from typing import Tuple
import weakref
import itertools

from .. import errors


class ModelCache:
    _defaults = { 0, 0.0, True }

    def __init__(self, model):
        self.model = model
        self.replacements = weakref.WeakKeyDictionary()
        self.constraint_only_replacements = weakref.WeakKeyDictionary()

    def __hash__(self):
        if not hasattr(self, '_hash'):
            self._hash = hash(frozenset(self.model.items())) #pylint:disable=attribute-defined-outside-init
        return self._hash

    def __eq__(self, other):
        return self.model == other.model

    def __getstate__(self):
        return (self.model,)

    def __setstate__(self, s):
        self.model = s[0]
        self.replacements = weakref.WeakKeyDictionary()
        self.constraint_only_replacements = weakref.WeakKeyDictionary()

    #
    # Splitting support
    #

    def filter(self, variables):
        return ModelCache({ k:self.model[k] for k in self.model if k in variables })

    @staticmethod
    def combine(*models):
        return ModelCache(dict(itertools.chain.from_iterable(m.model.items() for m in models)))

    #
    # Model-driven evaluation
    #

    def _leaf_op(self, a):
        return (
            all_operations.BVV(self.model.get(a.args[0], 0), a.length) if a.op == 'BVS' else
            all_operations.BoolV(self.model.get(a.args[0], True)) if a.op == 'BoolS' else
            all_operations.FPV(self.model.get(a.args[0], 0.0), a.args[1]) if a.op == 'FPS' else
            all_operations.StringV(self.model.get(a.args[0], "")) if a.op == 'StringS' else
            a
        )

    def _leaf_op_existonly(self, a):
        return (
            all_operations.BVV(self.model[a.args[0]], a.length) if a.op == 'BVS' else
            all_operations.BoolV(self.model[a.args[0]]) if a.op == 'BoolS' else
            all_operations.FPV(self.model[a.args[0]], a.args[1]) if a.op == 'FPS' else
            all_operations.StringV(self.model[a.args[0]]) if a.op == 'StringS' else
            a
        )

    def eval_ast(self, ast, allow_unconstrained: bool=True):
        """
        Eval the ast, replacing symbols by their last value in the model.

        :param ast:                 The AST to evaluate.
        :param allow_unconstrained: When set to True, we will treat non-existent variables as unconstrained variables
                                    and will use arbitrary concrete values for them during evaluation. Otherwise, raise
                                    KeyErrors for non-existent variables.
        """

        if allow_unconstrained:
            new_ast = ast.replace_dict(self.replacements, leaf_operation=self._leaf_op)
        else:
            new_ast = ast.replace_dict(self.constraint_only_replacements, leaf_operation=self._leaf_op_existonly)
        return backends.concrete.eval(new_ast, 1)[0]

    def eval_constraints(self, constraints):
        """Returns whether the constraints is satisfied trivially by using the
        last model."""
        # eval_ast is concretizing symbols and evaluating them, this can raise
        # exceptions.
        try:
            return all(self.eval_ast(c) for c in constraints)
        except errors.ClaripyZeroDivisionError:
            return False

    def eval_list(self, asts, allow_unconstrained: bool=True) -> Tuple:
        """
        Evaluate a list of ASTs.

        :param asts:                A list of ASTs to evaluate.
        :param allow_unconstrained: When set to True, we will treat non-existent variables as unconstrained variables
                                    and will use arbitrary concrete values for them during evaluation. Otherwise, raise
                                    KeyErrors for non-existent variables.
        :return:                    A tuple of evaluated results, one element per AST.
        """

        return tuple(self.eval_ast(c, allow_unconstrained=allow_unconstrained) for c in asts)


class ModelCacheMixin:
    def __init__(self, *args, **kwargs):
        super(ModelCacheMixin, self).__init__(*args, **kwargs)
        self._models = set()
        self._exhausted = False
        self._eval_exhausted = weakref.WeakSet()
        self._max_exhausted = weakref.WeakSet()
        self._min_exhausted = weakref.WeakSet()
        self._max_signed_exhausted = weakref.WeakSet()
        self._min_signed_exhausted = weakref.WeakSet()

    def _blank_copy(self, c):
        super(ModelCacheMixin, self)._blank_copy(c)
        c._models = set()
        c._exhausted = False
        c._eval_exhausted = weakref.WeakSet()
        c._max_exhausted = weakref.WeakSet()
        c._min_exhausted = weakref.WeakSet()
        c._max_signed_exhausted = weakref.WeakSet()
        c._min_signed_exhausted = weakref.WeakSet()

    def _copy(self, c):
        super(ModelCacheMixin, self)._copy(c)
        c._models = set(self._models)
        c._exhausted = self._exhausted
        c._eval_exhausted = weakref.WeakSet(self._eval_exhausted)
        c._max_exhausted = weakref.WeakSet(self._max_exhausted)
        c._min_exhausted = weakref.WeakSet(self._min_exhausted)
        c._max_signed_exhausted = weakref.WeakSet(self._max_signed_exhausted)
        c._min_signed_exhausted = weakref.WeakSet(self._min_signed_exhausted)

    def __setstate__(self, base_state):
        super().__setstate__(base_state)
        self._models = set()
        self._exhausted = False
        self._eval_exhausted = weakref.WeakSet()
        self._max_exhausted = weakref.WeakSet()
        self._min_exhausted = weakref.WeakSet()
        self._max_signed_exhausted = weakref.WeakSet()
        self._min_signed_exhausted = weakref.WeakSet()

    #
    # Model cleaning
    #

    def simplify(self, *args, **kwargs):
        results = super(ModelCacheMixin, self).simplify(*args, **kwargs)
        if len(results) > 0 and any(c is false for c in results):
            self._models.clear()
        return results

    def _trivial_model_optimization(self):
        c = self.constraints[0]
        if not (
            c.depth == 2 and
            c.op == '__eq__' and
            len(c.variables) == 1 and
            c.args[0].symbolic and not c.args[1].symbolic and
            c.args[0].op == 'BVS'
        ):
            return

        self._models.add(ModelCache({
            next(iter(c.args[0].variables)): backends.concrete.eval(c.args[1], 1)[0]
        }))
        self._eval_exhausted.add(c.args[0].cache_key)
        self._max_exhausted.add(c.args[0].cache_key)
        self._min_exhausted.add(c.args[0].cache_key)
        self._max_signed_exhausted.add(c.args[0].cache_key)
        self._min_signed_exhausted.add(c.args[0].cache_key)

    def add(self, constraints, invalidate_cache=True, **kwargs):
        if len(constraints) == 0:
            return constraints

        old_vars = frozenset(self.variables)
        added = super(ModelCacheMixin, self).add(constraints, **kwargs)
        if len(added) == 0:
            return added

        if len(self.constraints) == 1 and len(self._models) == 0:
            self._trivial_model_optimization()

        new_vars = any(a.variables - old_vars for a in added)
        if new_vars or invalidate_cache:
            # shortcut for unsat
            if any(c is false for c in constraints):
                self._models.clear()

            still_valid = set(self._get_models(extra_constraints=added))
            if len(still_valid) != len(self._models):
                self._exhausted = False
                self._eval_exhausted.clear()
                self._max_exhausted.clear()
                self._min_exhausted.clear()
                self._max_signed_exhausted.clear()
                self._min_signed_exhausted.clear()
                self._models = still_valid

        return added

    def split(self):
        results = super(ModelCacheMixin, self).split()
        for r in results:
            r._models = { m.filter(r.variables) for m in self._models }
        return results

    def combine(self, others):
        combined = super(ModelCacheMixin, self).combine(others)

        if any(len(o._models) == 0 for o in others) or len(self._models) == 0:
            # this would need a solve anyways, so screw it
            return combined

        vars_count = len(self.variables) + sum(len(s.variables) for s in others)
        all_vars = self.variables.union(*[s.variables for s in others])
        if vars_count != len(all_vars):
            # this is the case where there are variables missing from the models.
            # We'll need more intelligence here to handle it
            return combined

        model_lists = [ self._models ]
        model_lists.extend(o._models for o in others)
        combined._models.update(
            ModelCache.combine(*product) for product in
            itertools.islice(itertools.product(*model_lists), len(self._models))
        )
        return combined

    def update(self, other):
        """
        Updates this cache mixin with results discovered by the other split off one.
        """

        acceptable_models = [ m for m in other._models if set(m.model.keys()) == self.variables ]
        self._models.update(acceptable_models)
        self._eval_exhausted.update(other._eval_exhausted)
        self._max_exhausted.update(other._max_exhausted)
        self._min_exhausted.update(other._min_exhausted)
        self._max_signed_exhausted.update(other._max_signed_exhausted)
        self._min_signed_exhausted.update(other._min_signed_exhausted)

    #
    # Cache retrieval
    #

    def _model_hook(self, m):
        # Z3 might give us solutions for variables that we did not ask for. so we create a new dict with solutions for
        # only the variables that are under the solver's control
        m_ = dict((k, v) for k, v in m.items() if k in self.variables)
        if m_:
            model = ModelCache(m_)
            self._models.add(model)

    def _get_models(self, extra_constraints=()):
        for m in self._models:
            if m.eval_constraints(extra_constraints):
                yield m

    def _get_batch_solutions(self, asts, n=None, extra_constraints=(), allow_unconstrained=True):
        results = set()

        for m in self._get_models(extra_constraints):
            try:
                results.add(m.eval_list(asts, allow_unconstrained=allow_unconstrained))
            except (ZeroDivisionError, KeyError):
                continue
            if len(results) == n:
                break

        return results

    def _get_solutions(self, e, n=None, extra_constraints=(), allow_unconstrained=True):
        return tuple(v[0] for v in self._get_batch_solutions(
            [e], n=n, extra_constraints=extra_constraints, allow_unconstrained=allow_unconstrained,
        ))


    #
    # Cached functions
    #


    def satisfiable(self, extra_constraints=(), **kwargs):
        for _ in self._get_models(extra_constraints=extra_constraints):
            return True
        return super(ModelCacheMixin, self).satisfiable(extra_constraints=extra_constraints, **kwargs)

    def batch_eval(self, asts, n, extra_constraints=(), **kwargs):
        results = self._get_batch_solutions(asts, n=n, extra_constraints=extra_constraints)

        if len(results) == n or (len(asts) == 1 and asts[0].cache_key in self._eval_exhausted):
            return results

        remaining = n - len(results)

        # TODO: faster to concat?
        if len(results) != 0:
            constraints = (all_operations.And(*[
                all_operations.Or(*[a!=v for a,v in zip(asts, r)]) for r in results
            ]),) + tuple(extra_constraints)
        else:
            constraints = extra_constraints

        try:
            results.update(super(ModelCacheMixin, self).batch_eval(
                asts, remaining, extra_constraints=constraints, **kwargs
            ))
        except UnsatError:
            if len(results) == 0:
                raise

        if len(extra_constraints) == 0 and len(results) < n:
            for e in asts:
                # only mark an AST as eval-exhausted if e.variables is a subset of variables that the current solver
                # knows about (from its constraints)
                if self.variables.issuperset(e.variables):
                    self._eval_exhausted.add(e.cache_key)

        return results

    def eval(self, e, n, **kwargs):
        return tuple( r[0] for r in ModelCacheMixin.batch_eval(self, [e], n=n, **kwargs) )

    def min(self, e, extra_constraints=(), signed=False, **kwargs):
        cached = [ ]
        if e.cache_key in self._eval_exhausted or e.cache_key in self._min_exhausted:
            # we set allow_unconstrained to False because we expect all returned values for e are returned by Z3,
            # instead of some arbitrarily assigned concrete values.
            cached = self._get_solutions(e, extra_constraints=extra_constraints, allow_unconstrained=False)

        if len(cached) > 0:
            signed_key = lambda v: v if v < 2**(len(e)-1) else v - 2**len(e)
            return min(cached, key=signed_key if signed else lambda v: v)
        else:
            m = super(ModelCacheMixin, self).min(e, extra_constraints=extra_constraints, signed=signed, **kwargs)
            if len(extra_constraints) == 0:
                (self._min_signed_exhausted if signed else self._min_exhausted).add(e.cache_key)
            return m

    def max(self, e, extra_constraints=(), signed=False, **kwargs):
        cached = [ ]
        if e.cache_key in self._eval_exhausted or e.cache_key in self._max_exhausted:
            cached = self._get_solutions(e, extra_constraints=extra_constraints, allow_unconstrained=False)

        if len(cached) > 0:
            signed_key = lambda v: v if v < 2**(len(e)-1) else v - 2**len(e)
            return max(cached, key=signed_key if signed else lambda v: v)
        else:
            m = super(ModelCacheMixin, self).max(e, extra_constraints=extra_constraints, signed=signed, **kwargs)
            if len(extra_constraints) == 0:
                (self._max_signed_exhausted if signed else self._max_exhausted).add(e.cache_key)
            return m

    def solution(self, e, v, extra_constraints=(), **kwargs):
        if isinstance(v, Base):
            cached = self._get_batch_solutions([e,v], extra_constraints=extra_constraints)
            if any(ec == vc for ec,vc in cached):
                return True
        else:
            cached = self._get_solutions(e, extra_constraints=extra_constraints)
            if v in cached:
                return True

        return super(ModelCacheMixin, self).solution(e, v, extra_constraints=extra_constraints, **kwargs)


from .. import backends, false
from ..errors import UnsatError
from ..ast import all_operations, Base
