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[tests] remove TF tests (uses of require_tf) (#38944)

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* remove uses of require_tf

* remove redundant import guards

* this class has no tests

* nits

* del tf rng comment
This commit is contained in:
Joao Gante
2025-06-25 18:29:10 +01:00
committed by GitHub
parent d37f751797
commit 1d45d90e5d
44 changed files with 21 additions and 2504 deletions
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33
tests/test_sequence_feature_extraction_common.py
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@@ -16,7 +16,7 @@
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from transformers.testing_utils import require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
@@ -76,24 +76,6 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
== (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size)
)
@require_tf
def test_batch_feature_tf(self):
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True)
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
input_name = feat_extract.model_input_names[0]
processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="tf")
batch_features_input = processed_features[input_name]
if len(batch_features_input.shape) < 3:
batch_features_input = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size)
)
def _check_padding(self, numpify=False):
def _inputs_have_equal_length(input):
length = len(input[0])
@@ -372,19 +354,6 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
self.assertTrue(abs(input_np.astype(np.float32).sum() - input_pt.numpy().astype(np.float32).sum()) < 1e-2)
@require_tf
def test_padding_accepts_tensors_tf(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
input_name = feat_extract.model_input_names[0]
processed_features = BatchFeature({input_name: speech_inputs})
input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name]
input_tf = feat_extract.pad(processed_features, padding="longest", return_tensors="tf")[input_name]
self.assertTrue(abs(input_np.astype(np.float32).sum() - input_tf.numpy().astype(np.float32).sum()) < 1e-2)
def test_attention_mask(self):
feat_dict = self.feat_extract_dict
feat_dict["return_attention_mask"] = True