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"""``TensorflowModelDataset`` is a data set implementation which can save and load
TensorFlow models.
"""
import copy
import tempfile
from pathlib import PurePosixPath
from typing import Any, Callable, Dict

import fsspec
import tensorflow as tf

from kedro.io.core import (
    AbstractVersionedDataSet,
    DataSetError,
    Version,
    get_filepath_str,
    get_protocol_and_path,
)


class TensorFlowModelDataset(AbstractVersionedDataSet):
    """``TensorflowModelDataset`` loads and saves TensorFlow models.
    The underlying functionality is supported by, and passes input arguments through to,
    TensorFlow 2.X load_model and save_model methods.

    Example:
    ::

        >>> from kedro.extras.datasets.tensorflow import TensorFlowModelDataset
        >>> import tensorflow as tf
        >>> import numpy as np
        >>>
        >>> data_set = TensorFlowModelDataset("saved_model_path")
        >>> model = tf.keras.Model()
        >>> predictions = model.predict([...])
        >>>
        >>> data_set.save(model)
        >>> loaded_model = data_set.load()
        >>> new_predictions = loaded_model.predict([...])
        >>> np.testing.assert_allclose(predictions, new_predictions, rtol=1e-6, atol=1e-6)

    """

    DEFAULT_LOAD_ARGS = {}  # type: Dict[str, Any]
    DEFAULT_SAVE_ARGS = {"save_format": "tf"}  # type: Dict[str, Any]

    # pylint: disable=too-many-arguments
    def __init__(
        self,
        filepath: str,
        load_args: Dict[str, Any] = None,
        save_args: Dict[str, Any] = None,
        version: Version = None,
        credentials: Dict[str, Any] = None,
        fs_args: Dict[str, Any] = None,
    ) -> None:
        """Creates a new instance of ``TensorFlowModelDataset``.

        Args:
            filepath: Filepath to a TensorFlow model directory prefixed with a protocol
                like `s3://`. If prefix is not provided `file` protocol (local filesystem)
                will be used. The prefix should be any protocol supported by ``fsspec``.
                Note: `http(s)` doesn't support versioning.
            load_args: TensorFlow options for loading models.
                Here you can find all available arguments:
                https://www.tensorflow.org/api_docs/python/tf/keras/models/load_model
                All defaults are preserved.
            save_args: TensorFlow options for saving models.
                Here you can find all available arguments:
                https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model
                All defaults are preserved, except for "save_format", which is set to "tf".
            version: If specified, should be an instance of
                ``kedro.io.core.Version``. If its ``load`` attribute is
                None, the latest version will be loaded. If its ``save``
                attribute is None, save version will be autogenerated.
            credentials: Credentials required to get access to the underlying filesystem.
                E.g. for ``GCSFileSystem`` it should look like `{'token': None}`.
            fs_args: Extra arguments to pass into underlying filesystem class constructor
                (e.g. `{"project": "my-project"}` for ``GCSFileSystem``).
        """
        _fs_args = copy.deepcopy(fs_args) or {}
        _credentials = copy.deepcopy(credentials) or {}
        protocol, path = get_protocol_and_path(filepath, version)
        if protocol == "file":
            _fs_args.setdefault("auto_mkdir", True)

        self._protocol = protocol
        self._fs = fsspec.filesystem(self._protocol, **_credentials, **_fs_args)
        super().__init__(
            filepath=PurePosixPath(path),
            version=version,
            exists_function=self._fs.exists,
            glob_function=self._fs.glob,
        )

        self._tmp_prefix = "kedro_tensorflow_tmp"  # temp prefix pattern

        # Handle default load and save arguments
        self._load_args = copy.deepcopy(self.DEFAULT_LOAD_ARGS)
        if load_args is not None:
            self._load_args.update(load_args)
        self._save_args = copy.deepcopy(self.DEFAULT_SAVE_ARGS)
        if save_args is not None:
            self._save_args.update(save_args)

        if self._save_args.get("save_format") == "h5":
            self._tmpfile_callable = tempfile.NamedTemporaryFile  # type: Callable
        else:
            self._tmpfile_callable = tempfile.TemporaryDirectory

    def _load(self) -> tf.keras.Model:
        load_path = get_filepath_str(self._get_load_path(), self._protocol)

        with self._tmpfile_callable(prefix=self._tmp_prefix) as tmp:
            # first use fsspec to get TF model directory/file from ArbitraryFileSystem
            # and save to local tempfile directory/file
            path, is_dir = (tmp, True) if isinstance(tmp, str) else (tmp.name, False)

            if is_dir:
                self._fs.get(load_path, path, recursive=True)
            else:
                self._fs.copy(load_path, path)

            # then pass the local temporary directory path to keras.load_model
            return tf.keras.models.load_model(path, **self._load_args)

    def _save(self, data: tf.keras.Model) -> None:
        save_path = get_filepath_str(self._get_save_path(), self._protocol)

        with self._tmpfile_callable(prefix=self._tmp_prefix) as tmp:
            # first use keras.load_model to save TF model directory to local tempfile directory
            path, is_dir = (tmp, True) if isinstance(tmp, str) else (tmp.name, False)
            tf.keras.models.save_model(data, path, **self._save_args)

            # then use fsspec to take from local tempfile directory/file and
            # put in ArbitraryFileSystem
            if is_dir:
                self._fs.put(path, save_path, recursive=True)
            else:
                self._fs.copy(path, save_path)

    def _exists(self) -> bool:
        try:
            load_path = get_filepath_str(self._get_load_path(), self._protocol)
        except DataSetError:
            return False
        return self._fs.exists(load_path)

    def _describe(self) -> Dict[str, Any]:
        return dict(
            filepath=self._filepath,
            protocol=self._protocol,
            load_args=self._load_args,
            save_args=self._save_args,
            version=self._version,
        )

    def _release(self) -> None:
        super()._release()
        self._invalidate_cache()

    def _invalidate_cache(self) -> None:
        """Invalidate underlying filesystem caches."""
        filepath = get_filepath_str(self._filepath, self._protocol)
        self._fs.invalidate_cache(filepath)
