Bump black from 22.6.0 to 24.3.0 (#6820)

Dependabot upgrade #6802 failed since Python files need to be reformatted after `black` upgrade, bumping `black` version and linting the Python files in this PR.

#oncall

tensorboard/backend/event_processing/data_provider.py

@@ -152,13 +152,13 @@ def read_last_scalars(
             plugin_name, run_tag_filter, summary_pb2.DATA_CLASS_SCALAR
         )
         run_tag_to_last_scalar_datum = collections.defaultdict(dict)
-        for (run, tags_for_run) in index.items():
-            for (tag, metadata) in tags_for_run.items():
+        for run, tags_for_run in index.items():
+            for tag, metadata in tags_for_run.items():
                 events = self._multiplexer.Tensors(run, tag)
                 if events:
-                    run_tag_to_last_scalar_datum[run][
-                        tag
-                    ] = _convert_scalar_event(events[-1])
+                    run_tag_to_last_scalar_datum[run][tag] = (
+                        _convert_scalar_event(events[-1])
+                    )
 
         return run_tag_to_last_scalar_datum
 
@@ -222,11 +222,11 @@ def _index(self, plugin_name, run_tag_filter, data_class_filter):
             all_metadata = self._multiplexer.AllSummaryMetadata()
 
         result = {}
-        for (run, tag_to_metadata) in all_metadata.items():
+        for run, tag_to_metadata in all_metadata.items():
             if runs is not None and run not in runs:
                 continue
             result_for_run = {}
-            for (tag, metadata) in tag_to_metadata.items():
+            for tag, metadata in tag_to_metadata.items():
                 if tags is not None and tag not in tags:
                     continue
                 if metadata.data_class != data_class_filter:
@@ -250,10 +250,10 @@ def _list(self, construct_time_series, index):
           suitable to be returned from `list_scalars` or `list_tensors`.
         """
         result = {}
-        for (run, tag_to_metadata) in index.items():
+        for run, tag_to_metadata in index.items():
             result_for_run = {}
             result[run] = result_for_run
-            for (tag, summary_metadata) in tag_to_metadata.items():
+            for tag, summary_metadata in tag_to_metadata.items():
                 max_step = None
                 max_wall_time = None
                 for event in self._multiplexer.Tensors(run, tag):
@@ -286,10 +286,10 @@ def _read(self, convert_event, index, downsample):
           suitable to be returned from `read_scalars` or `read_tensors`.
         """
         result = {}
-        for (run, tags_for_run) in index.items():
+        for run, tags_for_run in index.items():
             result_for_run = {}
             result[run] = result_for_run
-            for (tag, metadata) in tags_for_run.items():
+            for tag, metadata in tags_for_run.items():
                 events = self._multiplexer.Tensors(run, tag)
                 data = [convert_event(e) for e in events]
                 result_for_run[tag] = _downsample(data, downsample)
@@ -304,10 +304,10 @@ def list_blob_sequences(
             plugin_name, run_tag_filter, summary_pb2.DATA_CLASS_BLOB_SEQUENCE
         )
         result = {}
-        for (run, tag_to_metadata) in index.items():
+        for run, tag_to_metadata in index.items():
             result_for_run = {}
             result[run] = result_for_run
-            for (tag, metadata) in tag_to_metadata.items():
+            for tag, metadata in tag_to_metadata.items():
                 max_step = None
                 max_wall_time = None
                 max_length = None
@@ -345,7 +345,7 @@ def read_blob_sequences(
             plugin_name, run_tag_filter, summary_pb2.DATA_CLASS_BLOB_SEQUENCE
         )
         result = {}
-        for (run, tags) in index.items():
+        for run, tags in index.items():
             result_for_run = {}
             result[run] = result_for_run
             for tag in tags:

tensorboard/backend/event_processing/data_provider_test.py

@@ -83,7 +83,7 @@ def setUp(self):
                 ("very smooth", (0.0, 0.25, 0.5, 0.75, 1.0), "uniform"),
                 ("very smoothn't", (0.0, 0.01, 0.99, 1.0), "bimodal"),
             ]
-            for (description, distribution, name) in data:
+            for description, distribution, name in data:
                 tensor = tf.constant([distribution], dtype=tf.float64)
                 for i in range(1, 11):
                     histogram_summary.histogram(
@@ -97,7 +97,7 @@ def setUp(self):
                 ("blue", (1, 91, 158), "bottom-left"),
                 ("yellow", (239, 220, 111), "bottom-right"),
             ]
-            for (name, color, description) in data:
+            for name, color, description in data:
                 image_1x1 = tf.constant([[[color]]], dtype=tf.uint8)
                 for i in range(1, 11):
                     # Use a non-monotonic sequence of sample sizes to
@@ -289,7 +289,7 @@ def test_read_scalars(self):
             for tag in result[run]:
                 tensor_events = multiplexer.Tensors(run, tag)
                 self.assertLen(result[run][tag], len(tensor_events))
-                for (datum, event) in zip(result[run][tag], tensor_events):
+                for datum, event in zip(result[run][tag], tensor_events):
                     self.assertEqual(datum.step, event.step)
                     self.assertEqual(datum.wall_time, event.wall_time)
                     self.assertEqual(
@@ -424,7 +424,7 @@ def test_read_tensors(self):
             for tag in result[run]:
                 tensor_events = multiplexer.Tensors(run, tag)
                 self.assertLen(result[run][tag], len(tensor_events))
-                for (datum, event) in zip(result[run][tag], tensor_events):
+                for datum, event in zip(result[run][tag], tensor_events):
                     self.assertEqual(datum.step, event.step)
                     self.assertEqual(datum.wall_time, event.wall_time)
                     np.testing.assert_equal(

tensorboard/backend/event_processing/event_multiplexer.py

@@ -96,7 +96,7 @@ def __init__(
                 "Event Multplexer doing initialization load for %s",
                 run_path_map,
             )
-            for (run, path) in run_path_map.items():
+            for run, path in run_path_map.items():
                 self.AddRun(path, run)
         logger.info("Event Multiplexer done initializing")
 

tensorboard/backend/event_processing/plugin_event_multiplexer.py

@@ -122,7 +122,7 @@ def __init__(
                 "Event Multplexer doing initialization load for %s",
                 run_path_map,
             )
-            for (run, path) in run_path_map.items():
+            for run, path in run_path_map.items():
                 self.AddRun(path, run)
         logger.info("Event Multiplexer done initializing")
 

tensorboard/compat/tensorflow_stub/io/gfile.py

@@ -602,9 +602,11 @@ def glob(self, filename):
         prefix = self._get_chain_protocol_prefix(filename)
 
         return [
-            file
-            if (self.SEPARATOR in file or self.CHAIN_SEPARATOR in file)
-            else prefix + file
+            (
+                file
+                if (self.SEPARATOR in file or self.CHAIN_SEPARATOR in file)
+                else prefix + file
+            )
             for file in files
         ]
 

tensorboard/compat/tensorflow_stub/io/gfile_tf_test.py

@@ -158,7 +158,7 @@ def testWalkInOrder(self):
         all_dirs = []
         all_subdirs = []
         all_files = []
-        for (w_dir, w_subdirs, w_files) in gfile.walk(dir_path, topdown=True):
+        for w_dir, w_subdirs, w_files in gfile.walk(dir_path, topdown=True):
             all_dirs.append(w_dir)
             all_subdirs.append(w_subdirs)
             all_files.append(w_files)
@@ -198,7 +198,7 @@ def testWalkPostOrder(self):
         all_dirs = []
         all_subdirs = []
         all_files = []
-        for (w_dir, w_subdirs, w_files) in gfile.walk(dir_path, topdown=False):
+        for w_dir, w_subdirs, w_files in gfile.walk(dir_path, topdown=False):
             all_dirs.append(w_dir)
             all_subdirs.append(w_subdirs)
             all_files.append(w_files)
@@ -237,7 +237,7 @@ def testWalkFailure(self):
         all_dirs = []
         all_subdirs = []
         all_files = []
-        for (w_dir, w_subdirs, w_files) in gfile.walk(dir_path, topdown=False):
+        for w_dir, w_subdirs, w_files in gfile.walk(dir_path, topdown=False):
             all_dirs.append(w_dir)
             all_subdirs.append(w_subdirs)
             all_files.append(w_files)

tensorboard/data/grpc_provider.py

@@ -140,7 +140,7 @@ def read_scalars(
                     series = []
                     tags[tag_entry.tag_name] = series
                     d = tag_entry.data
-                    for (step, wt, value) in zip(d.step, d.wall_time, d.value):
+                    for step, wt, value in zip(d.step, d.wall_time, d.value):
                         point = provider.ScalarDatum(
                             step=step,
                             wall_time=wt,
@@ -177,13 +177,13 @@ def read_last_scalars(
                     d = tag_entry.data
                     # There should be no more than one datum in
                     # `tag_entry.data` since downsample was set to 1.
-                    for (step, wt, value) in zip(d.step, d.wall_time, d.value):
-                        result[run_name][
-                            tag_entry.tag_name
-                        ] = provider.ScalarDatum(
-                            step=step,
-                            wall_time=wt,
-                            value=value,
+                    for step, wt, value in zip(d.step, d.wall_time, d.value):
+                        result[run_name][tag_entry.tag_name] = (
+                            provider.ScalarDatum(
+                                step=step,
+                                wall_time=wt,
+                                value=value,
+                            )
                         )
             return result
 
@@ -243,7 +243,7 @@ def read_tensors(
                     series = []
                     tags[tag_entry.tag_name] = series
                     d = tag_entry.data
-                    for (step, wt, value) in zip(d.step, d.wall_time, d.value):
+                    for step, wt, value in zip(d.step, d.wall_time, d.value):
                         point = provider.TensorDatum(
                             step=step,
                             wall_time=wt,
@@ -308,7 +308,7 @@ def read_blob_sequences(
                     series = []
                     tags[tag_entry.tag_name] = series
                     d = tag_entry.data
-                    for (step, wt, blob_sequence) in zip(
+                    for step, wt, blob_sequence in zip(
                         d.step, d.wall_time, d.values
                     ):
                         values = []

tensorboard/data/grpc_provider_test.py

@@ -513,7 +513,7 @@ def test_rpc_error(self):
             (grpc.StatusCode.NOT_FOUND, errors.NotFoundError),
             (grpc.StatusCode.PERMISSION_DENIED, errors.PermissionDeniedError),
         ]
-        for (code, error_type) in cases:
+        for code, error_type in cases:
             with self.subTest(code.name):
                 msg = "my favorite cause"
                 e = _grpc_error(code, msg)

tensorboard/data/server_ingester_test.py

@@ -64,6 +64,7 @@ def test(self):
         error_file = os.path.join(tmpdir.name, "startup_error")
 
         real_popen = subprocess.Popen
+
         # Stub out `subprocess.Popen` to write the port file.
         def fake_popen(subprocess_args, *args, **kwargs):
             def target():

tensorboard/examples/plugins/example_basic/tensorboard_plugin_example/plugin.py

@@ -74,8 +74,8 @@ def _serve_tags(self, request):
         )
 
         result = {run: {} for run in mapping}
-        for (run, tag_to_timeseries) in mapping.items():
-            for (tag, timeseries) in tag_to_timeseries.items():
+        for run, tag_to_timeseries in mapping.items():
+            for tag, timeseries in tag_to_timeseries.items():
                 result[run][tag] = {
                     "description": timeseries.description,
                 }

tensorboard/functionaltests/core_test.py

@@ -116,7 +116,7 @@ def testDashboardSelection(self):
             "reload_button": "paper-icon-button#reload-button",
         }
         elements = {}
-        for (name, selector) in selectors.items():
+        for name, selector in selectors.items():
             locator = (by.By.CSS_SELECTOR, selector)
             self.wait.until(
                 expected_conditions.presence_of_element_located(locator)

tensorboard/notebook.py

@@ -349,7 +349,7 @@ def _display_colab(port, height, display_handle):
         ("%PORT%", "%d" % port),
         ("%HEIGHT%", "%d" % height),
     ]
-    for (k, v) in replacements:
+    for k, v in replacements:
         shell = shell.replace(k, v)
     script = IPython.display.Javascript(shell)
 
@@ -398,7 +398,7 @@ def _display_ipython(port, height, display_handle):
             ("%URL%", json.dumps("/")),
         ]
 
-    for (k, v) in replacements:
+    for k, v in replacements:
         shell = shell.replace(k, v)
     iframe = IPython.display.HTML(shell)
     if display_handle:

tensorboard/pip_package/requirements_dev.txt

@@ -25,7 +25,7 @@ moto==1.3.7
 fsspec>=2021.06.0
 
 # For linting
-black==22.6.0
+black==24.3.0
 flake8==3.7.8
 yamllint==1.17.0
 

tensorboard/plugin_util.py

@@ -60,6 +60,7 @@
     "th",
 ]
 
+
 # Cache Markdown converter to avoid expensive initialization at each
 # call to `markdown_to_safe_html`. Cache a different instance per thread.
 class _MarkdownStore(threading.local):

tensorboard/plugins/audio/audio_plugin.py

@@ -101,8 +101,8 @@ def _index_impl(self, ctx, experiment):
             plugin_name=metadata.PLUGIN_NAME,
         )
         result = {run: {} for run in mapping}
-        for (run, tag_to_time_series) in mapping.items():
-            for (tag, time_series) in tag_to_time_series.items():
+        for run, tag_to_time_series in mapping.items():
+            for tag, time_series in tag_to_time_series.items():
                 md = metadata.parse_plugin_metadata(time_series.plugin_content)
                 if not self._version_checker.ok(md.version, run, tag):
                     continue

tensorboard/plugins/custom_scalar/custom_scalars_plugin_test.py

@@ -177,9 +177,9 @@ def createPlugin(self, logdir):
             custom_scalars_plugin_instance,
         ]
         for plugin_instance in plugin_instances:
-            plugin_name_to_instance[
-                plugin_instance.plugin_name
-            ] = plugin_instance
+            plugin_name_to_instance[plugin_instance.plugin_name] = (
+                plugin_instance
+            )
         return custom_scalars_plugin_instance
 
     def test_download_url_json(self):

tensorboard/plugins/debugger_v2/debugger_v2_plugin_test.py

@@ -116,6 +116,7 @@ def setUp(self):
         self.plugin = debugger_v2_plugin.DebuggerV2Plugin(context)
         wsgi_app = application.TensorBoardWSGI([self.plugin])
         self.server = werkzeug_test.Client(wsgi_app, wrappers.Response)
+
         # The multiplexer reads data asynchronously on a separate thread, so
         # as not to block the main thread of the TensorBoard backend. During
         # unit test, we disable the asynchronous behavior, so that we can

tensorboard/plugins/graph/graphs_plugin.py

@@ -108,8 +108,8 @@ def add_row_item(run, tag=None):
             experiment_id=experiment,
             plugin_name=metadata.PLUGIN_NAME_RUN_METADATA_WITH_GRAPH,
         )
-        for (run_name, tags) in mapping.items():
-            for (tag, tag_data) in tags.items():
+        for run_name, tags in mapping.items():
+            for tag, tag_data in tags.items():
                 # The Summary op is defined in TensorFlow and does not use a stringified proto
                 # as a content of plugin data. It contains single string that denotes a version.
                 # https://github.com/tensorflow/tensorflow/blob/11f4ecb54708865ec757ca64e4805957b05d7570/tensorflow/python/ops/summary_ops_v2.py#L789-L790
@@ -128,8 +128,8 @@ def add_row_item(run, tag=None):
             experiment_id=experiment,
             plugin_name=metadata.PLUGIN_NAME_RUN_METADATA,
         )
-        for (run_name, tags) in mapping.items():
-            for (tag, tag_data) in tags.items():
+        for run_name, tags in mapping.items():
+            for tag, tag_data in tags.items():
                 if tag_data.plugin_content != b"1":
                     logger.warning(
                         "Ignoring unrecognizable version of RunMetadata."
@@ -146,8 +146,8 @@ def add_row_item(run, tag=None):
             experiment_id=experiment,
             plugin_name=metadata.PLUGIN_NAME_KERAS_MODEL,
         )
-        for (run_name, tags) in mapping.items():
-            for (tag, tag_data) in tags.items():
+        for run_name, tags in mapping.items():
+            for tag, tag_data in tags.items():
                 if tag_data.plugin_content != b"1":
                     logger.warning(
                         "Ignoring unrecognizable version of RunMetadata."
@@ -161,7 +161,7 @@ def add_row_item(run, tag=None):
             experiment_id=experiment,
             plugin_name=metadata.PLUGIN_NAME,
         )
-        for (run_name, tags) in mapping.items():
+        for run_name, tags in mapping.items():
             if metadata.RUN_GRAPH_NAME in tags:
                 (run_item, _) = add_row_item(run_name, None)
                 run_item["run_graph"] = True
@@ -172,7 +172,7 @@ def add_row_item(run, tag=None):
             experiment_id=experiment,
             plugin_name=metadata.PLUGIN_NAME_TAGGED_RUN_METADATA,
         )
-        for (run_name, tags) in mapping.items():
+        for run_name, tags in mapping.items():
             for tag in tags:
                 (_, tag_item) = add_row_item(run_name, tag)
                 tag_item["profile"] = True