chore: blackify all

Signed-off-by: Joan Fontanals Martinez <joan.martinez@jina.ai>

annlite/container.py

@@ -188,9 +188,7 @@ def filter_cells(
 
         # reordering the results from multiple cells
         if order_by and len(cells) > 1:
-            result = sorted(
-                result, key=lambda d: d['order_by'], reverse=not ascending
-            )
+            result = sorted(result, key=lambda d: d['order_by'], reverse=not ascending)
             if limit > 0:
                 result = result[:limit]
 

annlite/index.py

@@ -53,23 +53,23 @@ class AnnLite(CellContainer):
     """
 
     def __init__(
-            self,
-            n_dim: int,
-            metric: Union[str, Metric] = 'cosine',
-            n_cells: int = 1,
-            n_subvectors: Optional[int] = None,
-            n_clusters: Optional[int] = 256,
-            n_probe: int = 16,
-            n_components: Optional[int] = None,
-            initial_size: Optional[int] = None,
-            expand_step_size: int = 10240,
-            columns: Optional[Union[Dict, List]] = None,
-            filterable_attrs: Optional[Dict] = None,
-            data_path: Union[Path, str] = Path('./data'),
-            create_if_missing: bool = True,
-            read_only: bool = False,
-            verbose: bool = False,
-            **kwargs,
+        self,
+        n_dim: int,
+        metric: Union[str, Metric] = 'cosine',
+        n_cells: int = 1,
+        n_subvectors: Optional[int] = None,
+        n_clusters: Optional[int] = 256,
+        n_probe: int = 16,
+        n_components: Optional[int] = None,
+        initial_size: Optional[int] = None,
+        expand_step_size: int = 10240,
+        columns: Optional[Union[Dict, List]] = None,
+        filterable_attrs: Optional[Dict] = None,
+        data_path: Union[Path, str] = Path('./data'),
+        create_if_missing: bool = True,
+        read_only: bool = False,
+        verbose: bool = False,
+        **kwargs,
     ):
         setup_logging(verbose)
 
@@ -81,7 +81,7 @@ def __init__(
 
         if n_subvectors:
             assert (
-                    n_dim % n_subvectors == 0
+                n_dim % n_subvectors == 0
             ), '"n_dim" needs to be divisible by "n_subvectors"'
         self.n_dim = n_dim
         self.n_components = n_components
@@ -186,7 +186,7 @@ def __init__(
     def _sanity_check(self, x: 'np.ndarray'):
         assert x.ndim == 2, 'inputs must be a 2D array'
         assert (
-                x.shape[1] == self.n_dim
+            x.shape[1] == self.n_dim
         ), f'inputs must have the same dimension as the index , got {x.shape[1]}, expected {self.n_dim}'
 
         return x.shape
@@ -230,7 +230,7 @@ def train(self, x: 'np.ndarray', auto_save: bool = True, force_train: bool = Fal
             self.dump_model()
 
     def partial_train(
-            self, x: np.ndarray, auto_save: bool = True, force_train: bool = False
+        self, x: np.ndarray, auto_save: bool = True, force_train: bool = False
     ):
         """Partially train the index with the given data.
 
@@ -293,11 +293,11 @@ def index(self, docs: 'List', **kwargs):
         return super(AnnLite, self).insert(x, assigned_cells, docs)
 
     def update(
-            self,
-            docs: 'List',
-            raise_errors_on_not_found: bool = False,
-            insert_if_not_found: bool = True,
-            **kwargs,
+        self,
+        docs: 'List',
+        raise_errors_on_not_found: bool = False,
+        insert_if_not_found: bool = True,
+        **kwargs,
     ):
         """Update the documents in the index.
 
@@ -331,12 +331,12 @@ def update(
         )
 
     def search(
-            self,
-            docs: 'List',
-            filter: Optional[dict] = None,
-            limit: int = 10,
-            include_metadata: bool = True,
-            **kwargs,
+        self,
+        docs: 'List',
+        filter: Optional[dict] = None,
+        limit: int = 10,
+        include_metadata: bool = True,
+        **kwargs,
     ):
         """Search the index, and attach matches to the query Documents in `docs`
 
@@ -356,11 +356,11 @@ def search(
         return match_docs
 
     def search_by_vectors(
-            self,
-            query_np: 'np.ndarray',
-            filter: Optional[dict] = None,
-            limit: int = 10,
-            include_metadata: bool = True,
+        self,
+        query_np: 'np.ndarray',
+        filter: Optional[dict] = None,
+        limit: int = 10,
+        include_metadata: bool = True,
     ):
         """Search the index by vectors, and return the matches.
 
@@ -384,13 +384,13 @@ def search_by_vectors(
         return match_dists, match_docs
 
     def filter(
-            self,
-            filter: Dict,
-            limit: int = 10,
-            offset: int = 0,
-            order_by: Optional[str] = None,
-            ascending: bool = True,
-            include_metadata: bool = True,
+        self,
+        filter: Dict,
+        limit: int = 10,
+        offset: int = 0,
+        order_by: Optional[str] = None,
+        ascending: bool = True,
+        include_metadata: bool = True,
     ):
         """Find the documents by the filter.
 
@@ -427,12 +427,12 @@ def get_doc_by_id(self, doc_id: str):
         return self._get_doc_by_id(doc_id)
 
     def get_docs(
-            self,
-            filter: Optional[dict] = None,
-            limit: int = 10,
-            offset: int = 0,
-            order_by: Optional[str] = None,
-            ascending: bool = True,
+        self,
+        filter: Optional[dict] = None,
+        limit: int = 10,
+        offset: int = 0,
+        order_by: Optional[str] = None,
+        ascending: bool = True,
     ):
         """Get the documents.
 
@@ -480,11 +480,11 @@ def _cell_selection(self, query_np, limit):
         return cells
 
     def search_numpy(
-            self,
-            query_np: 'np.ndarray',
-            filter: Dict = {},
-            limit: int = 10,
-            **kwargs,
+        self,
+        query_np: 'np.ndarray',
+        filter: Dict = {},
+        limit: int = 10,
+        **kwargs,
     ):
         """Search the index and return distances to the query and ids of the closest documents.
 
@@ -519,9 +519,9 @@ def _search_numpy(self, query_np: 'np.ndarray', filter: Dict = {}, limit: int =
         return dists, ids
 
     def delete(
-            self,
-            docs: Union['List[Dict]', List[str]],
-            raise_errors_on_not_found: bool = False,
+        self,
+        docs: Union['List[Dict]', List[str]],
+        raise_errors_on_not_found: bool = False,
     ):
         """Delete entries from the index by id
 
@@ -531,7 +531,8 @@ def delete(
 
         if len(docs) > 0:
             super().delete(
-                docs if isinstance(docs[0], str) else [doc['id'] for doc in docs], raise_errors_on_not_found
+                docs if isinstance(docs[0], str) else [doc['id'] for doc in docs],
+                raise_errors_on_not_found,
             )
 
     def clear(self):
@@ -617,9 +618,9 @@ def index_hash(self):
     def index_path(self):
         if self.index_hash:
             return (
-                    self.data_path
-                    / f'snapshot-{self.params_hash}'
-                    / f'{self.index_hash}-SNAPSHOT'
+                self.data_path
+                / f'snapshot-{self.params_hash}'
+                / f'{self.index_hash}-SNAPSHOT'
             )
         return None
 
@@ -863,14 +864,14 @@ def _rebuild_index_from_remote(self, source_name: str, token: str):
                     # default has only one cell
                     shutil.unpack_archive(zip_file, self.data_path / f'cell_{cell_id}')
                     for f in list(
-                            (
-                                    self.data_path
-                                    / f'cell_{cell_id}'
-                                    / zip_file.name.split('.zip')[0]
-                            ).iterdir()
+                        (
+                            self.data_path
+                            / f'cell_{cell_id}'
+                            / zip_file.name.split('.zip')[0]
+                        ).iterdir()
                     ):
                         origin_database_path = (
-                                self.data_path / f'cell_{cell_id}' / f.name
+                            self.data_path / f'cell_{cell_id}' / f.name
                         )
                         if origin_database_path.exists():
                             origin_database_path.unlink()

annlite/storage/kv.py

@@ -52,8 +52,8 @@ def insert(self, docs: 'List'):
         write_opt.sync = True
         batch_size = 0
         for doc in docs:
-            #TODO: How to serialize a dict
-            #write_batch.put(doc.id.encode(), doc.to_bytes(**self._serialize_config))
+            # TODO: How to serialize a dict
+            # write_batch.put(doc.id.encode(), doc.to_bytes(**self._serialize_config))
             write_batch.put(doc['id'].encode(), pickle.dumps(doc))
             batch_size += 1
         self._db.write(write_batch, write_opt=write_opt)
@@ -68,7 +68,7 @@ def update(self, docs: 'List'):
             if key not in self._db:
                 raise ValueError(f'The Doc ({doc["id"]}) does not exist in database!')
 
-            #write_batch.put(key, doc.to_bytes(**self._serialize_config))
+            # write_batch.put(key, doc.to_bytes(**self._serialize_config))
             # TODO: Serialize
             write_batch.put(key, pickle.dumps(doc))
         self._db.write(write_batch, write_opt=write_opt)
@@ -89,7 +89,7 @@ def get(self, doc_ids: Union[str, list]) -> List:
 
         for doc_bytes in self._db[[k.encode() for k in doc_ids]]:
             if doc_bytes:
-                #docs.append(Document.from_bytes(doc_bytes, **self._serialize_config))
+                # docs.append(Document.from_bytes(doc_bytes, **self._serialize_config))
                 # TODO: Deserialize
                 docs.append(pickle.loads(doc_bytes))
 
@@ -144,8 +144,8 @@ def batched_iterator(self, batch_size: int = 1, **kwargs) -> 'List':
         read_opt = ReadOptions()
 
         for value in self._db.values(read_opt=read_opt):
-            #doc = Document.from_bytes(value, **self._serialize_config)
-            #TODO: Deserialize
+            # doc = Document.from_bytes(value, **self._serialize_config)
+            # TODO: Deserialize
             docs.append(pickle.loads(value))
             count += 1
 

annlite/storage/table.py

@@ -231,10 +231,7 @@ def insert(
         for doc in docs:
             doc_value = tuple(
                 [doc['id']]
-                + [
-                    _converting(doc[c]) if c in doc else None
-                    for c in self.columns[2:]
-                ]
+                + [_converting(doc[c]) if c in doc else None for c in self.columns[2:]]
             )
             values.append(doc_value)
             docs_size += 1

scripts/get-all-test-paths.sh

@@ -5,8 +5,6 @@ set -ex
 BATCH_SIZE=5
 
 declare -a array1=( "tests/test_*.py" )
-declare -a array2=( "tests/docarray/test_*.py" )
-declare -a array3=( "tests/executor/test_*.py" )
-dest=( "${array1[@]}" "${array2[@]}" "${array3[@]}" )
+dest=( "${array1[@]}" )
 
 printf '%s\n' "${dest[@]}" | jq -R . | jq -cs .

tests/conftest.py

@@ -7,20 +7,20 @@
 @pytest.fixture(scope='session')
 def docs():
     return [
-            dict(id='doc1', embedding=np.array([1, 0, 0, 0])),
-            dict(id='doc2', embedding=np.array([0, 1, 0, 0])),
-            dict(id='doc3', embedding=np.array([0, 0, 1, 0])),
-            dict(id='doc4', embedding=np.array([0, 0, 0, 1])),
-            dict(id='doc5', embedding=np.array([1, 0, 1, 0])),
-            dict(id='doc6', embedding=np.array([0, 1, 0, 1])),
-        ]
+        dict(id='doc1', embedding=np.array([1, 0, 0, 0])),
+        dict(id='doc2', embedding=np.array([0, 1, 0, 0])),
+        dict(id='doc3', embedding=np.array([0, 0, 1, 0])),
+        dict(id='doc4', embedding=np.array([0, 0, 0, 1])),
+        dict(id='doc5', embedding=np.array([1, 0, 1, 0])),
+        dict(id='doc6', embedding=np.array([0, 1, 0, 1])),
+    ]
 
 
 @pytest.fixture(scope='session')
 def update_docs():
     return [
-            dict(id='doc1', embedding=np.array([0, 0, 0, 1])),
-        ]
+        dict(id='doc1', embedding=np.array([0, 0, 0, 1])),
+    ]
 
 
 @pytest.fixture(autouse=True)

tests/test_crud.py

@@ -42,7 +42,9 @@ def test_update_legal(annlite_with_data):
     index = annlite_with_data
 
     updated_X = np.random.random((Nt, D)).astype(np.float32)
-    updated_docs = [dict(id=f'{i}', embedding=updated_X[i], x=random.random()) for i in range(Nt)]
+    updated_docs = [
+        dict(id=f'{i}', embedding=updated_X[i], x=random.random()) for i in range(Nt)
+    ]
 
     index.update(updated_docs)
     matches = index.search(updated_docs)
@@ -56,9 +58,10 @@ def test_update_illegal(annlite_with_data):
     index = annlite_with_data
 
     updated_X = np.random.random((Nt, D)).astype(np.float32)
-    updated_docs = [dict(
-        id=f'{i}_wrong', embedding=updated_X[i], x=random.random()
-    ) for i in range(Nt)]
+    updated_docs = [
+        dict(id=f'{i}_wrong', embedding=updated_X[i], x=random.random())
+        for i in range(Nt)
+    ]
 
     with pytest.raises(Exception):
         index.update(

tests/test_filter.py

@@ -169,7 +169,10 @@ def test_filter_with_limit_offset(tmpfile, limit, offset, order_by, ascending):
     )
     X = np.random.random((N, D)).astype(np.float32)
 
-    docs = [dict(id=f'{i}', embedding=X[i], x=random.random(), y=random.random()) for i in range(N)]
+    docs = [
+        dict(id=f'{i}', embedding=X[i], x=random.random(), y=random.random())
+        for i in range(N)
+    ]
     index.index(docs)
 
     matches = index.filter(