# Copyright 2021 Google LLC
#
# 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.

from functools import reduce
import logging
import os
from typing import Any, Dict, Tuple

import tensorflow as tf
from tensorflow import keras


INPUTS_SPEC = {
    "distance_from_port": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
    "speed": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
    "course": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
    "lat": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
    "lon": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
}

OUTPUTS_SPEC = {
    "is_fishing": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
}

PADDING = 24


def validated(
    tensor_dict: Dict[str, tf.Tensor],
    spec_dict: Dict[str, tf.TypeSpec],
) -> Dict[str, tf.Tensor]:
    for field, spec in spec_dict.items():
        if field not in tensor_dict:
            raise KeyError(
                f"missing field '{field}', got={tensor_dict.keys()}, expected={spec_dict.keys()}"
            )
        if not spec.dtype.is_compatible_with(tensor_dict[field].dtype):
            raise TypeError(
                f"incompatible type in '{field}', got={tensor_dict[field].dtype}, expected={spec.dtype}"
            )
        if not spec.shape.is_compatible_with(tensor_dict[field].shape):
            raise ValueError(
                f"incompatible shape in '{field}', got={tensor_dict[field].shape}, expected={spec.shape}"
            )
    return tensor_dict


def serialize(value_dict: Dict[str, Any]) -> bytes:
    spec_dict = {**INPUTS_SPEC, **OUTPUTS_SPEC}
    tensor_dict = {
        field: tf.convert_to_tensor(value, spec_dict[field].dtype)
        for field, value in value_dict.items()
    }
    validated_tensor_dict = validated(tensor_dict, spec_dict)

    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                field: tf.train.Feature(
                    bytes_list=tf.train.BytesList(
                        value=[tf.io.serialize_tensor(value).numpy()]
                    )
                )
                for field, value in validated_tensor_dict.items()
            }
        )
    )
    return example.SerializeToString()


def deserialize(
    serialized_example: bytes,
) -> Tuple[Dict[str, tf.Tensor], Dict[str, tf.Tensor]]:
    features = {
        field: tf.io.FixedLenFeature(shape=(), dtype=tf.string)
        for field in [*INPUTS_SPEC.keys(), *OUTPUTS_SPEC.keys()]
    }
    example = tf.io.parse_example(serialized_example, features)

    def parse_tensor(bytes_value: bytes, spec: tf.TypeSpec) -> tf.Tensor:
        tensor = tf.io.parse_tensor(bytes_value, spec.dtype)
        tensor.set_shape(spec.shape)
        return tensor

    def parse_features(spec_dict: Dict[str, tf.TypeSpec]) -> Dict[str, tf.Tensor]:
        tensor_dict = {
            field: parse_tensor(bytes_value, spec_dict[field])
            for field, bytes_value in example.items()
            if field in spec_dict
        }
        return validated(tensor_dict, spec_dict)

    return parse_features(INPUTS_SPEC), parse_features(OUTPUTS_SPEC)


def create_dataset(data_dir: str, batch_size: int) -> tf.data.Dataset:
    file_names = tf.io.gfile.glob(f"{data_dir}/*")
    return (
        tf.data.TFRecordDataset(file_names, compression_type="GZIP")
        .map(deserialize, num_parallel_calls=tf.data.AUTOTUNE)
        .shuffle(batch_size * 128)
        .batch(batch_size, drop_remainder=True)
        .prefetch(tf.data.AUTOTUNE)
    )


def create_model(train_dataset: tf.data.Dataset) -> keras.Model:
    input_layers = {
        name: keras.layers.Input(shape=spec.shape, dtype=spec.dtype, name=name)
        for name, spec in INPUTS_SPEC.items()
    }

    def normalize(name: str) -> keras.layers.Layer:
        layer = keras.layers.Normalization(name=f"{name}_normalized")
        layer.adapt(train_dataset.map(lambda inputs, outputs: inputs[name]))
        return layer(input_layers[name])

    def direction(course_name: str) -> keras.layers.Layer:
        class Direction(keras.layers.Layer):
            def call(self: Any, course: tf.Tensor) -> tf.Tensor:
                x = tf.cos(course)
                y = tf.sin(course)
                return tf.concat([x, y], axis=-1)

        input_layer = input_layers[course_name]
        return Direction(name=f"{course_name}_direction")(input_layer)

    def geo_point(lat_name: str, lon_name: str) -> keras.layers.Layer:
        # We transform each (lat, lon) pair into a 3D point in the unit sphere.
        #   https://en.wikipedia.org/wiki/Spherical_coordinate_system#Cartesian_coordinates
        class GeoPoint(keras.layers.Layer):
            def call(self: Any, latlon: Tuple[tf.Tensor, tf.Tensor]) -> tf.Tensor:
                lat, lon = latlon
                x = tf.cos(lon) * tf.sin(lat)
                y = tf.sin(lon) * tf.sin(lat)
                z = tf.cos(lat)
                return tf.concat([x, y, z], axis=-1)

        lat_lon_input_layers = (input_layers[lat_name], input_layers[lon_name])
        return GeoPoint(name=f"{lat_name}_{lon_name}")(lat_lon_input_layers)

    def sequential_layers(
        first_layer: keras.layers.Layer, *layers: keras.layers.Layer
    ) -> keras.layers.Layer:
        return reduce(lambda layer, result: result(layer), layers, first_layer)

    preprocessed_inputs = [
        normalize("distance_from_port"),
        normalize("speed"),
        direction("course"),
        geo_point("lat", "lon"),
    ]

    output_layers = {
        "is_fishing": sequential_layers(
            keras.layers.concatenate(preprocessed_inputs, name="deep_layers"),
            keras.layers.Conv1D(
                filters=32,
                kernel_size=PADDING + 1,
                data_format="channels_last",
                activation="relu",
            ),
            keras.layers.Dense(16, activation="relu"),
            keras.layers.Dense(1, activation="sigmoid", name="is_fishing"),
        )
    }
    return keras.Model(input_layers, output_layers)


def run(
    train_data_dir: str,
    eval_data_dir: str,
    train_epochs: int,
    batch_size: int,
    model_dir: str,
    checkpoint_dir: str,
    tensorboard_dir: str,
) -> None:

    # For this sample we are using a mirrored distribution strategy,
    # which consists of a single machine with multiple GPUs.
    #   https://blog.tensorflow.org/2020/12/getting-started-with-distributed-tensorflow-on-gcp.html
    distributed_strategy = tf.distribute.MirroredStrategy()
    # distributed_strategy = tf.distribute.get_strategy()

    # Create the training and evaluation datasets from the TFRecord files.
    logging.info("Creating datasets")
    train_batch_size = batch_size * distributed_strategy.num_replicas_in_sync
    train_dataset = create_dataset(train_data_dir, train_batch_size)
    eval_dataset = create_dataset(eval_data_dir, batch_size)

    # Create and compile the model inside the distribution strategy scope.
    with distributed_strategy.scope():
        logging.info("Creating the model")
        model = create_model(train_dataset)

        logging.info("Compiling the model")
        model.compile(
            optimizer="adam",  # https://keras.io/api/optimizers
            loss={"is_fishing": "binary_crossentropy"},  # https://keras.io/api/losses
            metrics={"is_fishing": ["accuracy"]},  # https://keras.io/api/metrics
        )

    # Train the model.
    logging.info("Training the model")
    model.fit(
        train_dataset,
        epochs=train_epochs,
        validation_data=eval_dataset,
        callbacks=[
            keras.callbacks.TensorBoard(tensorboard_dir, update_freq="batch"),
            keras.callbacks.ModelCheckpoint(
                filepath=checkpoint_dir + "/{epoch}",
                save_best_only=True,  # Only save a model if `val_loss` has improved.
                monitor="val_loss",
                verbose=1,
            ),
        ],
    )

    # Save the trained model.
    logging.info(f"Saving the model: {model_dir}")
    model.save(model_dir)


if __name__ == "__main__":
    import argparse

    # TODO: Have either: hardcoded default values if possible, or have everything required
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--train-data-dir",
        required=True,
        help="Cloud Storage directory containing training TFRecord files.",
    )
    parser.add_argument(
        "--eval-data-dir",
        required=True,
        help="Cloud Storage directory containing evaluation TFRecord files.",
    )
    parser.add_argument(
        "--train-epochs",
        type=int,
        required=True,
        help="Number of times to go through the training dataset.",
    )
    parser.add_argument(
        "--batch-size",
        type=int,
        required=True,
        help="Batch size for the training and evaluation datasets.",
    )
    parser.add_argument(
        "--model-dir",
        default=os.environ.get("AIP_MODEL_DIR", "model"),
        help="Directory to save the trained model.",
    )
    parser.add_argument(
        "--checkpoint-dir",
        default=os.environ.get("AIP_CHECKPOINT_DIR", "checkpoints"),
        help="Directory to save model checkpoints during training.",
    )
    parser.add_argument(
        "--tensorboard-dir",
        default=os.environ.get("AIP_TENSORBOARD_LOG_DIR", "tensorboard"),
        help="Directory to save TensorBoard logs.",
    )
    args = parser.parse_args()

    logging.getLogger().setLevel(logging.INFO)
    run(
        train_data_dir=args.train_data_dir,
        eval_data_dir=args.eval_data_dir,
        train_epochs=args.train_epochs,
        batch_size=args.batch_size,
        model_dir=args.model_dir,
        checkpoint_dir=args.checkpoint_dir,
        tensorboard_dir=args.tensorboard_dir,
    )

# Copyright 2021 Google LLC
#
# 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.

from functools import reduce
import logging
import os
from typing import Any, Dict, Tuple

import tensorflow as tf
from tensorflow import keras


INPUTS_SPEC = {
    "distance_from_port": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
    "speed": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
    "course": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
    "lat": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
    "lon": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
}

OUTPUTS_SPEC = {
    "is_fishing": tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
}

PADDING = 24


def validated(
    tensor_dict: Dict[str, tf.Tensor],
    spec_dict: Dict[str, tf.TypeSpec],
) -> Dict[str, tf.Tensor]:
    for field, spec in spec_dict.items():
        if field not in tensor_dict:
            raise KeyError(
                f"missing field '{field}', got={tensor_dict.keys()}, expected={spec_dict.keys()}"
            )
        if not spec.dtype.is_compatible_with(tensor_dict[field].dtype):
            raise TypeError(
                f"incompatible type in '{field}', got={tensor_dict[field].dtype}, expected={spec.dtype}"
            )
        if not spec.shape.is_compatible_with(tensor_dict[field].shape):
            raise ValueError(
                f"incompatible shape in '{field}', got={tensor_dict[field].shape}, expected={spec.shape}"
            )
    return tensor_dict


def serialize(value_dict: Dict[str, Any]) -> bytes:
    spec_dict = {**INPUTS_SPEC, **OUTPUTS_SPEC}
    tensor_dict = {
        field: tf.convert_to_tensor(value, spec_dict[field].dtype)
        for field, value in value_dict.items()
    }
    validated_tensor_dict = validated(tensor_dict, spec_dict)

    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                field: tf.train.Feature(
                    bytes_list=tf.train.BytesList(
                        value=[tf.io.serialize_tensor(value).numpy()]
                    )
                )
                for field, value in validated_tensor_dict.items()
            }
        )
    )
    return example.SerializeToString()


def deserialize(
    serialized_example: bytes,
) -> Tuple[Dict[str, tf.Tensor], Dict[str, tf.Tensor]]:
    features = {
        field: tf.io.FixedLenFeature(shape=(), dtype=tf.string)
        for field in [*INPUTS_SPEC.keys(), *OUTPUTS_SPEC.keys()]
    }
    example = tf.io.parse_example(serialized_example, features)

    def parse_tensor(bytes_value: bytes, spec: tf.TypeSpec) -> tf.Tensor:
        tensor = tf.io.parse_tensor(bytes_value, spec.dtype)
        tensor.set_shape(spec.shape)
        return tensor

    def parse_features(spec_dict: Dict[str, tf.TypeSpec]) -> Dict[str, tf.Tensor]:
        tensor_dict = {
            field: parse_tensor(bytes_value, spec_dict[field])
            for field, bytes_value in example.items()
            if field in spec_dict
        }
        return validated(tensor_dict, spec_dict)

    return parse_features(INPUTS_SPEC), parse_features(OUTPUTS_SPEC)


def create_dataset(data_dir: str, batch_size: int) -> tf.data.Dataset:
    file_names = tf.io.gfile.glob(f"{data_dir}/*")
    return (
        tf.data.TFRecordDataset(file_names, compression_type="GZIP")
        .map(deserialize, num_parallel_calls=tf.data.AUTOTUNE)
        .shuffle(batch_size * 128)
        .batch(batch_size, drop_remainder=True)
        .prefetch(tf.data.AUTOTUNE)
    )


def create_model(train_dataset: tf.data.Dataset) -> keras.Model:
    input_layers = {
        name: keras.layers.Input(shape=spec.shape, dtype=spec.dtype, name=name)
        for name, spec in INPUTS_SPEC.items()
    }

    def normalize(name: str) -> keras.layers.Layer:
        layer = keras.layers.Normalization(name=f"{name}_normalized")
        layer.adapt(train_dataset.map(lambda inputs, outputs: inputs[name]))
        return layer(input_layers[name])

    def direction(course_name: str) -> keras.layers.Layer:
        class Direction(keras.layers.Layer):
            def call(self: Any, course: tf.Tensor) -> tf.Tensor:
                x = tf.cos(course)
                y = tf.sin(course)
                return tf.concat([x, y], axis=-1)

        input_layer = input_layers[course_name]
        return Direction(name=f"{course_name}_direction")(input_layer)

    def geo_point(lat_name: str, lon_name: str) -> keras.layers.Layer:
        # We transform each (lat, lon) pair into a 3D point in the unit sphere.
        #   https://en.wikipedia.org/wiki/Spherical_coordinate_system#Cartesian_coordinates
        class GeoPoint(keras.layers.Layer):
            def call(self: Any, latlon: Tuple[tf.Tensor, tf.Tensor]) -> tf.Tensor:
                lat, lon = latlon
                x = tf.cos(lon) * tf.sin(lat)
                y = tf.sin(lon) * tf.sin(lat)
                z = tf.cos(lat)
                return tf.concat([x, y, z], axis=-1)

        lat_lon_input_layers = (input_layers[lat_name], input_layers[lon_name])
        return GeoPoint(name=f"{lat_name}_{lon_name}")(lat_lon_input_layers)

    def sequential_layers(
        first_layer: keras.layers.Layer, *layers: keras.layers.Layer
    ) -> keras.layers.Layer:
        return reduce(lambda layer, result: result(layer), layers, first_layer)

    preprocessed_inputs = [
        normalize("distance_from_port"),
        normalize("speed"),
        direction("course"),
        geo_point("lat", "lon"),
    ]

    output_layers = {
        "is_fishing": sequential_layers(
            keras.layers.concatenate(preprocessed_inputs, name="deep_layers"),
            keras.layers.Conv1D(
                filters=32,
                kernel_size=PADDING + 1,
                data_format="channels_last",
                activation="relu",
            ),
            keras.layers.Dense(16, activation="relu"),
            keras.layers.Dense(1, activation="sigmoid", name="is_fishing"),
        )
    }
    return keras.Model(input_layers, output_layers)


def run(
    train_data_dir: str,
    eval_data_dir: str,
    train_epochs: int,
    batch_size: int,
    model_dir: str,
    checkpoint_dir: str,
    tensorboard_dir: str,
) -> None:

    # For this sample we are using a mirrored distribution strategy,
    # which consists of a single machine with multiple GPUs.
    #   https://blog.tensorflow.org/2020/12/getting-started-with-distributed-tensorflow-on-gcp.html
    distributed_strategy = tf.distribute.MirroredStrategy()
    # distributed_strategy = tf.distribute.get_strategy()

    # Create the training and evaluation datasets from the TFRecord files.
    logging.info("Creating datasets")
    train_batch_size = batch_size * distributed_strategy.num_replicas_in_sync
    train_dataset = create_dataset(train_data_dir, train_batch_size)
    eval_dataset = create_dataset(eval_data_dir, batch_size)

    # Create and compile the model inside the distribution strategy scope.
    with distributed_strategy.scope():
        logging.info("Creating the model")
        model = create_model(train_dataset)

        logging.info("Compiling the model")
        model.compile(
            optimizer="adam",  # https://keras.io/api/optimizers
            loss={"is_fishing": "binary_crossentropy"},  # https://keras.io/api/losses
            metrics={"is_fishing": ["accuracy"]},  # https://keras.io/api/metrics
        )

    # Train the model.
    logging.info("Training the model")
    model.fit(
        train_dataset,
        epochs=train_epochs,
        validation_data=eval_dataset,
        callbacks=[
            keras.callbacks.TensorBoard(tensorboard_dir, update_freq="batch"),
            keras.callbacks.ModelCheckpoint(
                filepath=checkpoint_dir + "/{epoch}",
                save_best_only=True,  # Only save a model if `val_loss` has improved.
                monitor="val_loss",
                verbose=1,
            ),
        ],
    )

    # Save the trained model.
    logging.info(f"Saving the model: {model_dir}")
    model.save(model_dir)


if __name__ == "__main__":
    import argparse

    # TODO: Have either: hardcoded default values if possible, or have everything required
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--train-data-dir",
        required=True,
        help="Cloud Storage directory containing training TFRecord files.",
    )
    parser.add_argument(
        "--eval-data-dir",
        required=True,
        help="Cloud Storage directory containing evaluation TFRecord files.",
    )
    parser.add_argument(
        "--train-epochs",
        type=int,
        required=True,
        help="Number of times to go through the training dataset.",
    )
    parser.add_argument(
        "--batch-size",
        type=int,
        required=True,
        help="Batch size for the training and evaluation datasets.",
    )
    parser.add_argument(
        "--model-dir",
        default=os.environ.get("AIP_MODEL_DIR", "model"),
        help="Directory to save the trained model.",
    )
    parser.add_argument(
        "--checkpoint-dir",
        default=os.environ.get("AIP_CHECKPOINT_DIR", "checkpoints"),
        help="Directory to save model checkpoints during training.",
    )
    parser.add_argument(
        "--tensorboard-dir",
        default=os.environ.get("AIP_TENSORBOARD_LOG_DIR", "tensorboard"),
        help="Directory to save TensorBoard logs.",
    )
    args = parser.parse_args()

    logging.getLogger().setLevel(logging.INFO)
    run(
        train_data_dir=args.train_data_dir,
        eval_data_dir=args.eval_data_dir,
        train_epochs=args.train_epochs,
        batch_size=args.batch_size,
        model_dir=args.model_dir,
        checkpoint_dir=args.checkpoint_dir,
        tensorboard_dir=args.tensorboard_dir,
    )

# Copyright 2021 Google LLC # # 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. # We transform each (lat, lon) pair into a 3D point in the unit sphere. #   https://en.wikipedia.org/wiki/Spherical_coordinate_system#Cartesian_coordinates # For this sample we are using a mirrored distribution strategy, # which consists of a single machine with multiple GPUs. #   https://blog.tensorflow.org/2020/12/getting-started-with-distributed-tensorflow-on-gcp.html # distributed_strategy = tf.distribute.get_strategy() # Create the training and evaluation datasets from the TFRecord files. # Create and compile the model inside the distribution strategy scope. # https://keras.io/api/optimizers # https://keras.io/api/losses # https://keras.io/api/metrics # Train the model. # Only save a model if `val_loss` has improved. # Save the trained model. # TODO: Have either: hardcoded default values if possible, or have everything required