"""Implementation of DPLL algorithm

Further improvements: eliminate calls to pl_true, implement branching rules,
efficient unit propagation.

References:
  - http://en.wikipedia.org/wiki/DPLL_algorithm
  - https://www.researchgate.net/publication/242384772_Implementations_of_the_DPLL_Algorithm
"""
from __future__ import print_function, division

from sympy.core.compatibility import range
from sympy import default_sort_key
from sympy.logic.boolalg import Or, Not, conjuncts, disjuncts, to_cnf, \
    to_int_repr, _find_predicates
from sympy.logic.inference import pl_true, literal_symbol


def dpll_satisfiable(expr):
    """
    Check satisfiability of a propositional sentence.
    It returns a model rather than True when it succeeds

    >>> from sympy.abc import A, B
    >>> from sympy.logic.algorithms.dpll import dpll_satisfiable
    >>> dpll_satisfiable(A & ~B)
    {A: True, B: False}
    >>> dpll_satisfiable(A & ~A)
    False

    """
    clauses = conjuncts(to_cnf(expr))
    if False in clauses:
        return False
    symbols = sorted(_find_predicates(expr), key=default_sort_key)
    symbols_int_repr = set(range(1, len(symbols) + 1))
    clauses_int_repr = to_int_repr(clauses, symbols)
    result = dpll_int_repr(clauses_int_repr, symbols_int_repr, {})
    if not result:
        return result
    output = {}
    for key in result:
        output.update({symbols[key - 1]: result[key]})
    return output


def dpll(clauses, symbols, model):
    """
    Compute satisfiability in a partial model.
    Clauses is an array of conjuncts.

    >>> from sympy.abc import A, B, D
    >>> from sympy.logic.algorithms.dpll import dpll
    >>> dpll([A, B, D], [A, B], {D: False})
    False

    """
    # compute DP kernel
    P, value = find_unit_clause(clauses, model)
    while P:
        model.update({P: value})
        symbols.remove(P)
        if not value:
            P = ~P
        clauses = unit_propagate(clauses, P)
        P, value = find_unit_clause(clauses, model)
    P, value = find_pure_symbol(symbols, clauses)
    while P:
        model.update({P: value})
        symbols.remove(P)
        if not value:
            P = ~P
        clauses = unit_propagate(clauses, P)
        P, value = find_pure_symbol(symbols, clauses)
    # end DP kernel
    unknown_clauses = []
    for c in clauses:
        val = pl_true(c, model)
        if val is False:
            return False
        if val is not True:
            unknown_clauses.append(c)
    if not unknown_clauses:
        return model
    if not clauses:
        return model
    P = symbols.pop()
    model_copy = model.copy()
    model.update({P: True})
    model_copy.update({P: False})
    symbols_copy = symbols[:]
    return (dpll(unit_propagate(unknown_clauses, P), symbols, model) or
            dpll(unit_propagate(unknown_clauses, Not(P)), symbols_copy, model_copy))


def dpll_int_repr(clauses, symbols, model):
    """
    Compute satisfiability in a partial model.
    Arguments are expected to be in integer representation

    >>> from sympy.logic.algorithms.dpll import dpll_int_repr
    >>> dpll_int_repr([{1}, {2}, {3}], {1, 2}, {3: False})
    False

    """
    # compute DP kernel
    P, value = find_unit_clause_int_repr(clauses, model)
    while P:
        model.update({P: value})
        symbols.remove(P)
        if not value:
            P = -P
        clauses = unit_propagate_int_repr(clauses, P)
        P, value = find_unit_clause_int_repr(clauses, model)
    P, value = find_pure_symbol_int_repr(symbols, clauses)
    while P:
        model.update({P: value})
        symbols.remove(P)
        if not value:
            P = -P
        clauses = unit_propagate_int_repr(clauses, P)
        P, value = find_pure_symbol_int_repr(symbols, clauses)
    # end DP kernel
    unknown_clauses = []
    for c in clauses:
        val = pl_true_int_repr(c, model)
        if val is False:
            return False
        if val is not True:
            unknown_clauses.append(c)
    if not unknown_clauses:
        return model
    P = symbols.pop()
    model_copy = model.copy()
    model.update({P: True})
    model_copy.update({P: False})
    symbols_copy = symbols.copy()
    return (dpll_int_repr(unit_propagate_int_repr(unknown_clauses, P), symbols, model) or
            dpll_int_repr(unit_propagate_int_repr(unknown_clauses, -P), symbols_copy, model_copy))

### helper methods for DPLL


def pl_true_int_repr(clause, model={}):
    """
    Lightweight version of pl_true.
    Argument clause represents the set of args of an Or clause. This is used
    inside dpll_int_repr, it is not meant to be used directly.

    >>> from sympy.logic.algorithms.dpll import pl_true_int_repr
    >>> pl_true_int_repr({1, 2}, {1: False})
    >>> pl_true_int_repr({1, 2}, {1: False, 2: False})
    False

    """
    result = False
    for lit in clause:
        if lit < 0:
            p = model.get(-lit)
            if p is not None:
                p = not p
        else:
            p = model.get(lit)
        if p is True:
            return True
        elif p is None:
            result = None
    return result


def unit_propagate(clauses, symbol):
    """
    Returns an equivalent set of clauses
    If a set of clauses contains the unit clause l, the other clauses are
    simplified by the application of the two following rules:

      1. every clause containing l is removed
      2. in every clause that contains ~l this literal is deleted

    Arguments are expected to be in CNF.

    >>> from sympy import symbols
    >>> from sympy.abc import A, B, D
    >>> from sympy.logic.algorithms.dpll import unit_propagate
    >>> unit_propagate([A | B, D | ~B, B], B)
    [D, B]

    """
    output = []
    for c in clauses:
        if c.func != Or:
            output.append(c)
            continue
        for arg in c.args:
            if arg == ~symbol:
                output.append(Or(*[x for x in c.args if x != ~symbol]))
                break
            if arg == symbol:
                break
        else:
            output.append(c)
    return output


def unit_propagate_int_repr(clauses, s):
    """
    Same as unit_propagate, but arguments are expected to be in integer
    representation

    >>> from sympy.logic.algorithms.dpll import unit_propagate_int_repr
    >>> unit_propagate_int_repr([{1, 2}, {3, -2}, {2}], 2)
    [{3}]

    """
    negated = {-s}
    return [clause - negated for clause in clauses if s not in clause]


def find_pure_symbol(symbols, unknown_clauses):
    """
    Find a symbol and its value if it appears only as a positive literal
    (or only as a negative) in clauses.

    >>> from sympy import symbols
    >>> from sympy.abc import A, B, D
    >>> from sympy.logic.algorithms.dpll import find_pure_symbol
    >>> find_pure_symbol([A, B, D], [A|~B,~B|~D,D|A])
    (A, True)

    """
    for sym in symbols:
        found_pos, found_neg = False, False
        for c in unknown_clauses:
            if not found_pos and sym in disjuncts(c):
                found_pos = True
            if not found_neg and Not(sym) in disjuncts(c):
                found_neg = True
        if found_pos != found_neg:
            return sym, found_pos
    return None, None


def find_pure_symbol_int_repr(symbols, unknown_clauses):
    """
    Same as find_pure_symbol, but arguments are expected
    to be in integer representation

    >>> from sympy.logic.algorithms.dpll import find_pure_symbol_int_repr
    >>> find_pure_symbol_int_repr({1,2,3},
    ...     [{1, -2}, {-2, -3}, {3, 1}])
    (1, True)

    """
    all_symbols = set().union(*unknown_clauses)
    found_pos = all_symbols.intersection(symbols)
    found_neg = all_symbols.intersection([-s for s in symbols])
    for p in found_pos:
        if -p not in found_neg:
            return p, True
    for p in found_neg:
        if -p not in found_pos:
            return -p, False
    return None, None


def find_unit_clause(clauses, model):
    """
    A unit clause has only 1 variable that is not bound in the model.

    >>> from sympy import symbols
    >>> from sympy.abc import A, B, D
    >>> from sympy.logic.algorithms.dpll import find_unit_clause
    >>> find_unit_clause([A | B | D, B | ~D, A | ~B], {A:True})
    (B, False)

    """
    for clause in clauses:
        num_not_in_model = 0
        for literal in disjuncts(clause):
            sym = literal_symbol(literal)
            if sym not in model:
                num_not_in_model += 1
                P, value = sym, not (literal.func is Not)
        if num_not_in_model == 1:
            return P, value
    return None, None


def find_unit_clause_int_repr(clauses, model):
    """
    Same as find_unit_clause, but arguments are expected to be in
    integer representation.

    >>> from sympy.logic.algorithms.dpll import find_unit_clause_int_repr
    >>> find_unit_clause_int_repr([{1, 2, 3},
    ...     {2, -3}, {1, -2}], {1: True})
    (2, False)

    """
    bound = set(model) | set(-sym for sym in model)
    for clause in clauses:
        unbound = clause - bound
        if len(unbound) == 1:
            p = unbound.pop()
            if p < 0:
                return -p, False
            else:
                return p, True
    return None, None
