update black to 23.7.0

Author: parthea

bigframes/_config/auth.py

@@ -30,9 +30,9 @@
 _cached_project_default: Optional[str] = None
 
 
-def get_default_credentials_with_project() -> tuple[
-    google.auth.credentials.Credentials, Optional[str]
-]:
+def get_default_credentials_with_project() -> (
+    tuple[google.auth.credentials.Credentials, Optional[str]]
+):
     global _AUTH_LOCK, _cached_credentials, _cached_project_default
 
     with _AUTH_LOCK:

bigframes/core/block_transforms.py

@@ -618,7 +618,6 @@ def skew(
     skew_column_ids: typing.Sequence[str],
     grouping_column_ids: typing.Sequence[str] = (),
 ) -> blocks.Block:
-
     original_columns = skew_column_ids
     column_labels = block.select_columns(original_columns).column_labels
 

bigframes/core/blocks.py

@@ -650,7 +650,6 @@ def _get_sampling_option(
         sampling_method: Optional[str] = None,
         random_state: Optional[int] = None,
     ) -> sampling_options.SamplingOptions:
-
         if (sampling_method is not None) and (sampling_method not in _SAMPLING_METHODS):
             raise NotImplementedError(
                 f"The downsampling method {sampling_method} is not implemented, "
@@ -693,7 +692,8 @@ def to_pandas_batches(
         """Download results one message at a time.
 
         page_size and max_results determine the size and number of batches,
-        see https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob#google_cloud_bigquery_job_QueryJob_result"""
+        see https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob#google_cloud_bigquery_job_QueryJob_result
+        """
 
         under_10gb = (
             (not allow_large_results)

bigframes/core/compile/ibis_compiler/scalar_op_registry.py

@@ -1888,7 +1888,6 @@ def struct_op_impl(
 def ai_generate(
     *values: ibis_types.Value, op: ops.AIGenerate
 ) -> ibis_types.StructValue:
-
     return ai_ops.AIGenerate(
         _construct_prompt(values, op.prompt_context),  # type: ignore
         op.connection_id,  # type: ignore
@@ -1903,7 +1902,6 @@ def ai_generate(
 def ai_generate_bool(
     *values: ibis_types.Value, op: ops.AIGenerateBool
 ) -> ibis_types.StructValue:
-
     return ai_ops.AIGenerateBool(
         _construct_prompt(values, op.prompt_context),  # type: ignore
         op.connection_id,  # type: ignore
@@ -1917,7 +1915,6 @@ def ai_generate_bool(
 def ai_generate_int(
     *values: ibis_types.Value, op: ops.AIGenerateInt
 ) -> ibis_types.StructValue:
-
     return ai_ops.AIGenerateInt(
         _construct_prompt(values, op.prompt_context),  # type: ignore
         op.connection_id,  # type: ignore
@@ -1931,7 +1928,6 @@ def ai_generate_int(
 def ai_generate_double(
     *values: ibis_types.Value, op: ops.AIGenerateDouble
 ) -> ibis_types.StructValue:
-
     return ai_ops.AIGenerateDouble(
         _construct_prompt(values, op.prompt_context),  # type: ignore
         op.connection_id,  # type: ignore
@@ -1943,7 +1939,6 @@ def ai_generate_double(
 
 @scalar_op_compiler.register_nary_op(ops.AIIf, pass_op=True)
 def ai_if(*values: ibis_types.Value, op: ops.AIIf) -> ibis_types.StructValue:
-
     return ai_ops.AIIf(
         _construct_prompt(values, op.prompt_context),  # type: ignore
         op.connection_id,  # type: ignore
@@ -1954,7 +1949,6 @@ def ai_if(*values: ibis_types.Value, op: ops.AIIf) -> ibis_types.StructValue:
 def ai_classify(
     *values: ibis_types.Value, op: ops.AIClassify
 ) -> ibis_types.StructValue:
-
     return ai_ops.AIClassify(
         _construct_prompt(values, op.prompt_context),  # type: ignore
         op.categories,  # type: ignore
@@ -1964,7 +1958,6 @@ def ai_classify(
 
 @scalar_op_compiler.register_nary_op(ops.AIScore, pass_op=True)
 def ai_score(*values: ibis_types.Value, op: ops.AIScore) -> ibis_types.StructValue:
-
     return ai_ops.AIScore(
         _construct_prompt(values, op.prompt_context),  # type: ignore
         op.connection_id,  # type: ignore

bigframes/core/expression_factoring.py

@@ -167,9 +167,9 @@ def graph_extract_scalar_exprs() -> Sequence[nodes.ColumnDef]:
         # TODO: We can prune expressions that won't be reused here,
         return tuple(nodes.ColumnDef(expr, id) for id, expr in results.items())
 
-    def graph_extract_window_expr() -> Optional[
-        Tuple[Sequence[nodes.ColumnDef], window_spec.WindowSpec]
-    ]:
+    def graph_extract_window_expr() -> (
+        Optional[Tuple[Sequence[nodes.ColumnDef], window_spec.WindowSpec]]
+    ):
         for id in graph.sinks:
             next_def = by_id[id]
             if isinstance(next_def.expression, agg_expressions.WindowExpression):

bigframes/core/indexes/base.py

@@ -210,7 +210,6 @@ def is_monotonic_increasing(self) -> bool:
     @property
     @validations.requires_ordering()
     def is_monotonic_decreasing(self) -> bool:
-
         return typing.cast(
             bool,
             self._block.is_monotonic_decreasing(self._block.index_columns),

bigframes/core/window/rolling.py

@@ -217,7 +217,6 @@ def create_range_window(
     grouping_keys: Sequence[str] = tuple(),
     drop_null_groups: bool = True,
 ) -> Window:
-
     if on is None:
         # Rolling on index
         index_dtypes = block.index.dtypes

bigframes/ml/cluster.py

@@ -44,7 +44,6 @@ class KMeans(
     base.UnsupervisedTrainablePredictor,
     bigframes_vendored.sklearn.cluster._kmeans.KMeans,
 ):
-
     __doc__ = bigframes_vendored.sklearn.cluster._kmeans.KMeans.__doc__
 
     def __init__(

bigframes/ml/decomposition.py

@@ -226,7 +226,6 @@ def __init__(
         # TODO: Add support for hyperparameter tuning.
         l2_reg: float = 1.0,
     ):
-
         feedback_type = feedback_type.lower()  # type: ignore
         if feedback_type not in ("explicit", "implicit"):
             raise ValueError("Expected feedback_type to be `explicit` or `implicit`.")

bigframes/ml/ensemble.py

@@ -213,7 +213,6 @@ class XGBClassifier(
     base.SupervisedTrainableWithEvaluationPredictor,
     bigframes_vendored.xgboost.sklearn.XGBClassifier,
 ):
-
     __doc__ = bigframes_vendored.xgboost.sklearn.XGBClassifier.__doc__
 
     def __init__(
@@ -370,7 +369,6 @@ class RandomForestRegressor(
     base.SupervisedTrainableWithEvaluationPredictor,
     bigframes_vendored.sklearn.ensemble._forest.RandomForestRegressor,
 ):
-
     __doc__ = bigframes_vendored.sklearn.ensemble._forest.RandomForestRegressor.__doc__
 
     def __init__(
@@ -536,7 +534,6 @@ class RandomForestClassifier(
     base.SupervisedTrainableWithEvaluationPredictor,
     bigframes_vendored.sklearn.ensemble._forest.RandomForestClassifier,
 ):
-
     __doc__ = bigframes_vendored.sklearn.ensemble._forest.RandomForestClassifier.__doc__
 
     def __init__(