Add C0326 bad-whitespace error to pylint sanity check.

PiperOrigin-RevId: 183689499

1 files changed, 22 insertions, 34 deletions

diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py
index 52fb1d39ae..2e84c5014f 100644
--- a/tensorflow/python/estimator/training.py
+++ b/tensorflow/python/estimator/training.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Classes and functions related to train_and_evaluate."""
 
 from __future__ import absolute_import
@@ -37,7 +36,6 @@ from tensorflow.python.training import server_lib
 from tensorflow.python.training import session_run_hook
 from tensorflow.python.util import compat
 
-
 _MAX_DELAY_SECS = 60
 _DELAY_SECS_PER_WORKER = 5
 _TF_CONFIG_ENV = 'TF_CONFIG'
@@ -50,8 +48,7 @@ _TRAINER_JOBS = (run_config_lib.TaskType.CHIEF, run_config_lib.TaskType.MASTER,
 def _validate_input_fn(input_fn):
   """Validates the `input_fn`."""
   if not callable(input_fn):
-    raise TypeError(
-        '`input_fn` must be callable, given: {}'.format(input_fn))
+    raise TypeError('`input_fn` must be callable, given: {}'.format(input_fn))
 
 
 def _validate_hooks(hooks):
@@ -125,10 +122,7 @@ class TrainSpec(
   duration. Optional hooks run at various stages of training.
   """
 
-  def __new__(cls,
-              input_fn,
-              max_steps=None,
-              hooks=None):
+  def __new__(cls, input_fn, max_steps=None, hooks=None):
     """Creates a validated `TrainSpec` instance.
 
     Args:
@@ -161,16 +155,13 @@ class TrainSpec(
     hooks = _validate_hooks(hooks)
 
     return super(TrainSpec, cls).__new__(
-        cls,
-        input_fn=input_fn,
-        max_steps=max_steps,
-        hooks=hooks)
+        cls, input_fn=input_fn, max_steps=max_steps, hooks=hooks)
 
 
 class EvalSpec(
     collections.namedtuple('EvalSpec', [
-        'input_fn', 'steps', 'name', 'hooks', 'exporters',
-        'start_delay_secs', 'throttle_secs'
+        'input_fn', 'steps', 'name', 'hooks', 'exporters', 'start_delay_secs',
+        'throttle_secs'
     ])):
   """Configuration for the "eval" part for the `train_and_evaluate` call.
 
@@ -417,8 +408,8 @@ def train_and_evaluate(estimator, train_spec, eval_spec):
   Raises:
     ValueError: if environment variable `TF_CONFIG` is incorrectly set.
   """
-  executor = _TrainingExecutor(estimator=estimator, train_spec=train_spec,
-                               eval_spec=eval_spec)
+  executor = _TrainingExecutor(
+      estimator=estimator, train_spec=train_spec, eval_spec=eval_spec)
 
   config = estimator.config
   if (config.task_type == run_config_lib.TaskType.EVALUATOR and
@@ -561,9 +552,8 @@ class _TrainingExecutor(object):
           self._timer.update_last_triggered_step(global_step_value)
           self._evaluator.evaluate_and_export()
         else:
-          logging.info(
-              'Skip the current checkpoint eval due to throttle secs '
-              '({} secs).'.format(self._eval_throttle_secs))
+          logging.info('Skip the current checkpoint eval due to throttle secs '
+                       '({} secs).'.format(self._eval_throttle_secs))
 
     # Final export signal: For any eval result with global_step >= train
     # max_steps, the evaluator will send the final export signal. There is a
@@ -576,8 +566,8 @@ class _TrainingExecutor(object):
     #
     # But here, throttle_secs will skip the next intermediate checkpoint and,
     # so, the double final export chance is very small.
-    evaluator = _TrainingExecutor._Evaluator(
-        self._estimator, self._eval_spec, self._train_spec.max_steps)
+    evaluator = _TrainingExecutor._Evaluator(self._estimator, self._eval_spec,
+                                             self._train_spec.max_steps)
 
     # When the underlying `Estimator` object saves a new checkpoint, we would
     # like this callback to be called so that evaluation and export can trigger.
@@ -617,8 +607,7 @@ class _TrainingExecutor(object):
       raise ValueError('eval_spec.throttle_secs should be positive, given: {}.'
                        'It is used do determine how long each training '
                        'iteration should go when train and evaluate '
-                       'locally.'.format(
-                           self._eval_spec.throttle_secs))
+                       'locally.'.format(self._eval_spec.throttle_secs))
 
     stop_hook = _StopAtSecsHook(self._eval_spec.throttle_secs)
     train_hooks = (
@@ -663,8 +652,9 @@ class _TrainingExecutor(object):
 
     if not config.master:
       jobs = config.cluster_spec.jobs
-      if (len(jobs) == 1 and len(config.cluster_spec.job_tasks(jobs[0])) == 1
-          and config.task_type in _TRAINER_JOBS):
+      if (len(jobs) == 1 and
+          len(config.cluster_spec.job_tasks(jobs[0])) == 1 and
+          config.task_type in _TRAINER_JOBS):
         # For distributed training, config.master is empty if and only if it has
         # a single node in the cluster spec. In this case, we should not start
         # the server.
@@ -679,9 +669,9 @@ class _TrainingExecutor(object):
     logging.info('Start Tensorflow server.')
 
     if config.session_config is None:
-      session_config=config_pb2.ConfigProto(log_device_placement=False)
+      session_config = config_pb2.ConfigProto(log_device_placement=False)
     else:
-      session_config=config_pb2.ConfigProto(
+      session_config = config_pb2.ConfigProto(
           log_device_placement=False,
           gpu_options=config.session_config.gpu_options)
 
@@ -744,8 +734,7 @@ class _TrainingExecutor(object):
             global_step >= self._train_spec.max_steps):
           logging.info(
               'Exiting evaluation, global_step=%s >= train max_steps=%s',
-              global_step,
-              self._train_spec.max_steps)
+              global_step, self._train_spec.max_steps)
           return
 
       latest_eval_result, should_early_stop = self._execute_evaluator_once(
@@ -781,10 +770,9 @@ class _TrainingExecutor(object):
 
     # Throttle if necessary.
     elapsed_time = time.time() - start
-    difference = throttle_secs  - elapsed_time
+    difference = throttle_secs - elapsed_time
     if difference > 0:
-      logging.info('Waiting %f secs before starting next eval run.',
-                   difference)
+      logging.info('Waiting %f secs before starting next eval run.', difference)
       time.sleep(difference)
 
     return (eval_result, should_early_stop)
@@ -929,8 +917,8 @@ class _EvalResult(
       if checkpoint_path:
         raise ValueError(
             'checkpoint must be `None` if status is not {}; got status {}, '
-            'checkpoint_path {}'.format(
-                _EvalStatus.EVALUATED, status, checkpoint_path))
+            'checkpoint_path {}'.format(_EvalStatus.EVALUATED, status,
+                                        checkpoint_path))
       return super(_EvalResult, cls).__new__(cls, status, metrics,
                                              checkpoint_path)