# Source code for rdflib.plugins.sparql.evaluate

    """
    These method recursively evaluate the SPARQL Algebra

    evalQuery is the entry-point, it will setup context and
    return the SPARQLResult object

    evalPart is called on each level and will delegate to the right method

    A rdflib.plugins.sparql.sparql.QueryContext is passed along, keeping
    information needed for evaluation

    A list of dicts (solution mappings) is returned, apart from GroupBy which may
    also return a dict of list of dicts

    """

    import collections

    from rdflib import Variable, Graph, BNode, URIRef, Literal

    from rdflib.plugins.sparql import CUSTOM_EVALS
    from rdflib.plugins.sparql.parserutils import value
    from rdflib.plugins.sparql.sparql import (
        QueryContext, AlreadyBound, FrozenBindings, SPARQLError)
    from rdflib.plugins.sparql.evalutils import (
        _filter, _eval, _join, _diff, _minus, _fillTemplate, _ebv, _val)

    from rdflib.plugins.sparql.aggregates import Aggregator
    from rdflib.plugins.sparql.algebra import Join, ToMultiSet, Values

    def evalBGP(ctx, bgp):

        """
        A basic graph pattern
        """

        if not bgp:
            yield ctx.solution()
            return

        s, p, o = bgp[0]

        _s = ctx[s]
        _p = ctx[p]
        _o = ctx[o]

        for ss, sp, so in ctx.graph.triples((_s, _p, _o)):
            if None in (_s, _p, _o):
                c = ctx.push()
            else:
                c = ctx

            if _s is None:
                c[s] = ss

            try:
                if _p is None:
                    c[p] = sp
            except AlreadyBound:
                continue

            try:
                if _o is None:
                    c[o] = so
            except AlreadyBound:
                continue

            for x in evalBGP(c, bgp[1:]):
                yield x

    def evalExtend(ctx, extend):
        # TODO: Deal with dict returned from evalPart from GROUP BY

        for c in evalPart(ctx, extend.p):
            try:
                e = _eval(extend.expr, c.forget(ctx, _except=extend._vars))
                if isinstance(e, SPARQLError):
                    raise e

                yield c.merge({extend.var: e})

            except SPARQLError:
                yield c

    def evalLazyJoin(ctx, join):
        """
        A lazy join will push the variables bound
        in the first part to the second part,
        essentially doing the join implicitly
        hopefully evaluating much fewer triples
        """
        for a in evalPart(ctx, join.p1):
            c = ctx.thaw(a)
            for b in evalPart(c, join.p2):
                yield b.merge(a) # merge, as some bindings may have been forgotten

    def evalJoin(ctx, join):

        # TODO: Deal with dict returned from evalPart from GROUP BY
        # only ever for join.p1

        if join.lazy:
            return evalLazyJoin(ctx, join)
        else:
            a = evalPart(ctx, join.p1)
            b = set(evalPart(ctx, join.p2))
            return _join(a, b)

    def evalUnion(ctx, union):
        res = set()
        for x in evalPart(ctx, union.p1):
            res.add(x)
            yield x
        for x in evalPart(ctx, union.p2):
            if x not in res:
                yield x

    def evalMinus(ctx, minus):
        a = evalPart(ctx, minus.p1)
        b = set(evalPart(ctx, minus.p2))
        return _minus(a, b)

    def evalLeftJoin(ctx, join):
        # import pdb; pdb.set_trace()
        for a in evalPart(ctx, join.p1):
            ok = False
            c = ctx.thaw(a)
            for b in evalPart(c, join.p2):
                if _ebv(join.expr, b.forget(ctx)):
                    ok = True
                    yield b
            if not ok:
                # we've cheated, the ctx above may contain
                # vars bound outside our scope
                # before we yield a solution without the OPTIONAL part
                # check that we would have had no OPTIONAL matches
                # even without prior bindings...
                p1_vars = join.p1._vars
                if p1_vars is None \
                or not any(_ebv(join.expr, b) for b in
                           evalPart(ctx.thaw(a.remember(p1_vars)), join.p2)):

                    yield a

    def evalFilter(ctx, part):
        # TODO: Deal with dict returned from evalPart!
        for c in evalPart(ctx, part.p):
            if _ebv(part.expr, c.forget(ctx, _except=part._vars) if not part.no_isolated_scope else c):
                yield c

    def evalGraph(ctx, part):

        if ctx.dataset is None:
            raise Exception(
                "Non-conjunctive-graph doesn't know about " +
                "graphs. Try a query without GRAPH.")

        ctx = ctx.clone()
        graph = ctx[part.term]
        if graph is None:

            for graph in ctx.dataset.contexts():

                # in SPARQL the default graph is NOT a named graph
                if graph == ctx.dataset.default_context:
                    continue

                c = ctx.pushGraph(graph)
                c = c.push()
                graphSolution = [{part.term: graph.identifier}]
                for x in _join(evalPart(c, part.p), graphSolution):
                    yield x

        else:
            c = ctx.pushGraph(ctx.dataset.get_context(graph))
            for x in evalPart(c, part.p):
                yield x

    def evalValues(ctx, part):
        for r in part.p.res:
            c = ctx.push()
            try:
                for k, v in r.items():
                    if v != 'UNDEF':
                        c[k] = v
            except AlreadyBound:
                continue

            yield c.solution()

    def evalMultiset(ctx, part):

        if part.p.name == 'values':
            return evalValues(ctx, part)

        return evalPart(ctx, part.p)

    def evalPart(ctx, part):

        # try custom evaluation functions
        for name, c in list(CUSTOM_EVALS.items()):
            try:
                return c(ctx, part)
            except NotImplementedError:
                pass  # the given custome-function did not handle this part

        if part.name == 'BGP':
            # Reorder triples patterns by number of bound nodes in the current ctx
            # Do patterns with more bound nodes first
            triples = sorted(part.triples, key=lambda t: len([n for n in t if ctx[n] is None]))

            return evalBGP(ctx, triples)
        elif part.name == 'Filter':
            return evalFilter(ctx, part)
        elif part.name == 'Join':
            return evalJoin(ctx, part)
        elif part.name == 'LeftJoin':
            return evalLeftJoin(ctx, part)
        elif part.name == 'Graph':
            return evalGraph(ctx, part)
        elif part.name == 'Union':
            return evalUnion(ctx, part)
        elif part.name == 'ToMultiSet':
            return evalMultiset(ctx, part)
        elif part.name == 'Extend':
            return evalExtend(ctx, part)
        elif part.name == 'Minus':
            return evalMinus(ctx, part)

        elif part.name == 'Project':
            return evalProject(ctx, part)
        elif part.name == 'Slice':
            return evalSlice(ctx, part)
        elif part.name == 'Distinct':
            return evalDistinct(ctx, part)
        elif part.name == 'Reduced':
            return evalReduced(ctx, part)

        elif part.name == 'OrderBy':
            return evalOrderBy(ctx, part)
        elif part.name == 'Group':
            return evalGroup(ctx, part)
        elif part.name == 'AggregateJoin':
            return evalAggregateJoin(ctx, part)

        elif part.name == 'SelectQuery':
            return evalSelectQuery(ctx, part)
        elif part.name == 'AskQuery':
            return evalAskQuery(ctx, part)
        elif part.name == 'ConstructQuery':
            return evalConstructQuery(ctx, part)

        elif part.name == 'ServiceGraphPattern':
            raise Exception('ServiceGraphPattern not implemented')

        elif part.name == 'DescribeQuery':
            raise Exception('DESCRIBE not implemented')

        else:
            # import pdb ; pdb.set_trace()
            raise Exception('I dont know: %s' % part.name)

    def evalGroup(ctx, group):

        """
        http://www.w3.org/TR/sparql11-query/#defn_algGroup
        """
        # grouping should be implemented by evalAggregateJoin
        return evalPart(ctx, group.p)

    def evalAggregateJoin(ctx, agg):
        # import pdb ; pdb.set_trace()
        p = evalPart(ctx, agg.p)
        # p is always a Group, we always get a dict back

        group_expr = agg.p.expr
        res = collections.defaultdict(lambda: Aggregator(aggregations=agg.A))

        if group_expr is None:
            # no grouping, just COUNT in SELECT clause
            # get 1 aggregator for counting
            aggregator = res[True]
            for row in p:
                aggregator.update(row)
        else:
            for row in p:
                # determine right group aggregator for row
                k = tuple(_eval(e, row, False) for e in group_expr)
                res[k].update(row)

        # all rows are done; yield aggregated values
        for aggregator in res.values():
            yield FrozenBindings(ctx, aggregator.get_bindings())

        # there were no matches
        if len(res) == 0:
            yield FrozenBindings(ctx)

    def evalOrderBy(ctx, part):

        res = evalPart(ctx, part.p)

        for e in reversed(part.expr):

            reverse = bool(e.order and e.order == 'DESC')
            res = sorted(res, key=lambda x: _val(value(x, e.expr, variables=True)), reverse=reverse)

        return res

    def evalSlice(ctx, slice):
        # import pdb; pdb.set_trace()
        res = evalPart(ctx, slice.p)
        i = 0
        while i < slice.start:
            next(res)
            i += 1
        i = 0
        for x in res:
            i += 1
            if slice.length is None:
                yield x
            else:
                if i <= slice.length:
                    yield x
                else:
                    break

    def evalReduced(ctx, part):
        """apply REDUCED to result

        REDUCED is not as strict as DISTINCT, but if the incoming rows were sorted
        it should produce the same result with limited extra memory and time per
        incoming row.
        """

        # This implementation uses a most recently used strategy and a limited
        # buffer size. It relates to a LRU caching algorithm:
        # https://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used_.28LRU.29
        MAX = 1
        # TODO: add configuration or determine "best" size for most use cases
        # 0: No reduction
        # 1: compare only with the last row, almost no reduction with
        #    unordered incoming rows
        # N: The greater the buffer size the greater the reduction but more
        #    memory and time are needed

        # mixed data structure: set for lookup, deque for append/pop/remove
        mru_set = set()
        mru_queue = collections.deque()

        for row in evalPart(ctx, part.p):
            if row in mru_set:
                # forget last position of row
                mru_queue.remove(row)
            else:
                #row seems to be new
                yield row
                mru_set.add(row)
                if len(mru_set) > MAX:
                    # drop the least recently used row from buffer
                    mru_set.remove(mru_queue.pop())
            # put row to the front
            mru_queue.appendleft(row)

    def evalDistinct(ctx, part):
        res = evalPart(ctx, part.p)

        done = set()
        for x in res:
            if x not in done:
                yield x
                done.add(x)

    def evalProject(ctx, project):
        res = evalPart(ctx, project.p)

        return (row.project(project.PV) for row in res)

    def evalSelectQuery(ctx, query):

        res = {}
        res["type_"] = "SELECT"
        res["bindings"] = evalPart(ctx, query.p)
        res["vars_"] = query.PV
        return res

    def evalAskQuery(ctx, query):
        res = {}
        res["type_"] = "ASK"
        res["askAnswer"] = False
        for x in evalPart(ctx, query.p):
            res["askAnswer"] = True
            break

        return res

    def evalConstructQuery(ctx, query):
        template = query.template

        if not template:
            # a construct-where query
            template = query.p.p.triples  # query->project->bgp ...

        graph = Graph()

        for c in evalPart(ctx, query.p):
            graph += _fillTemplate(template, c)

        res = {}
        res["type_"] = "CONSTRUCT"
        res["graph"] = graph

        return res

    def evalQuery(graph, query, initBindings, base=None):

        initBindings = dict( ( Variable(k),v ) for k,v in initBindings.items() )

        ctx = QueryContext(graph, initBindings=initBindings)

        ctx.prologue = query.prologue
        main = query.algebra

        if main.datasetClause:
            if ctx.dataset is None:
                raise Exception(
                    "Non-conjunctive-graph doesn't know about " +
                    "graphs! Try a query without FROM (NAMED).")

            ctx = ctx.clone()  # or push/pop?

            firstDefault = False
            for d in main.datasetClause:
                if d.default:

                    if firstDefault:
                        # replace current default graph
                        dg = ctx.dataset.get_context(BNode())
                        ctx = ctx.pushGraph(dg)
                        firstDefault = True

                    ctx.load(d.default, default=True)

                elif d.named:
                    g = d.named
                    ctx.load(g, default=False)

        return evalPart(ctx, main)