xref: /external/tensorflow/tensorflow/python/ops/check_ops.py

# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=g-short-docstring-punctuation
"""Asserts and Boolean Checks."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections

import numpy as np

from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.util import compat
from tensorflow.python.util import deprecation
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export

NUMERIC_TYPES = frozenset(
    [dtypes.float32, dtypes.float64, dtypes.int8, dtypes.int16, dtypes.int32,
     dtypes.int64, dtypes.uint8, dtypes.qint8, dtypes.qint32, dtypes.quint8,
     dtypes.complex64])

__all__ = [
    'assert_negative',
    'assert_positive',
    'assert_proper_iterable',
    'assert_non_negative',
    'assert_non_positive',
    'assert_equal',
    'assert_none_equal',
    'assert_near',
    'assert_integer',
    'assert_less',
    'assert_less_equal',
    'assert_greater',
    'assert_greater_equal',
    'assert_rank',
    'assert_rank_at_least',
    'assert_rank_in',
    'assert_same_float_dtype',
    'assert_scalar',
    'assert_type',
    'assert_shapes',
    'is_non_decreasing',
    'is_numeric_tensor',
    'is_strictly_increasing',
]


def _maybe_constant_value_string(t):
  if not isinstance(t, ops.Tensor):
    return str(t)
  const_t = tensor_util.constant_value(t)
  if const_t is not None:
    return str(const_t)
  return t


def _assert_static(condition, data):
  """Raises a InvalidArgumentError with as much information as possible."""
  if not condition:
    data_static = [_maybe_constant_value_string(x) for x in data]
    raise errors.InvalidArgumentError(node_def=None, op=None,
                                      message='\n'.join(data_static))


def _shape_and_dtype_str(tensor):
  """Returns a string containing tensor's shape and dtype."""
  return 'shape=%s dtype=%s' % (tensor.shape, tensor.dtype.name)


def _unary_assert_doc(sym, sym_name):
  """Common docstring for assert_* ops that evaluate a unary predicate over every element of a tensor.

  Args:
    sym: Mathematical symbol for the check performed on each element, i.e. "> 0"
    sym_name: English-language name for the op described by sym

  Returns:
    Decorator that adds the appropriate docstring to the function for symbol
    `sym`.
  """

  def _decorator(func):
    """Generated decorator that adds the appropriate docstring to the function for symbol `sym`.

    Args:
      func: Function for a TensorFlow op

    Returns:
      Version of `func` with documentation attached.
    """
    opname = func.__name__
    cap_sym_name = sym_name.capitalize()

    func.__doc__ = """
    Assert the condition `x {sym}` holds element-wise.

    When running in graph mode, you should add a dependency on this operation
    to ensure that it runs. Example of adding a dependency to an operation:

    ```python
    with tf.control_dependencies([tf.debugging.{opname}(x, y)]):
      output = tf.reduce_sum(x)
    ```

    {sym_name} means, for every element `x[i]` of `x`, we have `x[i] {sym}`.
    If `x` is empty this is trivially satisfied.

    Args:
      x:  Numeric `Tensor`.
      data:  The tensors to print out if the condition is False.  Defaults to
        error message and first few entries of `x`.
      summarize: Print this many entries of each tensor.
      message: A string to prefix to the default message.
      name: A name for this operation (optional).  Defaults to "{opname}".

    Returns:
      Op that raises `InvalidArgumentError` if `x {sym}` is False.
      @compatibility(eager)
        returns None
      @end_compatibility

    Raises:
      InvalidArgumentError: if the check can be performed immediately and
        `x {sym}` is False. The check can be performed immediately during
        eager execution or if `x` is statically known.
    """.format(
        sym=sym, sym_name=cap_sym_name, opname=opname)
    return func

  return _decorator


def _binary_assert_doc(sym):
  """Common docstring for most of the v1 assert_* ops that compare two tensors element-wise.

  Args:
    sym: Binary operation symbol, i.e. "=="

  Returns:
    Decorator that adds the appropriate docstring to the function for
  symbol `sym`.
  """

  def _decorator(func):
    """Generated decorator that adds the appropriate docstring to the function for symbol `sym`.

    Args:
      func: Function for a TensorFlow op

    Returns:
      A version of `func` with documentation attached.
    """
    opname = func.__name__

    func.__doc__ = """
    Assert the condition `x {sym} y` holds element-wise.

    This condition holds if for every pair of (possibly broadcast) elements
    `x[i]`, `y[i]`, we have `x[i] {sym} y[i]`.
    If both `x` and `y` are empty, this is trivially satisfied.

    When running in graph mode, you should add a dependency on this operation
    to ensure that it runs. Example of adding a dependency to an operation:

    ```python
    with tf.control_dependencies([tf.compat.v1.{opname}(x, y)]):
      output = tf.reduce_sum(x)
    ```

    Args:
      x:  Numeric `Tensor`.
      y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
      data:  The tensors to print out if the condition is False.  Defaults to
        error message and first few entries of `x`, `y`.
      summarize: Print this many entries of each tensor.
      message: A string to prefix to the default message.
      name: A name for this operation (optional).  Defaults to "{opname}".

    Returns:
      Op that raises `InvalidArgumentError` if `x {sym} y` is False.
      @compatibility(eager)
        returns None
      @end_compatibility

    Raises:
      InvalidArgumentError: if the check can be performed immediately and
        `x {sym} y` is False. The check can be performed immediately during
        eager execution or if `x` and `y` are statically known.
    """.format(
        sym=sym, opname=opname)
    return func

  return _decorator


def _make_assert_msg_data(sym, x, y, summarize, test_op):
  """Subroutine of _binary_assert that generates the components of the default error message when running in eager mode.

  Args:
    sym: Mathematical symbol for the test to apply to pairs of tensor elements,
      i.e. "=="
    x: First input to the assertion after applying `convert_to_tensor()`
    y: Second input to the assertion
    summarize: Value of the "summarize" parameter to the original assert_* call;
      tells how many elements of each tensor to print.
    test_op: TensorFlow op that returns a Boolean tensor with True in each
      position where the assertion is satisfied.

  Returns:
    List of tensors and scalars that, when stringified and concatenated,
    will produce the error message string.
  """
  # Prepare a message with first elements of x and y.
  data = []

  data.append('Condition x %s y did not hold.' % sym)

  if summarize > 0:
    if x.shape == y.shape and x.shape.as_list():
      # If the shapes of x and y are the same (and not scalars),
      # Get the values that actually differed and their indices.
      # If shapes are different this information is more confusing
      # than useful.
      mask = math_ops.logical_not(test_op)
      indices = array_ops.where(mask)
      indices_np = indices.numpy()
      x_vals = array_ops.boolean_mask(x, mask)
      y_vals = array_ops.boolean_mask(y, mask)
      num_vals = min(summarize, indices_np.shape[0])
      data.append('Indices of first %d different values:' % num_vals)
      data.append(indices_np[:num_vals])
      data.append('Corresponding x values:')
      data.append(x_vals.numpy().reshape((-1,))[:num_vals])
      data.append('Corresponding y values:')
      data.append(y_vals.numpy().reshape((-1,))[:num_vals])

    # reshape((-1,)) is the fastest way to get a flat array view.
    x_np = x.numpy().reshape((-1,))
    y_np = y.numpy().reshape((-1,))
    x_sum = min(x_np.size, summarize)
    y_sum = min(y_np.size, summarize)
    data.append('First %d elements of x:' % x_sum)
    data.append(x_np[:x_sum])
    data.append('First %d elements of y:' % y_sum)
    data.append(y_np[:y_sum])

  return data


def _pretty_print(data_item, summarize):
  """Format a data item for use in an error message in eager mode.

  Args:
    data_item: One of the items in the "data" argument to an assert_* function.
      Can be a Tensor or a scalar value.
    summarize: How many elements to retain of each tensor-valued entry in data.

  Returns:
    An appropriate string representation of data_item
  """
  if isinstance(data_item, ops.Tensor):
    arr = data_item.numpy()
    if np.isscalar(arr):
      # Tensor.numpy() returns a scalar for zero-dimensional tensors
      return str(arr)
    else:
      flat = arr.reshape((-1,))
      lst = [str(x) for x in flat[:summarize]]
      if len(lst) < flat.size:
        lst.append('...')
      return str(lst)
  else:
    return str(data_item)


def _binary_assert(sym, opname, op_func, static_func, x, y, data, summarize,
                   message, name):
  """Generic binary elementwise assertion.

  Implements the behavior described in _binary_assert_doc() above.
  Args:
    sym: Mathematical symbol for the test to apply to pairs of tensor elements,
      i.e. "=="
    opname: Name of the assert op in the public API, i.e. "assert_equal"
    op_func: Function that, if passed the two Tensor inputs to the assertion (x
      and y), will return the test to be passed to reduce_all() i.e.
    static_func: Function that, if passed numpy ndarray versions of the two
      inputs to the assertion, will return a Boolean ndarray with containing
      True in all positions where the assertion PASSES.
      i.e. np.equal for assert_equal()
    x:  Numeric `Tensor`.
    y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
    data:  The tensors to print out if the condition is False.  Defaults to
      error message and first few entries of `x`, `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to the value of
      `opname`.

  Returns:
    See docstring template in _binary_assert_doc().
  """
  with ops.name_scope(name, opname, [x, y, data]):
    x = ops.convert_to_tensor(x, name='x')
    y = ops.convert_to_tensor(y, name='y')

    if context.executing_eagerly():
      test_op = op_func(x, y)
      condition = math_ops.reduce_all(test_op)
      if condition:
        return

      # If we get here, the assertion has failed.
      # Default to printing 3 elements like control_flow_ops.Assert (used
      # by graph mode) does. Also treat negative values as "print
      # everything" for consistency with Tensor::SummarizeValue().
      if summarize is None:
        summarize = 3
      elif summarize < 0:
        summarize = 1e9  # Code below will find exact size of x and y.

      if data is None:
        data = _make_assert_msg_data(sym, x, y, summarize, test_op)

      if message is not None:
        data = [message] + list(data)

      raise errors.InvalidArgumentError(
          node_def=None,
          op=None,
          message=('\n'.join(_pretty_print(d, summarize) for d in data)))

    else:  # not context.executing_eagerly()
      if data is None:
        data = [
            'Condition x %s y did not hold element-wise:' % sym,
            'x (%s) = ' % x.name, x,
            'y (%s) = ' % y.name, y
        ]
      if message is not None:
        data = [message] + list(data)
      condition = math_ops.reduce_all(op_func(x, y))
      x_static = tensor_util.constant_value(x)
      y_static = tensor_util.constant_value(y)
      if x_static is not None and y_static is not None:
        condition_static = np.all(static_func(x_static, y_static))
        _assert_static(condition_static, data)
      return control_flow_ops.Assert(condition, data, summarize=summarize)


@tf_export(
    'debugging.assert_proper_iterable',
    v1=['debugging.assert_proper_iterable', 'assert_proper_iterable'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_proper_iterable')
def assert_proper_iterable(values):
  """Static assert that values is a "proper" iterable.

  `Ops` that expect iterables of `Tensor` can call this to validate input.
  Useful since `Tensor`, `ndarray`, byte/text type are all iterables themselves.

  Args:
    values:  Object to be checked.

  Raises:
    TypeError:  If `values` is not iterable or is one of
      `Tensor`, `SparseTensor`, `np.array`, `tf.compat.bytes_or_text_types`.
  """
  unintentional_iterables = (
      (ops.Tensor, sparse_tensor.SparseTensor, np.ndarray)
      + compat.bytes_or_text_types
  )
  if isinstance(values, unintentional_iterables):
    raise TypeError(
        'Expected argument "values" to be a "proper" iterable.  Found: %s' %
        type(values))

  if not hasattr(values, '__iter__'):
    raise TypeError(
        'Expected argument "values" to be iterable.  Found: %s' % type(values))


@tf_export('debugging.assert_negative', v1=[])
@dispatch.add_dispatch_support
def assert_negative_v2(x, message=None, summarize=None, name=None):
  """Assert the condition `x < 0` holds element-wise.

  This Op checks that `x[i] < 0` holds for every element of `x`. If `x` is
  empty, this is trivially satisfied.

  If `x` is not negative everywhere, `message`, as well as the first `summarize`
  entries of `x` are printed, and `InvalidArgumentError` is raised.

  Args:
    x:  Numeric `Tensor`.
    message: A string to prefix to the default message.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional).  Defaults to "assert_negative".

  Returns:
    Op raising `InvalidArgumentError` unless `x` is all negative. This can be
      used with `tf.control_dependencies` inside of `tf.function`s to block
      followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x[i] < 0` is False. The check can be performed immediately during eager
      execution or if `x` is statically known.
  """
  return assert_negative(x=x, message=message, summarize=summarize, name=name)


@tf_export(v1=['debugging.assert_negative', 'assert_negative'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_negative')
@_unary_assert_doc('< 0', 'negative')
def assert_negative(x, data=None, summarize=None, message=None, name=None):  # pylint: disable=missing-docstring
  message = message or ''
  with ops.name_scope(name, 'assert_negative', [x, data]):
    x = ops.convert_to_tensor(x, name='x')
    if data is None:
      if context.executing_eagerly():
        name = _shape_and_dtype_str(x)
      else:
        name = x.name
      data = [
          message,
          'Condition x < 0 did not hold element-wise:',
          'x (%s) = ' % name, x]
    zero = ops.convert_to_tensor(0, dtype=x.dtype)
    return assert_less(x, zero, data=data, summarize=summarize)


@tf_export('debugging.assert_positive', v1=[])
@dispatch.add_dispatch_support
def assert_positive_v2(x, message=None, summarize=None, name=None):
  """Assert the condition `x > 0` holds element-wise.

  This Op checks that `x[i] > 0` holds for every element of `x`. If `x` is
  empty, this is trivially satisfied.

  If `x` is not positive everywhere, `message`, as well as the first `summarize`
  entries of `x` are printed, and `InvalidArgumentError` is raised.

  Args:
    x:  Numeric `Tensor`.
    message: A string to prefix to the default message.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional). Defaults to "assert_positive".

  Returns:
    Op raising `InvalidArgumentError` unless `x` is all positive. This can be
      used with `tf.control_dependencies` inside of `tf.function`s to block
      followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x[i] > 0` is False. The check can be performed immediately during eager
      execution or if `x` is statically known.
  """
  return assert_positive(x=x, summarize=summarize, message=message, name=name)


@tf_export(v1=['debugging.assert_positive', 'assert_positive'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_positive')
@_unary_assert_doc('> 0', 'positive')
def assert_positive(x, data=None, summarize=None, message=None, name=None):  # pylint: disable=missing-docstring
  message = message or ''
  with ops.name_scope(name, 'assert_positive', [x, data]):
    x = ops.convert_to_tensor(x, name='x')
    if data is None:
      if context.executing_eagerly():
        name = _shape_and_dtype_str(x)
      else:
        name = x.name
      data = [
          message, 'Condition x > 0 did not hold element-wise:',
          'x (%s) = ' % name, x]
    zero = ops.convert_to_tensor(0, dtype=x.dtype)
    return assert_less(zero, x, data=data, summarize=summarize)


@tf_export('debugging.assert_non_negative', v1=[])
@dispatch.add_dispatch_support
def assert_non_negative_v2(x, message=None, summarize=None, name=None):
  """Assert the condition `x >= 0` holds element-wise.

  This Op checks that `x[i] >= 0` holds for every element of `x`. If `x` is
  empty, this is trivially satisfied.

  If `x` is not >= 0 everywhere, `message`, as well as the first `summarize`
  entries of `x` are printed, and `InvalidArgumentError` is raised.

  Args:
    x:  Numeric `Tensor`.
    message: A string to prefix to the default message.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional).  Defaults to
      "assert_non_negative".

  Returns:
    Op raising `InvalidArgumentError` unless `x` is all non-negative. This can
      be used with `tf.control_dependencies` inside of `tf.function`s to block
      followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x[i] >= 0` is False. The check can be performed immediately during eager
      execution or if `x` is statically known.
  """
  return assert_non_negative(x=x, summarize=summarize, message=message,
                             name=name)


@tf_export(v1=['debugging.assert_non_negative', 'assert_non_negative'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_non_negative')
@_unary_assert_doc('>= 0', 'non-negative')
def assert_non_negative(x, data=None, summarize=None, message=None, name=None):  # pylint: disable=missing-docstring
  message = message or ''
  with ops.name_scope(name, 'assert_non_negative', [x, data]):
    x = ops.convert_to_tensor(x, name='x')
    if data is None:
      if context.executing_eagerly():
        name = _shape_and_dtype_str(x)
      else:
        name = x.name
      data = [
          message,
          'Condition x >= 0 did not hold element-wise:',
          'x (%s) = ' % name, x]
    zero = ops.convert_to_tensor(0, dtype=x.dtype)
    return assert_less_equal(zero, x, data=data, summarize=summarize)


@tf_export('debugging.assert_non_positive', v1=[])
@dispatch.add_dispatch_support
def assert_non_positive_v2(x, message=None, summarize=None, name=None):
  """Assert the condition `x <= 0` holds element-wise.

  This Op checks that `x[i] <= 0` holds for every element of `x`. If `x` is
  empty, this is trivially satisfied.

  If `x` is not <= 0 everywhere, `message`, as well as the first `summarize`
  entries of `x` are printed, and `InvalidArgumentError` is raised.

  Args:
    x:  Numeric `Tensor`.
    message: A string to prefix to the default message.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional).  Defaults to
      "assert_non_positive".

  Returns:
    Op raising `InvalidArgumentError` unless `x` is all non-positive. This can
      be used with `tf.control_dependencies` inside of `tf.function`s to block
      followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x[i] <= 0` is False. The check can be performed immediately during eager
      execution or if `x` is statically known.
  """
  return assert_non_positive(x=x, summarize=summarize, message=message,
                             name=name)


@tf_export(v1=['debugging.assert_non_positive', 'assert_non_positive'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_non_positive')
@_unary_assert_doc('<= 0', 'non-positive')
def assert_non_positive(x, data=None, summarize=None, message=None, name=None):  # pylint: disable=missing-docstring
  message = message or ''
  with ops.name_scope(name, 'assert_non_positive', [x, data]):
    x = ops.convert_to_tensor(x, name='x')
    if data is None:
      if context.executing_eagerly():
        name = _shape_and_dtype_str(x)
      else:
        name = x.name
      data = [
          message,
          'Condition x <= 0 did not hold element-wise:'
          'x (%s) = ' % name, x]
    zero = ops.convert_to_tensor(0, dtype=x.dtype)
    return assert_less_equal(x, zero, data=data, summarize=summarize)


@tf_export('debugging.assert_equal', 'assert_equal', v1=[])
@dispatch.add_dispatch_support
def assert_equal_v2(x, y, message=None, summarize=None, name=None):
  """Assert the condition `x == y` holds element-wise.

  This Op checks that `x[i] == y[i]` holds for every pair of (possibly
  broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
  trivially satisfied.

  If `x` and `y` are not equal, `message`, as well as the first `summarize`
  entries of `x` and `y` are printed, and `InvalidArgumentError` is raised.

  Args:
    x:  Numeric `Tensor`.
    y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
    message: A string to prefix to the default message.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional).  Defaults to "assert_equal".

  Returns:
    Op that raises `InvalidArgumentError` if `x == y` is False. This can be
      used with `tf.control_dependencies` inside of `tf.function`s to block
      followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x == y` is False. The check can be performed immediately during eager
      execution or if `x` and `y` are statically known.
  """
  return assert_equal(x=x, y=y, summarize=summarize, message=message, name=name)


@tf_export(v1=['debugging.assert_equal', 'assert_equal'])
@dispatch.add_dispatch_support
@_binary_assert_doc('==')
def assert_equal(x, y, data=None, summarize=None, message=None, name=None):  # pylint: disable=missing-docstring
  with ops.name_scope(name, 'assert_equal', [x, y, data]):
    # Short-circuit if x and y are the same tensor.
    if x is y:
      return None if context.executing_eagerly() else control_flow_ops.no_op()
  return _binary_assert('==', 'assert_equal', math_ops.equal, np.equal, x, y,
                        data, summarize, message, name)


@tf_export('debugging.assert_none_equal', v1=[])
@dispatch.add_dispatch_support
def assert_none_equal_v2(x, y, summarize=None, message=None, name=None):
  """Assert the condition `x != y` holds for all elements.

  This Op checks that `x[i] != y[i]` holds for every pair of (possibly
  broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
  trivially satisfied.

  If any elements of `x` and `y` are equal, `message`, as well as the first
  `summarize` entries of `x` and `y` are printed, and `InvalidArgumentError`
  is raised.

  Args:
    x:  Numeric `Tensor`.
    y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to
    "assert_none_equal".

  Returns:
    Op that raises `InvalidArgumentError` if `x != y` is ever False. This can
      be used with `tf.control_dependencies` inside of `tf.function`s to block
      followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x != y` is False for any pair of elements in `x` and `y`. The check can
      be performed immediately during eager execution or if `x` and `y` are
      statically known.
  """
  return assert_none_equal(x=x, y=y, summarize=summarize, message=message,
                           name=name)


@tf_export(v1=['debugging.assert_none_equal', 'assert_none_equal'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_none_equal')
@_binary_assert_doc('!=')
def assert_none_equal(
    x, y, data=None, summarize=None, message=None, name=None):
  return _binary_assert('!=', 'assert_none_equal', math_ops.not_equal,
                        np.not_equal, x, y, data, summarize, message, name)


@tf_export('debugging.assert_near', v1=[])
@dispatch.add_dispatch_support
def assert_near_v2(x, y, rtol=None, atol=None, message=None, summarize=None,
                   name=None):
  """Assert the condition `x` and `y` are close element-wise.

  This Op checks that `x[i] - y[i] < atol + rtol * tf.abs(y[i])` holds for every
  pair of (possibly broadcast) elements of `x` and `y`. If both `x` and `y` are
  empty, this is trivially satisfied.

  If any elements of `x` and `y` are not close, `message`, as well as the first
  `summarize` entries of `x` and `y` are printed, and `InvalidArgumentError`
  is raised.

  The default `atol` and `rtol` is `10 * eps`, where `eps` is the smallest
  representable positive number such that `1 + eps != 1`.  This is about
  `1.2e-6` in `32bit`, `2.22e-15` in `64bit`, and `0.00977` in `16bit`.
  See `numpy.finfo`.

  Args:
    x: Float or complex `Tensor`.
    y: Float or complex `Tensor`, same dtype as and broadcastable to `x`.
    rtol:  `Tensor`.  Same `dtype` as, and broadcastable to, `x`.
      The relative tolerance.  Default is `10 * eps`.
    atol:  `Tensor`.  Same `dtype` as, and broadcastable to, `x`.
      The absolute tolerance.  Default is `10 * eps`.
    message: A string to prefix to the default message.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional).  Defaults to "assert_near".

  Returns:
    Op that raises `InvalidArgumentError` if `x` and `y` are not close enough.
      This can be used with `tf.control_dependencies` inside of `tf.function`s
      to block followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x != y` is False for any pair of elements in `x` and `y`. The check can
      be performed immediately during eager execution or if `x` and `y` are
      statically known.

  @compatibility(numpy)
  Similar to `numpy.testing.assert_allclose`, except tolerance depends on data
  type. This is due to the fact that `TensorFlow` is often used with `32bit`,
  `64bit`, and even `16bit` data.
  @end_compatibility
  """
  return assert_near(x=x, y=y, rtol=rtol, atol=atol, summarize=summarize,
                     message=message, name=name)


@tf_export(v1=['debugging.assert_near', 'assert_near'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_near')
def assert_near(
    x, y, rtol=None, atol=None, data=None, summarize=None, message=None,
    name=None):
  """Assert the condition `x` and `y` are close element-wise.

  Example of adding a dependency to an operation:

  ```python
  with tf.control_dependencies([tf.compat.v1.assert_near(x, y)]):
    output = tf.reduce_sum(x)
  ```

  This condition holds if for every pair of (possibly broadcast) elements
  `x[i]`, `y[i]`, we have

  ```tf.abs(x[i] - y[i]) <= atol + rtol * tf.abs(y[i])```.

  If both `x` and `y` are empty, this is trivially satisfied.

  The default `atol` and `rtol` is `10 * eps`, where `eps` is the smallest
  representable positive number such that `1 + eps != 1`.  This is about
  `1.2e-6` in `32bit`, `2.22e-15` in `64bit`, and `0.00977` in `16bit`.
  See `numpy.finfo`.

  Args:
    x:  Float or complex `Tensor`.
    y:  Float or complex `Tensor`, same `dtype` as, and broadcastable to, `x`.
    rtol:  `Tensor`.  Same `dtype` as, and broadcastable to, `x`.
      The relative tolerance.  Default is `10 * eps`.
    atol:  `Tensor`.  Same `dtype` as, and broadcastable to, `x`.
      The absolute tolerance.  Default is `10 * eps`.
    data:  The tensors to print out if the condition is False.  Defaults to
      error message and first few entries of `x`, `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to "assert_near".

  Returns:
    Op that raises `InvalidArgumentError` if `x` and `y` are not close enough.

  @compatibility(numpy)
  Similar to `numpy.testing.assert_allclose`, except tolerance depends on data
  type. This is due to the fact that `TensorFlow` is often used with `32bit`,
  `64bit`, and even `16bit` data.
  @end_compatibility
  """
  message = message or ''
  with ops.name_scope(name, 'assert_near', [x, y, rtol, atol, data]):
    x = ops.convert_to_tensor(x, name='x')
    y = ops.convert_to_tensor(y, name='y', dtype=x.dtype)

    dtype = x.dtype
    if dtype.is_complex:
      dtype = dtype.real_dtype
    eps = np.finfo(dtype.as_numpy_dtype).eps
    rtol = 10 * eps if rtol is None else rtol
    atol = 10 * eps if atol is None else atol

    rtol = ops.convert_to_tensor(rtol, name='rtol', dtype=dtype)
    atol = ops.convert_to_tensor(atol, name='atol', dtype=dtype)

    if context.executing_eagerly():
      x_name = _shape_and_dtype_str(x)
      y_name = _shape_and_dtype_str(y)
    else:
      x_name = x.name
      y_name = y.name

    if data is None:
      data = [
          message,
          'x and y not equal to tolerance rtol = %s, atol = %s' % (rtol, atol),
          'x (%s) = ' % x_name, x, 'y (%s) = ' % y_name, y
      ]
    tol = atol + rtol * math_ops.abs(y)
    diff = math_ops.abs(x - y)
    condition = math_ops.reduce_all(math_ops.less(diff, tol))
    return control_flow_ops.Assert(condition, data, summarize=summarize)


@tf_export('debugging.assert_less', 'assert_less', v1=[])
@dispatch.add_dispatch_support
def assert_less_v2(x, y, message=None, summarize=None, name=None):
  """Assert the condition `x < y` holds element-wise.

  This Op checks that `x[i] < y[i]` holds for every pair of (possibly
  broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
  trivially satisfied.

  If `x` is not less than `y` element-wise, `message`, as well as the first
  `summarize` entries of `x` and `y` are printed, and `InvalidArgumentError` is
  raised.

  Args:
    x:  Numeric `Tensor`.
    y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
    message: A string to prefix to the default message.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional).  Defaults to "assert_less".

  Returns:
    Op that raises `InvalidArgumentError` if `x < y` is False.
    This can be used with `tf.control_dependencies` inside of `tf.function`s
    to block followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x < y` is False. The check can be performed immediately during eager
      execution or if `x` and `y` are statically known.
  """
  return assert_less(x=x, y=y, summarize=summarize, message=message, name=name)


@tf_export(v1=['debugging.assert_less', 'assert_less'])
@dispatch.add_dispatch_support
@_binary_assert_doc('<')
def assert_less(x, y, data=None, summarize=None, message=None, name=None):
  return _binary_assert('<', 'assert_less', math_ops.less, np.less, x, y, data,
                        summarize, message, name)


@tf_export('debugging.assert_less_equal', v1=[])
@dispatch.add_dispatch_support
def assert_less_equal_v2(x, y, message=None, summarize=None, name=None):
  """Assert the condition `x <= y` holds element-wise.

  This Op checks that `x[i] <= y[i]` holds for every pair of (possibly
  broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
  trivially satisfied.

  If `x` is not less or equal than `y` element-wise, `message`, as well as the
  first `summarize` entries of `x` and `y` are printed, and
  `InvalidArgumentError` is raised.

  Args:
    x:  Numeric `Tensor`.
    y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
    message: A string to prefix to the default message.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional). Defaults to "assert_less_equal".

  Returns:
    Op that raises `InvalidArgumentError` if `x <= y` is False. This can be
      used with `tf.control_dependencies` inside of `tf.function`s to block
      followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x <= y` is False. The check can be performed immediately during eager
      execution or if `x` and `y` are statically known.
  """
  return assert_less_equal(x=x, y=y,
                           summarize=summarize, message=message, name=name)


@tf_export(v1=['debugging.assert_less_equal', 'assert_less_equal'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_less_equal')
@_binary_assert_doc('<=')
def assert_less_equal(x, y, data=None, summarize=None, message=None, name=None):
  return _binary_assert('<=', 'assert_less_equal', math_ops.less_equal,
                        np.less_equal, x, y, data, summarize, message, name)


@tf_export('debugging.assert_greater', 'assert_greater', v1=[])
@dispatch.add_dispatch_support
def assert_greater_v2(x, y, message=None, summarize=None, name=None):
  """Assert the condition `x > y` holds element-wise.

  This Op checks that `x[i] > y[i]` holds for every pair of (possibly
  broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
  trivially satisfied.

  If `x` is not greater than `y` element-wise, `message`, as well as the first
  `summarize` entries of `x` and `y` are printed, and `InvalidArgumentError` is
  raised.

  Args:
    x:  Numeric `Tensor`.
    y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
    message: A string to prefix to the default message.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional).  Defaults to "assert_greater".

  Returns:
    Op that raises `InvalidArgumentError` if `x > y` is False. This can be
      used with `tf.control_dependencies` inside of `tf.function`s to block
      followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x > y` is False. The check can be performed immediately during eager
      execution or if `x` and `y` are statically known.
  """
  return assert_greater(x=x, y=y, summarize=summarize, message=message,
                        name=name)


@tf_export(v1=['debugging.assert_greater', 'assert_greater'])
@dispatch.add_dispatch_support
@_binary_assert_doc('>')
def assert_greater(x, y, data=None, summarize=None, message=None, name=None):  # pylint: disable=missing-docstring
  return _binary_assert('>', 'assert_greater', math_ops.greater, np.greater, x,
                        y, data, summarize, message, name)


@tf_export('debugging.assert_greater_equal', v1=[])
@dispatch.add_dispatch_support
def assert_greater_equal_v2(x, y, message=None, summarize=None, name=None):
  """Assert the condition `x >= y` holds element-wise.

  This Op checks that `x[i] >= y[i]` holds for every pair of (possibly
  broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
  trivially satisfied.

  If `x` is not greater or equal to `y` element-wise, `message`, as well as the
  first `summarize` entries of `x` and `y` are printed, and
  `InvalidArgumentError` is raised.

  Args:
    x:  Numeric `Tensor`.
    y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
    message: A string to prefix to the default message.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional).  Defaults to
    "assert_greater_equal".

  Returns:
    Op that raises `InvalidArgumentError` if `x >= y` is False. This can be
      used with `tf.control_dependencies` inside of `tf.function`s to block
      followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x >= y` is False. The check can be performed immediately during eager
      execution or if `x` and `y` are statically known.
  """
  return assert_greater_equal(x=x, y=y, summarize=summarize, message=message,
                              name=name)


@tf_export(v1=['debugging.assert_greater_equal', 'assert_greater_equal'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_greater_equal')
@_binary_assert_doc('>=')
def assert_greater_equal(x, y, data=None, summarize=None, message=None,
                         name=None):
  return _binary_assert('>=', 'assert_greater_equal', math_ops.greater_equal,
                        np.greater_equal, x, y, data, summarize, message, name)


def _assert_rank_condition(
    x, rank, static_condition, dynamic_condition, data, summarize):
  """Assert `x` has a rank that satisfies a given condition.

  Args:
    x:  Numeric `Tensor`.
    rank:  Scalar `Tensor`.
    static_condition:   A python function that takes `[actual_rank, given_rank]`
      and returns `True` if the condition is satisfied, `False` otherwise.
    dynamic_condition:  An `op` that takes [actual_rank, given_rank] and return
      `True` if the condition is satisfied, `False` otherwise.
    data:  The tensors to print out if the condition is false.  Defaults to
      error message and first few entries of `x`.
    summarize: Print this many entries of each tensor.

  Returns:
    Op raising `InvalidArgumentError` if `x` fails dynamic_condition.

  Raises:
    ValueError:  If static checks determine `x` fails static_condition.
  """
  assert_type(rank, dtypes.int32)

  # Attempt to statically defined rank.
  rank_static = tensor_util.constant_value(rank)
  if rank_static is not None:
    if rank_static.ndim != 0:
      raise ValueError('Rank must be a scalar.')

    x_rank_static = x.get_shape().ndims
    if x_rank_static is not None:
      if not static_condition(x_rank_static, rank_static):
        raise ValueError(
            'Static rank condition failed', x_rank_static, rank_static)
      return control_flow_ops.no_op(name='static_checks_determined_all_ok')

  condition = dynamic_condition(array_ops.rank(x), rank)

  # Add the condition that `rank` must have rank zero.  Prevents the bug where
  # someone does assert_rank(x, [n]), rather than assert_rank(x, n).
  if rank_static is None:
    this_data = ['Rank must be a scalar. Received rank: ', rank]
    rank_check = assert_rank(rank, 0, data=this_data)
    condition = control_flow_ops.with_dependencies([rank_check], condition)

  return control_flow_ops.Assert(condition, data, summarize=summarize)


@tf_export('debugging.assert_rank', 'assert_rank', v1=[])
@dispatch.add_dispatch_support
def assert_rank_v2(x, rank, message=None, name=None):
  """Assert that `x` has rank equal to `rank`.

  This Op checks that the rank of `x` is equal to `rank`.

  If `x` has a different rank, `message`, as well as the shape of `x` are
  printed, and `InvalidArgumentError` is raised.

  Args:
    x: `Tensor`.
    rank: Scalar integer `Tensor`.
    message: A string to prefix to the default message.
    name: A name for this operation (optional). Defaults to
      "assert_rank".

  Returns:
    Op raising `InvalidArgumentError` unless `x` has specified rank.
    If static checks determine `x` has correct rank, a `no_op` is returned.
    This can be used with `tf.control_dependencies` inside of `tf.function`s
    to block followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: if the check can be performed immediately and
      `x` does not have rank `rank`. The check can be performed immediately
      during eager execution or if the shape of `x` is statically known.
  """
  return assert_rank(x=x, rank=rank, message=message, name=name)


@tf_export(v1=['debugging.assert_rank', 'assert_rank'])
@dispatch.add_dispatch_support
def assert_rank(x, rank, data=None, summarize=None, message=None, name=None):
  """Assert `x` has rank equal to `rank`.

  Example of adding a dependency to an operation:

  ```python
  with tf.control_dependencies([tf.compat.v1.assert_rank(x, 2)]):
    output = tf.reduce_sum(x)
  ```

  Args:
    x:  Numeric `Tensor`.
    rank:  Scalar integer `Tensor`.
    data:  The tensors to print out if the condition is False.  Defaults to
      error message and the shape of `x`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to "assert_rank".

  Returns:
    Op raising `InvalidArgumentError` unless `x` has specified rank.
    If static checks determine `x` has correct rank, a `no_op` is returned.

  Raises:
    ValueError:  If static checks determine `x` has wrong rank.
  """
  with ops.name_scope(name, 'assert_rank', (x, rank) + tuple(data or [])):
    if not isinstance(x, sparse_tensor.SparseTensor):
      x = ops.convert_to_tensor(x, name='x')
    rank = ops.convert_to_tensor(rank, name='rank')
    message = message or ''

    static_condition = lambda actual_rank, given_rank: actual_rank == given_rank
    dynamic_condition = math_ops.equal

    if context.executing_eagerly() or isinstance(x, sparse_tensor.SparseTensor):
      name = ''
    else:
      name = x.name

    if data is None:
      data = [
          message,
          'Tensor %s must have rank' % name, rank, 'Received shape: ',
          array_ops.shape(x)
      ]

    try:
      assert_op = _assert_rank_condition(x, rank, static_condition,
                                         dynamic_condition, data, summarize)

    except ValueError as e:
      if e.args[0] == 'Static rank condition failed':
        raise ValueError(
            '%s.  Tensor %s must have rank %d.  Received rank %d, shape %s' %
            (message, name, e.args[2], e.args[1], x.get_shape()))
      else:
        raise

  return assert_op


@tf_export('debugging.assert_rank_at_least', v1=[])
@dispatch.add_dispatch_support
def assert_rank_at_least_v2(x, rank, message=None, name=None):
  """Assert that `x` has rank of at least `rank`.

  This Op checks that the rank of `x` is greater or equal to `rank`.

  If `x` has a rank lower than `rank`, `message`, as well as the shape of `x`
  are printed, and `InvalidArgumentError` is raised.

  Args:
    x: `Tensor`.
    rank: Scalar integer `Tensor`.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to
      "assert_rank_at_least".

  Returns:
    Op raising `InvalidArgumentError` unless `x` has specified rank or higher.
    If static checks determine `x` has correct rank, a `no_op` is returned.
    This can be used with `tf.control_dependencies` inside of `tf.function`s
    to block followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: `x` does not have rank at least `rank`, but the rank
      cannot be statically determined.
    ValueError: If static checks determine `x` has mismatched rank.
  """
  return assert_rank_at_least(x=x, rank=rank, message=message, name=name)


@tf_export(v1=['debugging.assert_rank_at_least', 'assert_rank_at_least'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_rank_at_least')
def assert_rank_at_least(
    x, rank, data=None, summarize=None, message=None, name=None):
  """Assert `x` has rank equal to `rank` or higher.

  Example of adding a dependency to an operation:

  ```python
  with tf.control_dependencies([tf.compat.v1.assert_rank_at_least(x, 2)]):
    output = tf.reduce_sum(x)
  ```

  Args:
    x:  Numeric `Tensor`.
    rank:  Scalar `Tensor`.
    data:  The tensors to print out if the condition is False.  Defaults to
      error message and first few entries of `x`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).
      Defaults to "assert_rank_at_least".

  Returns:
    Op raising `InvalidArgumentError` unless `x` has specified rank or higher.
    If static checks determine `x` has correct rank, a `no_op` is returned.

  Raises:
    ValueError:  If static checks determine `x` has wrong rank.
  """
  with ops.name_scope(
      name, 'assert_rank_at_least', (x, rank) + tuple(data or [])):
    x = ops.convert_to_tensor(x, name='x')
    rank = ops.convert_to_tensor(rank, name='rank')
    message = message or ''

    static_condition = lambda actual_rank, given_rank: actual_rank >= given_rank
    dynamic_condition = math_ops.greater_equal

    if context.executing_eagerly():
      name = ''
    else:
      name = x.name

    if data is None:
      data = [
          message,
          'Tensor %s must have rank at least' % name, rank,
          'Received shape: ', array_ops.shape(x)
      ]

    try:
      assert_op = _assert_rank_condition(x, rank, static_condition,
                                         dynamic_condition, data, summarize)

    except ValueError as e:
      if e.args[0] == 'Static rank condition failed':
        raise ValueError(
            '%s.  Tensor %s must have rank at least %d.  Received rank %d, '
            'shape %s' % (message, name, e.args[2], e.args[1], x.get_shape()))
      else:
        raise

  return assert_op


def _static_rank_in(actual_rank, given_ranks):
  return actual_rank in given_ranks


def _dynamic_rank_in(actual_rank, given_ranks):
  if len(given_ranks) < 1:
    return ops.convert_to_tensor(False)
  result = math_ops.equal(given_ranks[0], actual_rank)
  for given_rank in given_ranks[1:]:
    result = math_ops.logical_or(
        result, math_ops.equal(given_rank, actual_rank))
  return result


def _assert_ranks_condition(
    x, ranks, static_condition, dynamic_condition, data, summarize):
  """Assert `x` has a rank that satisfies a given condition.

  Args:
    x:  Numeric `Tensor`.
    ranks:  Scalar `Tensor`.
    static_condition:   A python function that takes
      `[actual_rank, given_ranks]` and returns `True` if the condition is
      satisfied, `False` otherwise.
    dynamic_condition:  An `op` that takes [actual_rank, given_ranks]
      and return `True` if the condition is satisfied, `False` otherwise.
    data:  The tensors to print out if the condition is false.  Defaults to
      error message and first few entries of `x`.
    summarize: Print this many entries of each tensor.

  Returns:
    Op raising `InvalidArgumentError` if `x` fails dynamic_condition.

  Raises:
    ValueError:  If static checks determine `x` fails static_condition.
  """
  for rank in ranks:
    assert_type(rank, dtypes.int32)

  # Attempt to statically defined rank.
  ranks_static = tuple([tensor_util.constant_value(rank) for rank in ranks])
  if not any(r is None for r in ranks_static):
    for rank_static in ranks_static:
      if rank_static.ndim != 0:
        raise ValueError('Rank must be a scalar.')

    x_rank_static = x.get_shape().ndims
    if x_rank_static is not None:
      if not static_condition(x_rank_static, ranks_static):
        raise ValueError(
            'Static rank condition failed', x_rank_static, ranks_static)
      return control_flow_ops.no_op(name='static_checks_determined_all_ok')

  condition = dynamic_condition(array_ops.rank(x), ranks)

  # Add the condition that `rank` must have rank zero.  Prevents the bug where
  # someone does assert_rank(x, [n]), rather than assert_rank(x, n).
  for rank, rank_static in zip(ranks, ranks_static):
    if rank_static is None:
      this_data = ['Rank must be a scalar. Received rank: ', rank]
      rank_check = assert_rank(rank, 0, data=this_data)
      condition = control_flow_ops.with_dependencies([rank_check], condition)

  return control_flow_ops.Assert(condition, data, summarize=summarize)


@tf_export('debugging.assert_rank_in', v1=[])
@dispatch.add_dispatch_support
def assert_rank_in_v2(x, ranks, message=None, name=None):
  """Assert that `x` has a rank in `ranks`.

  This Op checks that the rank of `x` is in `ranks`.

  If `x` has a different rank, `message`, as well as the shape of `x` are
  printed, and `InvalidArgumentError` is raised.

  Args:
    x: `Tensor`.
    ranks: `Iterable` of scalar `Tensor` objects.
    message: A string to prefix to the default message.
    name: A name for this operation (optional). Defaults to "assert_rank_in".

  Returns:
    Op raising `InvalidArgumentError` unless rank of `x` is in `ranks`.
    If static checks determine `x` has matching rank, a `no_op` is returned.
    This can be used with `tf.control_dependencies` inside of `tf.function`s
    to block followup computation until the check has executed.
    @compatibility(eager)
    returns None
    @end_compatibility

  Raises:
    InvalidArgumentError: `x` does not have rank in `ranks`, but the rank cannot
      be statically determined.
    ValueError: If static checks determine `x` has mismatched rank.
  """
  return assert_rank_in(x=x, ranks=ranks, message=message, name=name)


@tf_export(v1=['debugging.assert_rank_in', 'assert_rank_in'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_rank_in')
def assert_rank_in(
    x, ranks, data=None, summarize=None, message=None, name=None):
  """Assert `x` has rank in `ranks`.

  Example of adding a dependency to an operation:

  ```python
  with tf.control_dependencies([tf.compat.v1.assert_rank_in(x, (2, 4))]):
    output = tf.reduce_sum(x)
  ```

  Args:
    x:  Numeric `Tensor`.
    ranks:  Iterable of scalar `Tensor` objects.
    data:  The tensors to print out if the condition is False.  Defaults to
      error message and first few entries of `x`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).
      Defaults to "assert_rank_in".

  Returns:
    Op raising `InvalidArgumentError` unless rank of `x` is in `ranks`.
    If static checks determine `x` has matching rank, a `no_op` is returned.

  Raises:
    ValueError:  If static checks determine `x` has mismatched rank.
  """
  with ops.name_scope(
      name, 'assert_rank_in', (x,) + tuple(ranks) + tuple(data or [])):
    if not isinstance(x, sparse_tensor.SparseTensor):
      x = ops.convert_to_tensor(x, name='x')
    ranks = tuple([ops.convert_to_tensor(rank, name='rank') for rank in ranks])
    message = message or ''

    if context.executing_eagerly() or isinstance(x, sparse_tensor.SparseTensor):
      name = ''
    else:
      name = x.name

    if data is None:
      data = [
          message, 'Tensor %s must have rank in' % name
      ] + list(ranks) + [
          'Received shape: ', array_ops.shape(x)
      ]

    try:
      assert_op = _assert_ranks_condition(x, ranks, _static_rank_in,
                                          _dynamic_rank_in, data, summarize)

    except ValueError as e:
      if e.args[0] == 'Static rank condition failed':
        raise ValueError(
            '%s.  Tensor %s must have rank in %s.  Received rank %d, '
            'shape %s' % (message, name, e.args[2], e.args[1], x.get_shape()))
      else:
        raise

  return assert_op


@tf_export('debugging.assert_integer', v1=[])
@dispatch.add_dispatch_support
def assert_integer_v2(x, message=None, name=None):
  """Assert that `x` is of integer dtype.

  If `x` has a non-integer type, `message`, as well as the dtype of `x` are
  printed, and `InvalidArgumentError` is raised.

  This can always be checked statically, so this method returns nothing.

  Args:
    x: A `Tensor`.
    message: A string to prefix to the default message.
    name: A name for this operation (optional). Defaults to "assert_integer".

  Raises:
    TypeError:  If `x.dtype` is not a non-quantized integer type.
  """
  assert_integer(x=x, message=message, name=name)


@tf_export(v1=['debugging.assert_integer', 'assert_integer'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_integer')
def assert_integer(x, message=None, name=None):
  """Assert that `x` is of integer dtype.

  Example of adding a dependency to an operation:

  ```python
  with tf.control_dependencies([tf.compat.v1.assert_integer(x)]):
    output = tf.reduce_sum(x)
  ```

  Args:
    x: `Tensor` whose basetype is integer and is not quantized.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to "assert_integer".

  Raises:
    TypeError:  If `x.dtype` is anything other than non-quantized integer.

  Returns:
    A `no_op` that does nothing.  Type can be determined statically.
  """
  message = message or ''
  with ops.name_scope(name, 'assert_integer', [x]):
    x = ops.convert_to_tensor(x, name='x')
    if not x.dtype.is_integer:
      if context.executing_eagerly():
        name = 'tensor'
      else:
        name = x.name
      err_msg = (
          '%s  Expected "x" to be integer type.  Found: %s of dtype %s'
          % (message, name, x.dtype))
      raise TypeError(err_msg)

    return control_flow_ops.no_op('statically_determined_was_integer')


@tf_export('debugging.assert_type', v1=[])
@dispatch.add_dispatch_support
def assert_type_v2(tensor, tf_type, message=None, name=None):
  """Asserts that the given `Tensor` is of the specified type.

  This can always be checked statically, so this method returns nothing.

  Args:
    tensor: A `Tensor` or `SparseTensor`.
    tf_type: A tensorflow type (`dtypes.float32`, `tf.int64`, `dtypes.bool`,
      etc).
    message: A string to prefix to the default message.
    name:  A name for this operation. Defaults to "assert_type"

  Raises:
    TypeError: If the tensor's data type doesn't match `tf_type`.
  """
  assert_type(tensor=tensor, tf_type=tf_type, message=message, name=name)


@tf_export(v1=['debugging.assert_type', 'assert_type'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_type')
def assert_type(tensor, tf_type, message=None, name=None):
  """Statically asserts that the given `Tensor` is of the specified type.

  Args:
    tensor: A `Tensor` or `SparseTensor`.
    tf_type: A tensorflow type (`dtypes.float32`, `tf.int64`, `dtypes.bool`,
      etc).
    message: A string to prefix to the default message.
    name:  A name to give this `Op`.  Defaults to "assert_type"

  Raises:
    TypeError: If the tensors data type doesn't match `tf_type`.

  Returns:
    A `no_op` that does nothing.  Type can be determined statically.
  """
  message = message or ''
  tf_type = dtypes.as_dtype(tf_type)
  with ops.name_scope(name, 'assert_type', [tensor]):
    if not isinstance(tensor, sparse_tensor.SparseTensor):
      tensor = ops.convert_to_tensor(tensor, name='tensor')
    if tensor.dtype != tf_type:
      if context.executing_eagerly():
        raise TypeError('%s tensor must be of type %s' % (message, tf_type))
      else:
        raise TypeError(
            '%s  %s must be of type %s' %
            (message, tensor.name if hasattr(tensor, 'name') else '', tf_type))

    return control_flow_ops.no_op('statically_determined_correct_type')


def _dimension_sizes(x):
  """Gets the dimension sizes of a tensor `x`.

  If a size can be determined statically it is returned as an integer,
  otherwise as a tensor.

  If `x` is a scalar it is treated as rank 1 size 1.

  Args:
    x: A `Tensor`.

  Returns:
    Dimension sizes.
  """
  dynamic_shape = array_ops.shape(x)
  rank = x.get_shape().rank
  rank_is_known = rank is not None
  if rank_is_known and rank == 0:
    return (1,)
  if rank_is_known and rank > 0:
    static_shape = x.get_shape().as_list()
    sizes = [
        int(size) if size is not None else dynamic_shape[i]
        for i, size in enumerate(static_shape)
    ]
    return sizes
  has_rank_zero = math_ops.equal(array_ops.rank(x), 0)
  return control_flow_ops.cond(
      has_rank_zero, lambda: array_ops.constant([1]), lambda: dynamic_shape)


def _symbolic_dimension_sizes(symbolic_shape):
  # If len(symbolic_shape) == 0 construct a tuple
  if not symbolic_shape:
    return tuple([1])

  return symbolic_shape


def _has_known_value(dimension_size):
  not_none = dimension_size is not None
  try:
    int(dimension_size)
    can_be_parsed_as_int = True
  except (ValueError, TypeError):
    can_be_parsed_as_int = False
  return not_none and can_be_parsed_as_int


def _is_symbol_for_any_size(symbol):
  return symbol in [None, '.']


_TensorDimSizes = collections.namedtuple(
    '_TensorDimSizes',
    ['x', 'unspecified_dim', 'actual_sizes', 'symbolic_sizes'])


@tf_export('debugging.assert_shapes', v1=[])
@dispatch.add_dispatch_support
def assert_shapes_v2(shapes, data=None, summarize=None, message=None,
                     name=None):
  """Assert tensor shapes and dimension size relationships between tensors.

  This Op checks that a collection of tensors shape relationships
  satisfies given constraints.

  Example:

  >>> n = 10
  >>> q = 3
  >>> d = 7
  >>> x = tf.zeros([n,q])
  >>> y = tf.ones([n,d])
  >>> param = tf.Variable([1.0, 2.0, 3.0])
  >>> scalar = 1.0
  >>> tf.debugging.assert_shapes([
  ...  (x, ('N', 'Q')),
  ...  (y, ('N', 'D')),
  ...  (param, ('Q',)),
  ...  (scalar, ()),
  ... ])

  >>> tf.debugging.assert_shapes([
  ...   (x, ('N', 'D')),
  ...   (y, ('N', 'D'))
  ... ])
  Traceback (most recent call last):
  ...
  ValueError: ...

  If `x`, `y`, `param` or `scalar` does not have a shape that satisfies
  all specified constraints, `message`, as well as the first `summarize` entries
  of the first encountered violating tensor are printed, and
  `InvalidArgumentError` is raised.

  Size entries in the specified shapes are checked against other entries by
  their __hash__, except:
    - a size entry is interpreted as an explicit size if it can be parsed as an
      integer primitive.
    - a size entry is interpreted as *any* size if it is None or '.'.

  If the first entry of a shape is `...` (type `Ellipsis`) or '*' that indicates
  a variable number of outer dimensions of unspecified size, i.e. the constraint
  applies to the inner-most dimensions only.

  Scalar tensors and specified shapes of length zero (excluding the 'inner-most'
  prefix) are both treated as having a single dimension of size one.

  Args:
    shapes: dictionary with (`Tensor` to shape) items, or a list of
      (`Tensor`, shape) tuples. A shape must be an iterable.
    data: The tensors to print out if the condition is False.  Defaults to error
      message and first few entries of the violating tensor.
    summarize: Print this many entries of the tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to "assert_shapes".

  Raises:
    ValueError:  If static checks determine any shape constraint is violated.
  """
  assert_shapes(
      shapes, data=data, summarize=summarize, message=message, name=name)


@tf_export(v1=['debugging.assert_shapes'])
@dispatch.add_dispatch_support
def assert_shapes(shapes, data=None, summarize=None, message=None, name=None):
  """Assert tensor shapes and dimension size relationships between tensors.

  This Op checks that a collection of tensors shape relationships
  satisfies given constraints.

  Example:

  >>> n = 10
  >>> q = 3
  >>> d = 7
  >>> x = tf.zeros([n,q])
  >>> y = tf.ones([n,d])
  >>> param = tf.Variable([1.0, 2.0, 3.0])
  >>> scalar = 1.0
  >>> tf.debugging.assert_shapes([
  ...  (x, ('N', 'Q')),
  ...  (y, ('N', 'D')),
  ...  (param, ('Q',)),
  ...  (scalar, ()),
  ... ])

  >>> tf.debugging.assert_shapes([
  ...   (x, ('N', 'D')),
  ...   (y, ('N', 'D'))
  ... ])
  Traceback (most recent call last):
  ...
  ValueError: ...

  Example of adding a dependency to an operation:

  ```python
  with tf.control_dependencies([tf.assert_shapes(shapes)]):
    output = tf.matmul(x, y, transpose_a=True)
  ```

  If `x`, `y`, `param` or `scalar` does not have a shape that satisfies
  all specified constraints, `message`, as well as the first `summarize` entries
  of the first encountered violating tensor are printed, and
  `InvalidArgumentError` is raised.

  Size entries in the specified shapes are checked against other entries by
  their __hash__, except:
    - a size entry is interpreted as an explicit size if it can be parsed as an
      integer primitive.
    - a size entry is interpreted as *any* size if it is None or '.'.

  If the first entry of a shape is `...` (type `Ellipsis`) or '*' that indicates
  a variable number of outer dimensions of unspecified size, i.e. the constraint
  applies to the inner-most dimensions only.

  Scalar tensors and specified shapes of length zero (excluding the 'inner-most'
  prefix) are both treated as having a single dimension of size one.

  Args:
    shapes: A list of (`Tensor`, `shape`) tuples, wherein `shape` is the
      expected shape of `Tensor`. See the example code above. The `shape` must
      be an iterable. Each element of the iterable can be either a concrete
      integer value or a string that abstractly represents the dimension.
      For example,
        - `('N', 'Q')` specifies a 2D shape wherein the first and second
          dimensions of shape may or may not be equal.
        - `('N', 'N', 'Q')` specifies a 3D shape wherein the first and second
          dimensions are equal.
        - `(1, 'N')` specifies a 2D shape wherein the first dimension is
          exactly 1 and the second dimension can be any value.
      Note that the abstract dimension letters take effect across different
      tuple elements of the list. For example,
      `tf.debugging.assert_shapes([(x, ('N', 'A')), (y, ('N', 'B'))]` asserts
      that both `x` and `y` are rank-2 tensors and their first dimensions are
      equal (`N`).
      `shape` can also be a `tf.TensorShape`.
    data: The tensors to print out if the condition is False.  Defaults to error
      message and first few entries of the violating tensor.
    summarize: Print this many entries of the tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to "assert_shapes".

  Returns:
    Op raising `InvalidArgumentError` unless all shape constraints are
    satisfied.
    If static checks determine all constraints are satisfied, a `no_op` is
    returned.

  Raises:
    ValueError:  If static checks determine any shape constraint is violated.
  """
  # If the user manages to assemble a dict containing tensors (possible in
  # Graph mode only), make sure we still accept that.
  if isinstance(shapes, dict):
    shapes = shapes.items()

  message = message or ''
  with ops.name_scope(name, 'assert_shapes', [shapes, data]):
    # Shape specified as None implies no constraint
    shape_constraints = [(x if isinstance(x, sparse_tensor.SparseTensor) else
                          ops.convert_to_tensor(x), s)
                         for x, s in shapes if s is not None]

    executing_eagerly = context.executing_eagerly()

    def tensor_name(x):
      if executing_eagerly or isinstance(x, sparse_tensor.SparseTensor):
        return _shape_and_dtype_str(x)
      return x.name

    tensor_dim_sizes = []
    for tensor, symbolic_shape in shape_constraints:
      is_iterable = (
          hasattr(symbolic_shape, '__iter__') or
          hasattr(symbolic_shape, '__getitem__')  # For Python 2 compat.
      )
      if not is_iterable:
        raise ValueError(
            '%s.  '
            'Tensor %s.  Specified shape must be an iterable.  '
            'An iterable has the attribute `__iter__` or `__getitem__`.  '
            'Received specified shape: %s' %
            (message, tensor_name(tensor), symbolic_shape))

      # We convert this into a tuple to handle strings, lists and numpy arrays
      symbolic_shape_tuple = tuple(symbolic_shape)

      tensors_specified_innermost = False
      for i, symbol in enumerate(symbolic_shape_tuple):
        if symbol not in [Ellipsis, '*']:
          continue

        if i != 0:
          raise ValueError(
              '%s.  '
              'Tensor %s specified shape index %d.  '
              'Symbol `...` or `*` for a variable number of '
              'unspecified dimensions is only allowed as the first entry' %
              (message, tensor_name(tensor), i))

        tensors_specified_innermost = True

      # Only include the size of the specified dimensions since the 0th symbol
      # is either ellipsis or *
      tensor_dim_sizes.append(
          _TensorDimSizes(
              tensor, tensors_specified_innermost, _dimension_sizes(tensor),
              _symbolic_dimension_sizes(
                  symbolic_shape_tuple[1:]
                  if tensors_specified_innermost else symbolic_shape_tuple)))

    rank_assertions = []
    for sizes in tensor_dim_sizes:
      rank = len(sizes.symbolic_sizes)
      rank_zero_or_one = rank in [0, 1]
      if sizes.unspecified_dim:
        if rank_zero_or_one:
          # No assertion of rank needed as `x` only need to have rank at least
          # 0. See elif rank_zero_or_one case comment.
          continue
        assertion = assert_rank_at_least(
            x=sizes.x,
            rank=rank,
            data=data,
            summarize=summarize,
            message=message,
            name=name)
      elif rank_zero_or_one:
        # Rank 0 is treated as rank 1 size 1, i.e. there is
        # no distinction between the two in terms of rank.
        # See _dimension_sizes.
        assertion = assert_rank_in(
            x=sizes.x,
            ranks=[0, 1],
            data=data,
            summarize=summarize,
            message=message,
            name=name)
      else:
        assertion = assert_rank(
            x=sizes.x,
            rank=rank,
            data=data,
            summarize=summarize,
            message=message,
            name=name)
      rank_assertions.append(assertion)

    size_assertions = []
    size_specifications = {}
    for sizes in tensor_dim_sizes:
      for i, size_symbol in enumerate(sizes.symbolic_sizes):

        if _is_symbol_for_any_size(size_symbol):
          # Size specified as any implies no constraint
          continue

        if sizes.unspecified_dim:
          tensor_dim = i - len(sizes.symbolic_sizes)
        else:
          tensor_dim = i

        if size_symbol in size_specifications or _has_known_value(size_symbol):
          if _has_known_value(size_symbol):
            specified_size = int(size_symbol)
            size_check_message = 'Specified explicitly'
          else:
            specified_size, specified_by_y, specified_at_dim = \
                size_specifications[size_symbol]
            size_check_message = (
                'Specified by tensor %s dimension %d' %
                (tensor_name(specified_by_y), specified_at_dim))

          # This is extremely subtle. If actual_sizes is dynamic, we must
          # make sure a control dependency is inserted here so that this slice
          # can not execute until the rank is asserted to be enough for the
          # slice to not fail.
          with ops.control_dependencies(rank_assertions):
            actual_size = sizes.actual_sizes[tensor_dim]
          if _has_known_value(actual_size) and _has_known_value(specified_size):
            if int(actual_size) != int(specified_size):
              raise ValueError(
                  '%s.  %s.  Tensor %s dimension %s must have size %d.  '
                  'Received size %d, shape %s' %
                  (message, size_check_message, tensor_name(sizes.x),
                   tensor_dim, specified_size, actual_size,
                   sizes.x.get_shape()))
            # No dynamic assertion needed
            continue

          condition = math_ops.equal(
              ops.convert_to_tensor(actual_size),
              ops.convert_to_tensor(specified_size))
          data_ = data
          if data is None:
            data_ = [
                message, size_check_message,
                'Tensor %s dimension' % tensor_name(sizes.x), tensor_dim,
                'must have size', specified_size, 'Received shape: ',
                array_ops.shape(sizes.x)
            ]
          size_assertions.append(
              control_flow_ops.Assert(condition, data_, summarize=summarize))
        else:
          # Not sure if actual_sizes is a constant, but for safety, guard
          # on rank. See explanation above about actual_sizes need for safety.
          with ops.control_dependencies(rank_assertions):
            size = sizes.actual_sizes[tensor_dim]
          size_specifications[size_symbol] = (size, sizes.x, tensor_dim)

  # Ensure both assertions actually occur.
  with ops.control_dependencies(rank_assertions):
    shapes_assertion = control_flow_ops.group(size_assertions)

  return shapes_assertion


# pylint: disable=line-too-long
def _get_diff_for_monotonic_comparison(x):
  """Gets the difference x[1:] - x[:-1]."""
  x = array_ops.reshape(x, [-1])
  if not is_numeric_tensor(x):
    raise TypeError('Expected x to be numeric, instead found: %s' % x)

  # If x has less than 2 elements, there is nothing to compare.  So return [].
  is_shorter_than_two = math_ops.less(array_ops.size(x), 2)
  short_result = lambda: ops.convert_to_tensor([], dtype=x.dtype)

  # With 2 or more elements, return x[1:] - x[:-1]
  s_len = array_ops.shape(x) - 1
  diff = lambda: array_ops.strided_slice(x, [1], [1] + s_len)- array_ops.strided_slice(x, [0], s_len)
  return control_flow_ops.cond(is_shorter_than_two, short_result, diff)


@tf_export(
    'debugging.is_numeric_tensor',
    v1=['debugging.is_numeric_tensor', 'is_numeric_tensor'])
@deprecation.deprecated_endpoints('is_numeric_tensor')
def is_numeric_tensor(tensor):
  """Returns `True` if the elements of `tensor` are numbers.

  Specifically, returns `True` if the dtype of `tensor` is one of the following:

  * `tf.float32`
  * `tf.float64`
  * `tf.int8`
  * `tf.int16`
  * `tf.int32`
  * `tf.int64`
  * `tf.uint8`
  * `tf.qint8`
  * `tf.qint32`
  * `tf.quint8`
  * `tf.complex64`

  Returns `False` if `tensor` is of a non-numeric type or if `tensor` is not
  a `tf.Tensor` object.
  """
  return isinstance(tensor, ops.Tensor) and tensor.dtype in NUMERIC_TYPES


@tf_export(
    'math.is_non_decreasing',
    v1=[
        'math.is_non_decreasing', 'debugging.is_non_decreasing',
        'is_non_decreasing'
    ])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('debugging.is_non_decreasing',
                                  'is_non_decreasing')
def is_non_decreasing(x, name=None):
  """Returns `True` if `x` is non-decreasing.

  Elements of `x` are compared in row-major order.  The tensor `[x[0],...]`
  is non-decreasing if for every adjacent pair we have `x[i] <= x[i+1]`.
  If `x` has less than two elements, it is trivially non-decreasing.

  See also:  `is_strictly_increasing`

  >>> x1 = tf.constant([1.0, 1.0, 3.0])
  >>> tf.math.is_non_decreasing(x1)
  <tf.Tensor: shape=(), dtype=bool, numpy=True>
  >>> x2 = tf.constant([3.0, 1.0, 2.0])
  >>> tf.math.is_non_decreasing(x2)
  <tf.Tensor: shape=(), dtype=bool, numpy=False>

  Args:
    x: Numeric `Tensor`.
    name: A name for this operation (optional).  Defaults to "is_non_decreasing"

  Returns:
    Boolean `Tensor`, equal to `True` iff `x` is non-decreasing.

  Raises:
    TypeError: if `x` is not a numeric tensor.
  """
  with ops.name_scope(name, 'is_non_decreasing', [x]):
    diff = _get_diff_for_monotonic_comparison(x)
    # When len(x) = 1, diff = [], less_equal = [], and reduce_all([]) = True.
    zero = ops.convert_to_tensor(0, dtype=diff.dtype)
    return math_ops.reduce_all(math_ops.less_equal(zero, diff))


@tf_export(
    'math.is_strictly_increasing',
    v1=[
        'math.is_strictly_increasing', 'debugging.is_strictly_increasing',
        'is_strictly_increasing'
    ])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('debugging.is_strictly_increasing',
                                  'is_strictly_increasing')
def is_strictly_increasing(x, name=None):
  """Returns `True` if `x` is strictly increasing.

  Elements of `x` are compared in row-major order.  The tensor `[x[0],...]`
  is strictly increasing if for every adjacent pair we have `x[i] < x[i+1]`.
  If `x` has less than two elements, it is trivially strictly increasing.

  See also:  `is_non_decreasing`

  >>> x1 = tf.constant([1.0, 2.0, 3.0])
  >>> tf.math.is_strictly_increasing(x1)
  <tf.Tensor: shape=(), dtype=bool, numpy=True>
  >>> x2 = tf.constant([3.0, 1.0, 2.0])
  >>> tf.math.is_strictly_increasing(x2)
  <tf.Tensor: shape=(), dtype=bool, numpy=False>

  Args:
    x: Numeric `Tensor`.
    name: A name for this operation (optional).
      Defaults to "is_strictly_increasing"

  Returns:
    Boolean `Tensor`, equal to `True` iff `x` is strictly increasing.

  Raises:
    TypeError: if `x` is not a numeric tensor.
  """
  with ops.name_scope(name, 'is_strictly_increasing', [x]):
    diff = _get_diff_for_monotonic_comparison(x)
    # When len(x) = 1, diff = [], less = [], and reduce_all([]) = True.
    zero = ops.convert_to_tensor(0, dtype=diff.dtype)
    return math_ops.reduce_all(math_ops.less(zero, diff))


def _assert_same_base_type(items, expected_type=None):
  r"""Asserts all items are of the same base type.

  Args:
    items: List of graph items (e.g., `Variable`, `Tensor`, `SparseTensor`,
        `Operation`, or `IndexedSlices`). Can include `None` elements, which
        will be ignored.
    expected_type: Expected type. If not specified, assert all items are
        of the same base type.

  Returns:
    Validated type, or none if neither expected_type nor items provided.

  Raises:
    ValueError: If any types do not match.
  """
  original_expected_type = expected_type
  mismatch = False
  for item in items:
    if item is not None:
      item_type = item.dtype.base_dtype
      if not expected_type:
        expected_type = item_type
      elif expected_type != item_type:
        mismatch = True
        break
  if mismatch:
    # Loop back through and build up an informative error message (this is very
    # slow, so we don't do it unless we found an error above).
    expected_type = original_expected_type
    original_item_str = None
    for item in items:
      if item is not None:
        item_type = item.dtype.base_dtype
        if not expected_type:
          expected_type = item_type
          original_item_str = item.name if hasattr(item, 'name') else str(item)
        elif expected_type != item_type:
          raise ValueError('%s, type=%s, must be of the same type (%s)%s.' % (
              item.name if hasattr(item, 'name') else str(item),
              item_type, expected_type,
              (' as %s' % original_item_str) if original_item_str else ''))
    return expected_type  # Should be unreachable
  else:
    return expected_type


@tf_export(
    'debugging.assert_same_float_dtype',
    v1=['debugging.assert_same_float_dtype', 'assert_same_float_dtype'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_same_float_dtype')
def assert_same_float_dtype(tensors=None, dtype=None):
  """Validate and return float type based on `tensors` and `dtype`.

  For ops such as matrix multiplication, inputs and weights must be of the
  same float type. This function validates that all `tensors` are the same type,
  validates that type is `dtype` (if supplied), and returns the type. Type must
  be a floating point type. If neither `tensors` nor `dtype` is supplied,
  the function will return `dtypes.float32`.

  Args:
    tensors: Tensors of input values. Can include `None` elements, which will be
        ignored.
    dtype: Expected type.

  Returns:
    Validated type.

  Raises:
    ValueError: if neither `tensors` nor `dtype` is supplied, or result is not
        float, or the common type of the inputs is not a floating point type.
  """
  if tensors:
    dtype = _assert_same_base_type(tensors, dtype)
  if not dtype:
    dtype = dtypes.float32
  elif not dtype.is_floating:
    raise ValueError('Expected floating point type, got %s.' % dtype)
  return dtype


@tf_export('debugging.assert_scalar', v1=[])
@dispatch.add_dispatch_support
def assert_scalar_v2(tensor, message=None, name=None):
  """Asserts that the given `tensor` is a scalar.

  This function raises `ValueError` unless it can be certain that the given
  `tensor` is a scalar. `ValueError` is also raised if the shape of `tensor` is
  unknown.

  This is always checked statically, so this method returns nothing.

  Args:
    tensor: A `Tensor`.
    message: A string to prefix to the default message.
    name:  A name for this operation. Defaults to "assert_scalar"

  Raises:
    ValueError: If the tensor is not scalar (rank 0), or if its shape is
      unknown.
  """
  assert_scalar(tensor=tensor, message=message, name=name)


@tf_export(v1=['debugging.assert_scalar', 'assert_scalar'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('assert_scalar')
def assert_scalar(tensor, name=None, message=None):
  """Asserts that the given `tensor` is a scalar (i.e. zero-dimensional).

  This function raises `ValueError` unless it can be certain that the given
  `tensor` is a scalar. `ValueError` is also raised if the shape of `tensor` is
  unknown.

  Args:
    tensor: A `Tensor`.
    name:  A name for this operation. Defaults to "assert_scalar"
    message: A string to prefix to the default message.

  Returns:
    The input tensor (potentially converted to a `Tensor`).

  Raises:
    ValueError: If the tensor is not scalar (rank 0), or if its shape is
      unknown.
  """
  with ops.name_scope(name, 'assert_scalar', [tensor]) as name_scope:
    tensor = ops.convert_to_tensor(tensor, name=name_scope)
    shape = tensor.get_shape()
    if shape.ndims != 0:
      if context.executing_eagerly():
        raise ValueError('%sExpected scalar shape, saw shape: %s.'
                         % (message or '', shape,))
      else:
        raise ValueError('%sExpected scalar shape for %s, saw shape: %s.'
                         % (message or '', tensor.name, shape))
    return tensor


@tf_export('ensure_shape')
@dispatch.add_dispatch_support
def ensure_shape(x, shape, name=None):
  """Updates the shape of a tensor and checks at runtime that the shape holds.

  When executed, this operation asserts that the input tensor `x`'s shape
  is compatible with the `shape` argument.
  See `tf.TensorShape.is_compatible_with` for details.

  >>> x = tf.constant([[1, 2, 3],
  ...                  [4, 5, 6]])
  >>> x = tf.ensure_shape(x, [2, 3])

  Use `None` for unknown dimensions:

  >>> x = tf.ensure_shape(x, [None, 3])
  >>> x = tf.ensure_shape(x, [2, None])

  If the tensor's shape is not compatible with the `shape` argument, an error
  is raised:

  >>> x = tf.ensure_shape(x, [5])
  Traceback (most recent call last):
  ...
  tf.errors.InvalidArgumentError: Shape of tensor dummy_input [3] is not
    compatible with expected shape [5]. [Op:EnsureShape]

  During graph construction (typically tracing a `tf.function`),
  `tf.ensure_shape` updates the static-shape of the **result** tensor by
  merging the two shapes. See `tf.TensorShape.merge_with` for details.

  This is most useful when **you** know a shape that can't be determined
  statically by TensorFlow.

  The following trivial `tf.function` prints the input tensor's
  static-shape before and after `ensure_shape` is applied.

  >>> @tf.function
  ... def f(tensor):
  ...   print("Static-shape before:", tensor.shape)
  ...   tensor = tf.ensure_shape(tensor, [None, 3])
  ...   print("Static-shape after:", tensor.shape)
  ...   return tensor

  This lets you see the effect of `tf.ensure_shape` when the function is traced:
  >>> cf = f.get_concrete_function(tf.TensorSpec([None, None]))
  Static-shape before: (None, None)
  Static-shape after: (None, 3)

  >>> cf(tf.zeros([3, 3])) # Passes
  >>> cf(tf.constant([1, 2, 3])) # fails
  Traceback (most recent call last):
  ...
  InvalidArgumentError:  Shape of tensor x [3] is not compatible with expected shape [3,3].

  The above example raises `tf.errors.InvalidArgumentError`, because `x`'s
  shape, `(3,)`, is not compatible with the `shape` argument, `(None, 3)`

  Inside a `tf.function` or `v1.Graph` context it checks both the buildtime and
  runtime shapes. This is stricter than `tf.Tensor.set_shape` which only
  checks the buildtime shape.

  Note: This differs from `tf.Tensor.set_shape` in that it sets the static shape
  of the resulting tensor and enforces it at runtime, raising an error if the
  tensor's runtime shape is incompatible with the specified shape.
  `tf.Tensor.set_shape` sets the static shape of the tensor without enforcing it
  at runtime, which may result in inconsistencies between the statically-known
  shape of tensors and the runtime value of tensors.

  For example, of loading images of a known size:

  >>> @tf.function
  ... def decode_image(png):
  ...   image = tf.image.decode_png(png, channels=3)
  ...   # the `print` executes during tracing.
  ...   print("Initial shape: ", image.shape)
  ...   image = tf.ensure_shape(image,[28, 28, 3])
  ...   print("Final shape: ", image.shape)
  ...   return image

  When tracing a function, no ops are being executed, shapes may be unknown.
  See the [Concrete Functions Guide](https://www.tensorflow.org/guide/concrete_function)
  for details.

  >>> concrete_decode = decode_image.get_concrete_function(
  ...     tf.TensorSpec([], dtype=tf.string))
  Initial shape:  (None, None, 3)
  Final shape:  (28, 28, 3)

  >>> image = tf.random.uniform(maxval=255, shape=[28, 28, 3], dtype=tf.int32)
  >>> image = tf.cast(image,tf.uint8)
  >>> png = tf.image.encode_png(image)
  >>> image2 = concrete_decode(png)
  >>> print(image2.shape)
  (28, 28, 3)

  >>> image = tf.concat([image,image], axis=0)
  >>> print(image.shape)
  (56, 28, 3)
  >>> png = tf.image.encode_png(image)
  >>> image2 = concrete_decode(png)
  Traceback (most recent call last):
  ...
  tf.errors.InvalidArgumentError:  Shape of tensor DecodePng [56,28,3] is not
    compatible with expected shape [28,28,3].

  Caution: if you don't use the result of `tf.ensure_shape` the check may not
  run.

  >>> @tf.function
  ... def bad_decode_image(png):
  ...   image = tf.image.decode_png(png, channels=3)
  ...   # the `print` executes during tracing.
  ...   print("Initial shape: ", image.shape)
  ...   # BAD: forgot to use the returned tensor.
  ...   tf.ensure_shape(image,[28, 28, 3])
  ...   print("Final shape: ", image.shape)
  ...   return image

  >>> image = bad_decode_image(png)
  Initial shape:  (None, None, 3)
  Final shape:  (None, None, 3)
  >>> print(image.shape)
  (56, 28, 3)

  Args:
    x: A `Tensor`.
    shape: A `TensorShape` representing the shape of this tensor, a
      `TensorShapeProto`, a list, a tuple, or None.
    name: A name for this operation (optional). Defaults to "EnsureShape".

  Returns:
    A `Tensor`. Has the same type and contents as `x`.

  Raises:
    tf.errors.InvalidArgumentError: If `shape` is incompatible with the shape
    of `x`.
  """
  if not isinstance(shape, tensor_shape.TensorShape):
    shape = tensor_shape.TensorShape(shape)

  return array_ops.ensure_shape(x, shape, name=name)


@ops.RegisterGradient('EnsureShape')
def _ensure_shape_grad(op, grad):
  del op  # Unused.
  return grad