"""
Boolean algebra module for SymPy
"""
from __future__ import print_function, division

from collections import defaultdict
from itertools import combinations, product

from sympy.core.basic import Basic
from sympy.core.cache import cacheit
from sympy.core.numbers import Number
from sympy.core.operations import LatticeOp
from sympy.core.function import Application, Derivative
from sympy.core.compatibility import ordered, range, with_metaclass, as_int
from sympy.core.sympify import converter, _sympify, sympify
from sympy.core.singleton import Singleton, S


class Boolean(Basic):
    """A boolean object is an object for which logic operations make sense."""

    __slots__ = []

    def __and__(self, other):
        """Overloading for & operator"""
        return And(self, other)

    __rand__ = __and__

    def __or__(self, other):
        """Overloading for |"""
        return Or(self, other)

    __ror__ = __or__

    def __invert__(self):
        """Overloading for ~"""
        return Not(self)

    def __rshift__(self, other):
        """Overloading for >>"""
        return Implies(self, other)

    def __lshift__(self, other):
        """Overloading for <<"""
        return Implies(other, self)

    __rrshift__ = __lshift__
    __rlshift__ = __rshift__

    def __xor__(self, other):
        return Xor(self, other)

    __rxor__ = __xor__

    def equals(self, other):
        """
        Returns True if the given formulas have the same truth table.
        For two formulas to be equal they must have the same literals.

        Examples
        ========

        >>> from sympy.abc import A, B, C
        >>> from sympy.logic.boolalg import And, Or, Not
        >>> (A >> B).equals(~B >> ~A)
        True
        >>> Not(And(A, B, C)).equals(And(Not(A), Not(B), Not(C)))
        False
        >>> Not(And(A, Not(A))).equals(Or(B, Not(B)))
        False
        """
        from sympy.logic.inference import satisfiable
        from sympy.core.relational import Relational

        if self.has(Relational) or other.has(Relational):
            raise NotImplementedError('handling of relationals')
        return self.atoms() == other.atoms() and \
                not satisfiable(Not(Equivalent(self, other)))


class BooleanAtom(Boolean):
    """
    Base class of BooleanTrue and BooleanFalse.
    """
    is_Boolean = True
    _op_priority = 11  # higher than Expr

    @property
    def canonical(self):
        return self

    def _noop(self, other=None):
        raise TypeError('BooleanAtom not allowed in this context.')

    __add__ = _noop
    __radd__ = _noop
    __sub__ = _noop
    __rsub__ = _noop
    __mul__ = _noop
    __rmul__ = _noop
    __pow__ = _noop
    __rpow__ = _noop
    __rdiv__ = _noop
    __truediv__ = _noop
    __div__ = _noop
    __rtruediv__ = _noop
    __mod__ = _noop
    __rmod__ = _noop
    _eval_power = _noop


class BooleanTrue(with_metaclass(Singleton, BooleanAtom)):
    """
    SymPy version of True, a singleton that can be accessed via S.true.

    This is the SymPy version of True, for use in the logic module. The
    primary advantage of using true instead of True is that shorthand boolean
    operations like ~ and >> will work as expected on this class, whereas with
    True they act bitwise on 1. Functions in the logic module will return this
    class when they evaluate to true.

    Notes
    =====

    There is liable to be some confusion as to when ``True`` should
    be used and when ``S.true`` should be used in various contexts
    throughout SymPy. An important thing to remember is that
    ``sympify(True)`` returns ``S.true``. This means that for the most
    part, you can just use ``True`` and it will automatically be converted
    to ``S.true`` when necessary, similar to how you can generally use 1
    instead of ``S.One``.

    The rule of thumb is:

    "If the boolean in question can be replaced by an arbitrary symbolic
    ``Boolean``, like ``Or(x, y)`` or ``x > 1``, use ``S.true``.
    Otherwise, use ``True``"

    In other words, use ``S.true`` only on those contexts where the
    boolean is being used as a symbolic representation of truth.
    For example, if the object ends up in the ``.args`` of any expression,
    then it must necessarily be ``S.true`` instead of ``True``, as
    elements of ``.args`` must be ``Basic``. On the other hand,
    ``==`` is not a symbolic operation in SymPy, since it always returns
    ``True`` or ``False``, and does so in terms of structural equality
    rather than mathematical, so it should return ``True``. The assumptions
    system should use ``True`` and ``False``. Aside from not satisfying
    the above rule of thumb, the
    assumptions system uses a three-valued logic (``True``, ``False``, ``None``),
    whereas ``S.true`` and ``S.false`` represent a two-valued logic. When in
    doubt, use ``True``.

    "``S.true == True is True``."

    While "``S.true is True``" is ``False``, "``S.true == True``"
    is ``True``, so if there is any doubt over whether a function or
    expression will return ``S.true`` or ``True``, just use ``==``
    instead of ``is`` to do the comparison, and it will work in either
    case.  Finally, for boolean flags, it's better to just use ``if x``
    instead of ``if x is True``. To quote PEP 8:

    Don't compare boolean values to ``True`` or ``False``
    using ``==``.

    * Yes:   ``if greeting:``
    * No:    ``if greeting == True:``
    * Worse: ``if greeting is True:``

    Examples
    ========

    >>> from sympy import sympify, true, Or
    >>> sympify(True)
    True
    >>> ~true
    False
    >>> ~True
    -2
    >>> Or(True, False)
    True

    See Also
    ========
    sympy.logic.boolalg.BooleanFalse

    """
    def __nonzero__(self):
        return True

    __bool__ = __nonzero__

    def __hash__(self):
        return hash(True)

    def as_set(self):
        """
        Rewrite logic operators and relationals in terms of real sets.

        Examples
        ========

        >>> from sympy import true
        >>> true.as_set()
        UniversalSet()
        """
        return S.UniversalSet


class BooleanFalse(with_metaclass(Singleton, BooleanAtom)):
    """
    SymPy version of False, a singleton that can be accessed via S.false.

    This is the SymPy version of False, for use in the logic module. The
    primary advantage of using false instead of False is that shorthand boolean
    operations like ~ and >> will work as expected on this class, whereas with
    False they act bitwise on 0. Functions in the logic module will return this
    class when they evaluate to false.

    Notes
    ======
    See note in :py:class`sympy.logic.boolalg.BooleanTrue`

    Examples
    ========

    >>> from sympy import sympify, false, Or, true
    >>> sympify(False)
    False
    >>> false >> false
    True
    >>> False >> False
    0
    >>> Or(True, False)
    True

    See Also
    ========
    sympy.logic.boolalg.BooleanTrue

    """
    def __nonzero__(self):
        return False

    __bool__ = __nonzero__

    def __hash__(self):
        return hash(False)

    def as_set(self):
        """
        Rewrite logic operators and relationals in terms of real sets.

        Examples
        ========

        >>> from sympy import false
        >>> false.as_set()
        EmptySet()
        """
        from sympy.sets.sets import EmptySet
        return EmptySet()

true = BooleanTrue()
false = BooleanFalse()
# We want S.true and S.false to work, rather than S.BooleanTrue and
# S.BooleanFalse, but making the class and instance names the same causes some
# major issues (like the inability to import the class directly from this
# file).
S.true = true
S.false = false

converter[bool] = lambda x: S.true if x else S.false

class BooleanFunction(Application, Boolean):
    """Boolean function is a function that lives in a boolean space
    It is used as base class for And, Or, Not, etc.
    """
    is_Boolean = True

    def _eval_simplify(self, ratio, measure):
        return simplify_logic(self)

    def to_nnf(self, simplify=True):
        return self._to_nnf(*self.args, simplify=simplify)

    @classmethod
    def _to_nnf(cls, *args, **kwargs):
        simplify = kwargs.get('simplify', True)
        argset = set([])
        for arg in args:
            if not is_literal(arg):
                arg = arg.to_nnf(simplify)
            if simplify:
                if isinstance(arg, cls):
                    arg = arg.args
                else:
                    arg = (arg,)
                for a in arg:
                    if Not(a) in argset:
                        return cls.zero
                    argset.add(a)
            else:
                argset.add(arg)
        return cls(*argset)


class And(LatticeOp, BooleanFunction):
    """
    Logical AND function.

    It evaluates its arguments in order, giving False immediately
    if any of them are False, and True if they are all True.

    Examples
    ========

    >>> from sympy.core import symbols
    >>> from sympy.abc import x, y
    >>> from sympy.logic.boolalg import And
    >>> x & y
    And(x, y)

    Notes
    =====

    The ``&`` operator is provided as a convenience, but note that its use
    here is different from its normal use in Python, which is bitwise
    and. Hence, ``And(a, b)`` and ``a & b`` will return different things if
    ``a`` and ``b`` are integers.

    >>> And(x, y).subs(x, 1)
    y

    """
    zero = false
    identity = true

    nargs = None

    @classmethod
    def _new_args_filter(cls, args):
        newargs = []
        rel = []
        for x in reversed(list(args)):
            if isinstance(x, Number) or x in (0, 1):
                newargs.append(True if x else False)
                continue
            if x.is_Relational:
                c = x.canonical
                if c in rel:
                    continue
                nc = (~c).canonical
                if any(r == nc for r in rel):
                    return [S.false]
                rel.append(c)
            newargs.append(x)
        return LatticeOp._new_args_filter(newargs, And)

    def as_set(self):
        """
        Rewrite logic operators and relationals in terms of real sets.

        Examples
        ========

        >>> from sympy import And, Symbol
        >>> x = Symbol('x', real=True)
        >>> And(x<2, x>-2).as_set()
        (-2, 2)
        """
        from sympy.sets.sets import Intersection
        if len(self.free_symbols) == 1:
            return Intersection(*[arg.as_set() for arg in self.args])
        else:
            raise NotImplementedError("Sorry, And.as_set has not yet been"
                                      " implemented for multivariate"
                                      " expressions")


class Or(LatticeOp, BooleanFunction):
    """
    Logical OR function

    It evaluates its arguments in order, giving True immediately
    if any of them are True, and False if they are all False.

    Examples
    ========

    >>> from sympy.core import symbols
    >>> from sympy.abc import x, y
    >>> from sympy.logic.boolalg import Or
    >>> x | y
    Or(x, y)

    Notes
    =====

    The ``|`` operator is provided as a convenience, but note that its use
    here is different from its normal use in Python, which is bitwise
    or. Hence, ``Or(a, b)`` and ``a | b`` will return different things if
    ``a`` and ``b`` are integers.

    >>> Or(x, y).subs(x, 0)
    y

    """
    zero = true
    identity = false

    @classmethod
    def _new_args_filter(cls, args):
        newargs = []
        rel = []
        for x in args:
            if isinstance(x, Number) or x in (0, 1):
                newargs.append(True if x else False)
                continue
            if x.is_Relational:
                c = x.canonical
                if c in rel:
                    continue
                nc = (~c).canonical
                if any(r == nc for r in rel):
                    return [S.true]
                rel.append(c)
            newargs.append(x)
        return LatticeOp._new_args_filter(newargs, Or)

    def as_set(self):
        """
        Rewrite logic operators and relationals in terms of real sets.

        Examples
        ========

        >>> from sympy import Or, Symbol
        >>> x = Symbol('x', real=True)
        >>> Or(x>2, x<-2).as_set()
        (-oo, -2) U (2, oo)
        """
        from sympy.sets.sets import Union
        if len(self.free_symbols) == 1:
            return Union(*[arg.as_set() for arg in self.args])
        else:
            raise NotImplementedError("Sorry, Or.as_set has not yet been"
                                      " implemented for multivariate"
                                      " expressions")


class Not(BooleanFunction):
    """
    Logical Not function (negation)


    Returns True if the statement is False
    Returns False if the statement is True

    Examples
    ========

    >>> from sympy.logic.boolalg import Not, And, Or
    >>> from sympy.abc import x, A, B
    >>> Not(True)
    False
    >>> Not(False)
    True
    >>> Not(And(True, False))
    True
    >>> Not(Or(True, False))
    False
    >>> Not(And(And(True, x), Or(x, False)))
    Not(x)
    >>> ~x
    Not(x)
    >>> Not(And(Or(A, B), Or(~A, ~B)))
    Not(And(Or(A, B), Or(Not(A), Not(B))))

    Notes
    =====

    - The ``~`` operator is provided as a convenience, but note that its use
      here is different from its normal use in Python, which is bitwise
      not. In particular, ``~a`` and ``Not(a)`` will be different if ``a`` is
      an integer. Furthermore, since bools in Python subclass from ``int``,
      ``~True`` is the same as ``~1`` which is ``-2``, which has a boolean
      value of True.  To avoid this issue, use the SymPy boolean types
      ``true`` and ``false``.

    >>> from sympy import true
    >>> ~True
    -2
    >>> ~true
    False

    """

    is_Not = True

    @classmethod
    def eval(cls, arg):
        from sympy import (
            Equality, GreaterThan, LessThan,
            StrictGreaterThan, StrictLessThan, Unequality)
        if isinstance(arg, Number) or arg in (True, False):
            return false if arg else true
        if arg.is_Not:
            return arg.args[0]
        # Simplify Relational objects.
        if isinstance(arg, Equality):
            return Unequality(*arg.args)
        if isinstance(arg, Unequality):
            return Equality(*arg.args)
        if isinstance(arg, StrictLessThan):
            return GreaterThan(*arg.args)
        if isinstance(arg, StrictGreaterThan):
            return LessThan(*arg.args)
        if isinstance(arg, LessThan):
            return StrictGreaterThan(*arg.args)
        if isinstance(arg, GreaterThan):
            return StrictLessThan(*arg.args)

    def as_set(self):
        """
        Rewrite logic operators and relationals in terms of real sets.

        Examples
        ========

        >>> from sympy import Not, Symbol
        >>> x = Symbol('x', real=True)
        >>> Not(x>0).as_set()
        (-oo, 0]
        """
        if len(self.free_symbols) == 1:
            return self.args[0].as_set().complement(S.Reals)
        else:
            raise NotImplementedError("Sorry, Not.as_set has not yet been"
                                      " implemented for mutivariate"
                                      " expressions")

    def to_nnf(self, simplify=True):
        if is_literal(self):
            return self

        expr = self.args[0]

        func, args = expr.func, expr.args

        if func == And:
            return Or._to_nnf(*[~arg for arg in args], simplify=simplify)

        if func == Or:
            return And._to_nnf(*[~arg for arg in args], simplify=simplify)

        if func == Implies:
            a, b = args
            return And._to_nnf(a, ~b, simplify=simplify)

        if func == Equivalent:
            return And._to_nnf(Or(*args), Or(*[~arg for arg in args]), simplify=simplify)

        if func == Xor:
            result = []
            for i in range(1, len(args)+1, 2):
                for neg in combinations(args, i):
                    clause = [~s if s in neg else s for s in args]
                    result.append(Or(*clause))
            return And._to_nnf(*result, simplify=simplify)

        if func == ITE:
            a, b, c = args
            return And._to_nnf(Or(a, ~c), Or(~a, ~b), simplify=simplify)

        raise ValueError("Illegal operator %s in expression" % func)


class Xor(BooleanFunction):
    """
    Logical XOR (exclusive OR) function.


    Returns True if an odd number of the arguments are True and the rest are
    False.

    Returns False if an even number of the arguments are True and the rest are
    False.

    Examples
    ========

    >>> from sympy.logic.boolalg import Xor
    >>> from sympy import symbols
    >>> x, y = symbols('x y')
    >>> Xor(True, False)
    True
    >>> Xor(True, True)
    False
    >>> Xor(True, False, True, True, False)
    True
    >>> Xor(True, False, True, False)
    False
    >>> x ^ y
    Xor(x, y)

    Notes
    =====

    The ``^`` operator is provided as a convenience, but note that its use
    here is different from its normal use in Python, which is bitwise xor. In
    particular, ``a ^ b`` and ``Xor(a, b)`` will be different if ``a`` and
    ``b`` are integers.

    >>> Xor(x, y).subs(y, 0)
    x

    """
    def __new__(cls, *args, **kwargs):
        argset = set([])
        obj = super(Xor, cls).__new__(cls, *args, **kwargs)
        for arg in obj._args:
            if isinstance(arg, Number) or arg in (True, False):
                if arg:
                    arg = true
                else:
                    continue
            if isinstance(arg, Xor):
                for a in arg.args:
                    argset.remove(a) if a in argset else argset.add(a)
            elif arg in argset:
                argset.remove(arg)
            else:
                argset.add(arg)
        rel = [(r, r.canonical, (~r).canonical) for r in argset if r.is_Relational]
        odd = False  # is number of complimentary pairs odd? start 0 -> False
        remove = []
        for i, (r, c, nc) in enumerate(rel):
            for j in range(i + 1, len(rel)):
                rj, cj = rel[j][:2]
                if cj == nc:
                    odd = ~odd
                    break
                elif cj == c:
                    break
            else:
                continue
            remove.append((r, rj))
        if odd:
            argset.remove(true) if true in argset else argset.add(true)
        for a, b in remove:
            argset.remove(a)
            argset.remove(b)
        if len(argset) == 0:
            return false
        elif len(argset) == 1:
            return argset.pop()
        elif True in argset:
            argset.remove(True)
            return Not(Xor(*argset))
        else:
            obj._args = tuple(ordered(argset))
            obj._argset = frozenset(argset)
            return obj

    @property
    @cacheit
    def args(self):
        return tuple(ordered(self._argset))

    def to_nnf(self, simplify=True):
        args = []
        for i in range(0, len(self.args)+1, 2):
            for neg in combinations(self.args, i):
                clause = [~s if s in neg else s for s in self.args]
                args.append(Or(*clause))
        return And._to_nnf(*args, simplify=simplify)


class Nand(BooleanFunction):
    """
    Logical NAND function.

    It evaluates its arguments in order, giving True immediately if any
    of them are False, and False if they are all True.

    Returns True if any of the arguments are False
    Returns False if all arguments are True

    Examples
    ========

    >>> from sympy.logic.boolalg import Nand
    >>> from sympy import symbols
    >>> x, y = symbols('x y')
    >>> Nand(False, True)
    True
    >>> Nand(True, True)
    False
    >>> Nand(x, y)
    Not(And(x, y))

    """
    @classmethod
    def eval(cls, *args):
        return Not(And(*args))


class Nor(BooleanFunction):
    """
    Logical NOR function.

    It evaluates its arguments in order, giving False immediately if any
    of them are True, and True if they are all False.

    Returns False if any argument is True
    Returns True if all arguments are False

    Examples
    ========

    >>> from sympy.logic.boolalg import Nor
    >>> from sympy import symbols
    >>> x, y = symbols('x y')

    >>> Nor(True, False)
    False
    >>> Nor(True, True)
    False
    >>> Nor(False, True)
    False
    >>> Nor(False, False)
    True
    >>> Nor(x, y)
    Not(Or(x, y))

    """
    @classmethod
    def eval(cls, *args):
        return Not(Or(*args))


class Implies(BooleanFunction):
    """
    Logical implication.

    A implies B is equivalent to !A v B

    Accepts two Boolean arguments; A and B.
    Returns False if A is True and B is False
    Returns True otherwise.

    Examples
    ========

    >>> from sympy.logic.boolalg import Implies
    >>> from sympy import symbols
    >>> x, y = symbols('x y')

    >>> Implies(True, False)
    False
    >>> Implies(False, False)
    True
    >>> Implies(True, True)
    True
    >>> Implies(False, True)
    True
    >>> x >> y
    Implies(x, y)
    >>> y << x
    Implies(x, y)

    Notes
    =====

    The ``>>`` and ``<<`` operators are provided as a convenience, but note
    that their use here is different from their normal use in Python, which is
    bit shifts. Hence, ``Implies(a, b)`` and ``a >> b`` will return different
    things if ``a`` and ``b`` are integers.  In particular, since Python
    considers ``True`` and ``False`` to be integers, ``True >> True`` will be
    the same as ``1 >> 1``, i.e., 0, which has a truth value of False.  To
    avoid this issue, use the SymPy objects ``true`` and ``false``.

    >>> from sympy import true, false
    >>> True >> False
    1
    >>> true >> false
    False

    """
    @classmethod
    def eval(cls, *args):
        try:
            newargs = []
            for x in args:
                if isinstance(x, Number) or x in (0, 1):
                    newargs.append(True if x else False)
                else:
                    newargs.append(x)
            A, B = newargs
        except ValueError:
            raise ValueError(
                "%d operand(s) used for an Implies "
                "(pairs are required): %s" % (len(args), str(args)))
        if A == True or A == False or B == True or B == False:
            return Or(Not(A), B)
        elif A == B:
            return S.true
        elif A.is_Relational and B.is_Relational:
            if A.canonical == B.canonical:
                return S.true
            if (~A).canonical == B.canonical:
                return B
        else:
            return Basic.__new__(cls, *args)

    def to_nnf(self, simplify=True):
        a, b = self.args
        return Or._to_nnf(~a, b, simplify=simplify)


class Equivalent(BooleanFunction):
    """
    Equivalence relation.

    Equivalent(A, B) is True iff A and B are both True or both False

    Returns True if all of the arguments are logically equivalent.
    Returns False otherwise.

    Examples
    ========

    >>> from sympy.logic.boolalg import Equivalent, And
    >>> from sympy.abc import x, y
    >>> Equivalent(False, False, False)
    True
    >>> Equivalent(True, False, False)
    False
    >>> Equivalent(x, And(x, True))
    True
    """
    def __new__(cls, *args, **options):
        from sympy.core.relational import Relational
        args = [_sympify(arg) for arg in args]

        argset = set(args)
        for x in args:
            if isinstance(x, Number) or x in [True, False]: # Includes 0, 1
                argset.discard(x)
                argset.add(True if x else False)
        rel = []
        for r in argset:
            if isinstance(r, Relational):
                rel.append((r, r.canonical, (~r).canonical))
        remove = []
        for i, (r, c, nc) in enumerate(rel):
            for j in range(i + 1, len(rel)):
                rj, cj = rel[j][:2]
                if cj == nc:
                    return false
                elif cj == c:
                    remove.append((r, rj))
                    break
        for a, b in remove:
            argset.remove(a)
            argset.remove(b)
            argset.add(True)
        if len(argset) <= 1:
            return true
        if True in argset:
            argset.discard(True)
            return And(*argset)
        if False in argset:
            argset.discard(False)
            return And(*[~arg for arg in argset])
        _args = frozenset(argset)
        obj = super(Equivalent, cls).__new__(cls, _args)
        obj._argset = _args
        return obj

    @property
    @cacheit
    def args(self):
        return tuple(ordered(self._argset))

    def to_nnf(self, simplify=True):
        args = []
        for a, b in zip(self.args, self.args[1:]):
            args.append(Or(~a, b))
        args.append(Or(~self.args[-1], self.args[0]))
        return And._to_nnf(*args, simplify=simplify)


class ITE(BooleanFunction):
    """
    If then else clause.

    ITE(A, B, C) evaluates and returns the result of B if A is true
    else it returns the result of C

    Examples
    ========

    >>> from sympy.logic.boolalg import ITE, And, Xor, Or
    >>> from sympy.abc import x, y, z
    >>> ITE(True, False, True)
    False
    >>> ITE(Or(True, False), And(True, True), Xor(True, True))
    True
    >>> ITE(x, y, z)
    ITE(x, y, z)
    >>> ITE(True, x, y)
    x
    >>> ITE(False, x, y)
    y
    >>> ITE(x, y, y)
    y
    """
    @classmethod
    def eval(cls, *args):
        try:
            a, b, c = args
        except ValueError:
            raise ValueError("ITE expects exactly 3 arguments")
        if a == True:
            return b
        if a == False:
            return c
        if b == c:
            return b

    def to_nnf(self, simplify=True):
        a, b, c = self.args
        return And._to_nnf(Or(~a, b), Or(a, c), simplify=simplify)

    def _eval_derivative(self, x):
        return self.func(self.args[0], *[a.diff(x) for a in self.args[1:]])

    # the diff method below is copied from Expr class
    def diff(self, *symbols, **assumptions):
        new_symbols = list(map(sympify, symbols))  # e.g. x, 2, y, z
        assumptions.setdefault("evaluate", True)
        return Derivative(self, *new_symbols, **assumptions)


### end class definitions. Some useful methods


def conjuncts(expr):
    """Return a list of the conjuncts in the expr s.

    Examples
    ========

    >>> from sympy.logic.boolalg import conjuncts
    >>> from sympy.abc import A, B
    >>> conjuncts(A & B)
    frozenset([A, B])
    >>> conjuncts(A | B)
    frozenset([Or(A, B)])

    """
    return And.make_args(expr)


def disjuncts(expr):
    """Return a list of the disjuncts in the sentence s.

    Examples
    ========

    >>> from sympy.logic.boolalg import disjuncts
    >>> from sympy.abc import A, B
    >>> disjuncts(A | B)
    frozenset([A, B])
    >>> disjuncts(A & B)
    frozenset([And(A, B)])

    """
    return Or.make_args(expr)


def distribute_and_over_or(expr):
    """
    Given a sentence s consisting of conjunctions and disjunctions
    of literals, return an equivalent sentence in CNF.

    Examples
    ========

    >>> from sympy.logic.boolalg import distribute_and_over_or, And, Or, Not
    >>> from sympy.abc import A, B, C
    >>> distribute_and_over_or(Or(A, And(Not(B), Not(C))))
    And(Or(A, Not(B)), Or(A, Not(C)))
    """
    return _distribute((expr, And, Or))


def distribute_or_over_and(expr):
    """
    Given a sentence s consisting of conjunctions and disjunctions
    of literals, return an equivalent sentence in DNF.

    Note that the output is NOT simplified.

    Examples
    ========

    >>> from sympy.logic.boolalg import distribute_or_over_and, And, Or, Not
    >>> from sympy.abc import A, B, C
    >>> distribute_or_over_and(And(Or(Not(A), B), C))
    Or(And(B, C), And(C, Not(A)))
    """
    return _distribute((expr, Or, And))


def _distribute(info):
    """
    Distributes info[1] over info[2] with respect to info[0].
    """
    if info[0].func is info[2]:
        for arg in info[0].args:
            if arg.func is info[1]:
                conj = arg
                break
        else:
            return info[0]
        rest = info[2](*[a for a in info[0].args if a is not conj])
        return info[1](*list(map(_distribute,
            [(info[2](c, rest), info[1], info[2]) for c in conj.args])))
    elif info[0].func is info[1]:
        return info[1](*list(map(_distribute,
            [(x, info[1], info[2]) for x in info[0].args])))
    else:
        return info[0]


def to_nnf(expr, simplify=True):
    """
    Converts expr to Negation Normal Form.
    A logical expression is in Negation Normal Form (NNF) if it
    contains only And, Or and Not, and Not is applied only to literals.
    If simpify is True, the result contains no redundant clauses.

    Examples
    ========

    >>> from sympy.abc import A, B, C, D
    >>> from sympy.logic.boolalg import Not, Equivalent, to_nnf
    >>> to_nnf(Not((~A & ~B) | (C & D)))
    And(Or(A, B), Or(Not(C), Not(D)))
    >>> to_nnf(Equivalent(A >> B, B >> A))
    And(Or(A, And(A, Not(B)), Not(B)), Or(And(B, Not(A)), B, Not(A)))
    """
    if is_nnf(expr, simplify):
        return expr
    return expr.to_nnf(simplify)


def to_cnf(expr, simplify=False):
    """
    Convert a propositional logical sentence s to conjunctive normal form.
    That is, of the form ((A | ~B | ...) & (B | C | ...) & ...)
    If simplify is True, the expr is evaluated to its simplest CNF form.

    Examples
    ========

    >>> from sympy.logic.boolalg import to_cnf
    >>> from sympy.abc import A, B, D
    >>> to_cnf(~(A | B) | D)
    And(Or(D, Not(A)), Or(D, Not(B)))
    >>> to_cnf((A | B) & (A | ~A), True)
    Or(A, B)

    """
    expr = sympify(expr)
    if not isinstance(expr, BooleanFunction):
        return expr

    if simplify:
        return simplify_logic(expr, 'cnf', True)

    # Don't convert unless we have to
    if is_cnf(expr):
        return expr

    expr = eliminate_implications(expr)
    return distribute_and_over_or(expr)


def to_dnf(expr, simplify=False):
    """
    Convert a propositional logical sentence s to disjunctive normal form.
    That is, of the form ((A & ~B & ...) | (B & C & ...) | ...)
    If simplify is True, the expr is evaluated to its simplest DNF form.

    Examples
    ========

    >>> from sympy.logic.boolalg import to_dnf
    >>> from sympy.abc import A, B, C
    >>> to_dnf(B & (A | C))
    Or(And(A, B), And(B, C))
    >>> to_dnf((A & B) | (A & ~B) | (B & C) | (~B & C), True)
    Or(A, C)

    """
    expr = sympify(expr)
    if not isinstance(expr, BooleanFunction):
        return expr

    if simplify:
        return simplify_logic(expr, 'dnf', True)

    # Don't convert unless we have to
    if is_dnf(expr):
        return expr

    expr = eliminate_implications(expr)
    return distribute_or_over_and(expr)


def is_nnf(expr, simplified=True):
    """
    Checks if expr is in Negation Normal Form.
    A logical expression is in Negation Normal Form (NNF) if it
    contains only And, Or and Not, and Not is applied only to literals.
    If simpified is True, checks if result contains no redundant clauses.

    Examples
    ========

    >>> from sympy.abc import A, B, C
    >>> from sympy.logic.boolalg import Not, is_nnf
    >>> is_nnf(A & B | ~C)
    True
    >>> is_nnf((A | ~A) & (B | C))
    False
    >>> is_nnf((A | ~A) & (B | C), False)
    True
    >>> is_nnf(Not(A & B) | C)
    False
    >>> is_nnf((A >> B) & (B >> A))
    False
    """

    expr = sympify(expr)
    if is_literal(expr):
        return True

    stack = [expr]

    while stack:
        expr = stack.pop()
        if expr.func in (And, Or):
            if simplified:
                args = expr.args
                for arg in args:
                    if Not(arg) in args:
                        return False
            stack.extend(expr.args)

        elif not is_literal(expr):
            return False

    return True


def is_cnf(expr):
    """
    Test whether or not an expression is in conjunctive normal form.

    Examples
    ========

    >>> from sympy.logic.boolalg import is_cnf
    >>> from sympy.abc import A, B, C
    >>> is_cnf(A | B | C)
    True
    >>> is_cnf(A & B & C)
    True
    >>> is_cnf((A & B) | C)
    False

    """
    return _is_form(expr, And, Or)


def is_dnf(expr):
    """
    Test whether or not an expression is in disjunctive normal form.

    Examples
    ========

    >>> from sympy.logic.boolalg import is_dnf
    >>> from sympy.abc import A, B, C
    >>> is_dnf(A | B | C)
    True
    >>> is_dnf(A & B & C)
    True
    >>> is_dnf((A & B) | C)
    True
    >>> is_dnf(A & (B | C))
    False

    """
    return _is_form(expr, Or, And)


def _is_form(expr, function1, function2):
    """
    Test whether or not an expression is of the required form.

    """
    expr = sympify(expr)

    # Special case of an Atom
    if expr.is_Atom:
        return True

    # Special case of a single expression of function2
    if expr.func is function2:
        for lit in expr.args:
            if lit.func is Not:
                if not lit.args[0].is_Atom:
                    return False
            else:
                if not lit.is_Atom:
                    return False
        return True

    # Special case of a single negation
    if expr.func is Not:
        if not expr.args[0].is_Atom:
            return False

    if expr.func is not function1:
        return False

    for cls in expr.args:
        if cls.is_Atom:
            continue
        if cls.func is Not:
            if not cls.args[0].is_Atom:
                return False
        elif cls.func is not function2:
            return False
        for lit in cls.args:
            if lit.func is Not:
                if not lit.args[0].is_Atom:
                    return False
            else:
                if not lit.is_Atom:
                    return False

    return True


def eliminate_implications(expr):
    """
    Change >>, <<, and Equivalent into &, |, and ~. That is, return an
    expression that is equivalent to s, but has only &, |, and ~ as logical
    operators.

    Examples
    ========

    >>> from sympy.logic.boolalg import Implies, Equivalent, \
         eliminate_implications
    >>> from sympy.abc import A, B, C
    >>> eliminate_implications(Implies(A, B))
    Or(B, Not(A))
    >>> eliminate_implications(Equivalent(A, B))
    And(Or(A, Not(B)), Or(B, Not(A)))
    >>> eliminate_implications(Equivalent(A, B, C))
    And(Or(A, Not(C)), Or(B, Not(A)), Or(C, Not(B)))
    """
    return to_nnf(expr)


def is_literal(expr):
    """
    Returns True if expr is a literal, else False.

    Examples
    ========

    >>> from sympy import Or, Q
    >>> from sympy.abc import A, B
    >>> from sympy.logic.boolalg import is_literal
    >>> is_literal(A)
    True
    >>> is_literal(~A)
    True
    >>> is_literal(Q.zero(A))
    True
    >>> is_literal(A + B)
    True
    >>> is_literal(Or(A, B))
    False
    """
    if isinstance(expr, Not):
        return not isinstance(expr.args[0], BooleanFunction)
    else:
        return not isinstance(expr, BooleanFunction)


def to_int_repr(clauses, symbols):
    """
    Takes clauses in CNF format and puts them into an integer representation.

    Examples
    ========

    >>> from sympy.logic.boolalg import to_int_repr
    >>> from sympy.abc import x, y
    >>> to_int_repr([x | y, y], [x, y]) == [set([1, 2]), set([2])]
    True

    """

    # Convert the symbol list into a dict
    symbols = dict(list(zip(symbols, list(range(1, len(symbols) + 1)))))

    def append_symbol(arg, symbols):
        if arg.func is Not:
            return -symbols[arg.args[0]]
        else:
            return symbols[arg]

    return [set(append_symbol(arg, symbols) for arg in Or.make_args(c))
            for c in clauses]


def term_to_integer(term):
    """
    Return an integer corresponding to the base-2 digits given by ``term``.

    Parameters
    ==========

    term : a string or list of ones and zeros

    Examples
    ========

    >>> from sympy.logic.boolalg import term_to_integer
    >>> term_to_integer([1, 0, 0])
    4
    >>> term_to_integer('100')
    4

    """

    return int(''.join(list(map(str, list(term)))), 2)


def integer_to_term(k, n_bits=None):
    """
    Return a list of the base-2 digits in the integer, ``k``.

    Parameters
    ==========

    k : int
    n_bits : int
        If ``n_bits`` is given and the number of digits in the binary
        representation of ``k`` is smaller than ``n_bits`` then left-pad the
        list with 0s.

    Examples
    ========

    >>> from sympy.logic.boolalg import integer_to_term
    >>> integer_to_term(4)
    [1, 0, 0]
    >>> integer_to_term(4, 6)
    [0, 0, 0, 1, 0, 0]
    """

    s = '{0:0{1}b}'.format(abs(as_int(k)), as_int(abs(n_bits or 0)))
    return list(map(int, s))


def truth_table(expr, variables, input=True):
    """
    Return a generator of all possible configurations of the input variables,
    and the result of the boolean expression for those values.

    Parameters
    ==========

    expr : string or boolean expression
    variables : list of variables
    input : boolean (default True)
        indicates whether to return the input combinations.

    Examples
    ========

    >>> from sympy.logic.boolalg import truth_table
    >>> from sympy.abc import x,y
    >>> table = truth_table(x >> y, [x, y])
    >>> for t in table:
    ...     print('{0} -> {1}'.format(*t))
    [0, 0] -> True
    [0, 1] -> True
    [1, 0] -> False
    [1, 1] -> True

    >>> table = truth_table(x | y, [x, y])
    >>> list(table)
    [([0, 0], False), ([0, 1], True), ([1, 0], True), ([1, 1], True)]

    If input is false, truth_table returns only a list of truth values.
    In this case, the corresponding input values of variables can be
    deduced from the index of a given output.

    >>> from sympy.logic.boolalg import integer_to_term
    >>> vars = [y, x]
    >>> values = truth_table(x >> y, vars, input=False)
    >>> values = list(values)
    >>> values
    [True, False, True, True]

    >>> for i, value in enumerate(values):
    ...     print('{0} -> {1}'.format(list(zip(
    ...     vars, integer_to_term(i, len(vars)))), value))
    [(y, 0), (x, 0)] -> True
    [(y, 0), (x, 1)] -> False
    [(y, 1), (x, 0)] -> True
    [(y, 1), (x, 1)] -> True

    """
    variables = [sympify(v) for v in variables]

    expr = sympify(expr)
    if not isinstance(expr, BooleanFunction) and not is_literal(expr):
        return

    table = product([0, 1], repeat=len(variables))
    for term in table:
        term = list(term)
        value = expr.xreplace(dict(zip(variables, term)))

        if input:
            yield term, value
        else:
            yield value


def _check_pair(minterm1, minterm2):
    """
    Checks if a pair of minterms differs by only one bit. If yes, returns
    index, else returns -1.
    """
    index = -1
    for x, (i, j) in enumerate(zip(minterm1, minterm2)):
        if i != j:
            if index == -1:
                index = x
            else:
                return -1
    return index


def _convert_to_varsSOP(minterm, variables):
    """
    Converts a term in the expansion of a function from binary to it's
    variable form (for SOP).
    """
    temp = []
    for i, m in enumerate(minterm):
        if m == 0:
            temp.append(Not(variables[i]))
        elif m == 1:
            temp.append(variables[i])
        else:
            pass  # ignore the 3s
    return And(*temp)


def _convert_to_varsPOS(maxterm, variables):
    """
    Converts a term in the expansion of a function from binary to it's
    variable form (for POS).
    """
    temp = []
    for i, m in enumerate(maxterm):
        if m == 1:
            temp.append(Not(variables[i]))
        elif m == 0:
            temp.append(variables[i])
        else:
            pass  # ignore the 3s
    return Or(*temp)


def _simplified_pairs(terms):
    """
    Reduces a set of minterms, if possible, to a simplified set of minterms
    with one less variable in the terms using QM method.
    """
    simplified_terms = []
    todo = list(range(len(terms)))
    for i, ti in enumerate(terms[:-1]):
        for j_i, tj in enumerate(terms[(i + 1):]):
            index = _check_pair(ti, tj)
            if index != -1:
                todo[i] = todo[j_i + i + 1] = None
                newterm = ti[:]
                newterm[index] = 3
                if newterm not in simplified_terms:
                    simplified_terms.append(newterm)
    simplified_terms.extend(
        [terms[i] for i in [_ for _ in todo if _ is not None]])
    return simplified_terms


def _compare_term(minterm, term):
    """
    Return True if a binary term is satisfied by the given term. Used
    for recognizing prime implicants.
    """
    for i, x in enumerate(term):
        if x != 3 and x != minterm[i]:
            return False
    return True


def _rem_redundancy(l1, terms):
    """
    After the truth table has been sufficiently simplified, use the prime
    implicant table method to recognize and eliminate redundant pairs,
    and return the essential arguments.
    """
    essential = []
    for x in terms:
        temporary = []
        for y in l1:
            if _compare_term(x, y):
                temporary.append(y)
        if len(temporary) == 1:
            if temporary[0] not in essential:
                essential.append(temporary[0])
    for x in terms:
        for y in essential:
            if _compare_term(x, y):
                break
        else:
            for z in l1:
                if _compare_term(x, z):
                    if z not in essential:
                        essential.append(z)
                    break

    return essential


def SOPform(variables, minterms, dontcares=None):
    """
    The SOPform function uses simplified_pairs and a redundant group-
    eliminating algorithm to convert the list of all input combos that
    generate '1' (the minterms) into the smallest Sum of Products form.

    The variables must be given as the first argument.

    Return a logical Or function (i.e., the "sum of products" or "SOP"
    form) that gives the desired outcome. If there are inputs that can
    be ignored, pass them as a list, too.

    The result will be one of the (perhaps many) functions that satisfy
    the conditions.

    Examples
    ========

    >>> from sympy.logic import SOPform
    >>> from sympy import symbols
    >>> w, x, y, z = symbols('w x y z')
    >>> minterms = [[0, 0, 0, 1], [0, 0, 1, 1],
    ...             [0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 1, 1]]
    >>> dontcares = [[0, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 1]]
    >>> SOPform([w, x, y, z], minterms, dontcares)
    Or(And(Not(w), z), And(y, z))

    References
    ==========

    .. [1] en.wikipedia.org/wiki/Quine-McCluskey_algorithm

    """
    variables = [sympify(v) for v in variables]
    if minterms == []:
        return false

    minterms = [list(i) for i in minterms]
    dontcares = [list(i) for i in (dontcares or [])]
    for d in dontcares:
        if d in minterms:
            raise ValueError('%s in minterms is also in dontcares' % d)

    old = None
    new = minterms + dontcares
    while new != old:
        old = new
        new = _simplified_pairs(old)
    essential = _rem_redundancy(new, minterms)
    return Or(*[_convert_to_varsSOP(x, variables) for x in essential])


def POSform(variables, minterms, dontcares=None):
    """
    The POSform function uses simplified_pairs and a redundant-group
    eliminating algorithm to convert the list of all input combinations
    that generate '1' (the minterms) into the smallest Product of Sums form.

    The variables must be given as the first argument.

    Return a logical And function (i.e., the "product of sums" or "POS"
    form) that gives the desired outcome. If there are inputs that can
    be ignored, pass them as a list, too.

    The result will be one of the (perhaps many) functions that satisfy
    the conditions.

    Examples
    ========

    >>> from sympy.logic import POSform
    >>> from sympy import symbols
    >>> w, x, y, z = symbols('w x y z')
    >>> minterms = [[0, 0, 0, 1], [0, 0, 1, 1], [0, 1, 1, 1],
    ...             [1, 0, 1, 1], [1, 1, 1, 1]]
    >>> dontcares = [[0, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 1]]
    >>> POSform([w, x, y, z], minterms, dontcares)
    And(Or(Not(w), y), z)

    References
    ==========

    .. [1] en.wikipedia.org/wiki/Quine-McCluskey_algorithm

    """
    variables = [sympify(v) for v in variables]
    if minterms == []:
        return false

    minterms = [list(i) for i in minterms]
    dontcares = [list(i) for i in (dontcares or [])]
    for d in dontcares:
        if d in minterms:
            raise ValueError('%s in minterms is also in dontcares' % d)

    maxterms = []
    for t in product([0, 1], repeat=len(variables)):
        t = list(t)
        if (t not in minterms) and (t not in dontcares):
            maxterms.append(t)
    old = None
    new = maxterms + dontcares
    while new != old:
        old = new
        new = _simplified_pairs(old)
    essential = _rem_redundancy(new, maxterms)
    return And(*[_convert_to_varsPOS(x, variables) for x in essential])


def _find_predicates(expr):
    """Helper to find logical predicates in BooleanFunctions.

    A logical predicate is defined here as anything within a BooleanFunction
    that is not a BooleanFunction itself.

    """
    if not isinstance(expr, BooleanFunction):
        return set([expr])
    return set().union(*(_find_predicates(i) for i in expr.args))


def simplify_logic(expr, form=None, deep=True):
    """
    This function simplifies a boolean function to its simplified version
    in SOP or POS form. The return type is an Or or And object in SymPy.

    Parameters
    ==========

    expr : string or boolean expression
    form : string ('cnf' or 'dnf') or None (default).
        If 'cnf' or 'dnf', the simplest expression in the corresponding
        normal form is returned; if None, the answer is returned
        according to the form with fewest args (in CNF by default).
    deep : boolean (default True)
        indicates whether to recursively simplify any
        non-boolean functions contained within the input.

    Examples
    ========

    >>> from sympy.logic import simplify_logic
    >>> from sympy.abc import x, y, z
    >>> from sympy import S
    >>> b = (~x & ~y & ~z) | ( ~x & ~y & z)
    >>> simplify_logic(b)
    And(Not(x), Not(y))

    >>> S(b)
    Or(And(Not(x), Not(y), Not(z)), And(Not(x), Not(y), z))
    >>> simplify_logic(_)
    And(Not(x), Not(y))

    """

    if form == 'cnf' or form == 'dnf' or form is None:
        expr = sympify(expr)
        if not isinstance(expr, BooleanFunction):
            return expr
        variables = _find_predicates(expr)
        truthtable = []
        for t in product([0, 1], repeat=len(variables)):
            t = list(t)
            if expr.xreplace(dict(zip(variables, t))) == True:
                truthtable.append(t)
        if deep:
            from sympy.simplify.simplify import simplify
            variables = [simplify(v) for v in variables]
        if form == 'dnf' or \
           (form is None and len(truthtable) >= (2 ** (len(variables) - 1))):
            return SOPform(variables, truthtable)
        elif form == 'cnf' or form is None:
            return POSform(variables, truthtable)
    else:
        raise ValueError("form can be cnf or dnf only")


def _finger(eq):
    """
    Assign a 5-item fingerprint to each symbol in the equation:
    [
    # of times it appeared as a Symbol,
    # of times it appeared as a Not(symbol),
    # of times it appeared as a Symbol in an And or Or,
    # of times it appeared as a Not(Symbol) in an And or Or,
    sum of the number of arguments with which it appeared,
    counting Symbol as 1 and Not(Symbol) as 2
    ]

    >>> from sympy.logic.boolalg import _finger as finger
    >>> from sympy import And, Or, Not
    >>> from sympy.abc import a, b, x, y
    >>> eq = Or(And(Not(y), a), And(Not(y), b), And(x, y))
    >>> dict(finger(eq))
    {(0, 0, 1, 0, 2): [x], (0, 0, 1, 0, 3): [a, b], (0, 0, 1, 2, 8): [y]}

    So y and x have unique fingerprints, but a and b do not.
    """
    f = eq.free_symbols
    d = dict(list(zip(f, [[0] * 5 for fi in f])))
    for a in eq.args:
        if a.is_Symbol:
            d[a][0] += 1
        elif a.is_Not:
            d[a.args[0]][1] += 1
        else:
            o = len(a.args) + sum(ai.func is Not for ai in a.args)
            for ai in a.args:
                if ai.is_Symbol:
                    d[ai][2] += 1
                    d[ai][-1] += o
                else:
                    d[ai.args[0]][3] += 1
                    d[ai.args[0]][-1] += o
    inv = defaultdict(list)
    for k, v in ordered(iter(d.items())):
        inv[tuple(v)].append(k)
    return inv


def bool_map(bool1, bool2):
    """
    Return the simplified version of bool1, and the mapping of variables
    that makes the two expressions bool1 and bool2 represent the same
    logical behaviour for some correspondence between the variables
    of each.
    If more than one mappings of this sort exist, one of them
    is returned.
    For example, And(x, y) is logically equivalent to And(a, b) for
    the mapping {x: a, y:b} or {x: b, y:a}.
    If no such mapping exists, return False.

    Examples
    ========

    >>> from sympy import SOPform, bool_map, Or, And, Not, Xor
    >>> from sympy.abc import w, x, y, z, a, b, c, d
    >>> function1 = SOPform([x, z, y],[[1, 0, 1], [0, 0, 1]])
    >>> function2 = SOPform([a, b, c],[[1, 0, 1], [1, 0, 0]])
    >>> bool_map(function1, function2)
    (And(Not(z), y), {y: a, z: b})

    The results are not necessarily unique, but they are canonical. Here,
    ``(w, z)`` could be ``(a, d)`` or ``(d, a)``:

    >>> eq =  Or(And(Not(y), w), And(Not(y), z), And(x, y))
    >>> eq2 = Or(And(Not(c), a), And(Not(c), d), And(b, c))
    >>> bool_map(eq, eq2)
    (Or(And(Not(y), w), And(Not(y), z), And(x, y)), {w: a, x: b, y: c, z: d})
    >>> eq = And(Xor(a, b), c, And(c,d))
    >>> bool_map(eq, eq.subs(c, x))
    (And(Or(Not(a), Not(b)), Or(a, b), c, d), {a: a, b: b, c: d, d: x})

    """

    def match(function1, function2):
        """Return the mapping that equates variables between two
        simplified boolean expressions if possible.

        By "simplified" we mean that a function has been denested
        and is either an And (or an Or) whose arguments are either
        symbols (x), negated symbols (Not(x)), or Or (or an And) whose
        arguments are only symbols or negated symbols. For example,
        And(x, Not(y), Or(w, Not(z))).

        Basic.match is not robust enough (see issue 4835) so this is
        a workaround that is valid for simplified boolean expressions
        """

        # do some quick checks
        if function1.__class__ != function2.__class__:
            return None
        if len(function1.args) != len(function2.args):
            return None
        if function1.is_Symbol:
            return {function1: function2}

        # get the fingerprint dictionaries
        f1 = _finger(function1)
        f2 = _finger(function2)

        # more quick checks
        if len(f1) != len(f2):
            return False

        # assemble the match dictionary if possible
        matchdict = {}
        for k in f1.keys():
            if k not in f2:
                return False
            if len(f1[k]) != len(f2[k]):
                return False
            for i, x in enumerate(f1[k]):
                matchdict[x] = f2[k][i]
        return matchdict

    a = simplify_logic(bool1)
    b = simplify_logic(bool2)
    m = match(a, b)
    if m:
        return a, m
    return m is not None
