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Purge unused ModelTester code (#37085)

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* Purge correctly this time

* Remove more methods from recent PRs

* make fixup
This commit is contained in:
Matt
2025-04-03 17:48:35 +01:00
committed by GitHub
parent 1b29409d89
commit 2d46a08b63
72 changed files with 3 additions and 4286 deletions
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39
tests/models/fnet/test_modeling_fnet.py
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@@ -22,7 +22,7 @@ from transformers.models.auto import get_values
from transformers.testing_utils import require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
@@ -41,10 +41,6 @@ if is_torch_available():
FNetModel,
FNetTokenizerFast,
)
from transformers.models.fnet.modeling_fnet import (
FNetBasicFourierTransform,
is_scipy_available,
)
# Override ConfigTester
@@ -133,26 +129,6 @@ class FNetModelTester:
tpu_short_seq_length=self.seq_length,
)
@require_torch
def create_and_check_fourier_transform(self, config):
hidden_states = floats_tensor([self.batch_size, self.seq_length, config.hidden_size])
transform = FNetBasicFourierTransform(config)
fftn_output = transform(hidden_states)
config.use_tpu_fourier_optimizations = True
if is_scipy_available():
transform = FNetBasicFourierTransform(config)
dft_output = transform(hidden_states)
config.max_position_embeddings = 4097
transform = FNetBasicFourierTransform(config)
fft_output = transform(hidden_states)
if is_scipy_available():
self.parent.assertTrue(torch.allclose(fftn_output[0][0], dft_output[0][0], atol=1e-4))
self.parent.assertTrue(torch.allclose(fft_output[0][0], dft_output[0][0], atol=1e-4))
self.parent.assertTrue(torch.allclose(fftn_output[0][0], fft_output[0][0], atol=1e-4))
def create_and_check_model(self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels):
model = FNetModel(config=config)
model.to(torch_device)
@@ -185,19 +161,6 @@ class FNetModelTester:
result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_next_sentence_prediction(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
model = FNetForNextSentencePrediction(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
token_type_ids=token_type_ids,
next_sentence_label=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):