Black 20 release

tests/test_modeling_t5.py

@@ -101,7 +101,13 @@ class T5ModelTester:
         )
 
     def check_prepare_lm_labels_via_shift_left(
-        self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
+        self,
+        config,
+        input_ids,
+        decoder_input_ids,
+        attention_mask,
+        decoder_attention_mask,
+        lm_labels,
     ):
         model = T5Model(config=config)
         model.to(torch_device)
@@ -134,7 +140,13 @@ class T5ModelTester:
                 self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())
 
     def create_and_check_model(
-        self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
+        self,
+        config,
+        input_ids,
+        decoder_input_ids,
+        attention_mask,
+        decoder_attention_mask,
+        lm_labels,
     ):
         model = T5Model(config=config)
         model.to(torch_device)
@@ -160,7 +172,13 @@ class T5ModelTester:
         self.parent.assertEqual(len(decoder_past[1][0]), 4)
 
     def create_and_check_with_lm_head(
-        self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
+        self,
+        config,
+        input_ids,
+        decoder_input_ids,
+        attention_mask,
+        decoder_attention_mask,
+        lm_labels,
     ):
         model = T5ForConditionalGeneration(config=config).to(torch_device).eval()
         outputs = model(
@@ -174,7 +192,13 @@ class T5ModelTester:
         self.parent.assertEqual(outputs["loss"].size(), ())
 
     def create_and_check_decoder_model_past(
-        self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
+        self,
+        config,
+        input_ids,
+        decoder_input_ids,
+        attention_mask,
+        decoder_attention_mask,
+        lm_labels,
     ):
         model = T5Model(config=config).get_decoder().to(torch_device).eval()
         # first forward pass
@@ -205,7 +229,13 @@ class T5ModelTester:
         self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
 
     def create_and_check_decoder_model_attention_mask_past(
-        self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
+        self,
+        config,
+        input_ids,
+        decoder_input_ids,
+        attention_mask,
+        decoder_attention_mask,
+        lm_labels,
     ):
         model = T5Model(config=config).get_decoder()
         model.to(torch_device)
@@ -231,7 +261,8 @@ class T5ModelTester:
         # append to next input_ids and attn_mask
         next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
         attn_mask = torch.cat(
-            [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1,
+            [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
+            dim=1,
         )
 
         # get two different outputs
@@ -249,7 +280,13 @@ class T5ModelTester:
         self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
 
     def create_and_check_generate_with_past_key_value_states(
-        self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
+        self,
+        config,
+        input_ids,
+        decoder_input_ids,
+        attention_mask,
+        decoder_attention_mask,
+        lm_labels,
     ):
         model = T5ForConditionalGeneration(config=config).to(torch_device).eval()
         torch.manual_seed(0)
@@ -261,14 +298,26 @@ class T5ModelTester:
         self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
 
     def create_and_check_model_fp16_forward(
-        self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
+        self,
+        config,
+        input_ids,
+        decoder_input_ids,
+        attention_mask,
+        decoder_attention_mask,
+        lm_labels,
     ):
         model = T5Model(config=config).to(torch_device).half().eval()
         output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)["last_hidden_state"]
         self.parent.assertFalse(torch.isnan(output).any().item())
 
     def create_and_check_encoder_decoder_shared_weights(
-        self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
+        self,
+        config,
+        input_ids,
+        decoder_input_ids,
+        attention_mask,
+        decoder_attention_mask,
+        lm_labels,
     ):
         for model_class in [T5Model, T5ForConditionalGeneration]:
             torch.manual_seed(0)
@@ -339,7 +388,14 @@ class T5ModelTester:
 
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
-        (config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,) = config_and_inputs
+        (
+            config,
+            input_ids,
+            decoder_input_ids,
+            attention_mask,
+            decoder_attention_mask,
+            lm_labels,
+        ) = config_and_inputs
 
         inputs_dict = {
             "input_ids": input_ids,
@@ -412,7 +468,11 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
         model = T5Model(config_and_inputs[0]).to(torch_device)
         with tempfile.TemporaryDirectory() as tmpdirname:
             torch.onnx.export(
-                model, config_and_inputs[1], f"{tmpdirname}/t5_test.onnx", export_params=True, opset_version=9,
+                model,
+                config_and_inputs[1],
+                f"{tmpdirname}/t5_test.onnx",
+                export_params=True,
+                opset_version=9,
             )
 
 
@@ -469,7 +529,8 @@ class T5ModelIntegrationTests(unittest.TestCase):
         )
         decoded = tok.batch_decode(hypotheses_batch, skip_special_tokens=True, clean_up_tokenization_spaces=False)
         self.assertListEqual(
-            expected_summaries, decoded,
+            expected_summaries,
+            decoded,
         )
 
     @slow