# Copyright 2025 The Orbax Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
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"""StandardCheckpointHandler class."""

from __future__ import annotations

import dataclasses
import numbers
from typing import Any, List, Optional

from absl import logging
from etils import epath
import jax
import numpy as np
from orbax.checkpoint import checkpoint_args
from orbax.checkpoint import checkpoint_utils
from orbax.checkpoint import options as options_lib
from orbax.checkpoint._src import asyncio_utils
from orbax.checkpoint._src.futures import future
from orbax.checkpoint._src.handlers import async_checkpoint_handler
from orbax.checkpoint._src.handlers import pytree_checkpoint_handler
from orbax.checkpoint._src.metadata import pytree_metadata_options as pytree_metadata_options_lib
from orbax.checkpoint._src.metadata import tree as tree_metadata
from orbax.checkpoint._src.metadata import value as value_metadata
from orbax.checkpoint._src.tree import types as tree_types
from orbax.checkpoint._src.tree import utils as tree_utils


PyTree = Any
CheckpointArgs = checkpoint_args.CheckpointArgs
PyTreeMetadataOptions = pytree_metadata_options_lib.PyTreeMetadataOptions
register_with_handler = checkpoint_args.register_with_handler


class StandardCheckpointHandler(
    async_checkpoint_handler.AsyncCheckpointHandler
):
  """A CheckpointHandler implementation for any PyTree structure.

  See JAX documentation for more information on what constitutes a "PyTree".
  This handler is capable of saving and restoring PyTrees with leaves of type
  Python scalar, np.ndarray, and jax.Array

  As with all :py:class:`.CheckpointHandler` subclasses,
  `StandardCheckpointHandler` should only be used in conjunction with a
  :py:class:`.Checkpointer` (or subclass). By itself, the `CheckpointHandler` is
  non-atomic.

  Example::

    ckptr = Checkpointer(StandardCheckpointHandler())
    # OR
    ckptr = StandardCheckpointer()

  If you find that your use case is not covered by `StandardCheckpointHandler`,
  consider using the parent class directly, or explore a custom implementation
  of `CheckpointHandler`.
  """

  def __init__(
      self,
      *,
      save_concurrent_gb: int = 96,
      restore_concurrent_gb: int = 96,
      multiprocessing_options: options_lib.MultiprocessingOptions = (
          options_lib.MultiprocessingOptions()
      ),
      pytree_metadata_options: PyTreeMetadataOptions = (
          pytree_metadata_options_lib.PYTREE_METADATA_OPTIONS
      ),
      use_ocdbt: bool = True,
  ):
    """Creates StandardCheckpointHandler.

    Args:
      save_concurrent_gb: max concurrent GB that are allowed to be saved. Can
        help to reduce the possibility of OOM's when large checkpoints are
        saved.
      restore_concurrent_gb: max concurrent GB that are allowed to be restored.
        Can help to reduce the possibility of OOM's when large checkpoints are
        restored.
      multiprocessing_options: See orbax.checkpoint.options.
      pytree_metadata_options: Options to control types like tuple and
        namedtuple in pytree metadata.
      use_ocdbt: Whether to enable Tensorstore OCDBT driver.
    """
    self._supported_types = checkpoint_utils.STANDARD_ARRAY_TYPES
    self._impl = pytree_checkpoint_handler.PyTreeCheckpointHandler(
        save_concurrent_gb=save_concurrent_gb,
        restore_concurrent_gb=restore_concurrent_gb,
        multiprocessing_options=multiprocessing_options,
        pytree_metadata_options=pytree_metadata_options,
        use_ocdbt=use_ocdbt,
    )

  def _validate_save_state(
      self, item: PyTree, save_args: Optional[PyTree] = None
  ) -> PyTree:
    if item is None:
      raise ValueError('Must provide item to save.')
    if isinstance(item, jax.Array | numbers.Number):
      raise ValueError(
          'StandardCheckpointHandler / StandardSave does not support single '
          'arrays or scalars. Use ArrayCheckpointHandler / ArraySave'
      )
    if save_args is None:
      save_args = jax.tree.map(lambda x: None, item)

    def _check_input(k, x, arg):
      if arg is not None:
        if arg.aggregate:
          raise ValueError(f'Unsupported option `aggregate` for key: {k}.')
      if not isinstance(x, (np.ndarray, jax.Array)) and hasattr(x, '__array__'):
        x = np.asarray(x)
      if not isinstance(x, self._supported_types):
        k = tree_utils.tuple_path_from_keypath(k)
        raise ValueError(f'Unsupported type: {type(x)} for key: {k}.')
      return x

    return jax.tree_util.tree_map_with_path(_check_input, item, save_args)

  def _validate_restore_state(self, item: PyTree):
    def _check_input(k, x):
      if not isinstance(x, self._supported_types) and not isinstance(
          x, jax.ShapeDtypeStruct
      ):
        k = tree_utils.tuple_path_from_keypath(k)
        raise ValueError(f'Unsupported type: {type(x)} for key: {k}.')

    jax.tree_util.tree_map_with_path(_check_input, item)

  async def async_save(
      self,
      directory: epath.Path,
      item: Optional[PyTree] = None,
      save_args: Optional[PyTree] = None,
      args: Optional[StandardSaveArgs] = None,
  ) -> Optional[List[future.Future]]:
    """Saves a PyTree of array-like objects.

    See :py:class:`.PyTreeCheckpointHandler`.

    Args:
      directory: path to the directory where the checkpoint will be saved.
      item: Deprecated, use `args`.
      save_args: Deprecated, use `args`.
      args: `StandardSaveArgs` (see below).

    Returns:
      A list of futures that will be completed when the save is complete.
    """
    if isinstance(item, CheckpointArgs):
      raise ValueError(
          'Make sure to specify kwarg name `args=` when providing'
          ' `StandardSaveArgs`.'
      )
    custom_metadata = None
    if args is not None:
      item = args.item
      save_args = args.save_args
      custom_metadata = args.custom_metadata

    item = self._validate_save_state(item, save_args=save_args)
    return await self._impl.async_save(
        directory,
        args=pytree_checkpoint_handler.PyTreeSaveArgs(
            item=item,
            save_args=save_args,
            custom_metadata=custom_metadata,
        ),
    )

  def save(self, directory: epath.Path, *args, **kwargs):
    """Saves the provided item synchronously."""

    async def async_save(*args, **kwargs):
      commit_futures = await self.async_save(*args, **kwargs)  # pytype: disable=bad-return-type
      # Futures are already running, so sequential waiting is equivalent to
      # concurrent waiting.
      if commit_futures:  # May be None.
        for f in commit_futures:
          f.result()  # Block on result.

    asyncio_utils.run_sync(async_save(directory, *args, **kwargs))

  def restore(
      self,
      directory: epath.Path,
      item: Optional[PyTree] = None,
      args: Optional[StandardRestoreArgs] = None,
  ) -> PyTree:
    """Restores a PyTree. See :py:class:`.PyTreeCheckpointHandler`.

    Example::

      ckptr = StandardCheckpointer()
      item = {
          'layer0': {
              'w': jax.Array(...),
              'b': np.ndarray(...),
          },
      }
      ckptr.save(dir, StandardSaveArgs(item))

      target = {
          'layer0': {
              'w': jax.ShapeDtypeStruct(...),
              'b': jax.Array(...),
          },
      }
      ckptr.restore(dir, StandardRestoreArgs(target))

    Args:
      directory: path from which to restore.
      item: Deprecated, use `args`.
      args: `StandardRestoreArgs` (see below).

    Returns:
      a restored PyTree.
    """
    if isinstance(item, CheckpointArgs):
      raise ValueError(
          'Make sure to specify kwarg name `args=` when providing'
          ' `StandardRestoreArgs`.'
      )
    if not args:
      args = StandardRestoreArgs(item=item)
    if args.item is not None:
      self._validate_restore_state(args.item)
      restore_args = checkpoint_utils.construct_restore_args(
          args.item, support_layout=args.support_layout
      )
    else:
      logging.warning(
          '`StandardCheckpointHandler` expects a target tree to be provided for'
          ' restore. Not doing so is generally UNSAFE unless you know the'
          ' present topology to be the same one as the checkpoint was saved'
          ' under.'
      )
      restore_args = _construct_restore_args(
          self.metadata(directory),
          args.fallback_sharding,
      )

    def _replace_strict(
        arg: pytree_checkpoint_handler.RestoreArgs,
    ) -> pytree_checkpoint_handler.RestoreArgs:
      if hasattr(arg, 'strict'):
        return dataclasses.replace(arg, strict=False)
      return arg

    if not args.strict:
      restore_args = jax.tree.map(_replace_strict, restore_args)
    return self._impl.restore(
        directory,
        args=pytree_checkpoint_handler.PyTreeRestoreArgs(
            item=args.item, restore_args=restore_args
        ),
    )

  def metadata(self, directory: epath.Path) -> tree_metadata.TreeMetadata:
    """Returns metadata about the saved item."""
    return self._impl.metadata(directory)

  def finalize(self, directory: epath.Path) -> None:
    self._impl.finalize(directory)

  def close(self):
    self._impl.close()


@register_with_handler(StandardCheckpointHandler, for_save=True)
@dataclasses.dataclass
class StandardSaveArgs(CheckpointArgs):
  """Parameters for saving a standard PyTree.

  Also see :py:class:`.PyTreeSave` for additional options.

  Attributes:
    item (required): a PyTree to be saved.
    save_args: a PyTree with the same structure of `item`, which consists of
      `ocp.SaveArgs` objects as values. `None` can be used for values where no
      `SaveArgs` are specified.
    custom_metadata: User-provided custom metadata. An arbitrary
      JSON-serializable dictionary the user can use to store additional
      information. The field is treated as opaque by Orbax.
  """

  item: PyTree
  save_args: Optional[PyTree] = None
  custom_metadata: tree_types.JsonType | None = None

  def __post_init__(self):
    if isinstance(self.item, tree_metadata.TreeMetadata):
      raise ValueError('Cannot save TreeMetadata.')


def _construct_restore_args(
    target: tree_metadata.TreeMetadata,
    fallback_sharding: Optional[jax.sharding.Sharding],
) -> PyTree:
  """Creates restore_args given a target TreeMetadata with sharding overrides if required.

  Overrides the sharding in `target` with `default_sharding` if the sharding
  in `target` is not compatible with the current device mesh.

  Args:
    target: The returned TreeMetadata will match the structure of `target`.
    fallback_sharding: If provided, this sharding is used as fallback if the
      sharding in `target` fails to load from the checkpoint.

  Returns:
    A PyTree matching target of RestoreArgs (or ArrayRestoreArgs) objects.
  """
  if fallback_sharding is None:
    return checkpoint_utils.construct_restore_args(target)

  def _maybe_override_sharding(value):
    if (
        isinstance(value, value_metadata.ArrayMetadata)
        and value.sharding is not None
    ):
      try:
        return value.sharding.to_jax_sharding()
      except ValueError:
        return fallback_sharding
    return None

  sharding_tree = jax.tree.map(_maybe_override_sharding, target)
  return checkpoint_utils.construct_restore_args(target, sharding_tree)


@register_with_handler(StandardCheckpointHandler, for_restore=True)
@dataclasses.dataclass
class StandardRestoreArgs(CheckpointArgs):
  """Parameters for restoring a standard PyTree.

  Also see :py:class:`.PyTreeRestore` for additional options.

  Attributes (all optional):
    item: target PyTree. Currently non-optional. Values may be either real
        array or scalar values, or they may be jax.ShapeDtypeStruct, or
        `ocp.metadata.value.Metadata` objects (which come from calling the
        `metadata` method). If real values are provided,
        that value will be restored as the given type, with
        the given properties. If jax.ShapeDtypeStruct is provided, the value
        will be restored as np.ndarray, unless `sharding` is specified. If
        `item` is a custom PyTree class, the tree will be restored with the
        same structure as provided. If not provided, restores as a serialized
        nested dict representation of the custom class.
        `TreeMetadata` is also allowed as the tree used to
        define the restored structure.
    strict: if False, restoration allows silent truncating/padding of arrays if
        the stored array shape does not match the target shape. Otherwise,
        raises an error.
    support_layout: if True, restores with the layouts in `item`.
    fallback_sharding: If provided, this sharding will be used as a fallback
        if the saved sharding fails to load from the checkpoint.
  """

  item: Optional[PyTree] = None
  strict: bool = True
  support_layout: bool = False
  fallback_sharding: Optional[jax.sharding.Sharding] = None

  def __post_init__(self):
    if isinstance(self.item, tree_metadata.TreeMetadata):
      self.item = self.item.tree
