def test_flatten_raises_when_expected(self, list_of_lists, mapping):
multi_tokens = []
for s in list_of_lists:
multi_tokens.append(
unified_tokenizer.AbstractMultiToken(
spellings=s,
kind=unified_tokenizer.TokenKind.STRING,
metadata=unified_tokenizer.TokenMetadata()))
with self.assertRaises(ValueError):
unified_tokenizer.flatten_and_sanitize_subtoken_lists(
multi_tokens, sanitization_mapping=mapping, sentinel='^')
def test_flatten_returns_expected(self, subtoken_lists, mappings,
expected_subtoken_list):
multi_tokens = []
for s in subtoken_lists:
multi_tokens.append(
unified_tokenizer.AbstractMultiToken(
spellings=s,
kind=unified_tokenizer.TokenKind.STRING,
metadata=unified_tokenizer.TokenMetadata()))
subtokens = unified_tokenizer.flatten_and_sanitize_subtoken_lists(
multi_tokens, mappings, sentinel='^')
self.assertSequenceEqual(expected_subtoken_list, subtokens)
def test_split_agnostic_returns_expected(self, labelled_tokens, max_length,
expected_labelled_subtokens):
tokens = [
unified_tokenizer.AbstractToken(s, k, unified_tokenizer.TokenMetadata())
for s, k in labelled_tokens
]
labelled_subtokens = unified_tokenizer.split_agnostic_tokens(
tokens, max_length)
expected_multi_tokens = []
for spelling_list, kind in expected_labelled_subtokens:
expected_multi_tokens.append(
unified_tokenizer.AbstractMultiToken(
# We cast spellings to tuples, since we know that
# `split_agnostic_tokens` creates multi tokens with tuples rather
# than lists.
spellings=tuple(spelling_list),
kind=kind,
metadata=unified_tokenizer.TokenMetadata()))
self.assertSequenceEqual(expected_multi_tokens, labelled_subtokens)
