sdks/python: fix proposed linting and formatting issues

Author: moawnallah

sdks/python/apache_beam/ml/rag/embeddings/huggingface_test.py

@@ -24,78 +24,77 @@
 
 import apache_beam as beam
 from apache_beam.ml.rag.embeddings.huggingface import HuggingfaceTextEmbeddings
-from apache_beam.ml.rag.types import Chunk
-from apache_beam.ml.rag.types import Content
-from apache_beam.ml.rag.types import Embedding
 from apache_beam.ml.rag.test_utils import TestHelpers
+from apache_beam.ml.rag.types import Chunk, Content, Embedding
 from apache_beam.ml.transforms.base import MLTransform
 from apache_beam.testing.test_pipeline import TestPipeline
-from apache_beam.testing.util import assert_that
-from apache_beam.testing.util import equal_to
+from apache_beam.testing.util import assert_that, equal_to
 
 # pylint: disable=unused-import
 try:
-  from sentence_transformers import SentenceTransformer
-  SENTENCE_TRANSFORMERS_AVAILABLE = True
+    from sentence_transformers import SentenceTransformer
+
+    SENTENCE_TRANSFORMERS_AVAILABLE = True
 except ImportError:
-  SENTENCE_TRANSFORMERS_AVAILABLE = False
+    SENTENCE_TRANSFORMERS_AVAILABLE = False
 
 
 @pytest.mark.uses_transformers
 @unittest.skipIf(
-    not SENTENCE_TRANSFORMERS_AVAILABLE, "sentence-transformers not available")
+    not SENTENCE_TRANSFORMERS_AVAILABLE, "sentence-transformers not available"
+)
 class HuggingfaceTextEmbeddingsTest(unittest.TestCase):
-  def setUp(self):
-    self.artifact_location = tempfile.mkdtemp(prefix='sentence_transformers_')
-    self.test_chunks = [
-        Chunk(
-            content=Content(text="This is a test sentence."),
-            id="1",
-            metadata={
-                "source": "test.txt", "language": "en"
-            }),
-        Chunk(
-            content=Content(text="Another example."),
-            id="2",
-            metadata={
-                "source": "test.txt", "language": "en"
-            })
-    ]
+    def setUp(self):
+        self.artifact_location = tempfile.mkdtemp(prefix="sentence_transformers_")
+        self.test_chunks = [
+            Chunk(
+                content=Content(text="This is a test sentence."),
+                id="1",
+                metadata={"source": "test.txt", "language": "en"},
+            ),
+            Chunk(
+                content=Content(text="Another example."),
+                id="2",
+                metadata={"source": "test.txt", "language": "en"},
+            ),
+        ]
 
-  def tearDown(self) -> None:
-    shutil.rmtree(self.artifact_location)
+    def tearDown(self) -> None:
+        shutil.rmtree(self.artifact_location)
 
-  def test_embedding_pipeline(self):
-    expected = [
-        Chunk(
-            id="1",
-            embedding=Embedding(dense_embedding=[0.0] * 384),
-            metadata={
-                "source": "test.txt", "language": "en"
-            },
-            content=Content(text="This is a test sentence.")),
-        Chunk(
-            id="2",
-            embedding=Embedding(dense_embedding=[0.0] * 384),
-            metadata={
-                "source": "test.txt", "language": "en"
-            },
-            content=Content(text="Another example."))
-    ]
-    embedder = HuggingfaceTextEmbeddings(
-        model_name="sentence-transformers/all-MiniLM-L6-v2")
+    def test_embedding_pipeline(self):
+        expected = [
+            Chunk(
+                id="1",
+                embedding=Embedding(dense_embedding=[0.0] * 384),
+                metadata={"source": "test.txt", "language": "en"},
+                content=Content(text="This is a test sentence."),
+            ),
+            Chunk(
+                id="2",
+                embedding=Embedding(dense_embedding=[0.0] * 384),
+                metadata={"source": "test.txt", "language": "en"},
+                content=Content(text="Another example."),
+            ),
+        ]
+        embedder = HuggingfaceTextEmbeddings(
+            model_name="sentence-transformers/all-MiniLM-L6-v2"
+        )
 
-    with TestPipeline() as p:
-      embeddings = (
-          p
-          | beam.Create(self.test_chunks)
-          | MLTransform(write_artifact_location=self.artifact_location).
-          with_transform(embedder))
+        with TestPipeline() as p:
+            embeddings = (
+                p
+                | beam.Create(self.test_chunks)
+                | MLTransform(
+                    write_artifact_location=self.artifact_location
+                ).with_transform(embedder)
+            )
 
-      assert_that(
-          embeddings,
-          equal_to(expected, equals_fn=TestHelpers.chunk_approximately_equals))
+            assert_that(
+                embeddings,
+                equal_to(expected, equals_fn=TestHelpers.chunk_approximately_equals),
+            )
 
 
-if __name__ == '__main__':
-  unittest.main()
+if __name__ == "__main__":
+    unittest.main()