Merge pull request #41355 from lgeiger/cherry-pick-abc-collections

[CherryPick:r2.3] Fix deprecated usage of collections ABC

tensorflow/python/compiler/xla/xla.py

@@ -18,7 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import collections
 import contextlib
 
 from six.moves import xrange  # pylint: disable=redefined-builtin
@@ -37,6 +36,7 @@ from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import compat
 from tensorflow.python.util import nest
 from tensorflow.python.util import tf_inspect
+from tensorflow.python.util.compat import collections_abc
 from tensorflow.python.util.tf_export import tf_export
 
 _XLA_COMPILE_ATTR = '_xla_compile_id'
@@ -329,7 +329,7 @@ def _compile_internal(computation, inputs=None):
   if inputs is None:
     inputs = []
 
-  if not isinstance(inputs, collections.Sequence):
+  if not isinstance(inputs, collections_abc.Sequence):
     raise TypeError('inputs must be a list')
 
   # Flatten inputs.
@@ -428,15 +428,15 @@ def is_flat(outputs):
   """
   # If outputs is a list or tuple, check if it has any nested structure. If
   # there is, then outputs is non-flat.
-  if isinstance(outputs, collections.Sequence):
+  if isinstance(outputs, collections_abc.Sequence):
     for o in outputs:
-      if (isinstance(o, collections.Sequence) or
-          isinstance(o, collections.Mapping) or
+      if (isinstance(o, collections_abc.Sequence) or
+          isinstance(o, collections_abc.Mapping) or
           hasattr(o.__class__, '__attrs_attrs__')):
         return False
 
   # If outputs is a dict, it is non-flat.
-  if isinstance(outputs, collections.Mapping):
+  if isinstance(outputs, collections_abc.Mapping):
     return False
 
   # If outputs is from the attrs library, it is non-flat.
@@ -467,7 +467,7 @@ def _postprocess_flat_outputs(outputs):
   if outputs is None:
     outputs = tuple()
   # If the computation only returned one value, make it a tuple.
-  if not isinstance(outputs, collections.Sequence):
+  if not isinstance(outputs, collections_abc.Sequence):
     outputs = (outputs,)
 
   # Append `no_op` here so that return value of this function always contains

tensorflow/python/data/ops/dataset_ops.py

@@ -18,7 +18,6 @@ from __future__ import division
 from __future__ import print_function
 
 import abc
-import collections
 import functools
 import sys
 import threading
@@ -72,6 +71,7 @@ from tensorflow.python.util import deprecation
 from tensorflow.python.util import function_utils
 from tensorflow.python.util import lazy_loader
 from tensorflow.python.util import nest as tf_nest
+from tensorflow.python.util.compat import collections_abc
 from tensorflow.python.util.tf_export import tf_export
 
 # Loaded lazily due to a circular dependency (roughly
@@ -103,7 +103,7 @@ tf_export("data.UNKNOWN_CARDINALITY").export_constant(__name__, "UNKNOWN")
 
 @tf_export("data.Dataset", v1=[])
 @six.add_metaclass(abc.ABCMeta)
-class DatasetV2(collections.Iterable, tracking_base.Trackable,
+class DatasetV2(collections_abc.Iterable, tracking_base.Trackable,
                 composite_tensor.CompositeTensor):
   """Represents a potentially large set of elements.
 

tensorflow/python/data/ops/iterator_ops.py

@@ -18,7 +18,6 @@ from __future__ import division
 from __future__ import print_function
 
 import abc
-import collections
 import threading
 import warnings
 
@@ -41,6 +40,7 @@ from tensorflow.python.ops import gen_experimental_dataset_ops
 from tensorflow.python.training.saver import BaseSaverBuilder
 from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.util import deprecation
+from tensorflow.python.util.compat import collections_abc
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -543,7 +543,7 @@ class IteratorResourceDeleter(object):
 
 @tf_export("data.Iterator", v1=[])
 @six.add_metaclass(abc.ABCMeta)
-class IteratorBase(collections.Iterator, trackable.Trackable,
+class IteratorBase(collections_abc.Iterator, trackable.Trackable,
                    composite_tensor.CompositeTensor):
   """Represents an iterator of a `tf.data.Dataset`.
 

tensorflow/python/data/util/structure.py

@@ -440,7 +440,7 @@ def type_spec_from_value(element, use_fallback=True):
 
   if isinstance(element, tuple):
     if hasattr(element, "_fields") and isinstance(
-        element._fields, collections.Sequence) and all(
+        element._fields, collections_abc.Sequence) and all(
             isinstance(f, six.string_types) for f in element._fields):
       if isinstance(element, wrapt.ObjectProxy):
         element_type = type(element.__wrapped__)

tensorflow/python/debug/wrappers/framework.py

@@ -99,7 +99,6 @@ from __future__ import division
 from __future__ import print_function
 
 import abc
-import collections
 import re
 import threading
 
@@ -113,6 +112,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.platform import tf_logging
 from tensorflow.python.training import monitored_session
 from tensorflow.python.util import nest
+from tensorflow.python.util.compat import collections_abc
 
 
 # Helper function.
@@ -445,7 +445,7 @@ class BaseDebugWrapperSession(session.SessionInterface):
       """Check whether a possibly nested structure is empty."""
       if not nest.is_nested(x):
         return False
-      if isinstance(x, collections.Mapping):
+      if isinstance(x, collections_abc.Mapping):
         return is_empty(list(x.values()))
       for item in x:
         if not is_empty(item):

tensorflow/python/distribute/input_lib.py

@@ -18,7 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import collections
 import functools
 import sys
 
@@ -53,6 +52,7 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.ragged import ragged_tensor
 from tensorflow.python.types import distribute as distribute_types
 from tensorflow.python.util import nest
+from tensorflow.python.util.compat import collections_abc
 from tensorflow.python.util.deprecation import deprecated
 from tensorflow.python.util.tf_export import tf_export
 from tensorflow.tools.docs import doc_controls
@@ -143,7 +143,7 @@ def get_distributed_datasets_from_function(dataset_fn,
 
 
 @tf_export("distribute.DistributedIterator", v1=[])
-class DistributedIteratorInterface(collections.Iterator,
+class DistributedIteratorInterface(collections_abc.Iterator,
                                    distribute_types.Iterator):
   """An iterator over `tf.distribute.DistributedDataset`.
 
@@ -272,7 +272,7 @@ class DistributedIteratorInterface(collections.Iterator,
 
 
 @tf_export("distribute.DistributedDataset", v1=[])
-class DistributedDatasetInterface(collections.Iterable,
+class DistributedDatasetInterface(collections_abc.Iterable,
                                   distribute_types.Iterable):
   # pylint: disable=line-too-long
   """Represents a dataset distributed among devices and machines.

tensorflow/python/framework/indexed_slices.py

@@ -32,6 +32,7 @@ from tensorflow.python.framework import tensor_conversion_registry
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import type_spec
 from tensorflow.python.types import internal
+from tensorflow.python.util.compat import collections_abc
 from tensorflow.python.util.lazy_loader import LazyLoader
 from tensorflow.python.util.tf_export import tf_export
 
@@ -344,7 +345,7 @@ def internal_convert_n_to_tensor_or_indexed_slices(values,
     RuntimeError: If a registered conversion function returns an invalid
       value.
   """
-  if not isinstance(values, collections.Iterable):
+  if not isinstance(values, collections_abc.Iterable):
     raise TypeError("values must be iterable.")
   ret = []
   for i, value in enumerate(values):

tensorflow/python/keras/engine/training_utils.py

@@ -19,7 +19,6 @@ from __future__ import print_function
 
 import abc
 import atexit
-import collections
 from collections import OrderedDict
 import functools
 import multiprocessing.pool
@@ -617,7 +616,7 @@ def standardize_sample_or_class_weights(x_weight, output_names, weight_type):
                        'You should provide one `' + weight_type + '`'
                        'array per model output.')
     return x_weight
-  if isinstance(x_weight, collections.Mapping):
+  if isinstance(x_weight, collections_abc.Mapping):
     generic_utils.check_for_unexpected_keys(weight_type, x_weight, output_names)
     x_weights = []
     for name in output_names:
@@ -864,7 +863,7 @@ def collect_per_output_metric_info(metrics,
               [metrics_module.clone_metric(m) for m in metrics])
       else:
         nested_metrics = [metrics]
-  elif isinstance(metrics, collections.Mapping):
+  elif isinstance(metrics, collections_abc.Mapping):
     generic_utils.check_for_unexpected_keys('metrics', metrics, output_names)
     nested_metrics = []
     for name in output_names:
@@ -1443,7 +1442,7 @@ def prepare_sample_weight_modes(training_endpoints, sample_weight_mode):
     ValueError: In case of invalid `sample_weight_mode` input.
   """
 
-  if isinstance(sample_weight_mode, collections.Mapping):
+  if isinstance(sample_weight_mode, collections_abc.Mapping):
     generic_utils.check_for_unexpected_keys(
         'sample_weight_mode', sample_weight_mode,
         [e.output_name for e in training_endpoints])
@@ -1536,7 +1535,7 @@ def prepare_loss_weights(training_endpoints, loss_weights=None):
   if loss_weights is None:
     for e in training_endpoints:
       e.loss_weight = 1.
-  elif isinstance(loss_weights, collections.Mapping):
+  elif isinstance(loss_weights, collections_abc.Mapping):
     generic_utils.check_for_unexpected_keys(
         'loss_weights', loss_weights,
         [e.output_name for e in training_endpoints])

tensorflow/python/keras/layers/preprocessing/preprocessing_test_utils.py

@@ -18,11 +18,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import collections
-
 import numpy as np
 
 from tensorflow.python.platform import test
+from tensorflow.python.util.compat import collections_abc
 
 
 class PreprocessingLayerTest(test.TestCase):
@@ -38,7 +37,7 @@ class PreprocessingLayerTest(test.TestCase):
       self.assertEqual(len(a), len(b))
       for a_value, b_value in zip(a, b):
         self.assertAllCloseOrEqual(a_value, b_value, msg=msg)
-    elif isinstance(a, collections.Mapping):
+    elif isinstance(a, collections_abc.Mapping):
       self.assertEqual(len(a), len(b))
       for key, a_value in a.items():
         b_value = b[key]

tensorflow/python/keras/layers/recurrent.py

@@ -44,14 +44,10 @@ from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.training.tracking import data_structures
 from tensorflow.python.util import nest
+from tensorflow.python.util.compat import collections_abc
 from tensorflow.python.util.tf_export import keras_export
 from tensorflow.tools.docs import doc_controls
 
-try:
-  from collections import abc as collections_abc  # pylint: disable=g-import-not-at-top
-except ImportError:  # For Python 2
-  import collections as collections_abc  # pylint: disable=g-import-not-at-top
-
 
 RECURRENT_DROPOUT_WARNING_MSG = (
     'RNN `implementation=2` is not supported when `recurrent_dropout` is set. '

tensorflow/python/ops/math_ops.py

@@ -70,8 +70,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import collections
-
 import numpy as np
 import six
 from six.moves import builtins
@@ -100,6 +98,7 @@ from tensorflow.python.util import compat
 from tensorflow.python.util import deprecation
 from tensorflow.python.util import dispatch
 from tensorflow.python.util import nest
+from tensorflow.python.util.compat import collections_abc
 from tensorflow.python.util.tf_export import tf_export
 
 # Aliases for some automatically-generated names.
@@ -3493,7 +3492,7 @@ def add_n(inputs, name=None):
     ValueError: If `inputs` don't all have same shape and dtype or the shape
     cannot be inferred.
   """
-  if not inputs or not isinstance(inputs, collections.Iterable):
+  if not inputs or not isinstance(inputs, collections_abc.Iterable):
     raise ValueError("inputs must be an iterable of at least one "
                      "Tensor/IndexedSlices with the same dtype and shape")
   inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs)
@@ -3626,9 +3625,9 @@ def sigmoid(x, name=None):
 
   Returns:
     A Tensor with the same type as `x`.
-  
+
   Usage Example:
-  
+
   >>> x = tf.constant([-128.0, 0.0, 128.0], dtype=tf.float32)
   >>> tf.sigmoid(x)
   <tf.Tensor: shape=(3,), dtype=float32,

tensorflow/python/ops/nn_ops.py

@@ -18,7 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import collections
 import functools
 import numbers
 import os
@@ -3270,7 +3269,7 @@ def conv_transpose(input,  # pylint: disable=redefined-builtin
                       [input, filter, output_shape]) as name:
     if tensor_util.is_tensor(output_shape):
       n = output_shape.shape[0] - 2
-    elif isinstance(output_shape, collections.Sized):
+    elif isinstance(output_shape, collections_abc.Sized):
       n = len(output_shape) - 2
     else:
       raise ValueError("output_shape must be a tensor or sized collection.")

tensorflow/python/ops/variable_scope.py

@@ -18,7 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import collections as collections_lib
 import copy
 import enum  # pylint: disable=g-bad-import-order
 import functools
@@ -47,6 +46,7 @@ from tensorflow.python.util import deprecation
 from tensorflow.python.util import function_utils
 from tensorflow.python.util import tf_contextlib
 from tensorflow.python.util import tf_inspect
+from tensorflow.python.util.compat import collections_abc
 from tensorflow.python.util.tf_export import tf_export
 
 __all__ = [
@@ -77,13 +77,13 @@ class _PartitionInfo(object):
       ValueError: If `full_shape` or `var_offset` differ in length. If
         `var_offset` exceeds `full_shape` in any dimension.
     """
-    if not isinstance(full_shape, collections_lib.Sequence) or isinstance(
+    if not isinstance(full_shape, collections_abc.Sequence) or isinstance(
         full_shape, six.string_types):
       raise TypeError(
           "`full_shape` must be a sequence (like tuple or list) instead of " +
           type(full_shape).__name__)
 
-    if not isinstance(var_offset, collections_lib.Sequence) or isinstance(
+    if not isinstance(var_offset, collections_abc.Sequence) or isinstance(
         var_offset, six.string_types):
       raise TypeError(
           "`var_offset` must be a sequence (like tuple or list) instead of " +
@@ -151,7 +151,7 @@ class _PartitionInfo(object):
       ValueError: If `shape` is not the same length as `self.full_shape`. If
         the variable is partitioned in more than one dimension.
     """
-    if not isinstance(shape, collections_lib.Sequence) or isinstance(
+    if not isinstance(shape, collections_abc.Sequence) or isinstance(
         shape, six.string_types):
       raise TypeError(
           "`shape` must be a sequence (like tuple or list) instead of " +
@@ -451,7 +451,7 @@ class _VariableStore(object):
         synchronization=VariableSynchronization.AUTO,
         aggregation=VariableAggregation.NONE):
       is_scalar = (
-          shape is not None and isinstance(shape, collections_lib.Sequence) and
+          shape is not None and isinstance(shape, collections_abc.Sequence) and
           not shape)
       # Partitioned variable case
       if partitioner is not None and not is_scalar:
@@ -2511,7 +2511,7 @@ def _call_partitioner(partitioner, shape, dtype):
                      "shape: %s" % shape)
 
   slicing = partitioner(shape=shape, dtype=dtype)
-  if not isinstance(slicing, collections_lib.Sequence):
+  if not isinstance(slicing, collections_abc.Sequence):
     raise ValueError("Partitioner must return a sequence, but saw: %s" %
                      slicing)
   if len(slicing) != shape.ndims:

tensorflow/python/tools/saved_model_cli.py

@@ -24,7 +24,6 @@ from __future__ import division
 from __future__ import print_function
 
 import argparse
-import collections
 import os
 import re
 import sys
@@ -51,6 +50,7 @@ from tensorflow.python.saved_model import signature_constants
 from tensorflow.python.tools import saved_model_aot_compile
 from tensorflow.python.tools import saved_model_utils
 from tensorflow.python.tpu import tpu
+from tensorflow.python.util.compat import collections_abc
 
 
 _XLA_DEBUG_OPTIONS_URL = (
@@ -241,7 +241,7 @@ def _print_args(arguments, argument_type='Argument', indent=0):
       in_print('  %s' % element)
     elif isinstance(element, tensor_spec.TensorSpec):
       print((indent + 1) * '  ' + '%s: %s' % (element.name, repr(element)))
-    elif (isinstance(element, collections.Iterable) and
+    elif (isinstance(element, collections_abc.Iterable) and
           not isinstance(element, dict)):
       in_print('  DType: %s' % type(element).__name__)
       in_print('  Value: [', end='')