import copy
from datetime import datetime
import logging
import os
from pathlib import Path
import platform
import shutil
import sys
import tempfile
import time
from contextlib import redirect_stderr, redirect_stdout
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union, Type

import ray
from ray.air._internal.remote_storage import list_at_uri
from ray.air._internal.uri_utils import URI
from ray.air._internal.util import skip_exceptions, exception_cause
from ray.air.checkpoint import (
    Checkpoint,
    _DICT_CHECKPOINT_ADDITIONAL_FILE_KEY,
)
from ray.air.constants import (
    TIMESTAMP,
    TIME_THIS_ITER_S,
    TRAINING_ITERATION,
)
from ray.tune.result import (
    DEBUG_METRICS,
    DEFAULT_RESULTS_DIR,
    DONE,
    EPISODES_THIS_ITER,
    EPISODES_TOTAL,
    HOSTNAME,
    NODE_IP,
    PID,
    RESULT_DUPLICATE,
    SHOULD_CHECKPOINT,
    STDERR_FILE,
    STDOUT_FILE,
    TIME_TOTAL_S,
    TIMESTEPS_THIS_ITER,
    TIMESTEPS_TOTAL,
    TRIAL_ID,
    TRIAL_INFO,
)
from ray.tune import TuneError
from ray.tune.utils import UtilMonitor, warn_if_slow
from ray.tune.utils.callback import (
    DEFAULT_CALLBACK_CLASSES,
    _get_artifact_templates_for_callbacks,
)
from ray.tune.utils.log import disable_ipython
from ray.tune.execution.placement_groups import PlacementGroupFactory
from ray.tune.syncer import SyncConfig, get_node_to_storage_syncer
from ray.tune.trainable.util import TrainableUtil
from ray.tune.utils.util import Tee, _get_checkpoint_from_remote_node
from ray.util.annotations import PublicAPI

if TYPE_CHECKING:
    from ray.tune.logger import Logger

logger = logging.getLogger(__name__)

SETUP_TIME_THRESHOLD = 10


@PublicAPI
class Trainable:
    """Abstract class for trainable models, functions, etc.

    A call to ``train()`` on a trainable will execute one logical iteration of
    training. As a rule of thumb, the execution time of one train call should
    be large enough to avoid overheads (i.e. more than a few seconds), but
    short enough to report progress periodically (i.e. at most a few minutes).

    Calling ``save()`` should save the training state of a trainable to disk,
    and ``restore(path)`` should restore a trainable to the given state.

    Generally you only need to implement ``setup``, ``step``,
    ``save_checkpoint``, and ``load_checkpoint`` when subclassing Trainable.

    Other implementation methods that may be helpful to override are
    ``log_result``, ``reset_config``, ``cleanup``, and ``_export_model``.

    Tune will convert this class into a Ray actor, which runs on a separate process.
    By default, Tune will also change the current working directory of this process to
    its corresponding trial-level log directory ``self.logdir``.
    This is designed so that different trials that run on the same physical node won't
    accidently write to the same location and overstep each other.

    The behavior of changing the working directory can be disabled by setting the
    flag `chdir_to_trial_dir=False` in `tune.TuneConfig`. This allows access to files
    in the original working directory, but relative paths should be used for read only
    purposes, and you must make sure that the directory is synced on all nodes if
    running on multiple machines.

    The `TUNE_ORIG_WORKING_DIR` environment variable was the original workaround for
    accessing paths relative to the original working directory. This environment
    variable is deprecated, and the `chdir_to_trial_dir` flag described above should be
    used instead.

    This class supports checkpointing to and restoring from remote storage.


    """

    _checkpoint_cls: Type[Checkpoint] = Checkpoint

    def __init__(
        self,
        config: Dict[str, Any] = None,
        logger_creator: Callable[[Dict[str, Any]], "Logger"] = None,  # Deprecated (2.7)
        remote_checkpoint_dir: Optional[str] = None,
        sync_config: Optional[SyncConfig] = None,
    ):
        """Initialize a Trainable.

        Sets up logging and points ``self.logdir`` to a directory in which
        training outputs should be placed.

        Subclasses should prefer defining ``setup()`` instead of overriding
        ``__init__()`` directly.

        Args:
            config: Trainable-specific configuration data. By default
                will be saved as ``self.config``.
            logger_creator: (Deprecated) Function that creates a ray.tune.Logger
                object. If unspecified, a default logger is created.
            remote_checkpoint_dir: Upload directory (S3 or GS path).
                This is **per trial** directory,
                which is different from **per checkpoint** directory.
            sync_config: Configuration object for syncing.
                See :class:`~ray.tune.syncer.SyncConfig`.
        """

        self.config = config or {}
        trial_info = self.config.pop(TRIAL_INFO, None)

        if self.is_actor():
            disable_ipython()

        # TODO(ml-team): Remove `logger_creator` in 2.7.
        self._result_logger = self._logdir = None
        self._create_logger(self.config, logger_creator)

        self._stdout_context = self._stdout_fp = self._stdout_stream = None
        self._stderr_context = self._stderr_fp = self._stderr_stream = None
        self._stderr_logging_handler = None

        stdout_file = self.config.pop(STDOUT_FILE, None)
        stderr_file = self.config.pop(STDERR_FILE, None)
        self._open_logfiles(stdout_file, stderr_file)

        self._iteration = 0
        self._time_total = 0.0
        self._timesteps_total = None
        self._episodes_total = None
        self._time_since_restore = 0.0
        self._timesteps_since_restore = 0
        self._iterations_since_restore = 0
        self._last_result = None
        self._restored = False
        self._trial_info = trial_info
        self._stdout_file = stdout_file
        self._stderr_file = stderr_file

        self._start_time = time.time()
        self._local_ip = ray.util.get_node_ip_address()
        self.setup(copy.deepcopy(self.config))
        setup_time = time.time() - self._start_time
        if setup_time > SETUP_TIME_THRESHOLD:
            logger.info(
                "Trainable.setup took {:.3f} seconds. If your "
                "trainable is slow to initialize, consider setting "
                "reuse_actors=True to reduce actor creation "
                "overheads.".format(setup_time)
            )
        log_sys_usage = self.config.get("log_sys_usage", False)
        self._monitor = UtilMonitor(start=log_sys_usage)

        self.remote_checkpoint_dir = remote_checkpoint_dir
        # If no sync_config is provided, but we save to a remote_checkpoint_dir,
        # then provide a default syncer. `upload_dir` here is just a dummy directory
        # that tells the SyncConfig to create a default syncer.
        self.sync_config = sync_config or SyncConfig(
            upload_dir=self.remote_checkpoint_dir, syncer="auto"
        )

        # Resolves syncer="auto" to an actual syncer cloud storage is used
        # If sync_config.syncer is a custom Syncer instance, this is a no-op.
        self.sync_config.syncer = get_node_to_storage_syncer(
            self.sync_config, self.remote_checkpoint_dir
        )

        self.sync_num_retries = int(os.getenv("TUNE_CHECKPOINT_CLOUD_RETRY_NUM", "3"))
        self.sync_sleep_time = float(
            os.getenv("TUNE_CHECKPOINT_CLOUD_RETRY_WAIT_TIME_S", "1")
        )
        self._last_artifact_sync_iter = None

    @property
    def uses_cloud_checkpointing(self):
        return bool(self.remote_checkpoint_dir)

    def _remote_storage_path(self, local_path):
        """Converts a `local_path` to be based off of
        `self.remote_checkpoint_dir`."""
        return TrainableUtil.get_remote_storage_path(
            local_path=local_path,
            local_path_prefix=self.logdir,
            remote_path_prefix=self.remote_checkpoint_dir,
        )

    @classmethod
    def default_resource_request(
        cls, config: Dict[str, Any]
    ) -> Optional[PlacementGroupFactory]:
        """Provides a static resource requirement for the given configuration.

        This can be overridden by sub-classes to set the correct trial resource
        allocation, so the user does not need to.

        .. testcode::

            @classmethod
            def default_resource_request(cls, config):
                return PlacementGroupFactory([{"CPU": 1}, {"CPU": 1}])


        Args:
            config[Dict[str, Any]]: The Trainable's config dict.

        Returns:
            PlacementGroupFactory: A PlacementGroupFactory consumed by Tune
                for queueing.
        """
        return None

    @classmethod
    def resource_help(cls, config: Dict):
        """Returns a help string for configuring this trainable's resources.

        Args:
            config: The Trainer's config dict.
        """
        return ""

    def get_current_ip_pid(self):
        return self._local_ip, os.getpid()

    def get_auto_filled_metrics(
        self,
        now: Optional[datetime] = None,
        time_this_iter: Optional[float] = None,
        timestamp: Optional[int] = None,
        debug_metrics_only: bool = False,
    ) -> dict:
        """Return a dict with metrics auto-filled by the trainable.

        If ``debug_metrics_only`` is True, only metrics that don't
        require at least one iteration will be returned
        (``ray.tune.result.DEBUG_METRICS``).
        """
        if now is None:
            now = datetime.today()
        autofilled = {
            TRIAL_ID: self.trial_id,
            "date": now.strftime("%Y-%m-%d_%H-%M-%S"),
            "timestamp": timestamp if timestamp else int(time.mktime(now.timetuple())),
            TIME_THIS_ITER_S: time_this_iter,
            TIME_TOTAL_S: self._time_total,
            PID: os.getpid(),
            HOSTNAME: platform.node(),
            NODE_IP: self._local_ip,
            "config": self.config,
            "time_since_restore": self._time_since_restore,
            "iterations_since_restore": self._iterations_since_restore,
        }
        if self._timesteps_since_restore:
            autofilled["timesteps_since_restore"] = self._timesteps_since_restore

        if debug_metrics_only:
            autofilled = {k: v for k, v in autofilled.items() if k in DEBUG_METRICS}
        return autofilled

    def is_actor(self):
        try:
            actor_id = ray._private.worker.global_worker.actor_id
            return actor_id != actor_id.nil()
        except Exception:
            # If global_worker is not instantiated, we're not in an actor
            return False

    def train_buffered(self, buffer_time_s: float, max_buffer_length: int = 1000):
        """Runs multiple iterations of training.

        Calls ``train()`` internally. Collects and combines multiple results.
        This function will run ``self.train()`` repeatedly until one of
        the following conditions is met: 1) the maximum buffer length is
        reached, 2) the maximum buffer time is reached, or 3) a checkpoint
        was created. Even if the maximum time is reached, it will always
        block until at least one result is received.

        Args:
            buffer_time_s: Maximum time to buffer. The next result
                received after this amount of time has passed will return
                the whole buffer.
            max_buffer_length: Maximum number of results to buffer.

        """
        results = []

        now = time.time()
        send_buffer_at = now + buffer_time_s
        while now < send_buffer_at or not results:  # At least one result
            result = self.train()
            results.append(result)
            if result.get(DONE, False):
                # If the trial is done, return
                break
            elif result.get(SHOULD_CHECKPOINT, False):
                # If a checkpoint was created, return
                break
            elif result.get(RESULT_DUPLICATE):
                # If the function API trainable completed, return
                break
            elif len(results) >= max_buffer_length:
                # If the buffer is full, return
                break
            now = time.time()

        return results

    def train(self):
        """Runs one logical iteration of training.

        Calls ``step()`` internally. Subclasses should override ``step()``
        instead to return results.
        This method automatically fills the following fields in the result:

            `done` (bool): training is terminated. Filled only if not provided.

            `time_this_iter_s` (float): Time in seconds this iteration
            took to run. This may be overridden in order to override the
            system-computed time difference.

            `time_total_s` (float): Accumulated time in seconds for this
            entire experiment.

            `training_iteration` (int): The index of this
            training iteration, e.g. call to train(). This is incremented
            after `step()` is called.

            `pid` (str): The pid of the training process.

            `date` (str): A formatted date of when the result was processed.

            `timestamp` (str): A UNIX timestamp of when the result
            was processed. This may be overridden.

            `hostname` (str): Hostname of the machine hosting the training
            process.

            `node_ip` (str): Node ip of the machine hosting the training
            process.

        Returns:
            A dict that describes training progress.
        """
        start = time.time()
        try:
            result = self.step()
        except Exception as e:
            skipped = skip_exceptions(e)
            raise skipped from exception_cause(skipped)

        assert isinstance(result, dict), "step() needs to return a dict."

        # We do not modify internal state nor update this result if duplicate.
        if RESULT_DUPLICATE in result:
            return result

        result = result.copy()

        self._iteration += 1
        self._iterations_since_restore += 1

        if result.get(TIME_THIS_ITER_S) is not None:
            time_this_iter = result[TIME_THIS_ITER_S]
        else:
            time_this_iter = time.time() - start
        self._time_total += time_this_iter
        self._time_since_restore += time_this_iter

        result_timestamp = result.get(TIMESTAMP, None)

        result.setdefault(DONE, False)

        # self._timesteps_total should only be tracked if increments are provided
        if result.get(TIMESTEPS_THIS_ITER) is not None:
            if self._timesteps_total is None:
                self._timesteps_total = 0
            self._timesteps_total += result[TIMESTEPS_THIS_ITER]
            self._timesteps_since_restore += result[TIMESTEPS_THIS_ITER]

        # self._episodes_total should only be tracked if increments provided
        if result.get(EPISODES_THIS_ITER) is not None:
            if self._episodes_total is None:
                self._episodes_total = 0
            self._episodes_total += result[EPISODES_THIS_ITER]

        # self._timesteps_total should not override user-provided total
        if self._timesteps_total is not None:
            result.setdefault(TIMESTEPS_TOTAL, self._timesteps_total)
        if self._episodes_total is not None:
            result.setdefault(EPISODES_TOTAL, self._episodes_total)
        result.setdefault(TRAINING_ITERATION, self._iteration)

        now = datetime.today()
        result.update(
            self.get_auto_filled_metrics(
                now=now, time_this_iter=time_this_iter, timestamp=result_timestamp
            )
        )

        monitor_data = self._monitor.get_data()
        if monitor_data:
            result.update(monitor_data)

        self.log_result(result)

        if self._stdout_context:
            self._stdout_stream.flush()
        if self._stderr_context:
            self._stderr_stream.flush()

        self._last_result = result

        return result

    def get_state(self):
        return {
            "iteration": self._iteration,
            "timesteps_total": self._timesteps_total,
            "time_total": self._time_total,
            "episodes_total": self._episodes_total,
            "last_result": self._last_result,
            "ray_version": ray.__version__,
        }

    def _create_checkpoint_dir(
        self, checkpoint_dir: Optional[str] = None
    ) -> Optional[str]:
        # Create checkpoint_xxxxx directory and drop checkpoint marker
        checkpoint_dir = TrainableUtil.make_checkpoint_dir(
            checkpoint_dir or self.logdir, index=self.iteration, override=True
        )
        return checkpoint_dir

    def save(
        self, checkpoint_dir: Optional[str] = None, prevent_upload: bool = False
    ) -> str:
        """Saves the current model state to a checkpoint.

        Subclasses should override ``save_checkpoint()`` instead to save state.
        This method dumps additional metadata alongside the saved path.

        If a remote checkpoint dir is given, this will also sync up to remote
        storage.

        Args:
            checkpoint_dir: Optional dir to place the checkpoint.
            prevent_upload: If True, will not upload the saved checkpoint to cloud.

        Returns:
            The given or created checkpoint directory.

        Note the return path should match up with what is expected of
        `restore()`.
        """
        checkpoint_dir = self._create_checkpoint_dir(checkpoint_dir=checkpoint_dir)

        # User saves checkpoint
        checkpoint_dict_or_path = self.save_checkpoint(checkpoint_dir)

        if checkpoint_dict_or_path is None:
            # checkpoint_dict_or_path can only be None in class trainables.
            # In that case the default is to use the root checkpoint directory.
            assert checkpoint_dir
            checkpoint_dict_or_path = checkpoint_dir
        elif checkpoint_dir is None:
            # checkpoint_dir is only None in function trainables. In that case,
            # checkpoint_dict_or_path points to the already saved checkpoint dir.
            # This will be considered the root dir.
            assert isinstance(checkpoint_dict_or_path, str)
            checkpoint_dir = checkpoint_dict_or_path

        # Get trainable metadata
        metadata = self.get_state()

        if isinstance(checkpoint_dict_or_path, dict):
            metadata["relative_checkpoint_path"] = ""
            metadata["saved_as_dict"] = True
            self._checkpoint_cls.from_dict(checkpoint_dict_or_path).to_directory(
                checkpoint_dir
            )
            # Re-drop marker
            TrainableUtil.mark_as_checkpoint_dir(checkpoint_dir)
        else:
            # Make sure the checkpoint dir is contained
            if not checkpoint_dict_or_path.startswith(checkpoint_dir):
                raise ValueError(
                    f"The returned checkpoint path must be within the given "
                    f"checkpoint dir ({checkpoint_dir}): {checkpoint_dict_or_path}"
                )

            # Get relative path to returned checkpoint
            relative_checkpoint_path = os.path.relpath(
                checkpoint_dict_or_path, checkpoint_dir
            )
            metadata["relative_checkpoint_path"] = relative_checkpoint_path
            metadata["saved_as_dict"] = False

        TrainableUtil.write_metadata(checkpoint_dir, metadata)

        if not prevent_upload:
            # First, upload the new trial checkpoint to cloud
            self._maybe_save_to_cloud(checkpoint_dir)
            # Then, save other artifacts that live in the trial logdir
            self._maybe_save_artifacts_to_cloud()

        return checkpoint_dir

    def _get_latest_available_checkpoint(self) -> Optional[str]:
        latest_local_checkpoint = self._get_latest_local_available_checkpoint()
        latest_remote_checkpoint = self._get_latest_remote_available_checkpoint()

        if not latest_local_checkpoint:
            return latest_remote_checkpoint
        elif not latest_remote_checkpoint:
            return latest_local_checkpoint

        # Else, both are available
        return max([latest_local_checkpoint, latest_remote_checkpoint])

    def _get_latest_local_available_checkpoint(self) -> Optional[str]:
        checkpoint_candidates = []
        for name in os.listdir(self._logdir):
            if not name.startswith("checkpoint_"):
                continue
            candidate_path = os.path.join(self._logdir, name)
            if not os.path.isdir(candidate_path):
                continue

            # On local storage it is cheap to check for valid checkpoints
            try:
                TrainableUtil.find_checkpoint_dir(candidate_path)
            except Exception:
                continue

            checkpoint_candidates.append(candidate_path)

        if not checkpoint_candidates:
            return None

        return max(checkpoint_candidates)

    def _get_latest_remote_available_checkpoint(self) -> Optional[str]:
        if not self.remote_checkpoint_dir:
            return None

        checkpoint_candidates = []
        for name in list_at_uri(self.remote_checkpoint_dir):
            if not name.startswith("checkpoint_"):
                continue
            candidate_path = os.path.join(self._logdir, name)
            checkpoint_candidates.append(candidate_path)

        if not checkpoint_candidates:
            return None

        return max(checkpoint_candidates)

    @property
    def _should_upload_artifacts(self) -> bool:
        if not self.sync_config.sync_artifacts:
            return False

        if self._last_artifact_sync_iter == self.iteration:
            # No need to sync again, if we have already synced this iteration.
            return False

        # Check if any files have actually been written
        # (apart from checkpoints and driver logs)
        exclude = ["checkpoint_*"] + _get_artifact_templates_for_callbacks(
            DEFAULT_CALLBACK_CLASSES
        )
        excluded_files = []
        for exclude_template in exclude:
            excluded_files += [
                path.name for path in Path(self.logdir).glob(exclude_template)
            ]
        artifact_files = set(os.listdir(self.logdir)) - set(excluded_files)
        return bool(artifact_files)

    def _maybe_save_artifacts_to_cloud(self) -> bool:
        if not self._should_upload_artifacts:
            return False

        self._last_artifact_sync_iter = self.iteration
        with warn_if_slow(
            name="trial_artifact_cloud_upload",
            message=(
                "Uploading trial artifacts took {duration:.3f} s, which may be a "
                "performance bottleneck. Consider saving fewer/smaller artifacts to "
                "the trial log directory, or disable artifact syncing with "
                "`SyncConfig(sync_artifacts=False)`."
            ),
            # Log a warning if upload time surpasses 10s
            threshold=10,
            disable=not self.uses_cloud_checkpointing,
        ):
            # Avoid double uploading checkpoints and driver callback artifacts,
            # if those live in the same directory
            exclude = ["checkpoint_*"] + _get_artifact_templates_for_callbacks(
                DEFAULT_CALLBACK_CLASSES
            )
            uploaded = self._maybe_save_to_cloud(self.logdir, exclude=exclude)
        return uploaded

    def _maybe_save_to_cloud(self, local_dir: str, exclude: List[str] = None) -> bool:
        """Saves the given directory to the cloud. This is used for checkpoint
        and artifact uploads to cloud.

        Args:
            local_dir: The local directory to upload to the `remote_checkpoint_dir`

        Returns:
            bool: True if (successfully) saved to cloud
        """
        if not self.uses_cloud_checkpointing:
            return False

        syncer = self.sync_config.syncer
        assert syncer

        checkpoint_uri = self._remote_storage_path(local_dir)

        syncer.sync_up(local_dir=local_dir, remote_dir=checkpoint_uri, exclude=exclude)
        try:
            syncer.wait_or_retry(
                max_retries=self.sync_num_retries,
                backoff_s=self.sync_sleep_time,
            )
        except TuneError:
            num_retries = self.sync_num_retries
            logger.error(
                f"Could not upload checkpoint to {checkpoint_uri} even after "
                f"{num_retries} retries."
                f"Please check if the credentials expired and that the remote "
                f"filesystem is supported. For large checkpoints or artifacts, "
                f"consider increasing `SyncConfig(sync_timeout)` "
                f"(current value: {self.sync_config.sync_timeout} seconds)."
            )
            return False
        return True

    def _maybe_load_checkpoint_from_cloud(self, checkpoint_path: str) -> bool:
        """Loads a checkpoint from its corresponding location in remote storage
        to `checkpoint_path`.
        If a checkpoint already exists at `checkpoint_path`, the Trainable
        will continue restoring with that existing checkpoint.

        Args:
            checkpoint_path: The checkpoint path to download to, which should have
                `logdir` as part of its path prefix (e.g., `{logdir}/checkpoint_00000`)

        Return:
            bool: True if the checkpoint was synced down successfully from cloud.
        """
        if os.path.exists(checkpoint_path):
            try:
                TrainableUtil.find_checkpoint_dir(checkpoint_path)
            except Exception:
                pass
            else:
                # If the path exists locally, we don't have to download
                return False

        if not self.uses_cloud_checkpointing:
            return False

        rel_checkpoint_dir = TrainableUtil.find_rel_checkpoint_dir(
            self.logdir, checkpoint_path
        )
        external_uri = str(URI(self.remote_checkpoint_dir) / rel_checkpoint_dir)
        local_dir = os.path.join(self.logdir, rel_checkpoint_dir)
        path_existed_before = os.path.exists(local_dir)

        success = self._maybe_load_from_cloud(
            remote_dir=external_uri, local_dir=local_dir
        )
        if not success:
            # We may have created this dir when we tried to sync, so clean up
            if not path_existed_before and os.path.exists(local_dir):
                shutil.rmtree(local_dir)
        return success

    def _maybe_load_artifacts_from_cloud(self) -> bool:
        if not self.sync_config.sync_artifacts:
            return False

        remote_dir = self._remote_storage_path(self.logdir)
        with warn_if_slow(
            name="trial_artifact_cloud_download",
            message=(
                "Downloading trial artifacts took {duration:.3f} s, which may be a "
                "performance bottleneck. Consider saving fewer/smaller artifacts to "
                "the trial log directory, or disable artifact syncing with "
                "`SyncConfig(sync_artifacts=False)`."
            ),
            # Log a warning if upload time surpasses 10s
            threshold=10,
            disable=not self.uses_cloud_checkpointing,
        ):
            exclude = ["checkpoint_*"] + _get_artifact_templates_for_callbacks(
                DEFAULT_CALLBACK_CLASSES
            )
            downloaded = self._maybe_load_from_cloud(
                remote_dir=remote_dir, local_dir=self.logdir, exclude=exclude
            )
        return downloaded

    def _maybe_load_from_cloud(
        self, remote_dir: str, local_dir: str, exclude: List[str] = None
    ) -> bool:
        if not self.uses_cloud_checkpointing or not list_at_uri(remote_dir):
            return False

        syncer = self.sync_config.syncer
        assert syncer

        syncer.sync_down(remote_dir=remote_dir, local_dir=local_dir, exclude=exclude)
        try:
            syncer.wait_or_retry(
                max_retries=self.sync_num_retries,
                backoff_s=self.sync_sleep_time,
            )
        except TuneError:
            num_retries = self.sync_num_retries
            logger.error(
                f"Could not download from {remote_dir} even after {num_retries} "
                f"retries. "
                f"Please check if the credentials expired and that the remote "
                f"filesystem is supported. For large checkpoints or artifacts, "
                f"consider increasing `SyncConfig(sync_timeout)` "
                f"(current value: {self.sync_config.sync_timeout} seconds)."
            )
            return False

        return True

    def save_to_object(self):
        """Saves the current model state to a Python object.

        It also saves to disk but does not return the checkpoint path.
        It does not save the checkpoint to cloud storage.

        Returns:
            Object holding checkpoint data.
        """
        temp_container_dir = tempfile.mkdtemp("save_to_object", dir=self.logdir)
        checkpoint_dir = self.save(temp_container_dir, prevent_upload=True)

        obj_ref = self._checkpoint_cls.from_directory(checkpoint_dir).to_bytes()
        shutil.rmtree(temp_container_dir)
        return obj_ref

    def _restore_from_checkpoint_obj(self, checkpoint: Checkpoint):
        with checkpoint.as_directory() as converted_checkpoint_path:
            return self.restore(
                checkpoint_path=converted_checkpoint_path,
                checkpoint_node_ip=None,
            )

    def restore(
        self,
        checkpoint_path: Union[str, Checkpoint],
        checkpoint_node_ip: Optional[str] = None,
        fallback_to_latest: bool = False,
    ):
        """Restores training state from a given model checkpoint.

        These checkpoints are returned from calls to save().

        Subclasses should override ``load_checkpoint()`` instead to
        restore state.
        This method restores additional metadata saved with the checkpoint.

        `checkpoint_path` should match with the return from ``save()``.

        `checkpoint_path` can be
        `~/ray_results/exp/MyTrainable_abc/
        checkpoint_00000/checkpoint`. Or,
        `~/ray_results/exp/MyTrainable_abc/checkpoint_00000`.

        `self.logdir` should generally be corresponding to `checkpoint_path`,
        for example, `~/ray_results/exp/MyTrainable_abc`.

        `self.remote_checkpoint_dir` in this case, is something like,
        `REMOTE_CHECKPOINT_BUCKET/exp/MyTrainable_abc`

        Args:
            checkpoint_path: Path to restore checkpoint from. If this
                path does not exist on the local node, it will be fetched
                from external (cloud) storage if available, or restored
                from a remote node.
            checkpoint_node_ip: If given, try to restore
                checkpoint from this node if it doesn't exist locally or
                on cloud storage.
            fallback_to_latest: If True, will try to recover the
                latest available checkpoint if the given ``checkpoint_path``
                could not be found.

        """
        # Ensure Checkpoints are converted
        if isinstance(checkpoint_path, Checkpoint):
            return self._restore_from_checkpoint_obj(checkpoint_path)

        synced_from_cloud = self._maybe_load_checkpoint_from_cloud(checkpoint_path)

        if not synced_from_cloud and (
            # If a checkpoint source IP is given
            checkpoint_node_ip
            # And the checkpoint does not currently exist on the local node
            and not os.path.exists(checkpoint_path)
            # And the source IP is different to the current IP
            and checkpoint_node_ip != ray.util.get_node_ip_address()
        ):
            checkpoint = _get_checkpoint_from_remote_node(
                checkpoint_path, checkpoint_node_ip
            )
            if checkpoint:
                checkpoint.to_directory(checkpoint_path)

        if not os.path.exists(checkpoint_path):
            if fallback_to_latest:
                logger.info(
                    f"Checkpoint path was not available, trying to recover from latest "
                    f"available checkpoint instead. Unavailable checkpoint path: "
                    f"{checkpoint_path}"
                )
                checkpoint_path = self._get_latest_available_checkpoint()
                if checkpoint_path:
                    logger.info(
                        f"Trying to recover from latest available checkpoint: "
                        f"{checkpoint_path}"
                    )
                    return self.restore(checkpoint_path, fallback_to_latest=False)

            # Else, raise
            raise ValueError(
                f"Could not recover from checkpoint as it does not exist on local "
                f"disk and was not available on cloud storage or another Ray node. "
                f"Got checkpoint path: {checkpoint_path} and IP {checkpoint_node_ip}"
            )

        checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)
        metadata = TrainableUtil.load_metadata(checkpoint_dir)

        if metadata["saved_as_dict"]:
            # If data was saved as a dict (e.g. from a class trainable),
            # also pass the dict to `load_checkpoint()`.
            checkpoint_dict = self._checkpoint_cls.from_directory(
                checkpoint_dir
            ).to_dict()
            # If other files were added to the directory after converting from the
            # original dict (e.g. marker files), clean these up
            checkpoint_dict.pop(_DICT_CHECKPOINT_ADDITIONAL_FILE_KEY, None)
            to_load = checkpoint_dict
        else:
            # Otherwise, pass the relative checkpoint path
            relative_checkpoint_path = metadata["relative_checkpoint_path"]
            to_load = os.path.join(checkpoint_dir, relative_checkpoint_path)

        # Set metadata
        self._iteration = metadata["iteration"]
        self._timesteps_total = metadata["timesteps_total"]
        self._time_total = metadata["time_total"]
        self._episodes_total = metadata["episodes_total"]

        # Actually load checkpoint
        self.load_checkpoint(to_load)

        # Then, download artifacts from cloud if applicable
        if synced_from_cloud:
            self._maybe_load_artifacts_from_cloud()

        self._time_since_restore = 0.0
        self._timesteps_since_restore = 0
        self._iterations_since_restore = 0
        self._restored = True

        logger.info(
            "Restored on %s from checkpoint: %s", self._local_ip, checkpoint_dir
        )
        state = {
            "_iteration": self._iteration,
            "_timesteps_total": self._timesteps_total,
            "_time_total": self._time_total,
            "_episodes_total": self._episodes_total,
        }
        logger.info("Current state after restoring: %s", state)

    def restore_from_object(self, obj):
        """Restores training state from a checkpoint object.

        These checkpoints are returned from calls to save_to_object().
        """
        checkpoint = self._checkpoint_cls.from_bytes(obj)

        with checkpoint.as_directory() as checkpoint_path:
            self.restore(checkpoint_path)

    def delete_checkpoint(self, checkpoint_path: Union[str, Checkpoint]):
        """Deletes local copy of checkpoint.

        Args:
            checkpoint_path: Path to checkpoint.
        """
        # Ensure Checkpoints are converted
        if isinstance(checkpoint_path, Checkpoint) and checkpoint_path._local_path:
            checkpoint_path = checkpoint_path._local_path

        try:
            checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)
        except FileNotFoundError:
            # The checkpoint won't exist locally if the
            # trial was rescheduled to another worker.
            logger.debug(
                f"Local checkpoint not found during garbage collection: "
                f"{self.trial_id} - {checkpoint_path}"
            )
            return
        else:
            if self.uses_cloud_checkpointing:
                syncer = self.sync_config.syncer
                assert syncer

                checkpoint_uri = self._remote_storage_path(checkpoint_dir)
                syncer.delete(checkpoint_uri)
                try:
                    syncer.wait_or_retry(
                        max_retries=self.sync_num_retries,
                        backoff_s=self.sync_sleep_time,
                    )
                except TuneError:
                    num_retries = self.sync_num_retries
                    logger.error(
                        f"Could not delete checkpoint even after {num_retries} "
                        f"retries: {checkpoint_uri}"
                    )

        if os.path.exists(checkpoint_dir):
            shutil.rmtree(checkpoint_dir)

    def export_model(
        self, export_formats: Union[List[str], str], export_dir: Optional[str] = None
    ):
        """Exports model based on export_formats.

        Subclasses should override _export_model() to actually
        export model to local directory.

        Args:
            export_formats: Format or list of (str) formats
                that should be exported.
            export_dir: Optional dir to place the exported model.
                Defaults to self.logdir.

        Returns:
            A dict that maps ExportFormats to successfully exported models.
        """
        if isinstance(export_formats, str):
            export_formats = [export_formats]
        export_dir = export_dir or self.logdir
        return self._export_model(export_formats, export_dir)

    def reset(self, new_config, logger_creator=None, remote_checkpoint_dir=None):
        """Resets trial for use with new config.

        Subclasses should override reset_config() to actually
        reset actor behavior for the new config."""
        # Save artifacts one last time, if this actor has been swapped to a
        # different trial.
        if remote_checkpoint_dir != self.remote_checkpoint_dir:
            self._maybe_save_artifacts_to_cloud()

        self.config = new_config

        trial_info = new_config.pop(TRIAL_INFO, None)
        if trial_info:
            self._trial_info = trial_info

        self._result_logger.flush()
        self._result_logger.close()

        if logger_creator:
            logger.debug("Logger reset.")
            self._create_logger(new_config.copy(), logger_creator)
        else:
            logger.debug(
                "Did not reset logger. Got: "
                f"trainable.reset(logger_creator={logger_creator})."
            )

        stdout_file = new_config.pop(STDOUT_FILE, None)
        stderr_file = new_config.pop(STDERR_FILE, None)

        self._close_logfiles()
        self._open_logfiles(stdout_file, stderr_file)

        success = self.reset_config(new_config)
        if not success:
            return False

        # Reset attributes. Will be overwritten by `restore` if a checkpoint
        # is provided.
        self._iteration = 0
        self._time_total = 0.0
        self._timesteps_total = None
        self._episodes_total = None
        self._time_since_restore = 0.0
        self._timesteps_since_restore = 0
        self._iterations_since_restore = 0
        self.remote_checkpoint_dir = remote_checkpoint_dir
        self._last_artifact_sync_iter = None
        self._restored = False

        return True

    def reset_config(self, new_config: Dict):
        """Resets configuration without restarting the trial.

        This method is optional, but can be implemented to speed up algorithms
        such as PBT, and to allow performance optimizations such as running
        experiments with reuse_actors=True.

        Args:
            new_config: Updated hyperparameter configuration
                for the trainable.

        Returns:
            True if reset was successful else False.
        """
        return False

    def _create_logger(
        self,
        config: Dict[str, Any],
        logger_creator: Callable[[Dict[str, Any]], "Logger"] = None,
    ):
        """Create logger from logger creator.

        Sets _logdir and _result_logger.

        `_logdir` is the **per trial** directory for the Trainable.
        """
        if logger_creator:
            self._result_logger = logger_creator(config)
            self._logdir = self._result_logger.logdir
        else:
            from ray.tune.logger import UnifiedLogger

            logdir_prefix = datetime.today().strftime("%Y-%m-%d_%H-%M-%S")
            ray._private.utils.try_to_create_directory(DEFAULT_RESULTS_DIR)
            self._logdir = tempfile.mkdtemp(
                prefix=logdir_prefix, dir=DEFAULT_RESULTS_DIR
            )
            self._result_logger = UnifiedLogger(config, self._logdir, loggers=None)

    def _open_logfiles(self, stdout_file, stderr_file):
        """Create loggers. Open stdout and stderr logfiles."""
        if stdout_file:
            stdout_path = os.path.expanduser(os.path.join(self._logdir, stdout_file))
            self._stdout_fp = open(stdout_path, "a+")
            self._stdout_stream = Tee(sys.stdout, self._stdout_fp)
            self._stdout_context = redirect_stdout(self._stdout_stream)
            self._stdout_context.__enter__()

        if stderr_file:
            stderr_path = os.path.expanduser(os.path.join(self._logdir, stderr_file))
            self._stderr_fp = open(stderr_path, "a+")
            self._stderr_stream = Tee(sys.stderr, self._stderr_fp)
            self._stderr_context = redirect_stderr(self._stderr_stream)
            self._stderr_context.__enter__()

            # Add logging handler to root ray logger
            formatter = logging.Formatter(
                "[%(levelname)s %(asctime)s] "
                "%(filename)s: %(lineno)d  "
                "%(message)s"
            )
            self._stderr_logging_handler = logging.StreamHandler(self._stderr_fp)
            self._stderr_logging_handler.setFormatter(formatter)
            ray.logger.addHandler(self._stderr_logging_handler)

    def _close_logfiles(self):
        """Close stdout and stderr logfiles."""
        if self._stderr_logging_handler:
            ray.logger.removeHandler(self._stderr_logging_handler)

        if self._stdout_context:
            self._stdout_stream.flush()
            self._stdout_context.__exit__(None, None, None)
            self._stdout_fp.close()
            self._stdout_context = None
        if self._stderr_context:
            self._stderr_stream.flush()
            self._stderr_context.__exit__(None, None, None)
            self._stderr_fp.close()
            self._stderr_context = None

    def stop(self):
        """Releases all resources used by this trainable.

        Calls ``Trainable.cleanup`` internally. Subclasses should override
        ``Trainable.cleanup`` for custom cleanup procedures.
        """
        self._maybe_save_artifacts_to_cloud()

        self._result_logger.flush()
        self._result_logger.close()
        if self._monitor.is_alive():
            self._monitor.stop()
            self._monitor.join()
        self.cleanup()

        self._close_logfiles()

    @property
    def logdir(self):
        """Directory of the results and checkpoints for this Trainable.

        Tune will automatically sync this folder with the driver if execution
        is distributed.

        Note that the current working directory will also be changed to this.

        """
        return os.path.join(self._logdir, "")

    @property
    def trial_name(self):
        """Trial name for the corresponding trial of this Trainable.

        This is not set if not using Tune.

        .. testcode::

            from ray.tune import Trainable

            name = Trainable().trial_name
        """
        if self._trial_info:
            return self._trial_info.trial_name
        else:
            return "default"

    @property
    def trial_id(self):
        """Trial ID for the corresponding trial of this Trainable.

        This is not set if not using Tune.

        .. testcode::

            from ray.tune import Trainable

            trial_id = Trainable().trial_id
        """
        if self._trial_info:
            return self._trial_info.trial_id
        else:
            return "default"

    @property
    def trial_resources(self) -> Optional[PlacementGroupFactory]:
        """Resources currently assigned to the trial of this Trainable.

        This is not set if not using Tune.

        .. testcode::

            from ray.tune import Trainable

            trial_resources = Trainable().trial_resources
        """
        if self._trial_info:
            return self._trial_info.trial_resources
        else:
            return None

    @property
    def iteration(self):
        """Current training iteration.

        This value is automatically incremented every time `train()` is called
        and is automatically inserted into the training result dict.

        """
        return self._iteration

    @property
    def training_iteration(self):
        """Current training iteration (same as `self.iteration`).

        This value is automatically incremented every time `train()` is called
        and is automatically inserted into the training result dict.

        """
        return self._iteration

    def get_config(self):
        """Returns configuration passed in by Tune."""
        return self.config

    def step(self):
        """Subclasses should override this to implement train().

        The return value will be automatically passed to the loggers. Users
        can also return `tune.result.DONE` or `tune.result.SHOULD_CHECKPOINT`
        as a key to manually trigger termination or checkpointing of this
        trial. Note that manual checkpointing only works when subclassing
        Trainables.

        .. versionadded:: 0.8.7

        Returns:
            A dict that describes training progress.

        """
        raise NotImplementedError

    def save_checkpoint(self, checkpoint_dir: str) -> Optional[Union[str, Dict]]:
        """Subclasses should override this to implement ``save()``.

        Warning:
            Do not rely on absolute paths in the implementation of
            ``Trainable.save_checkpoint`` and ``Trainable.load_checkpoint``.

        Use ``validate_save_restore`` to catch ``Trainable.save_checkpoint``/
        ``Trainable.load_checkpoint`` errors before execution.

        >>> from ray.tune.utils import validate_save_restore
        >>> MyTrainableClass = ... # doctest: +SKIP
        >>> validate_save_restore(MyTrainableClass) # doctest: +SKIP
        >>> validate_save_restore( # doctest: +SKIP
        ...     MyTrainableClass, use_object_store=True)

        .. versionadded:: 0.8.7

        Args:
            checkpoint_dir: The directory where the checkpoint
                file must be stored. In a Tune run, if the trial is paused,
                the provided path may be temporary and moved.

        Returns:
            A dict or string. If string, the return value is expected to be
            prefixed by `checkpoint_dir`. If dict, the return value will
            be automatically serialized by Tune. In both cases, the return value
            is exactly what will be passed to ``Trainable.load_checkpoint()``
            upon restore.

        Example:
            >>> trainable, trainable1, trainable2 = ... # doctest: +SKIP
            >>> print(trainable1.save_checkpoint("/tmp/checkpoint_1")) # doctest: +SKIP
            "/tmp/checkpoint_1"
            >>> print(trainable2.save_checkpoint("/tmp/checkpoint_2")) # doctest: +SKIP
            {"some": "data"}
            >>> trainable.save_checkpoint("/tmp/bad_example") # doctest: +SKIP
            "/tmp/NEW_CHECKPOINT_PATH/my_checkpoint_file" # This will error.
        """
        raise NotImplementedError

    def load_checkpoint(self, checkpoint: Union[Dict, str]):
        """Subclasses should override this to implement restore().

        Warning:
            In this method, do not rely on absolute paths. The absolute
            path of the checkpoint_dir used in ``Trainable.save_checkpoint``
            may be changed.

        If ``Trainable.save_checkpoint`` returned a prefixed string, the
        prefix of the checkpoint string returned by
        ``Trainable.save_checkpoint`` may be changed.
        This is because trial pausing depends on temporary directories.

        The directory structure under the checkpoint_dir provided to
        ``Trainable.save_checkpoint`` is preserved.

        See the examples below.

        Example:
            >>> import os
            >>> from ray.tune.trainable import Trainable
            >>> class Example(Trainable):
            ...    def save_checkpoint(self, checkpoint_path):
            ...        my_checkpoint_path = os.path.join(checkpoint_path, "my/path")
            ...        return my_checkpoint_path
            ...    def load_checkpoint(self, my_checkpoint_path):
            ...        print(my_checkpoint_path)
            >>> trainer = Example()
            >>> # This is used when PAUSED.
            >>> obj = trainer.save_to_object() # doctest: +SKIP
            <logdir>/tmpc8k_c_6hsave_to_object/checkpoint_0/my/path
            >>> # Note the different prefix.
            >>> trainer.restore_from_object(obj) # doctest: +SKIP
            <logdir>/tmpb87b5axfrestore_from_object/checkpoint_0/my/path

        If `Trainable.save_checkpoint` returned a dict, then Tune will directly pass
        the dict data as the argument to this method.

        Example:
            >>> from ray.tune.trainable import Trainable
            >>> class Example(Trainable):
            ...    def save_checkpoint(self, checkpoint_path):
            ...        return {"my_data": 1}
            ...    def load_checkpoint(self, checkpoint_dict):
            ...        print(checkpoint_dict["my_data"])

        .. versionadded:: 0.8.7

        Args:
            checkpoint: If dict, the return value is as
                returned by `save_checkpoint`. If a string, then it is
                a checkpoint path that may have a different prefix than that
                returned by `save_checkpoint`. The directory structure
                underneath the `checkpoint_dir` from `save_checkpoint` is preserved.
        """
        raise NotImplementedError

    def setup(self, config: Dict):
        """Subclasses should override this for custom initialization.

        .. versionadded:: 0.8.7

        Args:
            config: Hyperparameters and other configs given.
                Copy of `self.config`.

        """
        pass

    def log_result(self, result: Dict):
        """Subclasses can optionally override this to customize logging.

        The logging here is done on the worker process rather than
        the driver.

        .. versionadded:: 0.8.7

        Args:
            result: Training result returned by step().
        """
        self._result_logger.on_result(result)

    def cleanup(self):
        """Subclasses should override this for any cleanup on stop.

        If any Ray actors are launched in the Trainable (i.e., with a RLlib
        trainer), be sure to kill the Ray actor process here.

        This process should be lightweight. Per default,

        You can kill a Ray actor by calling `ray.kill(actor)`
        on the actor or removing all references to it and waiting for garbage
        collection

        .. versionadded:: 0.8.7
        """
        pass

    def _export_model(self, export_formats: List[str], export_dir: str):
        """Subclasses should override this to export model.

        Args:
            export_formats: List of formats that should be exported.
            export_dir: Directory to place exported models.

        Return:
            A dict that maps ExportFormats to successfully exported models.
        """
        return {}

    def _implements_method(self, key):
        return hasattr(self, key) and callable(getattr(self, key))
