# Copyright 2018 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.
# ==============================================================================
"""Keras estimator API."""

import tensorflow.compat.v2 as tf

# isort: off
from tensorflow.python.util.tf_export import keras_export

# Keras has undeclared dependency on tensorflow/estimator:estimator_py.
# As long as you depend //third_party/py/tensorflow:tensorflow target
# everything will work as normal.

_model_to_estimator_usage_gauge = tf.__internal__.monitoring.BoolGauge(
    "/tensorflow/api/keras/model_to_estimator",
    "Whether tf.keras.estimator.model_to_estimator() is called.",
    "version",
)


# LINT.IfChange
@keras_export(v1=["keras.estimator.model_to_estimator"])
def model_to_estimator(
    keras_model=None,
    keras_model_path=None,
    custom_objects=None,
    model_dir=None,
    config=None,
    checkpoint_format="saver",
    metric_names_map=None,
    export_outputs=None,
):
    """Constructs an `Estimator` instance from given keras model.

    If you use infrastructure or other tooling that relies on Estimators, you
    can still build a Keras model and use model_to_estimator to convert the
    Keras model to an Estimator for use with downstream systems.

    For usage example, please see:
    [Creating estimators from Keras Models](
    https://www.tensorflow.org/guide/estimator#create_an_estimator_from_a_keras_model).

    Sample Weights:
    Estimators returned by `model_to_estimator` are configured so that they can
    handle sample weights (similar to `keras_model.fit(x, y, sample_weights)`).

    To pass sample weights when training or evaluating the Estimator, the first
    item returned by the input function should be a dictionary with keys
    `features` and `sample_weights`. Example below:

    ```python
    keras_model = tf.keras.Model(...)
    keras_model.compile(...)

    estimator = tf.keras.estimator.model_to_estimator(keras_model)

    def input_fn():
      return dataset_ops.Dataset.from_tensors(
          ({'features': features, 'sample_weights': sample_weights},
           targets))

    estimator.train(input_fn, steps=1)
    ```

    Example with customized export signature:
    ```python
    inputs = {'a': tf.keras.Input(..., name='a'),
              'b': tf.keras.Input(..., name='b')}
    outputs = {'c': tf.keras.layers.Dense(..., name='c')(inputs['a']),
               'd': tf.keras.layers.Dense(..., name='d')(inputs['b'])}
    keras_model = tf.keras.Model(inputs, outputs)
    keras_model.compile(...)
    export_outputs = {'c': tf.estimator.export.RegressionOutput,
                      'd': tf.estimator.export.ClassificationOutput}

    estimator = tf.keras.estimator.model_to_estimator(
        keras_model, export_outputs=export_outputs)

    def input_fn():
      return dataset_ops.Dataset.from_tensors(
          ({'features': features, 'sample_weights': sample_weights},
           targets))

    estimator.train(input_fn, steps=1)
    ```

    Args:
      keras_model: A compiled Keras model object. This argument is mutually
        exclusive with `keras_model_path`. Estimator's `model_fn` uses the
        structure of the model to clone the model. Defaults to `None`.
      keras_model_path: Path to a compiled Keras model saved on disk, in HDF5
        format, which can be generated with the `save()` method of a Keras
        model.  This argument is mutually exclusive with `keras_model`.
        Defaults to `None`.
      custom_objects: Dictionary for cloning customized objects. This is
        used with classes that is not part of this pip package. For example, if
        user maintains a `relu6` class that inherits from
        `tf.keras.layers.Layer`, then pass `custom_objects={'relu6': relu6}`.
        Defaults to `None`.
      model_dir: Directory to save `Estimator` model parameters, graph, summary
        files for TensorBoard, etc. If unset a directory will be created with
        `tempfile.mkdtemp`
      config: `RunConfig` to config `Estimator`. Allows setting up things in
        `model_fn` based on configuration such as `num_ps_replicas`, or
        `model_dir`. Defaults to `None`. If both `config.model_dir` and the
        `model_dir` argument (above) are specified the `model_dir` **argument**
        takes precedence.
      checkpoint_format: Sets the format of the checkpoint saved by the
        estimator when training. May be `saver` or `checkpoint`, depending on
        whether to save checkpoints from `tf.train.Saver` or
        `tf.train.Checkpoint`. This argument currently defaults to `saver`. When
        2.0 is released, the default will be `checkpoint`. Estimators use
        name-based `tf.train.Saver` checkpoints, while Keras models use
        object-based checkpoints from `tf.train.Checkpoint`. Currently, saving
        object-based checkpoints from `model_to_estimator` is only supported by
        Functional and Sequential models. Defaults to 'saver'.
      metric_names_map: Optional dictionary mapping Keras model output metric
        names to custom names. This can be used to override the default Keras
        model output metrics names in a multi IO model use case and provide
        custom names for the `eval_metric_ops` in Estimator.
        The Keras model metric names can be obtained using `model.metrics_names`
        excluding any loss metrics such as total loss and output losses.
        For example, if your Keras model has two outputs `out_1` and `out_2`,
        with `mse` loss and `acc` metric, then `model.metrics_names` will be
        `['loss', 'out_1_loss', 'out_2_loss', 'out_1_acc', 'out_2_acc']`.
        The model metric names excluding the loss metrics will be
        `['out_1_acc', 'out_2_acc']`.
      export_outputs: Optional dictionary. This can be used to override the
        default Keras model output exports in a multi IO model use case and
        provide custom names for the `export_outputs` in
        `tf.estimator.EstimatorSpec`. Default is None, which is equivalent to
        {'serving_default': `tf.estimator.export.PredictOutput`}. If not None,
        the keys must match the keys of `model.output_names`.
        A dict `{name: output}` where:
          * name: An arbitrary name for this output.
          * output: an `ExportOutput` class such as `ClassificationOutput`,
            `RegressionOutput`, or `PredictOutput`. Single-headed models only
            need to specify one entry in this dictionary. Multi-headed models
            should specify one entry for each head, one of which must be named
            using
            `tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`
            If no entry is provided, a default `PredictOutput` mapping to
            `predictions` will be created.

    Returns:
      An Estimator from given keras model.

    Raises:
      ValueError: If neither keras_model nor keras_model_path was given.
      ValueError: If both keras_model and keras_model_path was given.
      ValueError: If the keras_model_path is a GCS URI.
      ValueError: If keras_model has not been compiled.
      ValueError: If an invalid checkpoint_format was given.
    """

    try:
        # isort: off
        from tensorflow_estimator.python.estimator import (
            keras_lib,
        )
    except ImportError:
        raise NotImplementedError(
            "tf.keras.estimator.model_to_estimator function not available in "
            "your installation."
        )
    _model_to_estimator_usage_gauge.get_cell("v1").set(True)
    return keras_lib.model_to_estimator(
        keras_model=keras_model,
        keras_model_path=keras_model_path,
        custom_objects=custom_objects,
        model_dir=model_dir,
        config=config,
        checkpoint_format=checkpoint_format,
        use_v2_estimator=False,
        metric_names_map=metric_names_map,
        export_outputs=export_outputs,
    )


@keras_export("keras.estimator.model_to_estimator", v1=[])
def model_to_estimator_v2(
    keras_model=None,
    keras_model_path=None,
    custom_objects=None,
    model_dir=None,
    config=None,
    checkpoint_format="checkpoint",
    metric_names_map=None,
    export_outputs=None,
):
    """Constructs an `Estimator` instance from given keras model.

    If you use infrastructure or other tooling that relies on Estimators, you
    can still build a Keras model and use model_to_estimator to convert the
    Keras model to an Estimator for use with downstream systems.

    For usage example, please see:
    [Creating estimators from Keras Models](
    https://www.tensorflow.org/guide/estimators#creating_estimators_from_keras_models).

    Sample Weights:
    Estimators returned by `model_to_estimator` are configured so that they can
    handle sample weights (similar to `keras_model.fit(x, y, sample_weights)`).

    To pass sample weights when training or evaluating the Estimator, the first
    item returned by the input function should be a dictionary with keys
    `features` and `sample_weights`. Example below:

    ```python
    keras_model = tf.keras.Model(...)
    keras_model.compile(...)

    estimator = tf.keras.estimator.model_to_estimator(keras_model)

    def input_fn():
      return dataset_ops.Dataset.from_tensors(
          ({'features': features, 'sample_weights': sample_weights},
           targets))

    estimator.train(input_fn, steps=1)
    ```

    Example with customized export signature:
    ```python
    inputs = {'a': tf.keras.Input(..., name='a'),
              'b': tf.keras.Input(..., name='b')}
    outputs = {'c': tf.keras.layers.Dense(..., name='c')(inputs['a']),
               'd': tf.keras.layers.Dense(..., name='d')(inputs['b'])}
    keras_model = tf.keras.Model(inputs, outputs)
    keras_model.compile(...)
    export_outputs = {'c': tf.estimator.export.RegressionOutput,
                      'd': tf.estimator.export.ClassificationOutput}

    estimator = tf.keras.estimator.model_to_estimator(
        keras_model, export_outputs=export_outputs)

    def input_fn():
      return dataset_ops.Dataset.from_tensors(
          ({'features': features, 'sample_weights': sample_weights},
           targets))

    estimator.train(input_fn, steps=1)
    ```

    Note: We do not support creating weighted metrics in Keras and converting
    them to weighted metrics in the Estimator API using `model_to_estimator`.
    You will have to create these metrics directly on the estimator spec using
    the `add_metrics` function.

    To customize the estimator `eval_metric_ops` names, you can pass in the
    `metric_names_map` dictionary mapping the keras model output metric names
    to the custom names as follows:

    ```python
      input_a = tf.keras.layers.Input(shape=(16,), name='input_a')
      input_b = tf.keras.layers.Input(shape=(16,), name='input_b')
      dense = tf.keras.layers.Dense(8, name='dense_1')
      interm_a = dense(input_a)
      interm_b = dense(input_b)
      merged = tf.keras.layers.concatenate([interm_a, interm_b], name='merge')
      output_a = tf.keras.layers.Dense(3, activation='softmax', name='dense_2')(
              merged)
      output_b = tf.keras.layers.Dense(2, activation='softmax', name='dense_3')(
              merged)
      keras_model = tf.keras.models.Model(
          inputs=[input_a, input_b], outputs=[output_a, output_b])
      keras_model.compile(
          loss='categorical_crossentropy',
          optimizer='rmsprop',
          metrics={
              'dense_2': 'categorical_accuracy',
              'dense_3': 'categorical_accuracy'
          })

      metric_names_map = {
          'dense_2_categorical_accuracy': 'acc_1',
          'dense_3_categorical_accuracy': 'acc_2',
      }
      keras_est = tf.keras.estimator.model_to_estimator(
          keras_model=keras_model,
          config=config,
          metric_names_map=metric_names_map)
    ```

    Args:
      keras_model: A compiled Keras model object. This argument is mutually
        exclusive with `keras_model_path`. Estimator's `model_fn` uses the
        structure of the model to clone the model. Defaults to `None`.
      keras_model_path: Path to a compiled Keras model saved on disk, in HDF5
        format, which can be generated with the `save()` method of a Keras
        model.  This argument is mutually exclusive with `keras_model`.
        Defaults to `None`.
      custom_objects: Dictionary for cloning customized objects. This is
        used with classes that is not part of this pip package. For example, if
        user maintains a `relu6` class that inherits from
        `tf.keras.layers.Layer`, then pass `custom_objects={'relu6': relu6}`.
        Defaults to `None`.
      model_dir: Directory to save `Estimator` model parameters, graph, summary
        files for TensorBoard, etc. If unset a directory will be created with
        `tempfile.mkdtemp`
      config: `RunConfig` to config `Estimator`. Allows setting up things in
        `model_fn` based on configuration such as `num_ps_replicas`, or
        `model_dir`. Defaults to `None`. If both `config.model_dir` and the
        `model_dir` argument (above) are specified the `model_dir` **argument**
        takes precedence.
      checkpoint_format: Sets the format of the checkpoint saved by the
        estimator when training. May be `saver` or `checkpoint`, depending on
        whether to save checkpoints from `tf.compat.v1.train.Saver` or
        `tf.train.Checkpoint`.  The default is `checkpoint`. Estimators use
        name-based `tf.train.Saver` checkpoints, while Keras models use
        object-based checkpoints from `tf.train.Checkpoint`. Currently, saving
        object-based checkpoints from `model_to_estimator` is only supported by
        Functional and Sequential models. Defaults to 'checkpoint'.
      metric_names_map: Optional dictionary mapping Keras model output metric
        names to custom names. This can be used to override the default Keras
        model output metrics names in a multi IO model use case and provide
        custom names for the `eval_metric_ops` in Estimator.
        The Keras model metric names can be obtained using `model.metrics_names`
        excluding any loss metrics such as total loss and output losses.
        For example, if your Keras model has two outputs `out_1` and `out_2`,
        with `mse` loss and `acc` metric, then `model.metrics_names` will be
        `['loss', 'out_1_loss', 'out_2_loss', 'out_1_acc', 'out_2_acc']`.
        The model metric names excluding the loss metrics will be
        `['out_1_acc', 'out_2_acc']`.
      export_outputs: Optional dictionary. This can be used to override the
        default Keras model output exports in a multi IO model use case and
        provide custom names for the `export_outputs` in
        `tf.estimator.EstimatorSpec`. Default is None, which is equivalent to
        {'serving_default': `tf.estimator.export.PredictOutput`}. If not None,
        the keys must match the keys of `model.output_names`.
        A dict `{name: output}` where:
          * name: An arbitrary name for this output.
          * output: an `ExportOutput` class such as `ClassificationOutput`,
            `RegressionOutput`, or `PredictOutput`. Single-headed models only
            need to specify one entry in this dictionary. Multi-headed models
            should specify one entry for each head, one of which must be named
            using
            `tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`
            If no entry is provided, a default `PredictOutput` mapping to
            `predictions` will be created.

    Returns:
      An Estimator from given keras model.

    Raises:
      ValueError: If neither keras_model nor keras_model_path was given.
      ValueError: If both keras_model and keras_model_path was given.
      ValueError: If the keras_model_path is a GCS URI.
      ValueError: If keras_model has not been compiled.
      ValueError: If an invalid checkpoint_format was given.
    """

    try:
        # isort: off
        from tensorflow_estimator.python.estimator import (
            keras_lib,
        )
    except ImportError:
        raise NotImplementedError(
            "tf.keras.estimator.model_to_estimator function not available in "
            "your installation."
        )
    _model_to_estimator_usage_gauge.get_cell("v2").set(True)
    return keras_lib.model_to_estimator(
        keras_model=keras_model,
        keras_model_path=keras_model_path,
        custom_objects=custom_objects,
        model_dir=model_dir,
        config=config,
        checkpoint_format=checkpoint_format,
        use_v2_estimator=True,
        metric_names_map=metric_names_map,
        export_outputs=export_outputs,
    )


# LINT.ThenChange(//tensorflow_estimator/python/estimator/keras_lib.py)
