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[tests] remove pt_tf equivalence tests (#36253)

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This commit is contained in:
Joao Gante
2025-02-19 11:55:11 +00:00
committed by GitHub
parent 1a81d774b1
commit 0863eef248
60 changed files with 56 additions and 2438 deletions
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7
tests/models/tapas/test_modeling_tapas.py
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@@ -32,7 +32,7 @@ from transformers import (
is_torch_available,
)
from transformers.models.auto import get_values
from transformers.testing_utils import require_tensorflow_probability, require_torch, slow, torch_device
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
@@ -522,11 +522,6 @@ class TapasModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
@require_tensorflow_probability
@unittest.skip(reason="tfp is not defined even if installed. FIXME @Arthur in a followup PR!")
def test_pt_tf_model_equivalence(self):
pass
@unittest.skip(reason="tfp is not defined even if installed. FIXME @Arthur in a followup PR!")
def test_tf_from_pt_safetensors(self):
pass

4
tests/models/tapas/test_modeling_tf_tapas.py
View File

@@ -535,10 +535,6 @@ class TFTapasModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCas
def test_loss_computation(self):
pass
@unittest.skip("tfp is not defined even if installed. FIXME @Arthur in a followup PR!")
def test_pt_tf_model_equivalence(self):
pass
def prepare_tapas_single_inputs_for_inference():
# Here we prepare a single table-question pair to test TAPAS inference on:

49
tests/models/tapas/test_tokenization_tapas.py
View File

@@ -34,7 +34,6 @@ from transformers.models.tapas.tokenization_tapas import (
_is_whitespace,
)
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_pandas,
require_tensorflow_probability,
require_tokenizers,
@@ -1158,54 +1157,6 @@ class TapasTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
self.assertListEqual(encoding.input_ids[:2], expected_results)
@is_pt_tf_cross_test
def test_batch_encode_plus_tensors(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequences = [
"Testing batch encode plus",
"Testing batch encode plus with different sequence lengths",
"Testing batch encode plus with different sequence lengths correctly pads",
]
table = self.get_table(tokenizer, length=0)
# A Tensor cannot be build by sequences which are not the same size
self.assertRaises(ValueError, tokenizer.batch_encode_plus, table, sequences, return_tensors="pt")
self.assertRaises(ValueError, tokenizer.batch_encode_plus, table, sequences, return_tensors="tf")
if tokenizer.pad_token_id is None:
self.assertRaises(
ValueError,
tokenizer.batch_encode_plus,
table,
sequences,
padding=True,
return_tensors="pt",
)
self.assertRaises(
ValueError,
tokenizer.batch_encode_plus,
table,
sequences,
padding="longest",
return_tensors="tf",
)
else:
pytorch_tensor = tokenizer.batch_encode_plus(table, sequences, padding=True, return_tensors="pt")
tensorflow_tensor = tokenizer.batch_encode_plus(
table, sequences, padding="longest", return_tensors="tf"
)
encoded_sequences = tokenizer.batch_encode_plus(table, sequences, padding=True)
for key in encoded_sequences.keys():
pytorch_value = pytorch_tensor[key].tolist()
tensorflow_value = tensorflow_tensor[key].numpy().tolist()
encoded_value = encoded_sequences[key]
self.assertEqual(pytorch_value, tensorflow_value, encoded_value)
@slow
def test_tapas_integration_test(self):
data = {