def test_greedy_decode(mocker):
words = ['John', 'loves', 'Mary']
pos_tags = ['NNP', 'VBZ', 'NNP']
correct_actions = [
NonTerminalAction('S'),
NonTerminalAction('NP'),
ShiftAction(),
ReduceAction(),
NonTerminalAction('VP'),
ShiftAction(),
NonTerminalAction('NP'),
ShiftAction(),
ReduceAction(),
ReduceAction(),
ReduceAction(),
]
retvals = [
Variable(
torch.zeros(len(action_store)).scatter_(
0, torch.LongTensor([action_store[a]]), 1))
for a in correct_actions
]
parser = DiscriminativeRnnGrammar(word2id, pos2id, nt2id, action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
mocker.patch.object(parser, 'forward', side_effect=retvals)
result = greedy_decode(parser)
assert len(result) == len(correct_actions)
picked_actions, log_probs = zip(*result)
assert list(picked_actions) == correct_actions
def get_actions(cls, tree: Tree) -> List[Action]:
if len(tree) == 1 and not isinstance(tree[0], Tree):
return [cls.get_action_at_pos_node(tree)]
actions: List[Action] = [NonTerminalAction(tree.label())]
for child in tree:
actions.extend(cls.get_actions(child))
actions.append(ReduceAction())
return actions
class TestOracleDataset:
bracketed_sents = [
'(S (NP (NNP John)) (VP (VBZ loves) (NP (NNP Mary))))',
'(S (NP (NNP Mary)) (VP (VBZ hates) (NP (NNP John))))' # poor John
]
words = {'John', 'loves', 'hates', 'Mary'}
pos_tags = {'NNP', 'VBZ'}
nt_labels = {'S', 'NP', 'VP'}
actions = {
NonTerminalAction('S'),
NonTerminalAction('NP'),
NonTerminalAction('VP'),
ShiftAction(),
ReduceAction()
}
def test_init(self):
oracles = [
DiscriminativeOracle.from_parsed_sentence(Tree.fromstring(s))
for s in self.bracketed_sents
]
dataset = OracleDataset(oracles)
assert isinstance(dataset.word_store, ItemStore)
assert set(dataset.word_store) == self.words
assert isinstance(dataset.pos_store, ItemStore)
assert set(dataset.pos_store) == self.pos_tags
assert isinstance(dataset.nt_store, ItemStore)
assert set(dataset.nt_store) == self.nt_labels
assert isinstance(dataset.action_store, ItemStore)
assert set(dataset.action_store) == self.actions
def test_getitem(self):
oracles = [
DiscriminativeOracle.from_parsed_sentence(Tree.fromstring(s))
for s in self.bracketed_sents
]
dataset = OracleDataset(oracles)
assert oracles[0] is dataset[0]
assert oracles[1] is dataset[1]
def test_len(self):
oracles = [
DiscriminativeOracle.from_parsed_sentence(Tree.fromstring(s))
for s in self.bracketed_sents
]
dataset = OracleDataset(oracles)
assert len(dataset) == len(oracles)
def test_push_known_non_terminal_but_unknown_action(self):
actions = [NonTerminalAction('NP'), NonTerminalAction('VP'), ShiftAction(), ReduceAction()]
action_store = ItemStore()
for a in actions:
action_store.add(a)
words = ['John']
pos_tags = ['NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
with pytest.raises(KeyError):
parser.push_non_terminal('S')
def test_forward_with_illegal_actions(self):
words = ['John', 'loves', 'Mary']
pos_tags = ['NNP', 'VBZ', 'NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
action_probs = parser().exp().data
assert action_probs[self.action_store[NonTerminalAction('S')]] > 0.
assert action_probs[self.action_store[NonTerminalAction('NP')]] > 0.
assert action_probs[self.action_store[NonTerminalAction('VP')]] > 0.
assert -0.001 <= action_probs[self.action_store[ShiftAction()]] <= 0.001
assert -0.001 <= action_probs[self.action_store[ReduceAction()]] <= 0.001
def test_from_string(self):
s = 'asdf fdsa\nNNP VBZ\nNT(S)\nSHIFT\nSHIFT\nREDUCE'
oracle = DiscriminativeOracle.from_string(s)
assert isinstance(oracle, DiscriminativeOracle)
assert oracle.words == ['asdf', 'fdsa']
assert oracle.pos_tags == ['NNP', 'VBZ']
assert oracle.actions == [
NonTerminalAction('S'),
ShiftAction(),
ShiftAction(),
ReduceAction()
]
def test_from_string(self):
s = 'NNP VBZ\nNT(S)\nGEN(asdf)\nGEN(fdsa)\nREDUCE'
oracle = GenerativeOracle.from_string(s)
assert isinstance(oracle, GenerativeOracle)
assert oracle.words == ['asdf', 'fdsa']
assert oracle.pos_tags == ['NNP', 'VBZ']
assert oracle.actions == [
NonTerminalAction('S'),
GenerateAction('asdf'),
GenerateAction('fdsa'),
ReduceAction()
]
def test_from_parsed_sent(self):
s = '(S (NP (NNP John)) (VP (VBZ loves) (NP (NNP Mary))))'
expected_actions = [
NonTerminalAction('S'),
NonTerminalAction('NP'),
ShiftAction(),
ReduceAction(),
NonTerminalAction('VP'),
ShiftAction(),
NonTerminalAction('NP'),
ShiftAction(),
ReduceAction(),
ReduceAction(),
ReduceAction(),
]
expected_pos_tags = ['NNP', 'VBZ', 'NNP']
expected_words = ['John', 'loves', 'Mary']
oracle = DiscriminativeOracle.from_parsed_sentence(Tree.fromstring(s))
assert isinstance(oracle, DiscriminativeOracle)
assert oracle.actions == expected_actions
assert oracle.pos_tags == expected_pos_tags
assert oracle.words == expected_words
def test_do_reduce_action(self):
words = ['John', 'loves', 'Mary']
pos_tags = ['NNP', 'VBZ', 'NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
parser.push_non_terminal('S')
parser.push_non_terminal('NP')
parser.shift()
prev_input_buffer = parser.input_buffer
parser.reduce()
assert len(parser.stack_buffer) == 2
last = parser.stack_buffer[-1]
assert isinstance(last, Tree)
assert last.label() == 'NP'
assert len(last) == 1
assert last[0] == 'John'
assert parser.input_buffer == prev_input_buffer
assert len(parser.action_history) == 4
assert parser.action_history[-1] == ReduceAction()
assert not parser.finished
def reduce(self) -> None:
if not self.can_reduce():
raise IllegalActionError("Illegal REDUCE action attempted.")
self._reduce()
self._append_history(ReduceAction())
from torch.autograd import Variable
from rnng.models import DiscriminativeRnnGrammar
from rnng.utils import ItemStore
from rnng.actions import ShiftAction, ReduceAction, NonTerminalAction
from rnng.decoding import greedy_decode
word2id = {'John': 0, 'loves': 1, 'Mary': 2}
pos2id = {'NNP': 0, 'VBZ': 1}
nt2id = {'S': 0, 'NP': 1, 'VP': 2}
actions = [
NonTerminalAction('S'),
NonTerminalAction('NP'),
NonTerminalAction('VP'),
ShiftAction(),
ReduceAction()
]
action_store = ItemStore()
for a in actions:
action_store.add(a)
def test_greedy_decode(mocker):
words = ['John', 'loves', 'Mary']
pos_tags = ['NNP', 'VBZ', 'NNP']
correct_actions = [
NonTerminalAction('S'),
NonTerminalAction('NP'),
ShiftAction(),
ReduceAction(),
NonTerminalAction('VP'),
def test_eq(self):
a = GenerateAction('asdf')
assert a == GenerateAction(a.word)
assert a != GenerateAction('fdsa')
assert a != ReduceAction()
def test_from_invalid_string(self):
with pytest.raises(ValueError):
ReduceAction.from_string('asdf')
def test_from_string(self):
a = ReduceAction.from_string(self.as_str)
assert isinstance(a, ReduceAction)
def test_eq(self):
assert ReduceAction() == ReduceAction()
assert ReduceAction() != ShiftAction()
def test_hash(self):
a = ReduceAction()
assert hash(a) == hash(self.as_str)
def test_to_string(self):
a = ReduceAction()
assert str(a) == self.as_str
def __init__(self,
word2id: Mapping[Word, WordId],
pos2id: Mapping[POSTag, POSId],
non_terminal2id: Mapping[NonTerminalLabel, NonTerminalId],
action_store: ItemStore,
word_dim: int = 32,
pos_dim: int = 12,
non_terminal_dim: int = 60,
action_dim: int = 16,
input_dim: int = 128,
hidden_dim: int = 128,
num_layers: int = 2,
dropout: float = 0.) -> None:
if ShiftAction() not in action_store:
raise ValueError('SHIFT action ID must be specified')
if ReduceAction() not in action_store:
raise ValueError('REDUCE action ID must be specified')
num_words = len(word2id)
num_pos = len(pos2id)
num_non_terminals = len(non_terminal2id)
num_actions = len(action_store)
for wid in word2id.values():
if wid < 0 or wid >= num_words:
raise ValueError(f'word ID of {wid} is out of range')
for pid in pos2id.values():
if pid < 0 or pid >= num_pos:
raise ValueError(f'POS tag ID of {pid} is out of range')
for nid in non_terminal2id.values():
if nid < 0 or nid >= num_non_terminals:
raise ValueError(f'nonterminal ID of {nid} is out of range')
super().__init__()
self.word2id = word2id
self.pos2id = pos2id
self.nt2id = non_terminal2id
self.action_store = action_store
self.num_words = num_words
self.num_pos = num_pos
self.num_non_terminals = num_non_terminals
self.num_actions = num_actions
self.word_dim = word_dim
self.pos_dim = pos_dim
self.non_terminal_dim = non_terminal_dim
self.action_dim = action_dim
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.dropout = dropout
# Parser states
self._stack = [] # type: List[StackElement]
self._buffer = [] # type: List[Word]
self._history = [] # type: List[Action]
self._num_open_non_terminals = 0
self._started = False
# Parser state encoders
self.stack_lstm = StackLSTM(input_dim,
hidden_dim,
num_layers=num_layers,
dropout=dropout)
# can use an LSTM, but this is easier.
self.buffer_lstm = StackLSTM(input_dim,
hidden_dim,
num_layers=num_layers,
dropout=dropout)
# can use LSTM, but this is more efficient
self.history_lstm = StackLSTM(input_dim,
hidden_dim,
num_layers=num_layers,
dropout=dropout)
# Composition
self.compose_fwd_lstm = nn.LSTM(input_dim,
input_dim,
num_layers=num_layers,
dropout=dropout)
self.compose_bwd_lstm = nn.LSTM(input_dim,
input_dim,
num_layers=num_layers,
dropout=dropout)
# Transformations
self.word2lstm = nn.Sequential(
OrderedDict([('linear', nn.Linear(word_dim + pos_dim, input_dim)),
('relu', nn.ReLU())]))
self.nt2lstm = nn.Sequential(
OrderedDict([('linear', nn.Linear(non_terminal_dim, input_dim)),
('relu', nn.ReLU())]))
self.action2lstm = nn.Sequential(
OrderedDict([('linear', nn.Linear(action_dim, input_dim)),
('relu', nn.ReLU())]))
self.fwdbwd2composed = nn.Sequential(
OrderedDict([('linear', nn.Linear(2 * input_dim, input_dim)),
('relu', nn.ReLU())]))
self.lstms2summary = nn.Sequential(
OrderedDict([ # Stack LSTMs to parser summary
('dropout', nn.Dropout(dropout)),
('linear', nn.Linear(3 * hidden_dim, hidden_dim)),
('relu', nn.ReLU())
]))
self.summary2actions = nn.Linear(hidden_dim, num_actions)
# Embeddings
self.word_embedding = nn.Embedding(num_words, word_dim)
self.pos_embedding = nn.Embedding(num_pos, pos_dim)
self.non_terminal_embedding = nn.Embedding(num_non_terminals,
non_terminal_dim)
self.action_embedding = nn.Embedding(num_actions, action_dim)
# Guard parameters for stack, buffer, and action history
self.stack_guard = nn.Parameter(torch.Tensor(input_dim))
self.buffer_guard = nn.Parameter(torch.Tensor(input_dim))
self.history_guard = nn.Parameter(torch.Tensor(input_dim))
# Final embeddings
self._word_emb = {} # type: Dict[WordId, Variable]
self._nt_emb = {} # type: Variable
self._action_emb = {} # type: Variable
def test_init_no_shift_action(self):
action_store = ItemStore()
actions = [NonTerminalAction('S'), NonTerminalAction('NP'), NonTerminalAction('VP'), ReduceAction()]
for a in actions:
action_store.add(a)
with pytest.raises(ValueError):
DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, action_store)
class TestDiscRNNGrammar:
word2id = {'John': 0, 'loves': 1, 'Mary': 2}
pos2id = {'NNP': 0, 'VBZ': 1}
nt2id = {'S': 2, 'NP': 1, 'VP': 0}
action_store = ItemStore()
actions = [NonTerminalAction('S'), NonTerminalAction('NP'), NonTerminalAction('VP'), ShiftAction(), ReduceAction()]
for a in actions:
action_store.add(a)
def test_init(self):
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
assert len(parser.stack_buffer) == 0
assert len(parser.input_buffer) == 0
assert len(parser.action_history) == 0
assert not parser.finished
assert not parser.started
def test_init_no_shift_action(self):
action_store = ItemStore()
actions = [NonTerminalAction('S'), NonTerminalAction('NP'), NonTerminalAction('VP'), ReduceAction()]
for a in actions:
action_store.add(a)
with pytest.raises(ValueError):
DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, action_store)
def test_init_no_reduce_action(self):
action_store = ItemStore()
actions = [NonTerminalAction('S'), NonTerminalAction('NP'), NonTerminalAction('VP'), ShiftAction()]
for a in actions:
action_store.add(a)
with pytest.raises(ValueError):
DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, action_store)
def test_init_word_id_out_of_range(self):
word2id = dict(self.word2id)
word2id['John'] = len(word2id)
with pytest.raises(ValueError):
DiscriminativeRnnGrammar(word2id, self.pos2id, self.nt2id, self.action_store)
word2id['John'] = -1
with pytest.raises(ValueError):
DiscriminativeRnnGrammar(word2id, self.pos2id, self.nt2id, self.action_store)
def test_init_pos_id_out_of_range(self):
pos2id = dict(self.pos2id)
pos2id['NNP'] = len(pos2id)
with pytest.raises(ValueError):
DiscriminativeRnnGrammar(self.word2id, pos2id, self.nt2id, self.action_store)
pos2id['NNP'] = -1
with pytest.raises(ValueError):
DiscriminativeRnnGrammar(self.word2id, pos2id, self.nt2id, self.action_store)
def test_init_non_terminal_id_out_of_range(self):
nt2id = dict(self.nt2id)
nt2id['S'] = len(nt2id)
with pytest.raises(ValueError):
DiscriminativeRnnGrammar(self.word2id, self.pos2id, nt2id, self.action_store)
nt2id['S'] = -1
with pytest.raises(ValueError):
DiscriminativeRnnGrammar(self.word2id, self.pos2id, nt2id, self.action_store)
def test_initialise_stacks_and_buffers(self):
words = ['John', 'loves', 'Mary']
pos_tags = ['NNP', 'VBZ', 'NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
assert len(parser.stack_buffer) == 0
assert parser.input_buffer == words
assert len(parser.action_history) == 0
assert not parser.finished
assert parser.started
def test_initialise_with_empty_tagged_words(self):
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
with pytest.raises(ValueError):
parser.initialise_stacks_and_buffers([])
def test_initalise_with_invalid_word_or_pos(self):
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
with pytest.raises(ValueError):
parser.initialise_stacks_and_buffers([('Bob', 'NNP')])
with pytest.raises(ValueError):
parser.initialise_stacks_and_buffers([('John', 'VBD')])
def test_do_non_terminal_action(self):
words = ['John', 'loves', 'Mary']
pos_tags = ['NNP', 'VBZ', 'NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
prev_input_buffer = parser.input_buffer
parser.push_non_terminal('S')
assert len(parser.stack_buffer) == 1
last = parser.stack_buffer[-1]
assert isinstance(last, Tree)
assert last.label() == 'S'
assert len(last) == 0
assert parser.input_buffer == prev_input_buffer
assert len(parser.action_history) == 1
assert parser.action_history[-1] == NonTerminalAction('S')
assert not parser.finished
def test_do_illegal_push_non_terminal_action(self):
words = ['John']
pos_tags = ['NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
# Buffer is empty
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
parser.push_non_terminal('S')
parser.shift()
with pytest.raises(IllegalActionError):
parser.push_non_terminal('NP')
# More than 100 open nonterminals
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
for i in range(100):
parser.push_non_terminal('S')
with pytest.raises(IllegalActionError):
parser.push_non_terminal('NP')
def test_push_unknown_non_terminal(self):
words = ['John']
pos_tags = ['NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
with pytest.raises(KeyError):
parser.push_non_terminal('asdf')
def test_push_known_non_terminal_but_unknown_action(self):
actions = [NonTerminalAction('NP'), NonTerminalAction('VP'), ShiftAction(), ReduceAction()]
action_store = ItemStore()
for a in actions:
action_store.add(a)
words = ['John']
pos_tags = ['NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
with pytest.raises(KeyError):
parser.push_non_terminal('S')
def test_do_shift_action(self):
words = ['John', 'loves', 'Mary']
pos_tags = ['NNP', 'VBZ', 'NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
parser.push_non_terminal('S')
parser.push_non_terminal('NP')
parser.shift()
assert len(parser.stack_buffer) == 3
last = parser.stack_buffer[-1]
assert last == 'John'
assert parser.input_buffer == words[1:]
assert len(parser.action_history) == 3
assert parser.action_history[-1] == ShiftAction()
assert not parser.finished
def test_do_illegal_shift_action(self):
words = ['John']
pos_tags = ['NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
# No open nonterminal
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
with pytest.raises(IllegalActionError):
parser.shift()
# Buffer is empty
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
parser.push_non_terminal('S')
parser.shift()
with pytest.raises(IllegalActionError):
parser.shift()
def test_do_reduce_action(self):
words = ['John', 'loves', 'Mary']
pos_tags = ['NNP', 'VBZ', 'NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
parser.push_non_terminal('S')
parser.push_non_terminal('NP')
parser.shift()
prev_input_buffer = parser.input_buffer
parser.reduce()
assert len(parser.stack_buffer) == 2
last = parser.stack_buffer[-1]
assert isinstance(last, Tree)
assert last.label() == 'NP'
assert len(last) == 1
assert last[0] == 'John'
assert parser.input_buffer == prev_input_buffer
assert len(parser.action_history) == 4
assert parser.action_history[-1] == ReduceAction()
assert not parser.finished
def test_do_illegal_reduce_action(self):
words = ['John', 'loves']
pos_tags = ['NNP', 'VBZ']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
# Top of stack is an open nonterminal
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
parser.push_non_terminal('S')
with pytest.raises(IllegalActionError):
parser.reduce()
# Buffer is not empty and REDUCE will finish parsing
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
parser.push_non_terminal('S')
parser.shift()
with pytest.raises(IllegalActionError):
parser.reduce()
def test_do_action_when_not_started(self):
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
with pytest.raises(RuntimeError):
parser.push_non_terminal('S')
with pytest.raises(RuntimeError):
parser.shift()
with pytest.raises(RuntimeError):
parser.reduce()
def test_forward(self):
words = ['John', 'loves', 'Mary']
pos_tags = ['NNP', 'VBZ', 'NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
parser.push_non_terminal('S')
parser.push_non_terminal('NP')
parser.shift()
parser.reduce()
action_logprobs = parser()
assert isinstance(action_logprobs, Variable)
assert action_logprobs.size() == (len(self.action_store),)
sum_prob = action_logprobs.exp().sum().data[0]
assert 0.999 <= sum_prob <= 1.001
def test_forward_with_illegal_actions(self):
words = ['John', 'loves', 'Mary']
pos_tags = ['NNP', 'VBZ', 'NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
action_probs = parser().exp().data
assert action_probs[self.action_store[NonTerminalAction('S')]] > 0.
assert action_probs[self.action_store[NonTerminalAction('NP')]] > 0.
assert action_probs[self.action_store[NonTerminalAction('VP')]] > 0.
assert -0.001 <= action_probs[self.action_store[ShiftAction()]] <= 0.001
assert -0.001 <= action_probs[self.action_store[ReduceAction()]] <= 0.001
def test_forward_when_not_started(self):
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
with pytest.raises(RuntimeError):
parser()
def test_finished(self):
words = ['John', 'loves', 'Mary']
pos_tags = ['NNP', 'VBZ', 'NNP']
parser = DiscriminativeRnnGrammar(self.word2id, self.pos2id, self.nt2id, self.action_store)
exp_parse_tree = Tree('S', [Tree('NP', ['John']),
Tree('VP', ['loves', Tree('NP', ['Mary'])])])
parser.initialise_stacks_and_buffers(list(zip(words, pos_tags)))
parser.push_non_terminal('S')
parser.push_non_terminal('NP')
parser.shift()
parser.reduce()
parser.push_non_terminal('VP')
parser.shift()
parser.push_non_terminal('NP')
parser.shift()
parser.reduce()
parser.reduce()
parser.reduce()
assert parser.finished
parse_tree = parser.stack_buffer[-1]
assert parse_tree == exp_parse_tree
with pytest.raises(RuntimeError):
parser()
with pytest.raises(RuntimeError):
parser.push_non_terminal('NP')
with pytest.raises(RuntimeError):
parser.shift()
with pytest.raises(RuntimeError):
parser.reduce()