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[tests] remove tests from libraries with deprecated support (flax, tensorflow_text, ...) (#39051)

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* rm tf/flax tests

* more flax deletions

* revert fixture change

* reverted test that should not be deleted; rm tf/flax test

* revert

* fix a few add-model-like tests

* fix add-model-like checkpoint source

* a few more

* test_get_model_files_only_pt fix

* fix test_retrieve_info_for_model_with_xxx

* fix test_retrieve_model_classes

* relative paths are the devil

* add todo
This commit is contained in:
Joao Gante
2025-06-26 16:25:00 +01:00
committed by GitHub
parent cfff7ca9a2
commit 3e5cc12855
16 changed files with 156 additions and 691 deletions
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68
tests/utils/test_generic.py
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@@ -19,13 +19,12 @@ import numpy as np
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_outputs import BaseModelOutput
from transformers.testing_utils import require_flax, require_torch
from transformers.testing_utils import require_torch
from transformers.utils import (
can_return_tuple,
expand_dims,
filter_out_non_signature_kwargs,
flatten_dict,
is_flax_available,
is_torch_available,
reshape,
squeeze,
@@ -34,9 +33,6 @@ from transformers.utils import (
)
if is_flax_available():
import jax.numpy as jnp
if is_torch_available():
import torch
@@ -84,23 +80,6 @@ class GenericTester(unittest.TestCase):
t = torch.tensor(x)
self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), transpose(t, axes=(1, 2, 0)).numpy()))
@require_flax
def test_transpose_flax(self):
x = np.random.randn(3, 4)
t = jnp.array(x)
self.assertTrue(np.allclose(transpose(x), np.asarray(transpose(t))))
x = np.random.randn(3, 4, 5)
t = jnp.array(x)
self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), np.asarray(transpose(t, axes=(1, 2, 0)))))
def test_reshape_numpy(self):
x = np.random.randn(3, 4)
self.assertTrue(np.allclose(reshape(x, (4, 3)), np.reshape(x, (4, 3))))
x = np.random.randn(3, 4, 5)
self.assertTrue(np.allclose(reshape(x, (12, 5)), np.reshape(x, (12, 5))))
@require_torch
def test_reshape_torch(self):
x = np.random.randn(3, 4)
@@ -111,23 +90,6 @@ class GenericTester(unittest.TestCase):
t = torch.tensor(x)
self.assertTrue(np.allclose(reshape(x, (12, 5)), reshape(t, (12, 5)).numpy()))
@require_flax
def test_reshape_flax(self):
x = np.random.randn(3, 4)
t = jnp.array(x)
self.assertTrue(np.allclose(reshape(x, (4, 3)), np.asarray(reshape(t, (4, 3)))))
x = np.random.randn(3, 4, 5)
t = jnp.array(x)
self.assertTrue(np.allclose(reshape(x, (12, 5)), np.asarray(reshape(t, (12, 5)))))
def test_squeeze_numpy(self):
x = np.random.randn(1, 3, 4)
self.assertTrue(np.allclose(squeeze(x), np.squeeze(x)))
x = np.random.randn(1, 4, 1, 5)
self.assertTrue(np.allclose(squeeze(x, axis=2), np.squeeze(x, axis=2)))
@require_torch
def test_squeeze_torch(self):
x = np.random.randn(1, 3, 4)
@@ -138,16 +100,6 @@ class GenericTester(unittest.TestCase):
t = torch.tensor(x)
self.assertTrue(np.allclose(squeeze(x, axis=2), squeeze(t, axis=2).numpy()))
@require_flax
def test_squeeze_flax(self):
x = np.random.randn(1, 3, 4)
t = jnp.array(x)
self.assertTrue(np.allclose(squeeze(x), np.asarray(squeeze(t))))
x = np.random.randn(1, 4, 1, 5)
t = jnp.array(x)
self.assertTrue(np.allclose(squeeze(x, axis=2), np.asarray(squeeze(t, axis=2))))
def test_expand_dims_numpy(self):
x = np.random.randn(3, 4)
self.assertTrue(np.allclose(expand_dims(x, axis=1), np.expand_dims(x, axis=1)))
@@ -158,12 +110,6 @@ class GenericTester(unittest.TestCase):
t = torch.tensor(x)
self.assertTrue(np.allclose(expand_dims(x, axis=1), expand_dims(t, axis=1).numpy()))
@require_flax
def test_expand_dims_flax(self):
x = np.random.randn(3, 4)
t = jnp.array(x)
self.assertTrue(np.allclose(expand_dims(x, axis=1), np.asarray(expand_dims(t, axis=1))))
def test_to_py_obj_native(self):
self.assertTrue(to_py_obj(1) == 1)
self.assertTrue(to_py_obj([1, 2, 3]) == [1, 2, 3])
@@ -192,18 +138,6 @@ class GenericTester(unittest.TestCase):
self.assertTrue(to_py_obj([t1, t2]) == [x1, x2])
@require_flax
def test_to_py_obj_flax(self):
x1 = [[1, 2, 3], [4, 5, 6]]
t1 = jnp.array(x1)
self.assertTrue(to_py_obj(t1) == x1)
x2 = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
t2 = jnp.array(x2)
self.assertTrue(to_py_obj(t2) == x2)
self.assertTrue(to_py_obj([t1, t2]) == [x1, x2])
class ValidationDecoratorTester(unittest.TestCase):
def test_cases_no_warning(self):