# 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.
# ==============================================================================
"""Utilities for unit-testing Keras."""


import collections
import contextlib
import functools
import itertools
import threading
import unittest

import numpy as np
import tensorflow.compat.v2 as tf

from keras.src import backend
from keras.src import layers
from keras.src import models
from keras.src.engine import base_layer_utils
from keras.src.optimizers.legacy import adadelta as adadelta_v2
from keras.src.optimizers.legacy import adagrad as adagrad_v2
from keras.src.optimizers.legacy import adam as adam_v2
from keras.src.optimizers.legacy import adamax as adamax_v2
from keras.src.optimizers.legacy import gradient_descent as gradient_descent_v2
from keras.src.optimizers.legacy import nadam as nadam_v2
from keras.src.optimizers.legacy import rmsprop as rmsprop_v2
from keras.src.utils import tf_contextlib
from keras.src.utils import tf_inspect

# isort: off
from tensorflow.python.framework import (
    test_util as tf_test_utils,
)
from tensorflow.python.util.tf_export import keras_export


def string_test(actual, expected):
    np.testing.assert_array_equal(actual, expected)


def numeric_test(actual, expected):
    np.testing.assert_allclose(actual, expected, rtol=1e-3, atol=1e-6)


def get_test_data(
    train_samples, test_samples, input_shape, num_classes, random_seed=None
):
    """Generates test data to train a model on.

    Args:
      train_samples: Integer, how many training samples to generate.
      test_samples: Integer, how many test samples to generate.
      input_shape: Tuple of integers, shape of the inputs.
      num_classes: Integer, number of classes for the data and targets.
      random_seed: Integer, random seed used by numpy to generate data.

    Returns:
      A tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
    """
    if random_seed is not None:
        np.random.seed(random_seed)
    num_sample = train_samples + test_samples
    templates = 2 * num_classes * np.random.random((num_classes,) + input_shape)
    y = np.random.randint(0, num_classes, size=(num_sample,))
    x = np.zeros((num_sample,) + input_shape, dtype=np.float32)
    for i in range(num_sample):
        x[i] = templates[y[i]] + np.random.normal(
            loc=0, scale=1.0, size=input_shape
        )
    return (
        (x[:train_samples], y[:train_samples]),
        (x[train_samples:], y[train_samples:]),
    )


@keras_export("keras.__internal__.utils.layer_test", v1=[])
@tf_test_utils.disable_cudnn_autotune
def layer_test(
    layer_cls,
    kwargs=None,
    input_shape=None,
    input_dtype=None,
    input_data=None,
    expected_output=None,
    expected_output_dtype=None,
    expected_output_shape=None,
    validate_training=True,
    adapt_data=None,
    custom_objects=None,
    test_harness=None,
    supports_masking=None,
):
    """Test routine for a layer with a single input and single output.

    Args:
      layer_cls: Layer class object.
      kwargs: Optional dictionary of keyword arguments for instantiating the
        layer.
      input_shape: Input shape tuple.
      input_dtype: Data type of the input data.
      input_data: Numpy array of input data.
      expected_output: Numpy array of the expected output.
      expected_output_dtype: Data type expected for the output.
      expected_output_shape: Shape tuple for the expected shape of the output.
      validate_training: Whether to attempt to validate training on this layer.
        This might be set to False for non-differentiable layers that output
        string or integer values.
      adapt_data: Optional data for an 'adapt' call. If None, adapt() will not
        be tested for this layer. This is only relevant for PreprocessingLayers.
      custom_objects: Optional dictionary mapping name strings to custom objects
        in the layer class. This is helpful for testing custom layers.
      test_harness: The Tensorflow test, if any, that this function is being
        called in.
      supports_masking: Optional boolean to check the `supports_masking`
        property of the layer. If None, the check will not be performed.

    Returns:
      The output data (Numpy array) returned by the layer, for additional
      checks to be done by the calling code.

    Raises:
      ValueError: if `input_shape is None`.
    """
    if input_data is None:
        if input_shape is None:
            raise ValueError("input_shape is None")
        if not input_dtype:
            input_dtype = "float32"
        input_data_shape = list(input_shape)
        for i, e in enumerate(input_data_shape):
            if e is None:
                input_data_shape[i] = np.random.randint(1, 4)
        input_data = 10 * np.random.random(input_data_shape)
        if input_dtype[:5] == "float":
            input_data -= 0.5
        input_data = input_data.astype(input_dtype)
    elif input_shape is None:
        input_shape = input_data.shape
    if input_dtype is None:
        input_dtype = input_data.dtype
    if expected_output_dtype is None:
        expected_output_dtype = input_dtype

    if tf.as_dtype(expected_output_dtype) == tf.string:
        if test_harness:
            assert_equal = test_harness.assertAllEqual
        else:
            assert_equal = string_test
    else:
        if test_harness:
            assert_equal = test_harness.assertAllClose
        else:
            assert_equal = numeric_test

    # instantiation
    kwargs = kwargs or {}
    layer = layer_cls(**kwargs)

    if (
        supports_masking is not None
        and layer.supports_masking != supports_masking
    ):
        raise AssertionError(
            "When testing layer %s, the `supports_masking` property is %r"
            "but expected to be %r.\nFull kwargs: %s"
            % (
                layer_cls.__name__,
                layer.supports_masking,
                supports_masking,
                kwargs,
            )
        )

    # Test adapt, if data was passed.
    if adapt_data is not None:
        layer.adapt(adapt_data)

    # test get_weights , set_weights at layer level
    weights = layer.get_weights()
    layer.set_weights(weights)

    # test and instantiation from weights
    if "weights" in tf_inspect.getargspec(layer_cls.__init__):
        kwargs["weights"] = weights
        layer = layer_cls(**kwargs)

    # test in functional API
    x = layers.Input(shape=input_shape[1:], dtype=input_dtype)
    y = layer(x)
    if backend.dtype(y) != expected_output_dtype:
        raise AssertionError(
            "When testing layer %s, for input %s, found output "
            "dtype=%s but expected to find %s.\nFull kwargs: %s"
            % (
                layer_cls.__name__,
                x,
                backend.dtype(y),
                expected_output_dtype,
                kwargs,
            )
        )

    def assert_shapes_equal(expected, actual):
        """Asserts that the output shape from the layer matches the actual
        shape."""
        if len(expected) != len(actual):
            raise AssertionError(
                "When testing layer %s, for input %s, found output_shape="
                "%s but expected to find %s.\nFull kwargs: %s"
                % (layer_cls.__name__, x, actual, expected, kwargs)
            )

        for expected_dim, actual_dim in zip(expected, actual):
            if isinstance(expected_dim, tf.compat.v1.Dimension):
                expected_dim = expected_dim.value
            if isinstance(actual_dim, tf.compat.v1.Dimension):
                actual_dim = actual_dim.value
            if expected_dim is not None and expected_dim != actual_dim:
                raise AssertionError(
                    "When testing layer %s, for input %s, found output_shape="
                    "%s but expected to find %s.\nFull kwargs: %s"
                    % (layer_cls.__name__, x, actual, expected, kwargs)
                )

    if expected_output_shape is not None:
        assert_shapes_equal(tf.TensorShape(expected_output_shape), y.shape)

    # check shape inference
    model = models.Model(x, y)
    computed_output_shape = tuple(
        layer.compute_output_shape(tf.TensorShape(input_shape)).as_list()
    )
    computed_output_signature = layer.compute_output_signature(
        tf.TensorSpec(shape=input_shape, dtype=input_dtype)
    )
    actual_output = model.predict(input_data)
    actual_output_shape = actual_output.shape
    assert_shapes_equal(computed_output_shape, actual_output_shape)
    assert_shapes_equal(computed_output_signature.shape, actual_output_shape)
    if computed_output_signature.dtype != actual_output.dtype:
        raise AssertionError(
            "When testing layer %s, for input %s, found output_dtype="
            "%s but expected to find %s.\nFull kwargs: %s"
            % (
                layer_cls.__name__,
                x,
                actual_output.dtype,
                computed_output_signature.dtype,
                kwargs,
            )
        )
    if expected_output is not None:
        assert_equal(actual_output, expected_output)

    # test serialization, weight setting at model level
    model_config = model.get_config()
    recovered_model = models.Model.from_config(model_config, custom_objects)
    if model.weights:
        weights = model.get_weights()
        recovered_model.set_weights(weights)
        output = recovered_model.predict(input_data)
        assert_equal(output, actual_output)

    # test training mode (e.g. useful for dropout tests)
    # Rebuild the model to avoid the graph being reused between predict() and
    # See b/120160788 for more details. This should be mitigated after 2.0.
    layer_weights = (
        layer.get_weights()
    )  # Get the layer weights BEFORE training.
    if validate_training:
        model = models.Model(x, layer(x))
        if _thread_local_data.run_eagerly is not None:
            model.compile(
                "rmsprop",
                "mse",
                weighted_metrics=["acc"],
                run_eagerly=should_run_eagerly(),
            )
        else:
            model.compile("rmsprop", "mse", weighted_metrics=["acc"])
        model.train_on_batch(input_data, actual_output)

    # test as first layer in Sequential API
    layer_config = layer.get_config()
    layer_config["batch_input_shape"] = input_shape
    layer = layer.__class__.from_config(layer_config)

    # Test adapt, if data was passed.
    if adapt_data is not None:
        layer.adapt(adapt_data)

    model = models.Sequential()
    model.add(layers.Input(shape=input_shape[1:], dtype=input_dtype))
    model.add(layer)

    layer.set_weights(layer_weights)
    actual_output = model.predict(input_data)
    actual_output_shape = actual_output.shape
    for expected_dim, actual_dim in zip(
        computed_output_shape, actual_output_shape
    ):
        if expected_dim is not None:
            if expected_dim != actual_dim:
                raise AssertionError(
                    "When testing layer %s **after deserialization**, "
                    "for input %s, found output_shape="
                    "%s but expected to find inferred shape %s.\n"
                    "Full kwargs: %s"
                    % (
                        layer_cls.__name__,
                        x,
                        actual_output_shape,
                        computed_output_shape,
                        kwargs,
                    )
                )
    if expected_output is not None:
        assert_equal(actual_output, expected_output)

    # test serialization, weight setting at model level
    model_config = model.get_config()
    recovered_model = models.Sequential.from_config(
        model_config, custom_objects
    )
    if model.weights:
        weights = model.get_weights()
        recovered_model.set_weights(weights)
        output = recovered_model.predict(input_data)
        assert_equal(output, actual_output)

    # for further checks in the caller function
    return actual_output


_thread_local_data = threading.local()
_thread_local_data.model_type = None
_thread_local_data.run_eagerly = None
_thread_local_data.saved_model_format = None
_thread_local_data.save_kwargs = None


@tf_contextlib.contextmanager
def model_type_scope(value):
    """Provides a scope within which the model type to test is equal to `value`.

    The model type gets restored to its original value upon exiting the scope.

    Args:
       value: model type value

    Yields:
      The provided value.
    """
    previous_value = _thread_local_data.model_type
    try:
        _thread_local_data.model_type = value
        yield value
    finally:
        # Restore model type to initial value.
        _thread_local_data.model_type = previous_value


@tf_contextlib.contextmanager
def run_eagerly_scope(value):
    """Provides a scope within which we compile models to run eagerly or not.

    The boolean gets restored to its original value upon exiting the scope.

    Args:
       value: Bool specifying if we should run models eagerly in the active
         test. Should be True or False.

    Yields:
      The provided value.
    """
    previous_value = _thread_local_data.run_eagerly
    try:
        _thread_local_data.run_eagerly = value
        yield value
    finally:
        # Restore model type to initial value.
        _thread_local_data.run_eagerly = previous_value


def should_run_eagerly():
    """Returns whether the models we are testing should be run eagerly."""
    if _thread_local_data.run_eagerly is None:
        raise ValueError(
            "Cannot call `should_run_eagerly()` outside of a "
            "`run_eagerly_scope()` or `run_all_keras_modes` "
            "decorator."
        )

    return _thread_local_data.run_eagerly and tf.executing_eagerly()


@tf_contextlib.contextmanager
def saved_model_format_scope(value, **kwargs):
    """Provides a scope within which the savde model format to test is `value`.

    The saved model format gets restored to its original value upon exiting the
    scope.

    Args:
       value: saved model format value
       **kwargs: optional kwargs to pass to the save function.

    Yields:
      The provided value.
    """
    previous_format = _thread_local_data.saved_model_format
    previous_kwargs = _thread_local_data.save_kwargs
    try:
        _thread_local_data.saved_model_format = value
        _thread_local_data.save_kwargs = kwargs
        yield
    finally:
        # Restore saved model format to initial value.
        _thread_local_data.saved_model_format = previous_format
        _thread_local_data.save_kwargs = previous_kwargs


def get_save_format():
    if _thread_local_data.saved_model_format is None:
        raise ValueError(
            "Cannot call `get_save_format()` outside of a "
            "`saved_model_format_scope()` or "
            "`run_with_all_saved_model_formats` decorator."
        )
    return _thread_local_data.saved_model_format


def get_save_kwargs():
    if _thread_local_data.save_kwargs is None:
        raise ValueError(
            "Cannot call `get_save_kwargs()` outside of a "
            "`saved_model_format_scope()` or "
            "`run_with_all_saved_model_formats` decorator."
        )
    return _thread_local_data.save_kwargs or {}


def get_model_type():
    """Gets the model type that should be tested."""
    if _thread_local_data.model_type is None:
        raise ValueError(
            "Cannot call `get_model_type()` outside of a "
            "`model_type_scope()` or `run_with_all_model_types` "
            "decorator."
        )

    return _thread_local_data.model_type


def get_small_sequential_mlp(num_hidden, num_classes, input_dim=None):
    model = models.Sequential()
    if input_dim:
        model.add(
            layers.Dense(num_hidden, activation="relu", input_dim=input_dim)
        )
    else:
        model.add(layers.Dense(num_hidden, activation="relu"))
    activation = "sigmoid" if num_classes == 1 else "softmax"
    model.add(layers.Dense(num_classes, activation=activation))
    return model


def get_small_functional_mlp(num_hidden, num_classes, input_dim):
    inputs = layers.Input(shape=(input_dim,))
    outputs = layers.Dense(num_hidden, activation="relu")(inputs)
    activation = "sigmoid" if num_classes == 1 else "softmax"
    outputs = layers.Dense(num_classes, activation=activation)(outputs)
    return models.Model(inputs, outputs)


class SmallSubclassMLP(models.Model):
    """A subclass model based small MLP."""

    def __init__(
        self, num_hidden, num_classes, use_bn=False, use_dp=False, **kwargs
    ):
        super().__init__(name="test_model", **kwargs)
        self.num_hidden = num_hidden
        self.num_classes = num_classes
        self.use_bn = use_bn
        self.use_dp = use_dp

        self.layer_a = layers.Dense(num_hidden, activation="relu")
        activation = "sigmoid" if num_classes == 1 else "softmax"
        self.layer_b = layers.Dense(num_classes, activation=activation)
        if self.use_dp:
            self.dp = layers.Dropout(0.5)
        if self.use_bn:
            self.bn = layers.BatchNormalization(axis=-1)

    def call(self, inputs, **kwargs):
        x = self.layer_a(inputs)
        if self.use_dp:
            x = self.dp(x)
        if self.use_bn:
            x = self.bn(x)
        return self.layer_b(x)

    def get_config(self):
        config = super().get_config()
        config.update(
            {
                "num_hidden": self.num_hidden,
                "num_classes": self.num_classes,
                "use_bn": self.use_bn,
                "use_dp": self.use_dp,
            }
        )
        return config


class _SmallSubclassMLPCustomBuild(models.Model):
    """A subclass model small MLP that uses a custom build method."""

    def __init__(self, num_hidden, num_classes):
        super().__init__()
        self.layer_a = None
        self.layer_b = None
        self.num_hidden = num_hidden
        self.num_classes = num_classes

    def build(self, input_shape):
        self.layer_a = layers.Dense(self.num_hidden, activation="relu")
        activation = "sigmoid" if self.num_classes == 1 else "softmax"
        self.layer_b = layers.Dense(self.num_classes, activation=activation)

    def call(self, inputs, **kwargs):
        x = self.layer_a(inputs)
        return self.layer_b(x)


def get_small_subclass_mlp(num_hidden, num_classes):
    return SmallSubclassMLP(num_hidden, num_classes)


def get_small_subclass_mlp_with_custom_build(num_hidden, num_classes):
    return _SmallSubclassMLPCustomBuild(num_hidden, num_classes)


def get_small_mlp(num_hidden, num_classes, input_dim):
    """Get a small mlp of the model type specified by `get_model_type`."""
    model_type = get_model_type()
    if model_type == "subclass":
        return get_small_subclass_mlp(num_hidden, num_classes)
    if model_type == "subclass_custom_build":
        return get_small_subclass_mlp_with_custom_build(num_hidden, num_classes)
    if model_type == "sequential":
        return get_small_sequential_mlp(num_hidden, num_classes, input_dim)
    if model_type == "functional":
        return get_small_functional_mlp(num_hidden, num_classes, input_dim)
    raise ValueError(f"Unknown model type {model_type}")


class _SubclassModel(models.Model):
    """A Keras subclass model."""

    def __init__(self, model_layers, *args, **kwargs):
        """Instantiate a model.

        Args:
          model_layers: a list of layers to be added to the model.
          *args: Model's args
          **kwargs: Model's keyword args, at most one of input_tensor -> the
            input tensor required for ragged/sparse input.
        """

        inputs = kwargs.pop("input_tensor", None)
        super().__init__(*args, **kwargs)
        # Note that clone and build doesn't support lists of layers in
        # subclassed models. Adding each layer directly here.
        for i, layer in enumerate(model_layers):
            setattr(self, self._layer_name_for_i(i), layer)

        self.num_layers = len(model_layers)

        if inputs is not None:
            self._set_inputs(inputs)

    def _layer_name_for_i(self, i):
        return f"layer{i}"

    def call(self, inputs, **kwargs):
        x = inputs
        for i in range(self.num_layers):
            layer = getattr(self, self._layer_name_for_i(i))
            x = layer(x)
        return x

    def get_config(self):
        # This test model relies on the default Keras serialization of a model,
        # rather than providing the details of `model_layers`.
        raise NotImplementedError


class _SubclassModelCustomBuild(models.Model):
    """A Keras subclass model that uses a custom build method."""

    def __init__(self, layer_generating_func, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.all_layers = None
        self._layer_generating_func = layer_generating_func

    def build(self, input_shape):
        model_layers = []
        for layer in self._layer_generating_func():
            model_layers.append(layer)
        self.all_layers = model_layers

    def call(self, inputs, **kwargs):
        x = inputs
        for layer in self.all_layers:
            x = layer(x)
        return x


def get_model_from_layers(
    model_layers,
    input_shape=None,
    input_dtype=None,
    name=None,
    input_ragged=None,
    input_sparse=None,
    model_type=None,
):
    """Builds a model from a sequence of layers.

    Args:
      model_layers: The layers used to build the network.
      input_shape: Shape tuple of the input or 'TensorShape' instance.
      input_dtype: Datatype of the input.
      name: Name for the model.
      input_ragged: Boolean, whether the input data is a ragged tensor.
      input_sparse: Boolean, whether the input data is a sparse tensor.
      model_type: One of "subclass", "subclass_custom_build", "sequential", or
        "functional". When None, defaults to `get_model_type`.

    Returns:
      A Keras model.
    """
    if model_type is None:
        model_type = get_model_type()
    if model_type == "subclass":
        inputs = None
        if input_ragged or input_sparse:
            inputs = layers.Input(
                shape=input_shape,
                dtype=input_dtype,
                ragged=input_ragged,
                sparse=input_sparse,
            )
        return _SubclassModel(model_layers, name=name, input_tensor=inputs)

    if model_type == "subclass_custom_build":
        layer_generating_func = lambda: model_layers
        return _SubclassModelCustomBuild(layer_generating_func, name=name)

    if model_type == "sequential":
        model = models.Sequential(name=name)
        if input_shape:
            model.add(
                layers.InputLayer(
                    input_shape=input_shape,
                    dtype=input_dtype,
                    ragged=input_ragged,
                    sparse=input_sparse,
                )
            )
        for layer in model_layers:
            model.add(layer)
        return model

    if model_type == "functional":
        if not input_shape:
            raise ValueError(
                "Cannot create a functional model from layers with no "
                "input shape."
            )
        inputs = layers.Input(
            shape=input_shape,
            dtype=input_dtype,
            ragged=input_ragged,
            sparse=input_sparse,
        )
        outputs = inputs
        for layer in model_layers:
            outputs = layer(outputs)
        return models.Model(inputs, outputs, name=name)

    raise ValueError(f"Unknown model type {model_type}")


class Bias(layers.Layer):
    def build(self, input_shape):
        self.bias = self.add_weight("bias", (1,), initializer="zeros")

    def call(self, inputs):
        return inputs + self.bias


class _MultiIOSubclassModel(models.Model):
    """Multi IO Keras subclass model."""

    def __init__(
        self,
        branch_a,
        branch_b,
        shared_input_branch=None,
        shared_output_branch=None,
        name=None,
    ):
        super().__init__(name=name)
        self._shared_input_branch = shared_input_branch
        self._branch_a = branch_a
        self._branch_b = branch_b
        self._shared_output_branch = shared_output_branch

    def call(self, inputs, **kwargs):
        if self._shared_input_branch:
            for layer in self._shared_input_branch:
                inputs = layer(inputs)
            a = inputs
            b = inputs
        elif isinstance(inputs, dict):
            a = inputs["input_1"]
            b = inputs["input_2"]
        else:
            a, b = inputs

        for layer in self._branch_a:
            a = layer(a)
        for layer in self._branch_b:
            b = layer(b)
        outs = [a, b]

        if self._shared_output_branch:
            for layer in self._shared_output_branch:
                outs = layer(outs)

        return outs


class _MultiIOSubclassModelCustomBuild(models.Model):
    """Multi IO Keras subclass model that uses a custom build method."""

    def __init__(
        self,
        branch_a_func,
        branch_b_func,
        shared_input_branch_func=None,
        shared_output_branch_func=None,
    ):
        super().__init__()
        self._shared_input_branch_func = shared_input_branch_func
        self._branch_a_func = branch_a_func
        self._branch_b_func = branch_b_func
        self._shared_output_branch_func = shared_output_branch_func

        self._shared_input_branch = None
        self._branch_a = None
        self._branch_b = None
        self._shared_output_branch = None

    def build(self, input_shape):
        if self._shared_input_branch_func():
            self._shared_input_branch = self._shared_input_branch_func()
        self._branch_a = self._branch_a_func()
        self._branch_b = self._branch_b_func()

        if self._shared_output_branch_func():
            self._shared_output_branch = self._shared_output_branch_func()

    def call(self, inputs, **kwargs):
        if self._shared_input_branch:
            for layer in self._shared_input_branch:
                inputs = layer(inputs)
            a = inputs
            b = inputs
        else:
            a, b = inputs

        for layer in self._branch_a:
            a = layer(a)
        for layer in self._branch_b:
            b = layer(b)
        outs = a, b

        if self._shared_output_branch:
            for layer in self._shared_output_branch:
                outs = layer(outs)

        return outs


def get_multi_io_model(
    branch_a, branch_b, shared_input_branch=None, shared_output_branch=None
):
    """Builds a multi-io model that contains two branches.

    The produced model will be of the type specified by `get_model_type`.

    To build a two-input, two-output model:
      Specify a list of layers for branch a and branch b, but do not specify any
      shared input branch or shared output branch. The resulting model will
      apply each branch to a different input, to produce two outputs.

      The first value in branch_a must be the Keras 'Input' layer for branch a,
      and the first value in branch_b must be the Keras 'Input' layer for
      branch b.

      example usage:
      ```
      branch_a = [Input(shape=(2,), name='a'), Dense(), Dense()]
      branch_b = [Input(shape=(3,), name='b'), Dense(), Dense()]

      model = get_multi_io_model(branch_a, branch_b)
      ```

    To build a two-input, one-output model:
      Specify a list of layers for branch a and branch b, and specify a
      shared output branch. The resulting model will apply
      each branch to a different input. It will then apply the shared output
      branch to a tuple containing the intermediate outputs of each branch,
      to produce a single output. The first layer in the shared_output_branch
      must be able to merge a tuple of two tensors.

      The first value in branch_a must be the Keras 'Input' layer for branch a,
      and the first value in branch_b must be the Keras 'Input' layer for
      branch b.

      example usage:
      ```
      input_branch_a = [Input(shape=(2,), name='a'), Dense(), Dense()]
      input_branch_b = [Input(shape=(3,), name='b'), Dense(), Dense()]
      shared_output_branch = [Concatenate(), Dense(), Dense()]

      model = get_multi_io_model(input_branch_a, input_branch_b,
                                 shared_output_branch=shared_output_branch)
      ```
    To build a one-input, two-output model:
      Specify a list of layers for branch a and branch b, and specify a
      shared input branch. The resulting model will take one input, and apply
      the shared input branch to it. It will then respectively apply each branch
      to that intermediate result in parallel, to produce two outputs.

      The first value in the shared_input_branch must be the Keras 'Input' layer
      for the whole model. Branch a and branch b should not contain any Input
      layers.

      example usage:
      ```
      shared_input_branch = [Input(shape=(2,), name='in'), Dense(), Dense()]
      output_branch_a = [Dense(), Dense()]
      output_branch_b = [Dense(), Dense()]


      model = get_multi_io_model(output__branch_a, output_branch_b,
                                 shared_input_branch=shared_input_branch)
      ```

    Args:
      branch_a: A sequence of layers for branch a of the model.
      branch_b: A sequence of layers for branch b of the model.
      shared_input_branch: An optional sequence of layers to apply to a single
        input, before applying both branches to that intermediate result. If
        set, the model will take only one input instead of two. Defaults to
        None.
      shared_output_branch: An optional sequence of layers to merge the
        intermediate results produced by branch a and branch b. If set,
        the model will produce only one output instead of two. Defaults to None.

    Returns:
      A multi-io model of the type specified by `get_model_type`, specified
      by the different branches.
    """
    # Extract the functional inputs from the layer lists
    if shared_input_branch:
        inputs = shared_input_branch[0]
        shared_input_branch = shared_input_branch[1:]
    else:
        inputs = branch_a[0], branch_b[0]
        branch_a = branch_a[1:]
        branch_b = branch_b[1:]

    model_type = get_model_type()
    if model_type == "subclass":
        return _MultiIOSubclassModel(
            branch_a, branch_b, shared_input_branch, shared_output_branch
        )

    if model_type == "subclass_custom_build":
        return _MultiIOSubclassModelCustomBuild(
            (lambda: branch_a),
            (lambda: branch_b),
            (lambda: shared_input_branch),
            (lambda: shared_output_branch),
        )

    if model_type == "sequential":
        raise ValueError(
            "Cannot use `get_multi_io_model` to construct sequential models"
        )

    if model_type == "functional":
        if shared_input_branch:
            a_and_b = inputs
            for layer in shared_input_branch:
                a_and_b = layer(a_and_b)
            a = a_and_b
            b = a_and_b
        else:
            a, b = inputs

        for layer in branch_a:
            a = layer(a)
        for layer in branch_b:
            b = layer(b)
        outputs = a, b

        if shared_output_branch:
            for layer in shared_output_branch:
                outputs = layer(outputs)

        return models.Model(inputs, outputs)

    raise ValueError(f"Unknown model type {model_type}")


_V2_OPTIMIZER_MAP = {
    "adadelta": adadelta_v2.Adadelta,
    "adagrad": adagrad_v2.Adagrad,
    "adam": adam_v2.Adam,
    "adamax": adamax_v2.Adamax,
    "nadam": nadam_v2.Nadam,
    "rmsprop": rmsprop_v2.RMSprop,
    "sgd": gradient_descent_v2.SGD,
}


def get_v2_optimizer(name, **kwargs):
    """Get the v2 optimizer requested.

    This is only necessary until v2 are the default, as we are testing in Eager,
    and Eager + v1 optimizers fail tests. When we are in v2, the strings alone
    should be sufficient, and this mapping can theoretically be removed.

    Args:
      name: string name of Keras v2 optimizer.
      **kwargs: any kwargs to pass to the optimizer constructor.

    Returns:
      Initialized Keras v2 optimizer.

    Raises:
      ValueError: if an unknown name was passed.
    """
    try:
        return _V2_OPTIMIZER_MAP[name](**kwargs)
    except KeyError:
        raise ValueError(
            "Could not find requested v2 optimizer: "
            "{}\nValid choices: {}".format(name, list(_V2_OPTIMIZER_MAP.keys()))
        )


def get_expected_metric_variable_names(var_names, name_suffix=""):
    """Returns expected metric variable names given names and prefix/suffix."""
    if tf.__internal__.tf2.enabled() or tf.executing_eagerly():
        # In V1 eager mode and V2 variable names are not made unique.
        return [n + ":0" for n in var_names]
    # In V1 graph mode variable names are made unique using a suffix.
    return [n + name_suffix + ":0" for n in var_names]


def enable_v2_dtype_behavior(fn):
    """Decorator for enabling the layer V2 dtype behavior on a test."""
    return _set_v2_dtype_behavior(fn, True)


def disable_v2_dtype_behavior(fn):
    """Decorator for disabling the layer V2 dtype behavior on a test."""
    return _set_v2_dtype_behavior(fn, False)


def _set_v2_dtype_behavior(fn, enabled):
    """Returns version of 'fn' that runs with v2 dtype behavior on or off."""

    @functools.wraps(fn)
    def wrapper(*args, **kwargs):
        v2_dtype_behavior = base_layer_utils.V2_DTYPE_BEHAVIOR
        base_layer_utils.V2_DTYPE_BEHAVIOR = enabled
        try:
            return fn(*args, **kwargs)
        finally:
            base_layer_utils.V2_DTYPE_BEHAVIOR = v2_dtype_behavior

    return tf.__internal__.decorator.make_decorator(fn, wrapper)


@contextlib.contextmanager
def device(should_use_gpu):
    """Uses gpu when requested and available."""
    if should_use_gpu and tf.test.is_gpu_available():
        dev = "/device:GPU:0"
    else:
        dev = "/device:CPU:0"
    with tf.device(dev):
        yield


@contextlib.contextmanager
def use_gpu():
    """Uses gpu when requested and available."""
    with device(should_use_gpu=True):
        yield


def for_all_test_methods(decorator, *args, **kwargs):
    """Generate class-level decorator from given method-level decorator.

    It is expected for the given decorator to take some arguments and return
    a method that is then called on the test method to produce a decorated
    method.

    Args:
      decorator: The decorator to apply.
      *args: Positional arguments
      **kwargs: Keyword arguments
    Returns: Function that will decorate a given classes test methods with the
      decorator.
    """

    def all_test_methods_impl(cls):
        """Apply decorator to all test methods in class."""
        for name in dir(cls):
            value = getattr(cls, name)
            if (
                callable(value)
                and name.startswith("test")
                and (name != "test_session")
            ):
                setattr(cls, name, decorator(*args, **kwargs)(value))
        return cls

    return all_test_methods_impl


# The description is just for documentation purposes.
def run_without_tensor_float_32(description):
    """Execute test with TensorFloat-32 disabled.

    While almost every real-world deep learning model runs fine with
    TensorFloat-32, many tests use assertAllClose or similar methods.
    TensorFloat-32 matmuls typically will cause such methods to fail with the
    default tolerances.

    Args:
      description: A description used for documentation purposes, describing why
        the test requires TensorFloat-32 to be disabled.

    Returns:
      Decorator which runs a test with TensorFloat-32 disabled.
    """

    def decorator(f):
        @functools.wraps(f)
        def decorated(self, *args, **kwargs):
            allowed = tf.config.experimental.tensor_float_32_execution_enabled()
            try:
                tf.config.experimental.enable_tensor_float_32_execution(False)
                f(self, *args, **kwargs)
            finally:
                tf.config.experimental.enable_tensor_float_32_execution(allowed)

        return decorated

    return decorator


# The description is just for documentation purposes.
def run_all_without_tensor_float_32(
    description,
):
    """Execute all tests in a class with TensorFloat-32 disabled."""
    return for_all_test_methods(run_without_tensor_float_32, description)


def run_v2_only(obj=None):
    """Execute the decorated test only if running in v2 mode.

    This function is intended to be applied to tests that exercise v2 only
    functionality. If the test is run in v1 mode it will simply be skipped.

    See go/tf-test-decorator-cheatsheet for the decorators to use in different
    v1/v2/eager/graph combinations.

    Args:
      obj: function to be annotated. If None, return a
        decorator the can be applied to a function or class. If `obj` is not
        None, return the decorator applied to `obj`.

    Returns:
      Returns a decorator that will conditionally skip the decorated test
      method.
    """
    condition = not tf.__internal__.tf2.enabled()
    reason = "Test is only compatible with TF v2."

    def decorator(f):
        if tf_inspect.isclass(f):
            return unittest.skipIf(condition=condition, reason=reason)(obj)

        def decorated(self, *args, **kwargs):
            if condition:
                self.skipTest(reason)
            return f(self, *args, **kwargs)

        return decorated

    if obj is not None:
        return decorator(obj)

    return decorator


def generate_combinations_with_testcase_name(**kwargs):
    """Generate combinations based on its keyword arguments using combine().

    This function calls combine() and appends a testcase name to the list of
    dictionaries returned. The 'testcase_name' key is a required for named
    parameterized tests.

    Args:
      **kwargs: keyword arguments of form `option=[possibilities, ...]` or
        `option=the_only_possibility`.

    Returns:
      a list of dictionaries for each combination. Keys in the dictionaries are
      the keyword argument names.  Each key has one value - one of the
      corresponding keyword argument values.
    """
    sort_by_key = lambda k: k[0]
    combinations = []
    for key, values in sorted(kwargs.items(), key=sort_by_key):
        if not isinstance(values, list):
            values = [values]
        combinations.append([(key, value) for value in values])

    combinations = [
        collections.OrderedDict(result)
        for result in itertools.product(*combinations)
    ]
    named_combinations = []
    for combination in combinations:
        assert isinstance(combination, collections.OrderedDict)
        name = "".join(
            [
                "_{}_{}".format(
                    "".join(filter(str.isalnum, key)),
                    "".join(filter(str.isalnum, str(value))),
                )
                for key, value in combination.items()
            ]
        )
        named_combinations.append(
            collections.OrderedDict(
                list(combination.items()) + [("testcase_name", f"_test{name}")]
            )
        )

    return named_combinations

