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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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15
tests/models/vision_encoder_decoder/test_modeling_flax_vision_encoder_decoder.py
View File

@@ -70,7 +70,7 @@ class FlaxEncoderDecoderMixin:
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
**kwargs,
):
encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
self.assertTrue(encoder_decoder_config.decoder.is_decoder)
@@ -100,7 +100,7 @@ class FlaxEncoderDecoderMixin:
decoder_input_ids,
decoder_attention_mask,
return_dict,
**kwargs
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict}
@@ -126,7 +126,7 @@ class FlaxEncoderDecoderMixin:
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model}
@@ -162,7 +162,7 @@ class FlaxEncoderDecoderMixin:
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
**kwargs,
):
# make the decoder inputs a different shape from the encoder inputs to harden the test
decoder_input_ids = decoder_input_ids[:, :-1]
@@ -236,7 +236,6 @@ class FlaxEncoderDecoderMixin:
self.assertEqual(generated_sequences.shape, (pixel_values.shape[0],) + (decoder_config.max_length,))
def check_pt_flax_equivalence(self, pt_model, fx_model, inputs_dict):
pt_model.to(torch_device)
pt_model.eval()
@@ -278,7 +277,6 @@ class FlaxEncoderDecoderMixin:
self.assert_almost_equals(fx_output, pt_output_loaded.numpy(), 1e-5)
def check_equivalence_pt_to_flax(self, config, decoder_config, inputs_dict):
encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
pt_model = VisionEncoderDecoderModel(encoder_decoder_config)
@@ -290,7 +288,6 @@ class FlaxEncoderDecoderMixin:
self.check_pt_flax_equivalence(pt_model, fx_model, inputs_dict)
def check_equivalence_flax_to_pt(self, config, decoder_config, inputs_dict):
encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
pt_model = VisionEncoderDecoderModel(encoder_decoder_config)
@@ -330,7 +327,6 @@ class FlaxEncoderDecoderMixin:
@is_pt_flax_cross_test
def test_pt_flax_equivalence(self):
config_inputs_dict = self.prepare_config_and_inputs()
config = config_inputs_dict.pop("config")
decoder_config = config_inputs_dict.pop("decoder_config")
@@ -442,7 +438,6 @@ class FlaxVisionEncoderDecoderModelTest(unittest.TestCase):
)
def _check_configuration_tie(self, model):
module = model.module.bind(model.params)
assert id(module.decoder.config) == id(model.config.decoder)
@@ -465,7 +460,6 @@ def prepare_img():
class FlaxViT2GPT2ModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_coco_en(self):
loc = "ydshieh/vit-gpt2-coco-en"
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
@@ -501,7 +495,6 @@ class FlaxViT2GPT2ModelIntegrationTest(unittest.TestCase):
self.assertLessEqual(max_diff, 1e-4)
def generate_step(pixel_values):
outputs = model.generate(pixel_values, max_length=16, num_beams=4)
output_ids = outputs.sequences
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)

19
tests/models/vision_encoder_decoder/test_modeling_tf_vision_encoder_decoder.py
View File

@@ -84,7 +84,7 @@ class TFVisionEncoderDecoderMixin:
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
**kwargs,
):
encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
self.assertTrue(encoder_decoder_config.decoder.is_decoder)
@@ -114,7 +114,7 @@ class TFVisionEncoderDecoderMixin:
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
@@ -158,7 +158,7 @@ class TFVisionEncoderDecoderMixin:
decoder_input_ids,
decoder_attention_mask,
return_dict,
**kwargs
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict}
@@ -185,7 +185,7 @@ class TFVisionEncoderDecoderMixin:
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
@@ -223,7 +223,7 @@ class TFVisionEncoderDecoderMixin:
decoder_input_ids,
decoder_attention_mask,
labels,
**kwargs
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
@@ -253,7 +253,7 @@ class TFVisionEncoderDecoderMixin:
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
**kwargs,
):
# make the decoder inputs a different shape from the encoder inputs to harden the test
decoder_input_ids = decoder_input_ids[:, :-1]
@@ -403,7 +403,6 @@ class TFVisionEncoderDecoderMixin:
)
def prepare_pt_inputs_from_tf_inputs(self, tf_inputs_dict):
pt_inputs_dict = {}
for name, key in tf_inputs_dict.items():
if type(key) == bool:
@@ -423,7 +422,6 @@ class TFVisionEncoderDecoderMixin:
return pt_inputs_dict
def check_pt_tf_models(self, tf_model, pt_model, tf_inputs_dict):
pt_inputs_dict = self.prepare_pt_inputs_from_tf_inputs(tf_inputs_dict)
# send pytorch inputs to the correct device
@@ -463,7 +461,6 @@ class TFVisionEncoderDecoderMixin:
self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
def check_pt_to_tf_equivalence(self, config, decoder_config, tf_inputs_dict):
encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
# Output all for aggressive testing
encoder_decoder_config.output_hidden_states = True
@@ -479,7 +476,6 @@ class TFVisionEncoderDecoderMixin:
self.check_pt_tf_equivalence(tf_model, pt_model, tf_inputs_dict)
def check_tf_to_pt_equivalence(self, config, decoder_config, tf_inputs_dict):
encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
# Output all for aggressive testing
encoder_decoder_config.output_hidden_states = True
@@ -534,7 +530,6 @@ class TFVisionEncoderDecoderMixin:
@is_pt_tf_cross_test
def test_pt_tf_model_equivalence(self):
config_inputs_dict = self.prepare_config_and_inputs()
labels = config_inputs_dict.pop("decoder_token_labels")
@@ -839,7 +834,6 @@ class TFVisionEncoderDecoderModelSaveLoadTests(unittest.TestCase):
decoder_input_ids = decoder_tokenizer("Linda Davis", return_tensors="tf").input_ids
with tempfile.TemporaryDirectory() as tmp_dirname:
# Since most of HF's models don't have pretrained cross-attention layers, they are randomly
# initialized even if we create models using `from_pretrained` method.
# For the tests, the decoder need to be a model with pretrained cross-attention layers.
@@ -895,7 +889,6 @@ class TFVisionEncoderDecoderModelSaveLoadTests(unittest.TestCase):
class TFViT2GPT2ModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_coco_en(self):
loc = "ydshieh/vit-gpt2-coco-en"
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)

10
tests/models/vision_encoder_decoder/test_modeling_vision_encoder_decoder.py
View File

@@ -135,7 +135,7 @@ class EncoderDecoderMixin:
decoder_attention_mask,
return_dict,
pixel_values=None,
**kwargs
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict}
@@ -226,7 +226,7 @@ class EncoderDecoderMixin:
decoder_attention_mask,
labels=None,
pixel_values=None,
**kwargs
**kwargs,
):
# make the decoder inputs a different shape from the encoder inputs to harden the test
decoder_input_ids = decoder_input_ids[:, :-1]
@@ -402,7 +402,7 @@ class DeiT2RobertaModelTest(EncoderDecoderMixin, unittest.TestCase):
decoder_attention_mask,
labels=None,
pixel_values=None,
**kwargs
**kwargs,
):
# make the decoder inputs a different shape from the encoder inputs to harden the test
decoder_input_ids = decoder_input_ids[:, :-1]
@@ -590,7 +590,7 @@ class Swin2BartModelTest(EncoderDecoderMixin, unittest.TestCase):
decoder_attention_mask,
labels=None,
pixel_values=None,
**kwargs
**kwargs,
):
# make the decoder inputs a different shape from the encoder inputs to harden the test
decoder_input_ids = decoder_input_ids[:, :-1]
@@ -747,7 +747,6 @@ class TrOCRModelIntegrationTest(unittest.TestCase):
class ViT2GPT2ModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_coco_en(self):
loc = "ydshieh/vit-gpt2-coco-en"
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
@@ -787,7 +786,6 @@ class ViT2GPT2ModelIntegrationTest(unittest.TestCase):
self.assertLessEqual(max_diff, 1e-4)
def generate_step(pixel_values):
outputs = model.generate(
pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True, output_scores=True
)