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bmap-gemma-engine / gemma /gm /math /_misc_test.py

feat: initial commit for B Map AI backend and Gemma LLM service

2f86d9b 17 days ago

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# Copyright 2026 DeepMind Technologies Limited.
#
# 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.

from gemma.gm.math import _misc
import numpy as np
import pytest


@pytest.mark.parametrize(
    "input_list, expected",
    [
        ([], ()),
        ([1], ((1, 1),)),
        ([1, 2, 3], ((1, 1), (2, 1), (3, 1))),
        (
            [1, 1, 2, 2, 2, 3, 1, 1, 1, 2, 2],
            ((1, 2), (2, 3), (3, 1), (1, 3), (2, 2)),
        ),
        (
            np.array([1, 1, 2, 2, 2, 3]),
            ((1, 2), (2, 3), (3, 1)),
        ),
    ],
)
def test_count_consecutive(input_list, expected):
  assert _misc.count_consecutive(input_list) == expected

	# Copyright 2026 DeepMind Technologies Limited.
	#
	# 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.

	from gemma.gm.math import _misc
	import numpy as np
	import pytest


	@pytest.mark.parametrize(
	"input_list, expected",
	[
	([], ()),
	([1], ((1, 1),)),
	([1, 2, 3], ((1, 1), (2, 1), (3, 1))),
	(
	[1, 1, 2, 2, 2, 3, 1, 1, 1, 2, 2],
	((1, 2), (2, 3), (3, 1), (1, 3), (2, 2)),
	),
	(
	np.array([1, 1, 2, 2, 2, 3]),
	((1, 2), (2, 3), (3, 1)),
	),
	],
	)
	def test_count_consecutive(input_list, expected):
	assert _misc.count_consecutive(input_list) == expected