extended_real and _rhs.is_extended_real:
+            if (_lhs.is_infinite and _lhs.is_extended_positive) or (_rhs.is_infinite and _rhs.is_extended_negative):
+                return True
+            diff = lhs - rhs
+            if diff is not S.NaN:
+                rv = is_extended_nonnegative(diff, assumptions)
+                if rv is not None:
+                    return rv
+
+
+def is_neq(lhs, rhs, assumptions=None):
+    """Fuzzy bool for lhs does not equal rhs.
+
+    See the docstring for :func:`~.is_eq` for more.
+    """
+    return fuzzy_not(is_eq(lhs, rhs, assumptions))
+
+
+def is_eq(lhs, rhs, assumptions=None):
+    """
+    Fuzzy bool representing mathematical equality between *lhs* and *rhs*.
+
+    Parameters
+    ==========
+
+    lhs : Expr
+        The left-hand side of the expression, must be sympified.
+
+    rhs : Expr
+        The right-hand side of the expression, must be sympified.
+
+    assumptions: Boolean, optional
+        Assumptions taken to evaluate the equality.
+
+    Returns
+    =======
+
+    ``True`` if *lhs* is equal to *rhs*, ``False`` is *lhs* is not equal to *rhs*,
+    and ``None`` if the comparison between *lhs* and *rhs* is indeterminate.
+
+    Explanation
+    ===========
+
+    This function is intended to give a relatively fast determination and
+    deliberately does not attempt slow calculations that might help in
+    obtaining a determination of True or False in more difficult cases.
+
+    :func:`~.is_neq` calls this function to return its value, so supporting
+    new type with this function will ensure correct behavior for ``is_neq``
+    as well.
+
+    Examples
+    ========
+
+    >>> from sympy import Q, S
+    >>> from sympy.core.relational import is_eq, is_neq
+    >>> from sympy.abc import x
+    >>> is_eq(S(0), S(0))
+    True
+    >>> is_neq(S(0), S(0))
+    False
+    >>> is_eq(S(0), S(2))
+    False
+    >>> is_neq(S(0), S(2))
+    True
+
+    Assumptions can be passed to evaluate the equality which is otherwise
+    indeterminate.
+
+    >>> print(is_eq(x, S(0)))
+    None
+    >>> is_eq(x, S(0), assumptions=Q.zero(x))
+    True
+
+    New types can be supported by dispatching to ``_eval_is_eq``.
+
+    >>> from sympy import Basic, sympify
+    >>> from sympy.multipledispatch import dispatch
+    >>> class MyBasic(Basic):
+    ...     def __new__(cls, arg):
+    ...         return Basic.__new__(cls, sympify(arg))
+    ...     @property
+    ...     def value(self):
+    ...         return self.args[0]
+    ...
+    >>> @dispatch(MyBasic, MyBasic)
+    ... def _eval_is_eq(a, b):
+    ...     return is_eq(a.value, b.value)
+    ...
+    >>> a = MyBasic(1)
+    >>> b = MyBasic(1)
+    >>> is_eq(a, b)
+    True
+    >>> is_neq(a, b)
+    False
+
+    """
+    # here, _eval_Eq is only called for backwards compatibility
+    # new code should use is_eq with multiple dispatch as
+    # outlined in the docstring
+    for side1, side2 in (lhs, rhs), (rhs, lhs):
+        eval_func = getattr(side1, '_eval_Eq', None)
+        if eval_func is not None:
+            retval = eval_func(side2)
+            if retval is not None:
+                return retval
+
+    retval = _eval_is_eq(lhs, rhs)
+    if retval is not None:
+        return retval
+
+    if dispatch(type(lhs), type(rhs)) != dispatch(type(rhs), type(lhs)):
+        retval = _eval_is_eq(rhs, lhs)
+        if retval is not None:
+            return retval
+
+    # retval is still None, so go through the equality logic
+    # If expressions have the same structure, they must be equal.
+    if lhs == rhs:
+        return True  # e.g. True == True
+    elif all(isinstance(i, BooleanAtom) for i in (rhs, lhs)):
+        return False  # True != False
+    elif not (lhs.is_Symbol or rhs.is_Symbol) and (
+        isinstance(lhs, Boolean) !=
+        isinstance(rhs, Boolean)):
+        return False  # only Booleans can equal Booleans
+
+    from sympy.assumptions.wrapper import (AssumptionsWrapper,
+        is_infinite, is_extended_real)
+    from .add import Add
+
+    _lhs = AssumptionsWrapper(lhs, assumptions)
+    _rhs = AssumptionsWrapper(rhs, assumptions)
+
+    if _lhs.is_infinite or _rhs.is_infinite:
+        if fuzzy_xor([_lhs.is_infinite, _rhs.is_infinite]):
+            return False
+        if fuzzy_xor([_lhs.is_extended_real, _rhs.is_extended_real]):
+            return False
+        if fuzzy_and([_lhs.is_extended_real, _rhs.is_extended_real]):
+            return fuzzy_xor([_lhs.is_extended_positive, fuzzy_not(_rhs.is_extended_positive)])
+
+        # Try to split real/imaginary parts and equate them
+        I = S.ImaginaryUnit
+
+        def split_real_imag(expr):
+            real_imag = lambda t: (
+                'real' if is_extended_real(t, assumptions) else
+                'imag' if is_extended_real(I*t, assumptions) else None)
+            return sift(Add.make_args(expr), real_imag)
+
+        lhs_ri = split_real_imag(lhs)
+        if not lhs_ri[None]:
+            rhs_ri = split_real_imag(rhs)
+            if not rhs_ri[None]:
+                eq_real = is_eq(Add(*lhs_ri['real']), Add(*rhs_ri['real']), assumptions)
+                eq_imag = is_eq(I * Add(*lhs_ri['imag']), I * Add(*rhs_ri['imag']), assumptions)
+                return fuzzy_and(map(fuzzy_bool, [eq_real, eq_imag]))
+
+        from sympy.functions.elementary.complexes import arg
+        # Compare e.g. zoo with 1+I*oo by comparing args
+        arglhs = arg(lhs)
+        argrhs = arg(rhs)
+        # Guard against Eq(nan, nan) -> False
+        if not (arglhs == S.NaN and argrhs == S.NaN):
+            return fuzzy_bool(is_eq(arglhs, argrhs, assumptions))
+
+    if all(isinstance(i, Expr) for i in (lhs, rhs)):
+        # see if the difference evaluates
+        dif = lhs - rhs
+        _dif = AssumptionsWrapper(dif, assumptions)
+        z = _dif.is_zero
+        if z is not None:
+            if z is False and _dif.is_commutative:  # issue 10728
+                return False
+            if z:
+                return True
+
+        n2 = _n2(lhs, rhs)
+        if n2 is not None:
+            return _sympify(n2 == 0)
+
+        # see if the ratio evaluates
+        n, d = dif.as_numer_denom()
+        rv = None
+        _n = AssumptionsWrapper(n, assumptions)
+        _d = AssumptionsWrapper(d, assumptions)
+        if _n.is_zero:
+            rv = _d.is_nonzero
+        elif _n.is_finite:
+            if _d.is_infinite:
+                rv = True
+            elif _n.is_zero is False:
+                rv = _d.is_infinite
+                if rv is None:
+                    # if the condition that makes the denominator
+                    # infinite does not make the original expression
+                    # True then False can be returned
+                    from sympy.simplify.simplify import clear_coefficients
+                    l, r = clear_coefficients(d, S.Infinity)
+                    args = [_.subs(l, r) for _ in (lhs, rhs)]
+                    if args != [lhs, rhs]:
+                        rv = fuzzy_bool(is_eq(*args, assumptions))
+                        if rv is True:
+                            rv = None
+        elif any(is_infinite(a, assumptions) for a in Add.make_args(n)):
+            # (inf or nan)/x != 0
+            rv = False
+        if rv is not None:
+            return rv

venv/lib/python3.10/site-packages/sympy/core/sorting.py

@@ -0,0 +1,309 @@
+from collections import defaultdict
+
+from .sympify import sympify, SympifyError
+from sympy.utilities.iterables import iterable, uniq
+
+
+__all__ = ['default_sort_key', 'ordered']
+
+
+def default_sort_key(item, order=None):
+    """Return a key that can be used for sorting.
+
+    The key has the structure:
+
+    (class_key, (len(args), args), exponent.sort_key(), coefficient)
+
+    This key is supplied by the sort_key routine of Basic objects when
+    ``item`` is a Basic object or an object (other than a string) that
+    sympifies to a Basic object. Otherwise, this function produces the
+    key.
+
+    The ``order`` argument is passed along to the sort_key routine and is
+    used to determine how the terms *within* an expression are ordered.
+    (See examples below) ``order`` options are: 'lex', 'grlex', 'grevlex',
+    and reversed values of the same (e.g. 'rev-lex'). The default order
+    value is None (which translates to 'lex').
+
+    Examples
+    ========
+
+    >>> from sympy import S, I, default_sort_key, sin, cos, sqrt
+    >>> from sympy.core.function import UndefinedFunction
+    >>> from sympy.abc import x
+
+    The following are equivalent ways of getting the key for an object:
+
+    >>> x.sort_key() == default_sort_key(x)
+    True
+
+    Here are some examples of the key that is produced:
+
+    >>> default_sort_key(UndefinedFunction('f'))
+    ((0, 0, 'UndefinedFunction'), (1, ('f',)), ((1, 0, 'Number'),
+        (0, ()), (), 1), 1)
+    >>> default_sort_key('1')
+    ((0, 0, 'str'), (1, ('1',)), ((1, 0, 'Number'), (0, ()), (), 1), 1)
+    >>> default_sort_key(S.One)
+    ((1, 0, 'Number'), (0, ()), (), 1)
+    >>> default_sort_key(2)
+    ((1, 0, 'Number'), (0, ()), (), 2)
+
+    While sort_key is a method only defined for SymPy objects,
+    default_sort_key will accept anything as an argument so it is
+    more robust as a sorting key. For the following, using key=
+    lambda i: i.sort_key() would fail because 2 does not have a sort_key
+    method; that's why default_sort_key is used. Note, that it also
+    handles sympification of non-string items likes ints:
+
+    >>> a = [2, I, -I]
+    >>> sorted(a, key=default_sort_key)
+    [2, -I, I]
+
+    The returned key can be used anywhere that a key can be specified for
+    a function, e.g. sort, min, max, etc...:
+
+    >>> a.sort(key=default_sort_key); a[0]
+    2
+    >>> min(a, key=default_sort_key)
+    2
+
+    Notes
+    =====
+
+    The key returned is useful for getting items into a canonical order
+    that will be the same across platforms. It is not directly useful for
+    sorting lists of expressions:
+
+    >>> a, b = x, 1/x
+
+    Since ``a`` has only 1 term, its value of sort_key is unaffected by
+    ``order``:
+
+    >>> a.sort_key() == a.sort_key('rev-lex')
+    True
+
+    If ``a`` and ``b`` are combined then the key will differ because there
+    are terms that can be ordered:
+
+    >>> eq = a + b
+    >>> eq.sort_key() == eq.sort_key('rev-lex')
+    False
+    >>> eq.as_ordered_terms()
+    [x, 1/x]
+    >>> eq.as_ordered_terms('rev-lex')
+    [1/x, x]
+
+    But since the keys for each of these terms are independent of ``order``'s
+    value, they do not sort differently when they appear separately in a list:
+
+    >>> sorted(eq.args, key=default_sort_key)
+    [1/x, x]
+    >>> sorted(eq.args, key=lambda i: default_sort_key(i, order='rev-lex'))
+    [1/x, x]
+
+    The order of terms obtained when using these keys is the order that would
+    be obtained if those terms were *factors* in a product.
+
+    Although it is useful for quickly putting expressions in canonical order,
+    it does not sort expressions based on their complexity defined by the
+    number of operations, power of variables and others:
+
+    >>> sorted([sin(x)*cos(x), sin(x)], key=default_sort_key)
+    [sin(x)*cos(x), sin(x)]
+    >>> sorted([x, x**2, sqrt(x), x**3], key=default_sort_key)
+    [sqrt(x), x, x**2, x**3]
+
+    See Also
+    ========
+
+    ordered, sympy.core.expr.Expr.as_ordered_factors, sympy.core.expr.Expr.as_ordered_terms
+
+    """
+    from .basic import Basic
+    from .singleton import S
+
+    if isinstance(item, Basic):
+        return item.sort_key(order=order)
+
+    if iterable(item, exclude=str):
+        if isinstance(item, dict):
+            args = item.items()
+            unordered = True
+        elif isinstance(item, set):
+            args = item
+            unordered = True
+        else:
+            # e.g. tuple, list
+            args = list(item)
+            unordered = False
+
+        args = [default_sort_key(arg, order=order) for arg in args]
+
+        if unordered:
+            # e.g. dict, set
+            args = sorted(args)
+
+        cls_index, args = 10, (len(args), tuple(args))
+    else:
+        if not isinstance(item, str):
+            try:
+                item = sympify(item, strict=True)
+            except SympifyError:
+                # e.g. lambda x: x
+                pass
+            else:
+                if isinstance(item, Basic):
+                    # e.g int -> Integer
+                    return default_sort_key(item)
+                # e.g. UndefinedFunction
+
+        # e.g. str
+        cls_index, args = 0, (1, (str(item),))
+
+    return (cls_index, 0, item.__class__.__name__
+            ), args, S.One.sort_key(), S.One
+
+
+def _node_count(e):
+    # this not only counts nodes, it affirms that the
+    # args are Basic (i.e. have an args property). If
+    # some object has a non-Basic arg, it needs to be
+    # fixed since it is intended that all Basic args
+    # are of Basic type (though this is not easy to enforce).
+    if e.is_Float:
+        return 0.5
+    return 1 + sum(map(_node_count, e.args))
+
+
+def _nodes(e):
+    """
+    A helper for ordered() which returns the node count of ``e`` which
+    for Basic objects is the number of Basic nodes in the expression tree
+    but for other objects is 1 (unless the object is an iterable or dict
+    for which the sum of nodes is returned).
+    """
+    from .basic import Basic
+    from .function import Derivative
+
+    if isinstance(e, Basic):
+        if isinstance(e, Derivative):
+            return _nodes(e.expr) + sum(i[1] if i[1].is_Number else
+                _nodes(i[1]) for i in e.variable_count)
+        return _node_count(e)
+    elif iterable(e):
+        return 1 + sum(_nodes(ei) for ei in e)
+    elif isinstance(e, dict):
+        return 1 + sum(_nodes(k) + _nodes(v) for k, v in e.items())
+    else:
+        return 1
+
+
+def ordered(seq, keys=None, default=True, warn=False):
+    """Return an iterator of the seq where keys are used to break ties in
+    a conservative fashion: if, after applying a key, there are no ties
+    then no other keys will be computed.
+
+    Two default keys will be applied if 1) keys are not provided or 2) the
+    given keys do not resolve all ties (but only if ``default`` is True). The
+    two keys are ``_nodes`` (which places smaller expressions before large) and
+    ``default_sort_key`` which (if the ``sort_key`` for an object is defined
+    properly) should resolve any ties.
+
+    If ``warn`` is True then an error will be raised if there were no
+    keys remaining to break ties. This can be used if it was expected that
+    there should be no ties between items that are not identical.
+
+    Examples
+    ========
+
+    >>> from sympy import ordered, count_ops
+    >>> from sympy.abc import x, y
+
+    The count_ops is not sufficient to break ties in this list and the first
+    two items appear in their original order (i.e. the sorting is stable):
+
+    >>> list(ordered([y + 2, x + 2, x**2 + y + 3],
+    ...    count_ops, default=False, warn=False))
+    ...
+    [y + 2, x + 2, x**2 + y + 3]
+
+    The default_sort_key allows the tie to be broken:
+
+    >>> list(ordered([y + 2, x + 2, x**2 + y + 3]))
+    ...
+    [x + 2, y + 2, x**2 + y + 3]
+
+    Here, sequences are sorted by length, then sum:
+
+    >>> seq, keys = [[[1, 2, 1], [0, 3, 1], [1, 1, 3], [2], [1]], [
+    ...    lambda x: len(x),
+    ...    lambda x: sum(x)]]
+    ...
+    >>> list(ordered(seq, keys, default=False, warn=False))
+    [[1], [2], [1, 2, 1], [0, 3, 1], [1, 1, 3]]
+
+    If ``warn`` is True, an error will be raised if there were not
+    enough keys to break ties:
+
+    >>> list(ordered(seq, keys, default=False, warn=True))
+    Traceback (most recent call last):
+    ...
+    ValueError: not enough keys to break ties
+
+
+    Notes
+    =====
+
+    The decorated sort is one of the fastest ways to sort a sequence for
+    which special item comparison is desired: the sequence is decorated,
+    sorted on the basis of the decoration (e.g. making all letters lower
+    case) and then undecorated. If one wants to break ties for items that
+    have the same decorated value, a second key can be used. But if the
+    second key is expensive to compute then it is inefficient to decorate
+    all items with both keys: only those items having identical first key
+    values need to be decorated. This function applies keys successively
+    only when needed to break ties. By yielding an iterator, use of the
+    tie-breaker is delayed as long as possible.
+
+    This function is best used in cases when use of the first key is
+    expected to be a good hashing function; if there are no unique hashes
+    from application of a key, then that key should not have been used. The
+    exception, however, is that even if there are many collisions, if the
+    first group is small and one does not need to process all items in the
+    list then time will not be wasted sorting what one was not interested
+    in. For example, if one were looking for the minimum in a list and
+    there were several criteria used to define the sort order, then this
+    function would be good at returning that quickly if the first group
+    of candidates is small relative to the number of items being processed.
+
+    """
+
+    d = defaultdict(list)
+    if keys:
+        if isinstance(keys, (list, tuple)):
+            keys = list(keys)
+            f = keys.pop(0)
+        else:
+            f = keys
+            keys = []
+        for a in seq:
+            d[f(a)].append(a)
+    else:
+        if not default:
+            raise ValueError('if default=False then keys must be provided')
+        d[None].extend(seq)
+
+    for k, value in sorted(d.items()):
+        if len(value) > 1:
+            if keys:
+                value = ordered(value, keys, default, warn)
+            elif default:
+                value = ordered(value, (_nodes, default_sort_key,),
+                               default=False, warn=warn)
+            elif warn:
+                u = list(uniq(value))
+                if len(u) > 1:
+                    raise ValueError(
+                        'not enough keys to break ties: %s' % u)
+        yield from value

venv/lib/python3.10/site-packages/sympy/core/trace.py

@@ -0,0 +1,12 @@
+from sympy.utilities.exceptions import sympy_deprecation_warning
+
+sympy_deprecation_warning(
+    """
+    sympy.core.trace is deprecated. Use sympy.physics.quantum.trace
+    instead.
+    """,
+    deprecated_since_version="1.10",
+    active_deprecations_target="sympy-core-trace-deprecated",
+)
+
+from sympy.physics.quantum.trace import Tr # noqa:F401

venv/lib/python3.10/site-packages/sympy/solvers/benchmarks/bench_solvers.py

@@ -0,0 +1,12 @@
+from sympy.core.symbol import Symbol
+from sympy.matrices.dense import (eye, zeros)
+from sympy.solvers.solvers import solve_linear_system
+
+N = 8
+M = zeros(N, N + 1)
+M[:, :N] = eye(N)
+S = [Symbol('A%i' % i) for i in range(N)]
+
+
+def timeit_linsolve_trivial():
+    solve_linear_system(M, *S)

venv/lib/python3.10/site-packages/sympy/solvers/diophantine/__init__.py

@@ -0,0 +1,5 @@
+from .diophantine import diophantine, classify_diop, diop_solve
+
+__all__ = [
+    'diophantine', 'classify_diop', 'diop_solve'
+]

venv/lib/python3.10/site-packages/sympy/solvers/diophantine/tests/test_diophantine.py

@@ -0,0 +1,1037 @@
+from sympy.core.add import Add
+from sympy.core.mul import Mul
+from sympy.core.numbers import (Rational, oo, pi)
+from sympy.core.relational import Eq
+from sympy.core.singleton import S
+from sympy.core.symbol import symbols
+from sympy.matrices.dense import Matrix
+from sympy.ntheory.factor_ import factorint
+from sympy.simplify.powsimp import powsimp
+from sympy.core.function import _mexpand
+from sympy.core.sorting import default_sort_key, ordered
+from sympy.functions.elementary.trigonometric import sin
+from sympy.solvers.diophantine import diophantine
+from sympy.solvers.diophantine.diophantine import (diop_DN,
+    diop_solve, diop_ternary_quadratic_normal,
+    diop_general_pythagorean, diop_ternary_quadratic, diop_linear,
+    diop_quadratic, diop_general_sum_of_squares, diop_general_sum_of_even_powers,
+    descent, diop_bf_DN, divisible, equivalent, find_DN, ldescent, length,
+    reconstruct, partition, power_representation,
+    prime_as_sum_of_two_squares, square_factor, sum_of_four_squares,
+    sum_of_three_squares, transformation_to_DN, transformation_to_normal,
+    classify_diop, base_solution_linear, cornacchia, sqf_normal, gaussian_reduce, holzer,
+    check_param, parametrize_ternary_quadratic, sum_of_powers, sum_of_squares,
+    _diop_ternary_quadratic_normal, _nint_or_floor,
+    _odd, _even, _remove_gcd, _can_do_sum_of_squares, DiophantineSolutionSet, GeneralPythagorean,
+    BinaryQuadratic)
+
+from sympy.testing.pytest import slow, raises, XFAIL
+from sympy.utilities.iterables import (
+        signed_permutations)
+
+a, b, c, d, p, q, x, y, z, w, t, u, v, X, Y, Z = symbols(
+    "a, b, c, d, p, q, x, y, z, w, t, u, v, X, Y, Z", integer=True)
+t_0, t_1, t_2, t_3, t_4, t_5, t_6 = symbols("t_:7", integer=True)
+m1, m2, m3 = symbols('m1:4', integer=True)
+n1 = symbols('n1', integer=True)
+
+
+def diop_simplify(eq):
+    return _mexpand(powsimp(_mexpand(eq)))
+
+
+def test_input_format():
+    raises(TypeError, lambda: diophantine(sin(x)))
+    raises(TypeError, lambda: diophantine(x/pi - 3))
+
+
+def test_nosols():
+    # diophantine should sympify eq so that these are equivalent
+    assert diophantine(3) == set()
+    assert diophantine(S(3)) == set()
+
+
+def test_univariate():
+    assert diop_solve((x - 1)*(x - 2)**2) == {(1,), (2,)}
+    assert diop_solve((x - 1)*(x - 2)) == {(1,), (2,)}
+
+
+def test_classify_diop():
+    raises(TypeError, lambda: classify_diop(x**2/3 - 1))
+    raises(ValueError, lambda: classify_diop(1))
+    raises(NotImplementedError, lambda: classify_diop(w*x*y*z - 1))
+    raises(NotImplementedError, lambda: classify_diop(x**3 + y**3 + z**4 - 90))
+    assert classify_diop(14*x**2 + 15*x - 42) == (
+        [x], {1: -42, x: 15, x**2: 14}, 'univariate')
+    assert classify_diop(x*y + z) == (
+        [x, y, z], {x*y: 1, z: 1}, 'inhomogeneous_ternary_quadratic')
+    assert classify_diop(x*y + z + w + x**2) == (
+        [w, x, y, z], {x*y: 1, w: 1, x**2: 1, z: 1}, 'inhomogeneous_general_quadratic')
+    assert classify_diop(x*y + x*z + x**2 + 1) == (
+        [x, y, z], {x*y: 1, x*z: 1, x**2: 1, 1: 1}, 'inhomogeneous_general_quadratic')
+    assert classify_diop(x*y + z + w + 42) == (
+        [w, x, y, z], {x*y: 1, w: 1, 1: 42, z: 1}, 'inhomogeneous_general_quadratic')
+    assert classify_diop(x*y + z*w) == (
+        [w, x, y, z], {x*y: 1, w*z: 1}, 'homogeneous_general_quadratic')
+    assert classify_diop(x*y**2 + 1) == (
+        [x, y], {x*y**2: 1, 1: 1}, 'cubic_thue')
+    assert classify_diop(x**4 + y**4 + z**4 - (1 + 16 + 81)) == (
+        [x, y, z], {1: -98, x**4: 1, z**4: 1, y**4: 1}, 'general_sum_of_even_powers')
+    assert classify_diop(x**2 + y**2 + z**2) == (
+        [x, y, z], {x**2: 1, y**2: 1, z**2: 1}, 'homogeneous_ternary_quadratic_normal')
+
+
+def test_linear():
+    assert diop_solve(x) == (0,)
+    assert diop_solve(1*x) == (0,)
+    assert diop_solve(3*x) == (0,)
+    assert diop_solve(x + 1) == (-1,)
+    assert diop_solve(2*x + 1) == (None,)
+    assert diop_solve(2*x + 4) == (-2,)
+    assert diop_solve(y + x) == (t_0, -t_0)
+    assert diop_solve(y + x + 0) == (t_0, -t_0)
+    assert diop_solve(y + x - 0) == (t_0, -t_0)
+    assert diop_solve(0*x - y - 5) == (-5,)
+    assert diop_solve(3*y + 2*x - 5) == (3*t_0 - 5, -2*t_0 + 5)
+    assert diop_solve(2*x - 3*y - 5) == (3*t_0 - 5, 2*t_0 - 5)
+    assert diop_solve(-2*x - 3*y - 5) == (3*t_0 + 5, -2*t_0 - 5)
+    assert diop_solve(7*x + 5*y) == (5*t_0, -7*t_0)
+    assert diop_solve(2*x + 4*y) == (2*t_0, -t_0)
+    assert diop_solve(4*x + 6*y - 4) == (3*t_0 - 2, -2*t_0 + 2)
+    assert diop_solve(4*x + 6*y - 3) == (None, None)
+    assert diop_solve(0*x + 3*y - 4*z + 5) == (4*t_0 + 5, 3*t_0 + 5)
+    assert diop_solve(4*x + 3*y - 4*z + 5) == (t_0, 8*t_0 + 4*t_1 + 5, 7*t_0 + 3*t_1 + 5)
+    assert diop_solve(4*x + 3*y - 4*z + 5, None) == (0, 5, 5)
+    assert diop_solve(4*x + 2*y + 8*z - 5) == (None, None, None)
+    assert diop_solve(5*x + 7*y - 2*z - 6) == (t_0, -3*t_0 + 2*t_1 + 6, -8*t_0 + 7*t_1 + 18)
+    assert diop_solve(3*x - 6*y + 12*z - 9) == (2*t_0 + 3, t_0 + 2*t_1, t_1)
+    assert diop_solve(6*w + 9*x + 20*y - z) == (t_0, t_1, t_1 + t_2, 6*t_0 + 29*t_1 + 20*t_2)
+
+    # to ignore constant factors, use diophantine
+    raises(TypeError, lambda: diop_solve(x/2))
+
+
+def test_quadratic_simple_hyperbolic_case():
+    # Simple Hyperbolic case: A = C = 0 and B != 0
+    assert diop_solve(3*x*y + 34*x - 12*y + 1) == \
+           {(-133, -11), (5, -57)}
+    assert diop_solve(6*x*y + 2*x + 3*y + 1) == set()
+    assert diop_solve(-13*x*y + 2*x - 4*y - 54) == {(27, 0)}
+    assert diop_solve(-27*x*y - 30*x - 12*y - 54) == {(-14, -1)}
+    assert diop_solve(2*x*y + 5*x + 56*y + 7) == {(-161, -3), (-47, -6), (-35, -12),
+                                                  (-29, -69), (-27, 64), (-21, 7),
+                                                  (-9, 1), (105, -2)}
+    assert diop_solve(6*x*y + 9*x + 2*y + 3) == set()
+    assert diop_solve(x*y + x + y + 1) == {(-1, t), (t, -1)}
+    assert diophantine(48*x*y)
+
+
+def test_quadratic_elliptical_case():
+    # Elliptical case: B**2 - 4AC < 0
+
+    assert diop_solve(42*x**2 + 8*x*y + 15*y**2 + 23*x + 17*y - 4915) == {(-11, -1)}
+    assert diop_solve(4*x**2 + 3*y**2 + 5*x - 11*y + 12) == set()
+    assert diop_solve(x**2 + y**2 + 2*x + 2*y + 2) == {(-1, -1)}
+    assert diop_solve(15*x**2 - 9*x*y + 14*y**2 - 23*x - 14*y - 4950) == {(-15, 6)}
+    assert diop_solve(10*x**2 + 12*x*y + 12*y**2 - 34) == \
+           {(-1, -1), (-1, 2), (1, -2), (1, 1)}
+
+
+def test_quadratic_parabolic_case():
+    # Parabolic case: B**2 - 4AC = 0
+    assert check_solutions(8*x**2 - 24*x*y + 18*y**2 + 5*x + 7*y + 16)
+    assert check_solutions(8*x**2 - 24*x*y + 18*y**2 + 6*x + 12*y - 6)
+    assert check_solutions(8*x**2 + 24*x*y + 18*y**2 + 4*x + 6*y - 7)
+    assert check_solutions(-4*x**2 + 4*x*y - y**2 + 2*x - 3)
+    assert check_solutions(x**2 + 2*x*y + y**2 + 2*x + 2*y + 1)
+    assert check_solutions(x**2 - 2*x*y + y**2 + 2*x + 2*y + 1)
+    assert check_solutions(y**2 - 41*x + 40)
+
+
+def test_quadratic_perfect_square():
+    # B**2 - 4*A*C > 0
+    # B**2 - 4*A*C is a perfect square
+    assert check_solutions(48*x*y)
+    assert check_solutions(4*x**2 - 5*x*y + y**2 + 2)
+    assert check_solutions(-2*x**2 - 3*x*y + 2*y**2 -2*x - 17*y + 25)
+    assert check_solutions(12*x**2 + 13*x*y + 3*y**2 - 2*x + 3*y - 12)
+    assert check_solutions(8*x**2 + 10*x*y + 2*y**2 - 32*x - 13*y - 23)
+    assert check_solutions(4*x**2 - 4*x*y - 3*y- 8*x - 3)
+    assert check_solutions(- 4*x*y - 4*y**2 - 3*y- 5*x - 10)
+    assert check_solutions(x**2 - y**2 - 2*x - 2*y)
+    assert check_solutions(x**2 - 9*y**2 - 2*x - 6*y)
+    assert check_solutions(4*x**2 - 9*y**2 - 4*x - 12*y - 3)
+
+
+def test_quadratic_non_perfect_square():
+    # B**2 - 4*A*C is not a perfect square
+    # Used check_solutions() since the solutions are complex expressions involving
+    # square roots and exponents
+    assert check_solutions(x**2 - 2*x - 5*y**2)
+    assert check_solutions(3*x**2 - 2*y**2 - 2*x - 2*y)
+    assert check_solutions(x**2 - x*y - y**2 - 3*y)
+    assert check_solutions(x**2 - 9*y**2 - 2*x - 6*y)
+    assert BinaryQuadratic(x**2 + y**2 + 2*x + 2*y + 2).solve() == {(-1, -1)}
+
+
+def test_issue_9106():
+    eq = -48 - 2*x*(3*x - 1) + y*(3*y - 1)
+    v = (x, y)
+    for sol in diophantine(eq):
+        assert not diop_simplify(eq.xreplace(dict(zip(v, sol))))
+
+
+def test_issue_18138():
+    eq = x**2 - x - y**2
+    v = (x, y)
+    for sol in diophantine(eq):
+        assert not diop_simplify(eq.xreplace(dict(zip(v, sol))))
+
+
+@slow
+def test_quadratic_non_perfect_slow():
+    assert check_solutions(8*x**2 + 10*x*y - 2*y**2 - 32*x - 13*y - 23)
+    # This leads to very large numbers.
+    # assert check_solutions(5*x**2 - 13*x*y + y**2 - 4*x - 4*y - 15)
+    assert check_solutions(-3*x**2 - 2*x*y + 7*y**2 - 5*x - 7)
+    assert check_solutions(-4 - x + 4*x**2 - y - 3*x*y - 4*y**2)
+    assert check_solutions(1 + 2*x + 2*x**2 + 2*y + x*y - 2*y**2)
+
+
+def test_DN():
+    # Most of the test cases were adapted from,
+    # Solving the generalized Pell equation x**2 - D*y**2 = N, John P. Robertson, July 31, 2004.
+    # https://web.archive.org/web/20160323033128/http://www.jpr2718.org/pell.pdf
+    # others are verified using Wolfram Alpha.
+
+    # Covers cases where D <= 0 or D > 0 and D is a square or N = 0
+    # Solutions are straightforward in these cases.
+    assert diop_DN(3, 0) == [(0, 0)]
+    assert diop_DN(-17, -5) == []
+    assert diop_DN(-19, 23) == [(2, 1)]
+    assert diop_DN(-13, 17) == [(2, 1)]
+    assert diop_DN(-15, 13) == []
+    assert diop_DN(0, 5) == []
+    assert diop_DN(0, 9) == [(3, t)]
+    assert diop_DN(9, 0) == [(3*t, t)]
+    assert diop_DN(16, 24) == []
+    assert diop_DN(9, 180) == [(18, 4)]
+    assert diop_DN(9, -180) == [(12, 6)]
+    assert diop_DN(7, 0) == [(0, 0)]
+
+    # When equation is x**2 + y**2 = N
+    # Solutions are interchangeable
+    assert diop_DN(-1, 5) == [(2, 1), (1, 2)]
+    assert diop_DN(-1, 169) == [(12, 5), (5, 12), (13, 0), (0, 13)]
+
+    # D > 0 and D is not a square
+
+    # N = 1
+    assert diop_DN(13, 1) == [(649, 180)]
+    assert diop_DN(980, 1) == [(51841, 1656)]
+    assert diop_DN(981, 1) == [(158070671986249, 5046808151700)]
+    assert diop_DN(986, 1) == [(49299, 1570)]
+    assert diop_DN(991, 1) == [(379516400906811930638014896080, 12055735790331359447442538767)]
+    assert diop_DN(17, 1) == [(33, 8)]
+    assert diop_DN(19, 1) == [(170, 39)]
+
+    # N = -1
+    assert diop_DN(13, -1) == [(18, 5)]
+    assert diop_DN(991, -1) == []
+    assert diop_DN(41, -1) == [(32, 5)]
+    assert diop_DN(290, -1) == [(17, 1)]
+    assert diop_DN(21257, -1) == [(13913102721304, 95427381109)]
+    assert diop_DN(32, -1) == []
+
+    # |N| > 1
+    # Some tests were created using calculator at
+    # http://www.numbertheory.org/php/patz.html
+
+    assert diop_DN(13, -4) == [(3, 1), (393, 109), (36, 10)]
+    # Source I referred returned (3, 1), (393, 109) and (-3, 1) as fundamental solutions
+    # So (-3, 1) and (393, 109) should be in the same equivalent class
+    assert equivalent(-3, 1, 393, 109, 13, -4) == True
+
+    assert diop_DN(13, 27) == [(220, 61), (40, 11), (768, 213), (12, 3)]
+    assert set(diop_DN(157, 12)) == {(13, 1), (10663, 851), (579160, 46222),
+                                     (483790960, 38610722), (26277068347, 2097138361),
+                                     (21950079635497, 1751807067011)}
+    assert diop_DN(13, 25) == [(3245, 900)]
+    assert diop_DN(192, 18) == []
+    assert diop_DN(23, 13) == [(-6, 1), (6, 1)]
+    assert diop_DN(167, 2) == [(13, 1)]
+    assert diop_DN(167, -2) == []
+
+    assert diop_DN(123, -2) == [(11, 1)]
+    # One calculator returned [(11, 1), (-11, 1)] but both of these are in
+    # the same equivalence class
+    assert equivalent(11, 1, -11, 1, 123, -2)
+
+    assert diop_DN(123, -23) == [(-10, 1), (10, 1)]
+
+    assert diop_DN(0, 0, t) == [(0, t)]
+    assert diop_DN(0, -1, t) == []
+
+
+def test_bf_pell():
+    assert diop_bf_DN(13, -4) == [(3, 1), (-3, 1), (36, 10)]
+    assert diop_bf_DN(13, 27) == [(12, 3), (-12, 3), (40, 11), (-40, 11)]
+    assert diop_bf_DN(167, -2) == []
+    assert diop_bf_DN(1729, 1) == [(44611924489705, 1072885712316)]
+    assert diop_bf_DN(89, -8) == [(9, 1), (-9, 1)]
+    assert diop_bf_DN(21257, -1) == [(13913102721304, 95427381109)]
+    assert diop_bf_DN(340, -4) == [(756, 41)]
+    assert diop_bf_DN(-1, 0, t) == [(0, 0)]
+    assert diop_bf_DN(0, 0, t) == [(0, t)]
+    assert diop_bf_DN(4, 0, t) == [(2*t, t), (-2*t, t)]
+    assert diop_bf_DN(3, 0, t) == [(0, 0)]
+    assert diop_bf_DN(1, -2, t) == []
+
+
+def test_length():
+    assert length(2, 1, 0) == 1
+    assert length(-2, 4, 5) == 3
+    assert length(-5, 4, 17) == 4
+    assert length(0, 4, 13) == 6
+    assert length(7, 13, 11) == 23
+    assert length(1, 6, 4) == 2
+
+
+def is_pell_transformation_ok(eq):
+    """
+    Test whether X*Y, X, or Y terms are present in the equation
+    after transforming the equation using the transformation returned
+    by transformation_to_pell(). If they are not present we are good.
+    Moreover, coefficient of X**2 should be a divisor of coefficient of
+    Y**2 and the constant term.
+    """
+    A, B = transformation_to_DN(eq)
+    u = (A*Matrix([X, Y]) + B)[0]
+    v = (A*Matrix([X, Y]) + B)[1]
+    simplified = diop_simplify(eq.subs(zip((x, y), (u, v))))
+
+    coeff = dict([reversed(t.as_independent(*[X, Y])) for t in simplified.args])
+
+    for term in [X*Y, X, Y]:
+        if term in coeff.keys():
+            return False
+
+    for term in [X**2, Y**2, 1]:
+        if term not in coeff.keys():
+            coeff[term] = 0
+
+    if coeff[X**2] != 0:
+        return divisible(coeff[Y**2], coeff[X**2]) and \
+        divisible(coeff[1], coeff[X**2])
+
+    return True
+
+
+def test_transformation_to_pell():
+    assert is_pell_transformation_ok(-13*x**2 - 7*x*y + y**2 + 2*x - 2*y - 14)
+    assert is_pell_transformation_ok(-17*x**2 + 19*x*y - 7*y**2 - 5*x - 13*y - 23)
+    assert is_pell_transformation_ok(x**2 - y**2 + 17)
+    assert is_pell_transformation_ok(-x**2 + 7*y**2 - 23)
+    assert is_pell_transformation_ok(25*x**2 - 45*x*y + 5*y**2 - 5*x - 10*y + 5)
+    assert is_pell_transformation_ok(190*x**2 + 30*x*y + y**2 - 3*y - 170*x - 130)
+    assert is_pell_transformation_ok(x**2 - 2*x*y -190*y**2 - 7*y - 23*x - 89)
+    assert is_pell_transformation_ok(15*x**2 - 9*x*y + 14*y**2 - 23*x - 14*y - 4950)
+
+
+def test_find_DN():
+    assert find_DN(x**2 - 2*x - y**2) == (1, 1)
+    assert find_DN(x**2 - 3*y**2 - 5) == (3, 5)
+    assert find_DN(x**2 - 2*x*y - 4*y**2 - 7) == (5, 7)
+    assert find_DN(4*x**2 - 8*x*y - y**2 - 9) == (20, 36)
+    assert find_DN(7*x**2 - 2*x*y - y**2 - 12) == (8, 84)
+    assert find_DN(-3*x**2 + 4*x*y -y**2) == (1, 0)
+    assert find_DN(-13*x**2 - 7*x*y + y**2 + 2*x - 2*y -14) == (101, -7825480)
+
+
+def test_ldescent():
+    # Equations which have solutions
+    u = ([(13, 23), (3, -11), (41, -113), (4, -7), (-7, 4), (91, -3), (1, 1), (1, -1),
+        (4, 32), (17, 13), (123689, 1), (19, -570)])
+    for a, b in u:
+        w, x, y = ldescent(a, b)
+        assert a*x**2 + b*y**2 == w**2
+    assert ldescent(-1, -1) is None
+
+
+def test_diop_ternary_quadratic_normal():
+    assert check_solutions(234*x**2 - 65601*y**2 - z**2)
+    assert check_solutions(23*x**2 + 616*y**2 - z**2)
+    assert check_solutions(5*x**2 + 4*y**2 - z**2)
+    assert check_solutions(3*x**2 + 6*y**2 - 3*z**2)
+    assert check_solutions(x**2 + 3*y**2 - z**2)
+    assert check_solutions(4*x**2 + 5*y**2 - z**2)
+    assert check_solutions(x**2 + y**2 - z**2)
+    assert check_solutions(16*x**2 + y**2 - 25*z**2)
+    assert check_solutions(6*x**2 - y**2 + 10*z**2)
+    assert check_solutions(213*x**2 + 12*y**2 - 9*z**2)
+    assert check_solutions(34*x**2 - 3*y**2 - 301*z**2)
+    assert check_solutions(124*x**2 - 30*y**2 - 7729*z**2)
+
+
+def is_normal_transformation_ok(eq):
+    A = transformation_to_normal(eq)
+    X, Y, Z = A*Matrix([x, y, z])
+    simplified = diop_simplify(eq.subs(zip((x, y, z), (X, Y, Z))))
+
+    coeff = dict([reversed(t.as_independent(*[X, Y, Z])) for t in simplified.args])
+    for term in [X*Y, Y*Z, X*Z]:
+        if term in coeff.keys():
+            return False
+
+    return True
+
+
+def test_transformation_to_normal():
+    assert is_normal_transformation_ok(x**2 + 3*y**2 + z**2 - 13*x*y - 16*y*z + 12*x*z)
+    assert is_normal_transformation_ok(x**2 + 3*y**2 - 100*z**2)
+    assert is_normal_transformation_ok(x**2 + 23*y*z)
+    assert is_normal_transformation_ok(3*y**2 - 100*z**2 - 12*x*y)
+    assert is_normal_transformation_ok(x**2 + 23*x*y - 34*y*z + 12*x*z)
+    assert is_normal_transformation_ok(z**2 + 34*x*y - 23*y*z + x*z)
+    assert is_normal_transformation_ok(x**2 + y**2 + z**2 - x*y - y*z - x*z)
+    assert is_normal_transformation_ok(x**2 + 2*y*z + 3*z**2)
+    assert is_normal_transformation_ok(x*y + 2*x*z + 3*y*z)
+    assert is_normal_transformation_ok(2*x*z + 3*y*z)
+
+
+def test_diop_ternary_quadratic():
+    assert check_solutions(2*x**2 + z**2 + y**2 - 4*x*y)
+    assert check_solutions(x**2 - y**2 - z**2 - x*y - y*z)
+    assert check_solutions(3*x**2 - x*y - y*z - x*z)
+    assert check_solutions(x**2 - y*z - x*z)
+    assert check_solutions(5*x**2 - 3*x*y - x*z)
+    assert check_solutions(4*x**2 - 5*y**2 - x*z)
+    assert check_solutions(3*x**2 + 2*y**2 - z**2 - 2*x*y + 5*y*z - 7*y*z)
+    assert check_solutions(8*x**2 - 12*y*z)
+    assert check_solutions(45*x**2 - 7*y**2 - 8*x*y - z**2)
+    assert check_solutions(x**2 - 49*y**2 - z**2 + 13*z*y -8*x*y)
+    assert check_solutions(90*x**2 + 3*y**2 + 5*x*y + 2*z*y + 5*x*z)
+    assert check_solutions(x**2 + 3*y**2 + z**2 - x*y - 17*y*z)
+    assert check_solutions(x**2 + 3*y**2 + z**2 - x*y - 16*y*z + 12*x*z)
+    assert check_solutions(x**2 + 3*y**2 + z**2 - 13*x*y - 16*y*z + 12*x*z)
+    assert check_solutions(x*y - 7*y*z + 13*x*z)
+
+    assert diop_ternary_quadratic_normal(x**2 + y**2 + z**2) == (None, None, None)
+    assert diop_ternary_quadratic_normal(x**2 + y**2) is None
+    raises(ValueError, lambda:
+        _diop_ternary_quadratic_normal((x, y, z),
+        {x*y: 1, x**2: 2, y**2: 3, z**2: 0}))
+    eq = -2*x*y - 6*x*z + 7*y**2 - 3*y*z + 4*z**2
+    assert diop_ternary_quadratic(eq) == (7, 2, 0)
+    assert diop_ternary_quadratic_normal(4*x**2 + 5*y**2 - z**2) == \
+        (1, 0, 2)
+    assert diop_ternary_quadratic(x*y + 2*y*z) == \
+        (-2, 0, n1)
+    eq = -5*x*y - 8*x*z - 3*y*z + 8*z**2
+    assert parametrize_ternary_quadratic(eq) == \
+        (8*p**2 - 3*p*q, -8*p*q + 8*q**2, 5*p*q)
+    # this cannot be tested with diophantine because it will
+    # factor into a product
+    assert diop_solve(x*y + 2*y*z) == (-2*p*q, -n1*p**2 + p**2, p*q)
+
+
+def test_square_factor():
+    assert square_factor(1) == square_factor(-1) == 1
+    assert square_factor(0) == 1
+    assert square_factor(5) == square_factor(-5) == 1
+    assert square_factor(4) == square_factor(-4) == 2
+    assert square_factor(12) == square_factor(-12) == 2
+    assert square_factor(6) == 1
+    assert square_factor(18) == 3
+    assert square_factor(52) == 2
+    assert square_factor(49) == 7
+    assert square_factor(392) == 14
+    assert square_factor(factorint(-12)) == 2
+
+
+def test_parametrize_ternary_quadratic():
+    assert check_solutions(x**2 + y**2 - z**2)
+    assert check_solutions(x**2 + 2*x*y + z**2)
+    assert check_solutions(234*x**2 - 65601*y**2 - z**2)
+    assert check_solutions(3*x**2 + 2*y**2 - z**2 - 2*x*y + 5*y*z - 7*y*z)
+    assert check_solutions(x**2 - y**2 - z**2)
+    assert check_solutions(x**2 - 49*y**2 - z**2 + 13*z*y - 8*x*y)
+    assert check_solutions(8*x*y + z**2)
+    assert check_solutions(124*x**2 - 30*y**2 - 7729*z**2)
+    assert check_solutions(236*x**2 - 225*y**2 - 11*x*y - 13*y*z - 17*x*z)
+    assert check_solutions(90*x**2 + 3*y**2 + 5*x*y + 2*z*y + 5*x*z)
+    assert check_solutions(124*x**2 - 30*y**2 - 7729*z**2)
+
+
+def test_no_square_ternary_quadratic():
+    assert check_solutions(2*x*y + y*z - 3*x*z)
+    assert check_solutions(189*x*y - 345*y*z - 12*x*z)
+    assert check_solutions(23*x*y + 34*y*z)
+    assert check_solutions(x*y + y*z + z*x)
+    assert check_solutions(23*x*y + 23*y*z + 23*x*z)
+
+
+def test_descent():
+
+    u = ([(13, 23), (3, -11), (41, -113), (91, -3), (1, 1), (1, -1), (17, 13), (123689, 1), (19, -570)])
+    for a, b in u:
+        w, x, y = descent(a, b)
+        assert a*x**2 + b*y**2 == w**2
+    # the docstring warns against bad input, so these are expected results
+    # - can't both be negative
+    raises(TypeError, lambda: descent(-1, -3))
+    # A can't be zero unless B != 1
+    raises(ZeroDivisionError, lambda: descent(0, 3))
+    # supposed to be square-free
+    raises(TypeError, lambda: descent(4, 3))
+
+
+def test_diophantine():
+    assert check_solutions((x - y)*(y - z)*(z - x))
+    assert check_solutions((x - y)*(x**2 + y**2 - z**2))
+    assert check_solutions((x - 3*y + 7*z)*(x**2 + y**2 - z**2))
+    assert check_solutions(x**2 - 3*y**2 - 1)
+    assert check_solutions(y**2 + 7*x*y)
+    assert check_solutions(x**2 - 3*x*y + y**2)
+    assert check_solutions(z*(x**2 - y**2 - 15))
+    assert check_solutions(x*(2*y - 2*z + 5))
+    assert check_solutions((x**2 - 3*y**2 - 1)*(x**2 - y**2 - 15))
+    assert check_solutions((x**2 - 3*y**2 - 1)*(y - 7*z))
+    assert check_solutions((x**2 + y**2 - z**2)*(x - 7*y - 3*z + 4*w))
+    # Following test case caused problems in parametric representation
+    # But this can be solved by factoring out y.
+    # No need to use methods for ternary quadratic equations.
+    assert check_solutions(y**2 - 7*x*y + 4*y*z)
+    assert check_solutions(x**2 - 2*x + 1)
+
+    assert diophantine(x - y) == diophantine(Eq(x, y))
+    # 18196
+    eq = x**4 + y**4 - 97
+    assert diophantine(eq, permute=True) == diophantine(-eq, permute=True)
+    assert diophantine(3*x*pi - 2*y*pi) == {(2*t_0, 3*t_0)}
+    eq = x**2 + y**2 + z**2 - 14
+    base_sol = {(1, 2, 3)}
+    assert diophantine(eq) == base_sol
+    complete_soln = set(signed_permutations(base_sol.pop()))
+    assert diophantine(eq, permute=True) == complete_soln
+
+    assert diophantine(x**2 + x*Rational(15, 14) - 3) == set()
+    # test issue 11049
+    eq = 92*x**2 - 99*y**2 - z**2
+    coeff = eq.as_coefficients_dict()
+    assert _diop_ternary_quadratic_normal((x, y, z), coeff) == \
+           {(9, 7, 51)}
+    assert diophantine(eq) == {(
+        891*p**2 + 9*q**2, -693*p**2 - 102*p*q + 7*q**2,
+        5049*p**2 - 1386*p*q - 51*q**2)}
+    eq = 2*x**2 + 2*y**2 - z**2
+    coeff = eq.as_coefficients_dict()
+    assert _diop_ternary_quadratic_normal((x, y, z), coeff) == \
+           {(1, 1, 2)}
+    assert diophantine(eq) == {(
+        2*p**2 - q**2, -2*p**2 + 4*p*q - q**2,
+        4*p**2 - 4*p*q + 2*q**2)}
+    eq = 411*x**2+57*y**2-221*z**2
+    coeff = eq.as_coefficients_dict()
+    assert _diop_ternary_quadratic_normal((x, y, z), coeff) == \
+           {(2021, 2645, 3066)}
+    assert diophantine(eq) == \
+           {(115197*p**2 - 446641*q**2, -150765*p**2 + 1355172*p*q -
+             584545*q**2, 174762*p**2 - 301530*p*q + 677586*q**2)}
+    eq = 573*x**2+267*y**2-984*z**2
+    coeff = eq.as_coefficients_dict()
+    assert _diop_ternary_quadratic_normal((x, y, z), coeff) == \
+           {(49, 233, 127)}
+    assert diophantine(eq) == \
+           {(4361*p**2 - 16072*q**2, -20737*p**2 + 83312*p*q - 76424*q**2,
+             11303*p**2 - 41474*p*q + 41656*q**2)}
+    # this produces factors during reconstruction
+    eq = x**2 + 3*y**2 - 12*z**2
+    coeff = eq.as_coefficients_dict()
+    assert _diop_ternary_quadratic_normal((x, y, z), coeff) == \
+           {(0, 2, 1)}
+    assert diophantine(eq) == \
+           {(24*p*q, 2*p**2 - 24*q**2, p**2 + 12*q**2)}
+    # solvers have not been written for every type
+    raises(NotImplementedError, lambda: diophantine(x*y**2 + 1))
+
+    # rational expressions
+    assert diophantine(1/x) == set()
+    assert diophantine(1/x + 1/y - S.Half) == {(6, 3), (-2, 1), (4, 4), (1, -2), (3, 6)}
+    assert diophantine(x**2 + y**2 +3*x- 5, permute=True) == \
+           {(-1, 1), (-4, -1), (1, -1), (1, 1), (-4, 1), (-1, -1), (4, 1), (4, -1)}
+
+
+    #test issue 18186
+    assert diophantine(y**4 + x**4 - 2**4 - 3**4, syms=(x, y), permute=True) == \
+           {(-3, -2), (-3, 2), (-2, -3), (-2, 3), (2, -3), (2, 3), (3, -2), (3, 2)}
+    assert diophantine(y**4 + x**4 - 2**4 - 3**4, syms=(y, x), permute=True) == \
+           {(-3, -2), (-3, 2), (-2, -3), (-2, 3), (2, -3), (2, 3), (3, -2), (3, 2)}
+
+    # issue 18122
+    assert check_solutions(x**2-y)
+    assert check_solutions(y**2-x)
+    assert diophantine((x**2-y), t) == {(t, t**2)}
+    assert diophantine((y**2-x), t) == {(t**2, -t)}
+
+
+def test_general_pythagorean():
+    from sympy.abc import a, b, c, d, e
+
+    assert check_solutions(a**2 + b**2 + c**2 - d**2)
+    assert check_solutions(a**2 + 4*b**2 + 4*c**2 - d**2)
+    assert check_solutions(9*a**2 + 4*b**2 + 4*c**2 - d**2)
+    assert check_solutions(9*a**2 + 4*b**2 - 25*d**2 + 4*c**2 )
+    assert check_solutions(9*a**2 - 16*d**2 + 4*b**2 + 4*c**2)
+    assert check_solutions(-e**2 + 9*a**2 + 4*b**2 + 4*c**2 + 25*d**2)
+    assert check_solutions(16*a**2 - b**2 + 9*c**2 + d**2 + 25*e**2)
+
+    assert GeneralPythagorean(a**2 + b**2 + c**2 - d**2).solve(parameters=[x, y, z]) == \
+           {(x**2 + y**2 - z**2, 2*x*z, 2*y*z, x**2 + y**2 + z**2)}
+
+
+def test_diop_general_sum_of_squares_quick():
+    for i in range(3, 10):
+        assert check_solutions(sum(i**2 for i in symbols(':%i' % i)) - i)
+
+    assert diop_general_sum_of_squares(x**2 + y**2 - 2) is None
+    assert diop_general_sum_of_squares(x**2 + y**2 + z**2 + 2) == set()
+    eq = x**2 + y**2 + z**2 - (1 + 4 + 9)
+    assert diop_general_sum_of_squares(eq) == \
+           {(1, 2, 3)}
+    eq = u**2 + v**2 + x**2 + y**2 + z**2 - 1313
+    assert len(diop_general_sum_of_squares(eq, 3)) == 3
+    # issue 11016
+    var = symbols(':5') + (symbols('6', negative=True),)
+    eq = Add(*[i**2 for i in var]) - 112
+
+    base_soln = {(0, 1, 1, 5, 6, -7), (1, 1, 1, 3, 6, -8), (2, 3, 3, 4, 5, -7), (0, 1, 1, 1, 3, -10),
+                 (0, 0, 4, 4, 4, -8), (1, 2, 3, 3, 5, -8), (0, 1, 2, 3, 7, -7), (2, 2, 4, 4, 6, -6),
+                 (1, 1, 3, 4, 6, -7), (0, 2, 3, 3, 3, -9), (0, 0, 2, 2, 2, -10), (1, 1, 2, 3, 4, -9),
+                 (0, 1, 1, 2, 5, -9), (0, 0, 2, 6, 6, -6), (1, 3, 4, 5, 5, -6), (0, 2, 2, 2, 6, -8),
+                 (0, 3, 3, 3, 6, -7), (0, 2, 3, 5, 5, -7), (0, 1, 5, 5, 5, -6)}
+    assert diophantine(eq) == base_soln
+    assert len(diophantine(eq, permute=True)) == 196800
+
+    # handle negated squares with signsimp
+    assert diophantine(12 - x**2 - y**2 - z**2) == {(2, 2, 2)}
+    # diophantine handles simplification, so classify_diop should
+    # not have to look for additional patterns that are removed
+    # by diophantine
+    eq = a**2 + b**2 + c**2 + d**2 - 4
+    raises(NotImplementedError, lambda: classify_diop(-eq))
+
+
+def test_issue_23807():
+    # fixes recursion error
+    eq = x**2 + y**2 + z**2 - 1000000
+    base_soln = {(0, 0, 1000), (0, 352, 936), (480, 600, 640), (24, 640, 768), (192, 640, 744),
+                 (192, 480, 856), (168, 224, 960), (0, 600, 800), (280, 576, 768), (152, 480, 864),
+                 (0, 280, 960), (352, 360, 864), (424, 480, 768), (360, 480, 800), (224, 600, 768),
+                 (96, 360, 928), (168, 576, 800), (96, 480, 872)}
+
+    assert diophantine(eq) == base_soln
+
+
+def test_diop_partition():
+    for n in [8, 10]:
+        for k in range(1, 8):
+            for p in partition(n, k):
+                assert len(p) == k
+    assert list(partition(3, 5)) == []
+    assert [list(p) for p in partition(3, 5, 1)] == [
+        [0, 0, 0, 0, 3], [0, 0, 0, 1, 2], [0, 0, 1, 1, 1]]
+    assert list(partition(0)) == [()]
+    assert list(partition(1, 0)) == [()]
+    assert [list(i) for i in partition(3)] == [[1, 1, 1], [1, 2], [3]]
+
+
+def test_prime_as_sum_of_two_squares():
+    for i in [5, 13, 17, 29, 37, 41, 2341, 3557, 34841, 64601]:
+        a, b = prime_as_sum_of_two_squares(i)
+        assert a**2 + b**2 == i
+    assert prime_as_sum_of_two_squares(7) is None
+    ans = prime_as_sum_of_two_squares(800029)
+    assert ans == (450, 773) and type(ans[0]) is int
+
+
+def test_sum_of_three_squares():
+    for i in [0, 1, 2, 34, 123, 34304595905, 34304595905394941, 343045959052344,
+              800, 801, 802, 803, 804, 805, 806]:
+        a, b, c = sum_of_three_squares(i)
+        assert a**2 + b**2 + c**2 == i
+
+    assert sum_of_three_squares(7) is None
+    assert sum_of_three_squares((4**5)*15) is None
+    assert sum_of_three_squares(25) == (5, 0, 0)
+    assert sum_of_three_squares(4) == (0, 0, 2)
+
+
+def test_sum_of_four_squares():
+    from sympy.core.random import randint
+
+    # this should never fail
+    n = randint(1, 100000000000000)
+    assert sum(i**2 for i in sum_of_four_squares(n)) == n
+
+    assert sum_of_four_squares(0) == (0, 0, 0, 0)
+    assert sum_of_four_squares(14) == (0, 1, 2, 3)
+    assert sum_of_four_squares(15) == (1, 1, 2, 3)
+    assert sum_of_four_squares(18) == (1, 2, 2, 3)
+    assert sum_of_four_squares(19) == (0, 1, 3, 3)
+    assert sum_of_four_squares(48) == (0, 4, 4, 4)
+
+
+def test_power_representation():
+    tests = [(1729, 3, 2), (234, 2, 4), (2, 1, 2), (3, 1, 3), (5, 2, 2), (12352, 2, 4),
+             (32760, 2, 3)]
+
+    for test in tests:
+        n, p, k = test
+        f = power_representation(n, p, k)
+
+        while True:
+            try:
+                l = next(f)
+                assert len(l) == k
+
+                chk_sum = 0
+                for l_i in l:
+                    chk_sum = chk_sum + l_i**p
+                assert chk_sum == n
+
+            except StopIteration:
+                break
+
+    assert list(power_representation(20, 2, 4, True)) == \
+        [(1, 1, 3, 3), (0, 0, 2, 4)]
+    raises(ValueError, lambda: list(power_representation(1.2, 2, 2)))
+    raises(ValueError, lambda: list(power_representation(2, 0, 2)))
+    raises(ValueError, lambda: list(power_representation(2, 2, 0)))
+    assert list(power_representation(-1, 2, 2)) == []
+    assert list(power_representation(1, 1, 1)) == [(1,)]
+    assert list(power_representation(3, 2, 1)) == []
+    assert list(power_representation(4, 2, 1)) == [(2,)]
+    assert list(power_representation(3**4, 4, 6, zeros=True)) == \
+        [(1, 2, 2, 2, 2, 2), (0, 0, 0, 0, 0, 3)]
+    assert list(power_representation(3**4, 4, 5, zeros=False)) == []
+    assert list(power_representation(-2, 3, 2)) == [(-1, -1)]
+    assert list(power_representation(-2, 4, 2)) == []
+    assert list(power_representation(0, 3, 2, True)) == [(0, 0)]
+    assert list(power_representation(0, 3, 2, False)) == []
+    # when we are dealing with squares, do feasibility checks
+    assert len(list(power_representation(4**10*(8*10 + 7), 2, 3))) == 0
+    # there will be a recursion error if these aren't recognized
+    big = 2**30
+    for i in [13, 10, 7, 5, 4, 2, 1]:
+        assert list(sum_of_powers(big, 2, big - i)) == []
+
+
+def test_assumptions():
+    """
+    Test whether diophantine respects the assumptions.
+    """
+    #Test case taken from the below so question regarding assumptions in diophantine module
+    #https://stackoverflow.com/questions/23301941/how-can-i-declare-natural-symbols-with-sympy
+    m, n = symbols('m n', integer=True, positive=True)
+    diof = diophantine(n**2 + m*n - 500)
+    assert diof == {(5, 20), (40, 10), (95, 5), (121, 4), (248, 2), (499, 1)}
+
+    a, b = symbols('a b', integer=True, positive=False)
+    diof = diophantine(a*b + 2*a + 3*b - 6)
+    assert diof == {(-15, -3), (-9, -4), (-7, -5), (-6, -6), (-5, -8), (-4, -14)}
+
+
+def check_solutions(eq):
+    """
+    Determines whether solutions returned by diophantine() satisfy the original
+    equation. Hope to generalize this so we can remove functions like check_ternay_quadratic,
+    check_solutions_normal, check_solutions()
+    """
+    s = diophantine(eq)
+
+    factors = Mul.make_args(eq)
+
+    var = list(eq.free_symbols)
+    var.sort(key=default_sort_key)
+
+    while s:
+        solution = s.pop()
+        for f in factors:
+            if diop_simplify(f.subs(zip(var, solution))) == 0:
+                break
+        else:
+            return False
+    return True
+
+
+def test_diopcoverage():
+    eq = (2*x + y + 1)**2
+    assert diop_solve(eq) == {(t_0, -2*t_0 - 1)}
+    eq = 2*x**2 + 6*x*y + 12*x + 4*y**2 + 18*y + 18
+    assert diop_solve(eq) == {(t, -t - 3), (2*t - 3, -t)}
+    assert diop_quadratic(x + y**2 - 3) == {(-t**2 + 3, -t)}
+
+    assert diop_linear(x + y - 3) == (t_0, 3 - t_0)
+
+    assert base_solution_linear(0, 1, 2, t=None) == (0, 0)
+    ans = (3*t - 1, -2*t + 1)
+    assert base_solution_linear(4, 8, 12, t) == ans
+    assert base_solution_linear(4, 8, 12, t=None) == tuple(_.subs(t, 0) for _ in ans)
+
+    assert cornacchia(1, 1, 20) is None
+    assert cornacchia(1, 1, 5) == {(2, 1)}
+    assert cornacchia(1, 2, 17) == {(3, 2)}
+
+    raises(ValueError, lambda: reconstruct(4, 20, 1))
+
+    assert gaussian_reduce(4, 1, 3) == (1, 1)
+    eq = -w**2 - x**2 - y**2 + z**2
+
+    assert diop_general_pythagorean(eq) == \
+        diop_general_pythagorean(-eq) == \
+            (m1**2 + m2**2 - m3**2, 2*m1*m3,
+            2*m2*m3, m1**2 + m2**2 + m3**2)
+
+    assert len(check_param(S(3) + x/3, S(4) + x/2, S(2), [x])) == 0
+    assert len(check_param(Rational(3, 2), S(4) + x, S(2), [x])) == 0
+    assert len(check_param(S(4) + x, Rational(3, 2), S(2), [x])) == 0
+
+    assert _nint_or_floor(16, 10) == 2
+    assert _odd(1) == (not _even(1)) == True
+    assert _odd(0) == (not _even(0)) == False
+    assert _remove_gcd(2, 4, 6) == (1, 2, 3)
+    raises(TypeError, lambda: _remove_gcd((2, 4, 6)))
+    assert sqf_normal(2*3**2*5, 2*5*11, 2*7**2*11)  == \
+        (11, 1, 5)
+
+    # it's ok if these pass some day when the solvers are implemented
+    raises(NotImplementedError, lambda: diophantine(x**2 + y**2 + x*y + 2*y*z - 12))
+    raises(NotImplementedError, lambda: diophantine(x**3 + y**2))
+    assert diop_quadratic(x**2 + y**2 - 1**2 - 3**4) == \
+           {(-9, -1), (-9, 1), (-1, -9), (-1, 9), (1, -9), (1, 9), (9, -1), (9, 1)}
+
+
+def test_holzer():
+    # if the input is good, don't let it diverge in holzer()
+    # (but see test_fail_holzer below)
+    assert holzer(2, 7, 13, 4, 79, 23) == (2, 7, 13)
+
+    # None in uv condition met; solution is not Holzer reduced
+    # so this will hopefully change but is here for coverage
+    assert holzer(2, 6, 2, 1, 1, 10) == (2, 6, 2)
+
+    raises(ValueError, lambda: holzer(2, 7, 14, 4, 79, 23))
+
+
+@XFAIL
+def test_fail_holzer():
+    eq = lambda x, y, z: a*x**2 + b*y**2 - c*z**2
+    a, b, c = 4, 79, 23
+    x, y, z = xyz = 26, 1, 11
+    X, Y, Z = ans = 2, 7, 13
+    assert eq(*xyz) == 0
+    assert eq(*ans) == 0
+    assert max(a*x**2, b*y**2, c*z**2) <= a*b*c
+    assert max(a*X**2, b*Y**2, c*Z**2) <= a*b*c
+    h = holzer(x, y, z, a, b, c)
+    assert h == ans  # it would be nice to get the smaller soln
+
+
+def test_issue_9539():
+    assert diophantine(6*w + 9*y + 20*x - z) == \
+           {(t_0, t_1, t_1 + t_2, 6*t_0 + 29*t_1 + 9*t_2)}
+
+
+def test_issue_8943():
+    assert diophantine(
+        3*(x**2 + y**2 + z**2) - 14*(x*y + y*z + z*x)) == \
+           {(0, 0, 0)}
+
+
+def test_diop_sum_of_even_powers():
+    eq = x**4 + y**4 + z**4 - 2673
+    assert diop_solve(eq) == {(3, 6, 6), (2, 4, 7)}
+    assert diop_general_sum_of_even_powers(eq, 2) == {(3, 6, 6), (2, 4, 7)}
+    raises(NotImplementedError, lambda: diop_general_sum_of_even_powers(-eq, 2))
+    neg = symbols('neg', negative=True)
+    eq = x**4 + y**4 + neg**4 - 2673
+    assert diop_general_sum_of_even_powers(eq) == {(-3, 6, 6)}
+    assert diophantine(x**4 + y**4 + 2) == set()
+    assert diop_general_sum_of_even_powers(x**4 + y**4 - 2, limit=0) == set()
+
+
+def test_sum_of_squares_powers():
+    tru = {(0, 0, 1, 1, 11), (0, 0, 5, 7, 7), (0, 1, 3, 7, 8), (0, 1, 4, 5, 9), (0, 3, 4, 7, 7), (0, 3, 5, 5, 8),
+           (1, 1, 2, 6, 9), (1, 1, 6, 6, 7), (1, 2, 3, 3, 10), (1, 3, 4, 4, 9), (1, 5, 5, 6, 6), (2, 2, 3, 5, 9),
+           (2, 3, 5, 6, 7), (3, 3, 4, 5, 8)}
+    eq = u**2 + v**2 + x**2 + y**2 + z**2 - 123
+    ans = diop_general_sum_of_squares(eq, oo)  # allow oo to be used
+    assert len(ans) == 14
+    assert ans == tru
+
+    raises(ValueError, lambda: list(sum_of_squares(10, -1)))
+    assert list(sum_of_squares(-10, 2)) == []
+    assert list(sum_of_squares(2, 3)) == []
+    assert list(sum_of_squares(0, 3, True)) == [(0, 0, 0)]
+    assert list(sum_of_squares(0, 3)) == []
+    assert list(sum_of_squares(4, 1)) == [(2,)]
+    assert list(sum_of_squares(5, 1)) == []
+    assert list(sum_of_squares(50, 2)) == [(5, 5), (1, 7)]
+    assert list(sum_of_squares(11, 5, True)) == [
+        (1, 1, 1, 2, 2), (0, 0, 1, 1, 3)]
+    assert list(sum_of_squares(8, 8)) == [(1, 1, 1, 1, 1, 1, 1, 1)]
+
+    assert [len(list(sum_of_squares(i, 5, True))) for i in range(30)] == [
+        1, 1, 1, 1, 2,
+        2, 1, 1, 2, 2,
+        2, 2, 2, 3, 2,
+        1, 3, 3, 3, 3,
+        4, 3, 3, 2, 2,
+        4, 4, 4, 4, 5]
+    assert [len(list(sum_of_squares(i, 5))) for i in range(30)] == [
+        0, 0, 0, 0, 0,
+        1, 0, 0, 1, 0,
+        0, 1, 0, 1, 1,
+        0, 1, 1, 0, 1,
+        2, 1, 1, 1, 1,
+        1, 1, 1, 1, 3]
+    for i in range(30):
+        s1 = set(sum_of_squares(i, 5, True))
+        assert not s1 or all(sum(j**2 for j in t) == i for t in s1)
+        s2 = set(sum_of_squares(i, 5))
+        assert all(sum(j**2 for j in t) == i for t in s2)
+
+    raises(ValueError, lambda: list(sum_of_powers(2, -1, 1)))
+    raises(ValueError, lambda: list(sum_of_powers(2, 1, -1)))
+    assert list(sum_of_powers(-2, 3, 2)) == [(-1, -1)]
+    assert list(sum_of_powers(-2, 4, 2)) == []
+    assert list(sum_of_powers(2, 1, 1)) == [(2,)]
+    assert list(sum_of_powers(2, 1, 3, True)) == [(0, 0, 2), (0, 1, 1)]
+    assert list(sum_of_powers(5, 1, 2, True)) == [(0, 5), (1, 4), (2, 3)]
+    assert list(sum_of_powers(6, 2, 2)) == []
+    assert list(sum_of_powers(3**5, 3, 1)) == []
+    assert list(sum_of_powers(3**6, 3, 1)) == [(9,)] and (9**3 == 3**6)
+    assert list(sum_of_powers(2**1000, 5, 2)) == []
+
+
+def test__can_do_sum_of_squares():
+    assert _can_do_sum_of_squares(3, -1) is False
+    assert _can_do_sum_of_squares(-3, 1) is False
+    assert _can_do_sum_of_squares(0, 1)
+    assert _can_do_sum_of_squares(4, 1)
+    assert _can_do_sum_of_squares(1, 2)
+    assert _can_do_sum_of_squares(2, 2)
+    assert _can_do_sum_of_squares(3, 2) is False
+
+
+def test_diophantine_permute_sign():
+    from sympy.abc import a, b, c, d, e
+    eq = a**4 + b**4 - (2**4 + 3**4)
+    base_sol = {(2, 3)}
+    assert diophantine(eq) == base_sol
+    complete_soln = set(signed_permutations(base_sol.pop()))
+    assert diophantine(eq, permute=True) == complete_soln
+
+    eq = a**2 + b**2 + c**2 + d**2 + e**2 - 234
+    assert len(diophantine(eq)) == 35
+    assert len(diophantine(eq, permute=True)) == 62000
+    soln = {(-1, -1), (-1, 2), (1, -2), (1, 1)}
+    assert diophantine(10*x**2 + 12*x*y + 12*y**2 - 34, permute=True) == soln
+
+
+@XFAIL
+def test_not_implemented():
+    eq = x**2 + y**4 - 1**2 - 3**4
+    assert diophantine(eq, syms=[x, y]) == {(9, 1), (1, 3)}
+
+
+def test_issue_9538():
+    eq = x - 3*y + 2
+    assert diophantine(eq, syms=[y,x]) == {(t_0, 3*t_0 - 2)}
+    raises(TypeError, lambda: diophantine(eq, syms={y, x}))
+
+
+def test_ternary_quadratic():
+    # solution with 3 parameters
+    s = diophantine(2*x**2 + y**2 - 2*z**2)
+    p, q, r = ordered(S(s).free_symbols)
+    assert s == {(
+        p**2 - 2*q**2,
+        -2*p**2 + 4*p*q - 4*p*r - 4*q**2,
+        p**2 - 4*p*q + 2*q**2 - 4*q*r)}
+    # solution with Mul in solution
+    s = diophantine(x**2 + 2*y**2 - 2*z**2)
+    assert s == {(4*p*q, p**2 - 2*q**2, p**2 + 2*q**2)}
+    # solution with no Mul in solution
+    s = diophantine(2*x**2 + 2*y**2 - z**2)
+    assert s == {(2*p**2 - q**2, -2*p**2 + 4*p*q - q**2,
+        4*p**2 - 4*p*q + 2*q**2)}
+    # reduced form when parametrized
+    s = diophantine(3*x**2 + 72*y**2 - 27*z**2)
+    assert s == {(24*p**2 - 9*q**2, 6*p*q, 8*p**2 + 3*q**2)}
+    assert parametrize_ternary_quadratic(
+        3*x**2 + 2*y**2 - z**2 - 2*x*y + 5*y*z - 7*y*z) == (
+        2*p**2 - 2*p*q - q**2, 2*p**2 + 2*p*q - q**2, 2*p**2 -
+        2*p*q + 3*q**2)
+    assert parametrize_ternary_quadratic(
+        124*x**2 - 30*y**2 - 7729*z**2) == (
+        -1410*p**2 - 363263*q**2, 2700*p**2 + 30916*p*q -
+        695610*q**2, -60*p**2 + 5400*p*q + 15458*q**2)
+
+
+def test_diophantine_solution_set():
+    s1 = DiophantineSolutionSet([], [])
+    assert set(s1) == set()
+    assert s1.symbols == ()
+    assert s1.parameters == ()
+    raises(ValueError, lambda: s1.add((x,)))
+    assert list(s1.dict_iterator()) == []
+
+    s2 = DiophantineSolutionSet([x, y], [t, u])
+    assert s2.symbols == (x, y)
+    assert s2.parameters == (t, u)
+    raises(ValueError, lambda: s2.add((1,)))
+    s2.add((3, 4))
+    assert set(s2) == {(3, 4)}
+    s2.update((3, 4), (-1, u))
+    assert set(s2) == {(3, 4), (-1, u)}
+    raises(ValueError, lambda: s1.update(s2))
+    assert list(s2.dict_iterator()) == [{x: -1, y: u}, {x: 3, y: 4}]
+
+    s3 = DiophantineSolutionSet([x, y, z], [t, u])
+    assert len(s3.parameters) == 2
+    s3.add((t**2 + u, t - u, 1))
+    assert set(s3) == {(t**2 + u, t - u, 1)}
+    assert s3.subs(t, 2) == {(u + 4, 2 - u, 1)}
+    assert s3(2) == {(u + 4, 2 - u, 1)}
+    assert s3.subs({t: 7, u: 8}) == {(57, -1, 1)}
+    assert s3(7, 8) == {(57, -1, 1)}
+    assert s3.subs({t: 5}) == {(u + 25, 5 - u, 1)}
+    assert s3(5) == {(u + 25, 5 - u, 1)}
+    assert s3.subs(u, -3) == {(t**2 - 3, t + 3, 1)}
+    assert s3(None, -3) == {(t**2 - 3, t + 3, 1)}
+    assert s3.subs({t: 2, u: 8}) == {(12, -6, 1)}
+    assert s3(2, 8) == {(12, -6, 1)}
+    assert s3.subs({t: 5, u: -3}) == {(22, 8, 1)}
+    assert s3(5, -3) == {(22, 8, 1)}
+    raises(ValueError, lambda: s3.subs(x=1))
+    raises(ValueError, lambda: s3.subs(1, 2, 3))
+    raises(ValueError, lambda: s3.add(()))
+    raises(ValueError, lambda: s3.add((1, 2, 3, 4)))
+    raises(ValueError, lambda: s3.add((1, 2)))
+    raises(ValueError, lambda: s3(1, 2, 3))
+    raises(TypeError, lambda: s3(t=1))
+
+    s4 = DiophantineSolutionSet([x, y], [t, u])
+    s4.add((t, 11*t))
+    s4.add((-t, 22*t))
+    assert s4(0, 0) == {(0, 0)}
+
+
+def test_quadratic_parameter_passing():
+    eq = -33*x*y + 3*y**2
+    solution = BinaryQuadratic(eq).solve(parameters=[t, u])
+    # test that parameters are passed all the way to the final solution
+    assert solution == {(t, 11*t), (-t, 22*t)}
+    assert solution(0, 0) == {(0, 0)}

venv/lib/python3.10/site-packages/sympy/solvers/ode/__init__.py

@@ -0,0 +1,16 @@
+from .ode import (allhints, checkinfsol, classify_ode,
+        constantsimp, dsolve, homogeneous_order)
+
+from .lie_group import infinitesimals
+
+from .subscheck import checkodesol
+
+from .systems import (canonical_odes, linear_ode_to_matrix,
+        linodesolve)
+
+
+__all__ = [
+    'allhints', 'checkinfsol', 'checkodesol', 'classify_ode', 'constantsimp',
+    'dsolve', 'homogeneous_order', 'infinitesimals', 'canonical_odes', 'linear_ode_to_matrix',
+    'linodesolve'
+]