# Copyright The Lightning AI team.
#
# 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.
import logging
import os
from typing import Any, Callable, Dict, Optional

from lightning_fabric.plugins.io.checkpoint_io import CheckpointIO
from lightning_fabric.utilities.cloud_io import _atomic_save
from lightning_fabric.utilities.cloud_io import _load as pl_load
from lightning_fabric.utilities.cloud_io import get_filesystem
from lightning_fabric.utilities.rank_zero import rank_zero_warn
from lightning_fabric.utilities.types import _PATH

log = logging.getLogger(__name__)


class TorchCheckpointIO(CheckpointIO):
    """CheckpointIO that utilizes :func:`torch.save` and :func:`torch.load` to save and load checkpoints
    respectively, common for most use cases."""

    def save_checkpoint(self, checkpoint: Dict[str, Any], path: _PATH, storage_options: Optional[Any] = None) -> None:
        """Save model/training states as a checkpoint file through state-dump and file-write.

        Args:
            checkpoint: dict containing model and trainer state
            path: write-target path
            storage_options: not used in ``TorchCheckpointIO.save_checkpoint``

        Raises:
            TypeError:
                If ``storage_options`` arg is passed in
        """
        if storage_options is not None:
            raise TypeError(
                "`Trainer.save_checkpoint(..., storage_options=...)` with `storage_options` arg"
                f" is not supported for `{self.__class__.__name__}`. Please implement your custom `CheckpointIO`"
                " to define how you'd like to use `storage_options`."
            )
        fs = get_filesystem(path)
        fs.makedirs(os.path.dirname(path), exist_ok=True)
        try:
            # write the checkpoint dictionary on the file
            _atomic_save(checkpoint, path)
        except AttributeError as err:
            # todo: is this try catch necessary still?
            # https://github.com/Lightning-AI/lightning/pull/431
            # TODO(fabric): Fabric doesn't support hyperparameters in the checkpoint, so this should be refactored
            key = "hyper_parameters"
            checkpoint.pop(key, None)
            rank_zero_warn(f"Warning, `{key}` dropped from checkpoint. An attribute is not picklable: {err}")
            _atomic_save(checkpoint, path)

    def load_checkpoint(
        self, path: _PATH, map_location: Optional[Callable] = lambda storage, loc: storage
    ) -> Dict[str, Any]:
        """Loads checkpoint using :func:`torch.load`, with additional handling for ``fsspec`` remote loading of
        files.

        Args:
            path: Path to checkpoint
            map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
                locations.

        Returns: The loaded checkpoint.

        Raises:
            FileNotFoundError: If ``path`` is not found by the ``fsspec`` filesystem
        """

        # Try to read the checkpoint at `path`. If not exist, do not restore checkpoint.
        fs = get_filesystem(path)
        if not fs.exists(path):
            raise FileNotFoundError(f"Checkpoint at {path} not found. Aborting training.")

        return pl_load(path, map_location=map_location)

    def remove_checkpoint(self, path: _PATH) -> None:
        """Remove checkpoint file from the filesystem.

        Args:
            path: Path to checkpoint
        """
        fs = get_filesystem(path)
        if fs.exists(path):
            fs.rm(path, recursive=True)
            log.debug(f"Removed checkpoint: {path}")
