Fix a subset of existing lint issues

.bazelrc

@@ -90,7 +90,7 @@ build --experimental_cc_shared_library
 # uses TensorFlow for all IO operations.
 build --define=use_tensorflow_io=1
 
-# TensorFlow Decision Forests need absl::anyInvocable support for new versions 
+# TensorFlow Decision Forests need absl::anyInvocable support for new versions
 # of Tensorflow.
 build --define use_absl_anyinvocable=1
 

WORKSPACE

@@ -130,4 +130,3 @@ load(
 )
 
 nccl_configure(name = "local_config_nccl")
-

ruff.toml

@@ -0,0 +1,123 @@
+line-length = 88
+
+[lint]
+select = [
+    # pycodestyle
+    "E",
+    "W",
+    # Pyflakes
+    "F",
+    # pyupgrade
+    "UP",
+    # flake8-bugbear
+    "B",
+    # flake8-simplify
+    "SIM",
+    # isort
+    "I",
+    # pep8 naming
+    "N",
+    # pydocstyle
+    "D",
+    # annotations
+    "ANN",
+    # debugger
+    "T10",
+    # flake8-pytest
+    "PT",
+    # flake8-return
+    "RET",
+    # flake8-unused-arguments
+    "ARG",
+    # flake8-fixme
+    "FIX",
+    # flake8-eradicate
+    "ERA",
+    # pandas-vet
+    "PD",
+    # numpy-specific rules
+    "NPY",
+]
+ignore = [
+    "D104", # Missing docstring in public package
+    "D100", # Missing docstring in public module
+    "D211", # No blank line before class
+    "D213", # Multiline summary second line
+    "PD901", # Avoid using 'df' for pandas dataframes. Perfectly fine in functions with limited scope
+    "ANN201", # Missing return type annotation for public function (makes no sense for NoneType return types...)
+    "ANN101", # Missing type annotation for `self`
+    "ANN102", # Missing type annotation for `cls` in classmethod
+    "ANN204", # Missing return type annotation for special method
+    "ANN206", # Missing return type annotation for classmethod
+    "ANN002", # Missing type annotation for `*args`
+    "ANN003", # Missing type annotation for `**kwargs`
+    "D105", # Missing docstring in magic method
+    "D203", # 1 blank line before after class docstring
+    "D204", # 1 blank line required after class docstring
+    "D413", # 1 blank line after parameters
+    "SIM108", # Simplify if/else to one line; not always clearer
+    "D206", # Advised to disable by ruff-format
+    "E501", # Advised to disable by ruff-format
+    "W191", # Advised to disable by ruff-format
+    "N802", # Function name should be lowercase; unittest uses mixed case
+    "D107", # Missing docstring in `__init__`
+
+    # These are issues which remain to be fixed
+    "D102", # Missing docstring in public method
+    "D103", # Missing docstring in public function
+    "D401", # First line of docstring should be in imperative mood
+    "D404", # First word of the docstring should not be "This"
+    "ANN001", # Missing type annotation for function argument
+    "ANN202", # Missing return type for private function
+    "ANN205", # Missing return type for staticmethod
+    "B024", # <class> is an abstract base class,  but it has no abstract methods
+    "FIX002", # Line contains TODO, consider resolving the issue
+    "ANN401", # Dynamically typed expressions (typing.Any) are disallowed
+    "UP008", # Use `super()` instead of `super(__class__, self)`
+    "SIM102", # Use a single `if` statement instead of nested `if` statements
+    "UP031", # Use format specifiers instead of percent format
+    "SIM115", # Use context handler for opening files
+    "B008", # Do not perform function call in argument defaults
+    "ARG001", # Unused function argument
+    "F841", # Local variable is assigned to but never used
+    "ERA001", # Found commented-out code
+    "RET505", # Unnecessary `else` after `return` statement
+    "RET503", # Missing explicit `return` at the end of function able to return non-`None` value
+    "UP029", # Unnecessary builtin import
+    "ARG005", # Unused lambda argument
+    "E402", # Module level import not at top of file
+    "F403", # Wildcard import
+    "NPY002", # Replace legacy numpy call
+    "D200", # One-line docstring should fit on one line
+    "D205", # 1 blank line required between summary line and description
+    "RET504", # Unnecessary assignment before return
+    "ARG002", # Unused method argument
+]
+
+[lint.per-file-ignores]
+"__init__.py" = [
+    "F401", # Unused import
+]
+"*_test_base.py" = [
+    "ANN001", # Type annotations aren't needed for tests; these are fixtures or parametrizations
+    "PT009", # Use a regular `assert` instead of a unittest-style `assertEqual`
+    "PT027", # Use `pytest.raises` instead of unittest-style `assertRaisesRegex`
+
+    # Missing docstrings; probably want to fill these out for tests. For now, we just disable
+    "D101", # Missing docstring in public class
+    "D102", # Missing docstring in public method
+
+]
+"*_test.py" = [
+    "ANN001", # Type annotations aren't needed for tests; these are fixtures or parametrizations
+    "PT009", # Use a regular `assert` instead of a unittest-style `assertEqual`
+    "PT027", # Use `pytest.raises` instead of unittest-style `assertRaisesRegex`
+
+    # Missing docstrings; probably want to fill these out for tests. For now, we just disable
+    "D101", # Missing docstring in public class
+    "D102", # Missing docstring in public method
+
+]
+
+[lint.pydocstyle]
+convention = "google"

tensorflow_serving/apis/model_service_pb2.py

@@ -18,19 +18,23 @@
 # python -m grpc.tools.protoc --python_out=. --grpc_python_out=. -I. tensorflow_serving/apis/model_service.proto
 
 import sys
+
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 from google.protobuf import descriptor as _descriptor
-from google.protobuf import message as _message
-from google.protobuf import reflection as _reflection
-from google.protobuf import symbol_database as _symbol_database
 from google.protobuf import descriptor_pb2
+from google.protobuf import symbol_database as _symbol_database
+
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
 
 
-from tensorflow_serving.apis import get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2
-from tensorflow_serving.apis import model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2
+from tensorflow_serving.apis import (
+    get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2,
+)
+from tensorflow_serving.apis import (
+    model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2,
+)
 
 DESCRIPTOR = _descriptor.FileDescriptor(
     name='tensorflow_serving/apis/model_service.proto',

tensorflow_serving/apis/model_service_pb2_grpc.py

@@ -19,11 +19,15 @@
 
 import grpc
 
-from tensorflow_serving.apis import get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2
-from tensorflow_serving.apis import model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2
+from tensorflow_serving.apis import (
+  get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2,
+)
+from tensorflow_serving.apis import (
+  model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2,
+)
 
 
-class ModelServiceStub(object):
+class ModelServiceStub:
   """ModelService provides methods to query and update the state of the server,
   e.g. which models/versions are being served.
   """
@@ -50,7 +54,7 @@ def __init__(self, channel):
     )
 
 
-class ModelServiceServicer(object):
+class ModelServiceServicer:
   """ModelService provides methods to query and update the state of the server,
   e.g. which models/versions are being served.
   """

tensorflow_serving/apis/prediction_service_pb2.py

@@ -22,23 +22,32 @@
 # source: tensorflow_serving/apis/prediction_service.proto
 
 import sys
+
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 from google.protobuf import descriptor as _descriptor
-from google.protobuf import message as _message
-from google.protobuf import reflection as _reflection
-from google.protobuf import symbol_database as _symbol_database
 from google.protobuf import descriptor_pb2
+from google.protobuf import symbol_database as _symbol_database
+
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
 
 
-from tensorflow_serving.apis import classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2
-from tensorflow_serving.apis import get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2
-from tensorflow_serving.apis import inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2
-from tensorflow_serving.apis import predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2
-from tensorflow_serving.apis import regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2
-
+from tensorflow_serving.apis import (
+  classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2,
+)
+from tensorflow_serving.apis import (
+  get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2,
+)
+from tensorflow_serving.apis import (
+  inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2,
+)
+from tensorflow_serving.apis import (
+  predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2,
+)
+from tensorflow_serving.apis import (
+  regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2,
+)
 
 DESCRIPTOR = _descriptor.FileDescriptor(
   name='tensorflow_serving/apis/prediction_service.proto',

tensorflow_serving/apis/prediction_service_pb2_grpc.py

@@ -18,14 +18,24 @@
 # python -m grpc.tools.protoc --python_out=. --grpc_python_out=. -I. tensorflow_serving/apis/prediction_service.proto
 import grpc
 
-from tensorflow_serving.apis import classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2
-from tensorflow_serving.apis import get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2
-from tensorflow_serving.apis import inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2
-from tensorflow_serving.apis import predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2
-from tensorflow_serving.apis import regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2
+from tensorflow_serving.apis import (
+  classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2,
+)
+from tensorflow_serving.apis import (
+  get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2,
+)
+from tensorflow_serving.apis import (
+  inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2,
+)
+from tensorflow_serving.apis import (
+  predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2,
+)
+from tensorflow_serving.apis import (
+  regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2,
+)
 
 
-class PredictionServiceStub(object):
+class PredictionServiceStub:
   """open source marker; do not remove
   PredictionService provides access to machine-learned models loaded by
   model_servers.
@@ -64,7 +74,7 @@ def __init__(self, channel):
         )
 
 
-class PredictionServiceServicer(object):
+class PredictionServiceServicer:
   """open source marker; do not remove
   PredictionService provides access to machine-learned models loaded by
   model_servers.

tensorflow_serving/batching/test_util/matrix_half_plus_two_saved_model.py

@@ -14,6 +14,7 @@
 # ==============================================================================
 
 import tensorflow.compat.v1 as tf
+
 FLAGS = tf.app.flags.FLAGS
 
 tf.app.flags.DEFINE_string("output_dir", "/tmp/matrix_half_plus_two/1",
@@ -22,13 +23,14 @@
 
 def _generate_saved_model_for_matrix_half_plus_two(export_dir):
   """Creates SavedModel for half plus two model that accepts batches of
-       3*3 matrices.
-       The model divides all elements in each matrix by 2 and adds 2 to them.
-       So, for one input matrix [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
-       the result will be [[2.5, 3, 3.5], [4, 4.5, 5], [5.5, 6, 6.5]].
-    Args:
-      export_dir: The directory where to write SavedModel files.
-    """
+     3*3 matrices.
+     The model divides all elements in each matrix by 2 and adds 2 to them.
+     So, for one input matrix [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
+     the result will be [[2.5, 3, 3.5], [4, 4.5, 5], [5.5, 6, 6.5]].
+
+  Args:
+    export_dir: The directory where to write SavedModel files.
+  """
   builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
   with tf.Session() as session:
     x = tf.placeholder(tf.float32, shape=[None, 3, 3], name="x")

tensorflow_serving/example/mnist_client.py

@@ -25,21 +25,17 @@
     mnist_client.py --num_tests=100 --server=localhost:9000
 """
 
-from __future__ import print_function
 
 import sys
 import threading
 
 # This is a placeholder for a Google-internal import.
-
 import grpc
+import mnist_input_data
 import numpy
 import tensorflow as tf
 
-from tensorflow_serving.apis import predict_pb2
-from tensorflow_serving.apis import prediction_service_pb2_grpc
-import mnist_input_data
-
+from tensorflow_serving.apis import predict_pb2, prediction_service_pb2_grpc
 
 tf.compat.v1.app.flags.DEFINE_integer(
     'concurrency', 1, 'maximum number of concurrent inference requests')
@@ -50,7 +46,7 @@
 FLAGS = tf.compat.v1.app.flags.FLAGS
 
 
-class _ResultCounter(object):
+class _ResultCounter:
   """Counter for the prediction results."""
 
   def __init__(self, num_tests, concurrency):
@@ -94,6 +90,7 @@ def _create_rpc_callback(label, result_counter):
   Args:
     label: The correct label for the predicted example.
     result_counter: Counter for the prediction result.
+
   Returns:
     The callback function.
   """

tensorflow_serving/example/mnist_input_data.py

@@ -17,7 +17,6 @@
 
 """Functions for downloading and reading MNIST data."""
 
-from __future__ import print_function
 
 import gzip
 import os
@@ -95,12 +94,11 @@ def extract_labels(filename, one_hot=False):
     return labels
 
 
-class DataSet(object):
+class DataSet:
   """Class encompassing test, validation and training MNIST data set."""
 
   def __init__(self, images, labels, fake_data=False, one_hot=False):
     """Construct a DataSet. one_hot arg is used only if fake_data is true."""
-
     if fake_data:
       self._num_examples = 10000
       self.one_hot = one_hot
@@ -171,7 +169,7 @@ def next_batch(self, batch_size, fake_data=False):
 def read_data_sets(train_dir, fake_data=False, one_hot=False):
   """Return training, validation and testing data sets."""
 
-  class DataSets(object):
+  class DataSets:
     pass
 
   data_sets = DataSets()