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# Copyright 2024 The etils Authors.
#
# 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.
"""Test utils."""
from __future__ import annotations
from typing import Callable, Iterable, Optional, TypeVar
from etils.enp import numpy_utils
import numpy as np
import pytest
lazy = numpy_utils.lazy
_FnT = TypeVar('_FnT')
@pytest.fixture(scope='module', autouse=True)
def set_tnp() -> None:
"""Enable numpy behavior (for `tensorflow`).
Note: The fixture has to be explicitly declared in the `_test.py`
file where it is used. This can be done by assigning
`set_tnp = enp.testing.set_tnp`.
"""
# This is required to have TF follow the same casting rules as numpy
lazy.tnp.experimental_enable_numpy_behavior(prefer_float32=True)
def parametrize_xnp(
*,
with_none: bool = False,
restrict: Optional[Iterable[str]] = None,
skip: Optional[Iterable[str]] = None,
) -> Callable[[_FnT], _FnT]:
"""Parametrize over the numpy modules.
Args:
with_none: If `True`, also yield `None` among the values (to test `list`)
restrict: If given, only test the given module (e.g. `restrict=['jnp']`)
skip: If given, skip the given module from test (e.g. `skip=['torch']`)
Returns:
The fixture to apply to the `def test_xyz()` function
"""
name_to_modules = {
'np': np,
'jnp': lazy.jnp,
'tnp': lazy.tnp,
'torch': lazy.torch,
}
keep = _normalize_set(
restrict, default=name_to_modules, valid=name_to_modules
)
skip = _normalize_set(skip, default=[], valid=name_to_modules)
name_to_modules = {
k: v for k, v in name_to_modules.items() if k not in skip and k in keep
}
if with_none:
# Allow to test without numpy module: `x = [1, 2]` vs `x = np.array([1, 2]`
name_to_modules['no_np'] = None
return pytest.mark.parametrize(
'xnp',
list(name_to_modules.values()),
ids=list(name_to_modules.keys()),
)
def _normalize_set(
values: Iterable[str], default: Iterable[str], valid: Iterable[str]
) -> set[str]:
# Normalize str -> list (e.g. skip='torch')
values = [values] if isinstance(values, str) else values
values = set(default if values is None else values)
if extra_elements := (values - set(valid)):
raise ValueError(f'Unexpected numpy module: {extra_elements}')
return values
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