File size: 2,213 Bytes
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import pytest
from verl.utils.tokenizer import normalize_token_ids
class DummyBatchEncoding:
def __init__(self, input_ids):
self.input_ids = input_ids
class DummyToList:
def __init__(self, data):
self._data = data
def tolist(self):
return self._data
@pytest.mark.parametrize(
("tokenized_output", "expected"),
[
# transformers v4-style direct token ids
([1, 2, 3], [1, 2, 3]),
((1, 2, 3), [1, 2, 3]),
# common list-like outputs with tolist()/ndarray paths
(DummyToList([1, 2, 3]), [1, 2, 3]),
(np.array([1, 2, 3], dtype=np.int64), [1, 2, 3]),
# transformers v5-like mapping / BatchEncoding-style outputs
({"input_ids": [1, 2, 3]}, [1, 2, 3]),
({"input_ids": DummyToList([1, 2, 3])}, [1, 2, 3]),
({"input_ids": [[1, 2, 3]]}, [1, 2, 3]),
(DummyBatchEncoding([1, 2, 3]), [1, 2, 3]),
(DummyBatchEncoding(DummyToList([[1, 2, 3]])), [1, 2, 3]),
# scalar item() support
([np.int64(1), np.int32(2), np.int16(3)], [1, 2, 3]),
],
)
def test_normalize_token_ids_valid_outputs(tokenized_output, expected):
assert normalize_token_ids(tokenized_output) == expected
@pytest.mark.parametrize(
"tokenized_output",
[
"not-token-ids",
{"attention_mask": [1, 1, 1]},
[[1, 2], [3, 4]], # ambiguous batched ids should fail fast
[1, object(), 3],
],
)
def test_normalize_token_ids_invalid_outputs(tokenized_output):
with pytest.raises(TypeError):
normalize_token_ids(tokenized_output)
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