v4.39 deprecations 🧼 (#29492)

tests/generation/test_tf_utils.py

@@ -41,7 +41,6 @@ if is_tf_available():
         TFBartForConditionalGeneration,
         TFLogitsProcessorList,
         TFMinLengthLogitsProcessor,
-        tf_top_k_top_p_filtering,
     )
     from transformers.modeling_tf_utils import keras
 
@@ -49,102 +48,6 @@ if is_tensorflow_text_available():
     import tensorflow_text as text
 
 
-@require_tf
-class UtilsFunctionsTest(unittest.TestCase):
-    # tests whether the top_k_top_p_filtering function behaves as expected
-    def test_top_k_top_p_filtering(self):
-        logits = tf.convert_to_tensor(
-            [
-                [
-                    8.2220991,  # 3rd highest value; idx. 0
-                    -0.5620044,
-                    5.23229752,
-                    4.0386393,
-                    -6.8798378,
-                    -0.54785802,
-                    -3.2012153,
-                    2.92777176,
-                    1.88171953,
-                    7.35341276,  # 5th highest value; idx. 9
-                    8.43207833,  # 2nd highest value; idx. 10
-                    -9.85711836,
-                    -5.96209236,
-                    -1.13039161,
-                    -7.1115294,
-                    -0.8369633,
-                    -5.3186408,
-                    7.06427407,
-                    0.81369344,
-                    -0.82023817,
-                    -5.9179796,
-                    0.58813443,
-                    -6.99778438,
-                    4.71551189,
-                    -0.18771637,
-                    7.44020759,  # 4th highest value; idx. 25
-                    9.38450987,  # 1st highest value; idx. 26
-                    2.12662941,
-                    -9.32562038,
-                    2.35652522,
-                ],  # cummulative prob of 5 highest values <= 0.6
-                [
-                    0.58425518,
-                    4.53139238,
-                    -5.57510464,
-                    -6.28030699,
-                    -7.19529503,
-                    -4.02122551,
-                    1.39337037,
-                    -6.06707057,
-                    1.59480517,
-                    -9.643119,
-                    0.03907799,
-                    0.67231762,
-                    -8.88206726,
-                    6.27115922,  # 4th highest value; idx. 13
-                    2.28520723,
-                    4.82767506,
-                    4.30421368,
-                    8.8275313,  # 2nd highest value; idx. 17
-                    5.44029958,  # 5th highest value; idx. 18
-                    -4.4735794,
-                    7.38579536,  # 3rd highest value; idx. 20
-                    -2.91051663,
-                    2.61946077,
-                    -2.5674762,
-                    -9.48959302,
-                    -4.02922645,
-                    -1.35416918,
-                    9.67702323,  # 1st highest value; idx. 27
-                    -5.89478553,
-                    1.85370467,
-                ],  # cummulative prob of 5 highest values <= 0.6
-            ],
-            dtype=tf.float32,
-        )
-
-        non_inf_expected_idx = tf.convert_to_tensor(
-            [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
-            dtype=tf.int32,
-        )  # expected non filtered idx as noted above
-
-        non_inf_expected_output = tf.convert_to_tensor(
-            [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],
-            dtype=tf.float32,
-        )  # expected non filtered values as noted above
-
-        output = tf_top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
-
-        non_inf_output = output[output != -float("inf")]
-        non_inf_idx = tf.cast(
-            tf.where(tf.not_equal(output, tf.constant(-float("inf"), dtype=tf.float32))),
-            dtype=tf.int32,
-        )
-
-        tf.debugging.assert_near(non_inf_output, non_inf_expected_output, rtol=1e-12)
-        tf.debugging.assert_equal(non_inf_idx, non_inf_expected_idx)
-
-
 @require_tf
 class TFGenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMixin):
     # setting framework_dependent_parameters needs to be gated, just like its contents' imports