fix the rest

keras/benchmarks/layer_benchmarks/run_xprof.py

@@ -16,6 +16,7 @@ from __future__ import absolute_import as _absolute_import
 from __future__ import division as _division
 from __future__ import print_function as _print_function
 
+import os
 import time
 import uuid
 

keras/callbacks.py

@@ -1564,7 +1564,7 @@ class ModelCheckpoint(Callback):
                         )
 
                 self._maybe_remove_file()
-            except IsADirectoryError as e:  # h5py 3.x
+            except IsADirectoryError:  # h5py 3.x
                 raise IOError(
                     "Please specify a non-directory filepath for "
                     "ModelCheckpoint. Filepath used is an existing "

keras/callbacks_test.py

@@ -33,8 +33,8 @@ from absl.testing import parameterized
 
 import keras
 from keras.callbacks import BackupAndRestore
-from keras.callbacks import Callback
 from keras.callbacks import BackupAndRestoreExperimental
+from keras.callbacks import Callback
 from keras.engine import sequential
 from keras.layers import Activation
 from keras.layers import Dense
@@ -387,7 +387,7 @@ class KerasCallbacksTest(test_combinations.TestCase):
                 if epoch == 5 or epoch == 12:
                     raise RuntimeError("Interruption")
 
-        log_dir = self.get_temp_dir()
+        self.get_temp_dir()
 
         # The following asserts that the train counter is fault tolerant.
         self.assertEqual(model._train_counter.numpy(), 0)
@@ -462,7 +462,8 @@ class KerasCallbacksTest(test_combinations.TestCase):
             )
 
         class InterruptingCallback(keras.callbacks.Callback):
-            """A callback to intentionally introduce interruption to training."""
+            """A callback to intentionally introduce interruption to
+            training."""
 
             batch_count = 0
 

keras/distribute/worker_training_state.py

@@ -62,8 +62,8 @@ class WorkerTrainingState:
         backend.set_value(
             self._ckpt_saved_batch, self.CKPT_SAVED_BATCH_UNUSED_VALUE
         )
-        # _ckpt_saved_epoch  and _ckpt_saved_batch gets tracked and is included in
-        # the checkpoint file when backing up.
+        # _ckpt_saved_epoch  and _ckpt_saved_batch gets tracked and is included
+        # in the checkpoint file when backing up.
         checkpoint = tf.train.Checkpoint(
             model=self._model,
             ckpt_saved_epoch=self._ckpt_saved_epoch,
@@ -155,8 +155,8 @@ class WorkerTrainingState:
         Returns:
           If the training is recovering from previous failure under multi-worker
           training setting, return the (epoch, step) the training is supposed to
-          continue at. Otherwise, return the `initial_epoch, initial_step` the user
-          passes in.
+          continue at. Otherwise, return the `initial_epoch, initial_step` the
+          user passes in.
         """
 
         initial_step = 0
@@ -165,19 +165,20 @@ class WorkerTrainingState:
         if mode == mode_keys.ModeKeys.TRAIN:
             if self._save_freq == "epoch":
                 if epoch >= 0:
-                    # The most recently saved epoch is one epoch prior to the epoch it
-                    # failed at, so return the value of 'self._ckpt_saved_epoch' plus one.
+                    # The most recently saved epoch is one epoch prior to the
+                    # epoch it failed at, so return the value of
+                    # 'self._ckpt_saved_epoch' plus one.
                     initial_epoch = epoch + 1
             else:
                 if batch >= 0 and epoch >= 0:
-                    # If the checkpoint was last saved at last batch of the epoch, return
-                    # the next epoch number and batch=0
+                    # If the checkpoint was last saved at last batch of the
+                    # epoch, return the next epoch number and batch=0
                     if batch == steps_per_epoch - 1:
                         initial_epoch = epoch + 1
                         initial_step = 0
                     else:
-                        # If the checkpoint was not last saved at last batch of the epoch,
-                        # return the same epoch and next batch number
+                        # If the checkpoint was not last saved at last batch of
+                        # the epoch, return the same epoch and next batch number
                         initial_epoch = epoch
                         initial_step = batch + 1
         return (initial_epoch, initial_step)

keras/dtensor/lazy_variable.py

@@ -181,9 +181,7 @@ class LazyInitVariable(resource_variable_ops.BaseResourceVariable):
     # TODO(scottzhu): This method and create_and_initialize might be removed if
     # we decide to just use the tf.Variable to replace this class.
     def initialize(self):
-        with ops.name_scope(
-            self._name, "Variable", skip_on_eager=False
-        ) as name:
+        with ops.name_scope(self._name, "Variable", skip_on_eager=False):
             with ops.colocate_with(self._handle), ops.name_scope("Initializer"):
                 if callable(self._initial_value):
                     initial_value = self._initial_value()

keras/engine/base_layer.py

@@ -685,6 +685,7 @@ class Layer(tf.Module, version_utils.LayerVersionSelector):
             and dtype.is_floating
         ):
             old_getter = getter
+
             # Wrap variable constructor to return an AutoCastVariable.
             def getter(*args, **kwargs):  # pylint: disable=function-redefined
                 variable = old_getter(*args, **kwargs)
@@ -3082,9 +3083,8 @@ class Layer(tf.Module, version_utils.LayerVersionSelector):
         if (
             name == "_self_setattr_tracking"
             or not getattr(self, "_self_setattr_tracking", True)
-            or
             # Exclude @property.setters from tracking
-            hasattr(self.__class__, name)
+            or hasattr(self.__class__, name)
         ):
             try:
                 super(tf.__internal__.tracking.AutoTrackable, self).__setattr__(

keras/engine/base_layer_v1.py

@@ -1279,10 +1279,9 @@ class Layer(base_layer.Layer):
         if (
             tf.distribute.has_strategy()
             and tf.distribute.in_cross_replica_context()
-            and
             # When saving the model, the distribution strategy context should be
             # ignored, following the default path for adding updates.
-            not call_context.saving
+            and not call_context.saving
         ):
             # Updates don't need to be run in a cross-replica context.
             return
@@ -2330,9 +2329,8 @@ class Layer(base_layer.Layer):
         if (
             name == "_self_setattr_tracking"
             or not getattr(self, "_self_setattr_tracking", True)
-            or
             # Exclude @property.setters from tracking
-            hasattr(self.__class__, name)
+            or hasattr(self.__class__, name)
         ):
             try:
                 super(tf.__internal__.tracking.AutoTrackable, self).__setattr__(

keras/engine/functional.py

@@ -1237,9 +1237,8 @@ def _should_skip_first_node(layer):
     if layer._self_tracked_trackables:
         return (
             isinstance(layer, Functional)
-            and
             # Filter out Sequential models without an input shape.
-            isinstance(
+            and isinstance(
                 layer._self_tracked_trackables[0], input_layer_module.InputLayer
             )
         )

keras/engine/saving.py

@@ -18,4 +18,4 @@
 Everything has been moved to keras/saving/. This file will be deleted soon.
 """
 
-from keras.saving import *  # noqa: F401
+from keras.saving import *  # noqa: F401,F403

keras/engine/sequential.py

@@ -333,7 +333,7 @@ class Sequential(functional.Functional):
                             # Create Functional API connection by calling the
                             # current layer
                             layer_output = layer(layer_input)
-                        except:  # pylint:disable=bare-except
+                        except:  # noqa: E722
                             # Functional API calls may fail for a number of
                             # reasons: 1) The layer may be buggy. In this case
                             # it will be easier for the user to debug if we fail
@@ -367,7 +367,7 @@ class Sequential(functional.Functional):
                         # not be supporting such layers.
                         self._init_graph_network(inputs, outputs)
                         self._graph_initialized = True
-                    except:  # pylint:disable=bare-except
+                    except:  # noqa: E722
                         self._use_legacy_deferred_behavior = True
                 self._inferred_input_shape = new_shape
 

keras/engine/training.py

@@ -1547,7 +1547,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
             (
                 data_handler._initial_epoch,
                 data_handler._initial_step,
-            ) = self._maybe_load_initial_counters_from_ckpt(  # pylint: disable=protected-access
+            ) = self._maybe_load_initial_counters_from_ckpt(
                 steps_per_epoch_inferred, initial_epoch
             )
             logs = None
@@ -3523,8 +3523,8 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
         Returns:
           If the training is recovering from previous failure under multi-worker
           training setting, return the (epoch, step) the training is supposed to
-          continue at. Otherwise, return the `initial_epoch, initial_step` the user
-          passes in.
+          continue at. Otherwise, return the `initial_epoch, initial_step` the
+          user passes in.
         """
         initial_step = 0
         if self._training_state is not None:

keras/engine/training_test.py

@@ -1723,7 +1723,7 @@ class TrainingTest(test_combinations.TestCase):
             "mse",
             run_eagerly=test_utils.should_run_eagerly(),
         )
-        history = model.fit(x, y, epochs=2)
+        model.fit(x, y, epochs=2)
         policy.set_global_policy("float32")
 
     @test_combinations.run_all_keras_modes
@@ -2368,10 +2368,8 @@ class LossWeightingTest(test_combinations.TestCase):
             y_train[:batch_size],
             class_weight=class_weight,
         )
-        ref_score = model.evaluate(
-            x_test, y_test, verbose=0
-        )  # pylint: disable=unused-variable
-        score = model.evaluate(  # pylint: disable=unused-variable
+        ref_score = model.evaluate(x_test, y_test, verbose=0)  # noqa: F841
+        score = model.evaluate(  # noqa: F841
             x_test[test_ids, :], y_test[test_ids, :], verbose=0
         )
         # TODO(b/152990697): Fix the class weights test here.

keras/integration_test/multi_worker_tutorial_test.py

@@ -70,7 +70,7 @@ class MultiWorkerTutorialTest(parameterized.TestCase, tf.test.TestCase):
     def skip_fetch_failure_exception(self):
         try:
             yield
-        except zipfile.BadZipfile as e:
+        except zipfile.BadZipfile:
             # There can be a race when multiple processes are downloading the
             # data.  Skip the test if that results in loading errors.
             self.skipTest(

keras/layers/normalization/batch_normalization.py

@@ -898,9 +898,7 @@ class BatchNormalizationBase(Layer):
         # Determine a boolean value for `training`: could be True, False, or
         # None.
         training_value = control_flow_util.constant_value(training)
-        if (
-            training_value == False
-        ):  # pylint: disable=singleton-comparison,g-explicit-bool-comparison
+        if training_value == False:  # noqa: E712
             mean, variance = self.moving_mean, self.moving_variance
         else:
             if self.adjustment:

keras/legacy_tf_layers/migration_utils_test.py

@@ -209,9 +209,8 @@ class DeterministicRandomTestToolTest(tf.test.TestCase):
             a_prime = tf.random.uniform(shape=(3, 1))
             a_prime = a_prime * 3
             error_string = "An exception should have been raised before this"
-            error_raised = "An exception should have been raised before this"
             try:
-                c = tf.random.uniform(shape=(3, 1))
+                tf.random.uniform(shape=(3, 1))
                 raise RuntimeError(error_string)
 
             except ValueError as err:

keras/mixed_precision/loss_scale_optimizer_test.py

@@ -385,17 +385,8 @@ class LossScaleOptimizerTest(tf.test.TestCase, parameterized.TestCase):
                 self.assertEqual(self.evaluate(opt.loss_scale), 8)
 
                 # Test Inf gradients are still skipped instead of being clipped
-<<<<<<< HEAD
                 loss = lambda: var * float("Inf")
                 run_fn = lambda: opt.minimize(loss, var_list=[var])
-=======
-                def run_fn():
-                    def loss():
-                        return var * float("Inf")
-
-                    return opt.minimize(loss, var_list=[var])
-
->>>>>>> 0bb24689 (fix F811)
                 run_op = strategy.experimental_run(run_fn)
                 self._run_if_in_graph_mode(run_op)
                 self.assertAllClose(
@@ -426,17 +417,8 @@ class LossScaleOptimizerTest(tf.test.TestCase, parameterized.TestCase):
             self.assertEqual(4.0, self.evaluate(opt.loss_scale))
 
             # Test optimizer with NaN gradients
-<<<<<<< HEAD
             loss = lambda: var * float("NaN")
             run_fn = lambda: opt.minimize(loss, var_list=[var])
-=======
-            def run_fn():
-                def loss():
-                    return var * float("NaN")
-
-                return opt.minimize(loss, var_list=[var])
-
->>>>>>> 0bb24689 (fix F811)
             run_op = strategy.experimental_run(run_fn)
             self._run_if_in_graph_mode(run_op)
             # Variable should not change from before, due to NaN gradients.

keras/saving/saved_model/load.py

@@ -713,9 +713,8 @@ class KerasObjectLoader:
         for node_id, (node, _) in self.loaded_nodes.items():
             if (
                 not isinstance(node, base_layer.Layer)
-                or
                 # Don't finalize models until all layers have finished loading.
-                node_id in self.model_layer_dependencies
+                or node_id in self.model_layer_dependencies
             ):
                 continue
 

keras/saving/saved_model/saved_model_test.py

@@ -1125,7 +1125,7 @@ class TestSavedModelFormat(tf.test.TestCase):
         class Model(keras.models.Model):
             def __init__(self):
                 super().__init__()
-                self.layer = CustomLayer()
+                self.layer = CustomLayer()  # noqa: F821
 
             @tf.function(input_signature=[tf.TensorSpec([None, 1])])
             def call(self, inputs):

keras/saving/saving_utils.py

@@ -365,7 +365,7 @@ def try_build_compiled_arguments(model):
                 model.compiled_loss.build(model.outputs)
             if not model.compiled_metrics.built:
                 model.compiled_metrics.build(model.outputs, model.outputs)
-        except:  # pylint: disable=bare-except
+        except:  # noqa: E722
             logging.warning(
                 "Compiled the loaded model, but the compiled metrics have "
                 "yet to be built. `model.compile_metrics` will be empty "

keras/saving/utils_v1/__init__.py

@@ -18,8 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-# pylint: disable=wildcard-import
-from keras.saving.utils_v1.export_output import *
+from keras.saving.utils_v1.export_output import *  # noqa: F403
 from keras.saving.utils_v1.export_utils import EXPORT_TAG_MAP
 from keras.saving.utils_v1.export_utils import SIGNATURE_KEY_MAP
 from keras.saving.utils_v1.export_utils import build_all_signature_defs
@@ -28,5 +27,4 @@ from keras.saving.utils_v1.export_utils import get_export_outputs
 from keras.saving.utils_v1.export_utils import get_temp_export_dir
 from keras.saving.utils_v1.export_utils import get_timestamped_export_dir
 
-# pylint: enable=wildcard-import
 # LINT.ThenChange(//tensorflow/python/saved_model/model_utils/__init__.py)

keras/testing_infra/keras_doctest_lib_test.py

@@ -55,7 +55,7 @@ class KerasDoctestOutputCheckerTest(parameterized.TestCase):
         ["text1.0 text", []],
         ["text 1.0text", []],
         ["text1.0text", []],
-        ["0x12e4", []],  #  not 12000
+        ["0x12e4", []],  # not 12000
         ["TensorBoard: http://128.0.0.1:8888", []],
         # With a newline
         ["1.0 text\n 2.0 3.0 text", [1.0, 2.0, 3.0]],

keras/tests/keras_doctest.py

@@ -32,7 +32,7 @@ tf.compat.v1.enable_v2_behavior()
 
 # We put doctest after absltest so that it picks up the unittest monkeypatch.
 # Otherwise doctest tests aren't runnable at all.
-import doctest  # pylint: disable=g-import-not-at-top,g-bad-import-order
+import doctest  # noqa: E402
 
 FLAGS = flags.FLAGS
 

keras/utils/data_utils.py

@@ -298,7 +298,7 @@ def get_file(
                 raise Exception(error_msg.format(origin, e.code, e.msg))
             except urllib.error.URLError as e:
                 raise Exception(error_msg.format(origin, e.errno, e.reason))
-        except (Exception, KeyboardInterrupt) as e:
+        except (Exception, KeyboardInterrupt):
             if os.path.exists(fpath):
                 os.remove(fpath)
             raise

keras/utils/mode_keys.py

@@ -15,6 +15,6 @@
 """Keras model mode constants."""
 
 # isort: off
-from tensorflow.python.saved_model.model_utils.mode_keys import (  # noqa: E501
+from tensorflow.python.saved_model.model_utils.mode_keys import (  # noqa: F401,E501
     KerasModeKeys as ModeKeys,
 )

setup.cfg

@@ -6,7 +6,5 @@ profile=black
 [flake8]
 # imported but unused in __init__.py, that's ok.
 per-file-ignores=**/__init__.py:F401
-ignore=E203,W503
+ignore=E203,W503,F632,E266,E731,E712,E741
 max-line-length=80
-# Only check line-too-long and ignore other errors.
-select=E501