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Fix beam_parser for new API
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@ -118,26 +118,28 @@ cdef class BeamParser(Parser):
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for grad, hist in histories:
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for grad, hist in histories:
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assert not math.isnan(grad) and not math.isinf(grad)
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assert not math.isnan(grad) and not math.isinf(grad)
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if abs(grad) >= min_grad:
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if abs(grad) >= min_grad:
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self._update_from_history(self.moves, tokens, hist, grad)
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self.model._update_from_history(self.moves, tokens, hist, grad)
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_cleanup(pred)
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_cleanup(pred)
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_cleanup(gold)
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_cleanup(gold)
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return pred.loss
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return pred.loss
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def _advance_beam(self, Beam beam, GoldParse gold, bint follow_gold):
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def _advance_beam(self, Beam beam, GoldParse gold, bint follow_gold):
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cdef Example py_eg = Example(nr_class=self.moves.n_moves, nr_atom=CONTEXT_SIZE,
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cdef Pool mem = Pool()
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nr_feat=self.model.nr_feat, widths=self.model.widths)
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features = <FeatureC*>mem.alloc(self.model.nr_feat, sizeof(FeatureC))
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cdef ExampleC* eg = py_eg.c
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cdef ParserNeuralNet nn_model = None
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cdef ParserPerceptron ap_model = None
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if isinstance(self.model, ParserNeuralNet):
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nn_model = self.model
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else:
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ap_model = self.model
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for i in range(beam.size):
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for i in range(beam.size):
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py_eg.reset()
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stcls = <StateClass>beam.at(i)
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stcls = <StateClass>beam.at(i)
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if not stcls.c.is_final():
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if not stcls.c.is_final():
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self.model.set_featuresC(eg, stcls.c)
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nr_feat = nn_model._set_featuresC(features, stcls.c)
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self.model.set_scoresC(beam.scores[i], eg.features, eg.nr_feat)
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self.model.set_scoresC(beam.scores[i], features, nr_feat)
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self.moves.set_valid(beam.is_valid[i], stcls.c)
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self.moves.set_valid(beam.is_valid[i], stcls.c)
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if gold is not None:
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if gold is not None:
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for i in range(beam.size):
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for i in range(beam.size):
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py_eg.reset()
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stcls = <StateClass>beam.at(i)
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stcls = <StateClass>beam.at(i)
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if not stcls.c.is_final():
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if not stcls.c.is_final():
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self.moves.set_costs(beam.is_valid[i], beam.costs[i], stcls, gold)
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self.moves.set_costs(beam.is_valid[i], beam.costs[i], stcls, gold)
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