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Update quality tooling for formatting (#21480)

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* Result of black 23.1

* Update target to Python 3.7

* Switch flake8 to ruff

* Configure isort

* Configure isort

* Apply isort with line limit

* Put the right black version

* adapt black in check copies

* Fix copies
This commit is contained in:
Sylvain Gugger
2023-02-06 18:10:56 -05:00
committed by GitHub
parent b7bb2b59f7
commit 6f79d26442
1211 changed files with 1532 additions and 2687 deletions
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6
tests/models/perceiver/test_modeling_perceiver.py
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@@ -263,7 +263,6 @@ class PerceiverModelTester:
@require_torch
class PerceiverModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
PerceiverModel,
@@ -739,7 +738,6 @@ class PerceiverModelTest(ModelTesterMixin, unittest.TestCase):
for problem_type in problem_types:
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
config.problem_type = problem_type["title"]
config.num_labels = problem_type["num_labels"]
@@ -849,7 +847,6 @@ def extract_image_patches(x, kernel, stride=1, dilation=1):
class PerceiverModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
tokenizer = PerceiverTokenizer.from_pretrained("deepmind/language-perceiver")
model = PerceiverForMaskedLM.from_pretrained("deepmind/language-perceiver")
model.to(torch_device)
@@ -884,7 +881,6 @@ class PerceiverModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_classification(self):
feature_extractor = PerceiverFeatureExtractor()
model = PerceiverForImageClassificationLearned.from_pretrained("deepmind/vision-perceiver-learned")
model.to(torch_device)
@@ -909,7 +905,6 @@ class PerceiverModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_classification_fourier(self):
feature_extractor = PerceiverFeatureExtractor()
model = PerceiverForImageClassificationFourier.from_pretrained("deepmind/vision-perceiver-fourier")
model.to(torch_device)
@@ -934,7 +929,6 @@ class PerceiverModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_classification_conv(self):
feature_extractor = PerceiverFeatureExtractor()
model = PerceiverForImageClassificationConvProcessing.from_pretrained("deepmind/vision-perceiver-conv")
model.to(torch_device)