text: import collections.abc as collections
  import numbers
  import os
  import stat
  import tempfile
  import warnings
  from abc import ABCMeta, abstractmethod
  from collections import OrderedDict
  from typing import IO, Any, Callable, Dict, List, Mapping, NamedTuple, Optional, Tuple, Union
  
  import torch
  import torch.nn as nn
  
  import ignite.distributed as idist
  from ignite.base import Serializable
  from ignite.engine import Engine, Events
  
  __all__ = ["Checkpoint", "DiskSaver", "ModelCheckpoint", "BaseSaveHandler"]
  
  
  class BaseSaveHandler(metaclass=ABCMeta):
      """Base class for save handlers
  
      Methods to override:
  
      - :meth:`~ignite.handlers.checkpoint.BaseSaveHandler.__call__`
      - :meth:`~ignite.handlers.checkpoint.BaseSaveHandler.remove`
  
  
      Note:
          In derived class, please, make sure that in distributed configuration overridden methods are called by a single
          process. Distributed configuration on XLA devices should be treated slightly differently: for saving checkpoint
          with `xm.save() <https://pytorch.org/xla/release/1.5/index.html#torch_xla.core.xla_model.save>`_  all processes
          should pass into the function. Otherwise, application gets stuck.
  
      """
  
      @abstractmethod
      def __call__(self, checkpoint: Mapping, filename: str, metadata: Optional[Mapping] = None) -> None:
          """Method to save `checkpoint` with `filename`. Additionally, metadata dictionary is provided.
  
          Metadata contains:
  
          - `basename`: file prefix (if provided) with checkpoint name, e.g. `epoch_checkpoint`.
          - `score_name`: score name if provided, e.g `val_acc`.
          - `priority`: checkpoint priority value (higher is better), e.g. `12` or `0.6554435`
  
          Args:
              checkpoint: checkpoint dictionary to save.
              filename: filename associated with checkpoint.
              metadata: metadata on checkpoint to save.
  
          """
  
      @abstractmethod
      def remove(self, filename: str) -> None:
          """Method to remove saved checkpoint.
  
          Args:
              filename: filename associated with checkpoint.
  
          """
  
  
  class Checkpoint(Serializable):
      """Checkpoint handler can be used to periodically save and load objects which have attribute
      ``state_dict/load_state_dict``. This class can use specific save handlers to store on the disk or a cloud
      storage, etc. The Checkpoint handler (if used with :class:`~ignite.handlers.DiskSaver`) also handles automatically
      moving data on TPU to CPU before writing the checkpoint.
  
      Args:
          to_save: Dictionary with the objects to save. Objects should have implemented ``state_dict`` and
              ``load_state_dict`` methods. If contains objects of type torch `DistributedDataParallel`_ or
              `DataParallel`_, their internal wrapped model is automatically saved (to avoid additional key ``module.`` in
              the state dictionary).
          save_handler: String, method or callable class
              used to save engine and other provided objects. Function receives two objects: checkpoint as a dictionary
              and filename. If ``save_handler`` is callable class, it can
              inherit of :class:`~ignite.handlers.checkpoint.BaseSaveHandler` and optionally implement ``remove`` method
              to keep a fixed number of saved checkpoints. In case if user needs to save engine's checkpoint on a disk,
              ``save_handler`` can be defined with :class:`~ignite.handlers.DiskSaver` or a string specifying
              directory name can be passed to ``save_handler``.
          filename_prefix: Prefix for the file name to which objects will be saved. See Note for details.
          score_function: If not None, it should be a function taking a single argument,
              :class:`~ignite.engine.engine.Engine` object, and returning a score (`float`). Objects with highest scores
              will be retained.
          score_name: If ``score_function`` not None, it is possible to store its value using
              ``score_name``. If ``score_function`` is None, ``score_name`` can be used alone to define ``score_function``
              as ``Checkpoint.get_default_score_fn(score_name)`` by default.
          n_saved: Number of objects that should be kept on disk. Older files will be removed. If set to
              `None`, all objects are kept.
          global_step_transform: global step transform function to output a desired global step.
              Input of the function is ``(engine, event_name)``. Output of function should be an integer.
              Default is None, global_step based on attached engine. If provided, uses function output as global_step.
              To setup global step from another engine, please use :meth:`~ignite.handlers.global_step_from_engine`.
          filename_pattern: If ``filename_pattern`` is provided, this pattern will be used to render
              checkpoint filenames. If the pattern is not defined, the default pattern would be used. See Note for
              details.
          include_self: Whether to include the `state_dict` of this object in the checkpoint. If `True`, then
              there must not be another object in ``to_save`` with key ``checkpointer``.
          greater_or_equal: if `True`, the latest equally scored model is stored. Otherwise, the first model.
              Default, `False`.
  
      .. _DistributedDataParallel: https://pytorch.org/docs/stable/generated/
          torch.nn.parallel.DistributedDataParallel.html
      .. _DataParallel: https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html
  
      Note:
          This class stores a single file as a dictionary of provided objects to save.
          The filename is defined by ``filename_pattern`` and by default has the following
          structure: ``{filename_prefix}_{name}_{suffix}.{ext}`` where
  
          - ``filename_prefix`` is the argument passed to the constructor,
          - `name` is the key in ``to_save`` if a single object is to store, otherwise `name` is "checkpoint".
          - `suffix` is composed as following ``{global_step}_{score_name}={score}``.
  
      +----------------+------------+-----------------------+----------------------------------------------+
      | score_function | score_name | global_step_transform |  suffix                                      |
      +================+============+=======================+==============================================+
      |      None      |   None     |        None           | ``{engine.state.iteration}``                 |
      +----------------+------------+-----------------------+----------------------------------------------+
      |       X        |   None     |        None           | ``{score}``                                  |
      +----------------+------------+-----------------------+----------------------------------------------+
      |       X        |   None     |         X             | ``{global_step}_{score}``                    |
      +----------------+------------+-----------------------+----------------------------------------------+
      |       X        |    X       |         X             | ``{global_step}_{score_name}={score}``       |
      +----------------+------------+-----------------------+----------------------------------------------+
      |      None      |   None     |         X             | ``{global_step}``                            |
      +----------------+------------+-----------------------+----------------------------------------------+
      |       X        |    X       |        None           | ``{score_name}={score}``                     |
      +----------------+------------+-----------------------+----------------------------------------------+
  
      Above `global_step` defined by the output of `global_step_transform` and `score` defined by the output
      of `score_function`.
  
      By default, none of ``score_function``, ``score_name``, ``global_step_transform`` is defined, then suffix is
      setup by attached engine's current iteration. The filename will be
      `{filename_prefix}_{name}_{engine.state.iteration}.{ext}`.
  
      For example, ``score_name="neg_val_loss"`` and ``score_function`` that returns `-loss` (as objects with highest
      scores will be retained), then saved filename will be ``{filename_prefix}_{name}_neg_val_loss=-0.1234.pt``.
  
      Note:
          If ``filename_pattern`` is given, it will be used to render the filenames. ``filename_pattern`` is a string
          that can contain ``{filename_prefix}``, ``{name}``, ``{score}``, ``{score_name}`` and ``{global_step}`` as
          templates.
  
          For example, let ``filename_pattern="{global_step}-{name}-{score}.pt"`` then the saved filename will be
          ``30000-checkpoint-94.pt``
  
          **Warning:** Please, keep in mind that if filename collide with already used one to saved a checkpoint,
          new checkpoint will replace the older one. This means that filename like ``checkpoint.pt`` will be saved
          every call and will always be overwritten by newer checkpoints.
  
      Note:
          To get the last stored filename, handler exposes attribute ``last_checkpoint``:
  
          .. code-block:: python
  
              handler = Checkpoint(...)
              ...
              print(handler.last_checkpoint)
              > checkpoint_12345.pt
  
      Note:
          This class is distributed configuration-friendly: it is not required to instantiate the class in rank 0 only
          process. This class supports automatically distributed configuration and if used with
          :class:`~ignite.handlers.DiskSaver`, checkpoint is stored by rank 0 process.
  
      .. warning::
  
          When running on XLA devices, it should be run in all processes, otherwise application can get stuck on
          saving the checkpoint.
  
          .. code-block:: python
  
              # Wrong:
              # if idist.get_rank() == 0:
              #     handler = Checkpoint(...)
              #     trainer.add_event_handler(Events.ITERATION_COMPLETED(every=1000), handler)
  
              # Correct:
              handler = Checkpoint(...)
              trainer.add_event_handler(Events.ITERATION_COMPLETED(every=1000), handler)
  
      Examples:
          Attach the handler to make checkpoints during training:
  
          .. code-block:: python
  
              from ignite.engine import Engine, Events
              from ignite.handlers import Checkpoint
  
              trainer = ...
              model = ...
              optimizer = ...
              lr_scheduler = ...
  
              to_save = {'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler, 'trainer': trainer}
  
              if (checkpoint_iters):
                  # A: Output is "checkpoint_<iteration>.pt"
                  handler = Checkpoint(
                      to_save, '/tmp/models', n_saved=2
                  )
                  trainer.add_event_handler(Events.ITERATION_COMPLETED(every=1000), handler)
              else:
                  # B:Output is "checkpoint_<epoch>.pt"
                  gst = lambda *_: trainer.state.epoch
                  handler = Checkpoint(
                      to_save, '/tmp/models', n_saved=2, global_step_transform=gst
                  )
                  trainer.add_event_handler(Events.EPOCH_COMPLETED, handler)
  
              trainer.run(data_loader, max_epochs=6)
              > A: ["checkpoint_7000.pt", "checkpoint_8000.pt", ]
              > B: ["checkpoint_5.pt", "checkpoint_6.pt", ]
  
          Attach the handler to an evaluator to save best model during the training
          according to computed validation metric:
  
          .. code-block:: python
  
              from ignite.engine import Engine, Events
              from ignite.handlers import Checkpoint, global_step_from_engine
  
              trainer = ...
              evaluator = ...
              # Setup Accuracy metric computation on evaluator.
              # evaluator.state.metrics contain 'accuracy',
              # which will be used to define ``score_function`` automatically.
              # Run evaluation on epoch completed event
              # ...
  
              to_save = {'model': model}
              handler = Checkpoint(
                  to_save, '/tmp/models',
                  n_saved=2, filename_prefix='best',
                  score_name="accuracy",
                  global_step_transform=global_step_from_engine(trainer)
              )
  
              evaluator.add_event_handler(Events.COMPLETED, handler)
  
              trainer.run(data_loader, max_epochs=10)
              > ["best_model_9_accuracy=0.77.pt", "best_model_10_accuracy=0.78.pt", ]
  
          Customise the ``save_handler``:
  
          .. code-block:: python
  
              handler = Checkpoint(
                  to_save, save_handler=DiskSaver('/tmp/models', create_dir=True, **kwargs), n_saved=2
              )
  
      .. versionchanged:: 0.4.3
  
          - Checkpoint can save model with same filename.
          - Added ``greater_or_equal`` argument.
  
      .. versionchanged:: 0.5.0
  
          - `score_name` can be used to define `score_function` automatically without providing `score_function`.
          - `save_handler` automatically saves to disk if path to directory is provided.
      """
  
      Item = NamedTuple("Item", [("priority", int), ("filename", str)])
      _state_dict_all_req_keys = ("saved",)
  
      def __init__(
          self,
          to_save: Mapping,
          save_handler: Union[str, Callable, BaseSaveHandler],
          filename_prefix: str = "",
          score_function: Optional[Callable] = None,
          score_name: Optional[str] = None,
          n_saved: Optional[int] = 1,
          global_step_transform: Optional[Callable] = None,
          filename_pattern: Optional[str] = None,
          include_self: bool = False,
          greater_or_equal: bool = False,
      ):
  
          if not isinstance(to_save, collections.Mapping):
              raise TypeError(f"Argument `to_save` should be a dictionary, but given {type(to_save)}")
  
          self._check_objects(to_save, "state_dict")
  
          if include_self:
              if not isinstance(to_save, collections.MutableMapping):
                  raise TypeError(
                      f"If `include_self` is True, then `to_save` must be mutable, but given {type(to_save)}."
                  )
  
              if "checkpointer" in to_save:
                  raise ValueError(f"Cannot have key 'checkpointer' if `include_self` is True: {to_save}")
  
          if not (isinstance(save_handler, str) or callable(save_handler) or isinstance(save_handler, BaseSaveHandler)):
              raise TypeError("Argument `save_handler` should be a string or callable or inherit from BaseSaveHandler")
  
          if global_step_transform is not None and not callable(global_step_transform):
              raise TypeError(f"global_step_transform should be a function, got {type(global_step_transform)} instead.")
  
          self.to_save = to_save
          self.filename_prefix = filename_prefix
          if isinstance(save_handler, str):
              self.save_handler = DiskSaver(save_handler, create_dir=True)
          else:
              self.save_handler = save_handler  # type: ignore
          self.score_function = score_function
          self.score_name = score_name
          if self.score_name is not None and self.score_function is None:
              self.score_function = self.get_default_score_fn(self.score_name)
          self.n_saved = n_saved
          self.ext = "pt"
          self.global_step_transform = global_step_transform
          self.filename_pattern = filename_pattern
          self._saved = []  # type: List["Checkpoint.Item"]
          self.include_self = include_self
          self.greater_or_equal = greater_or_equal
  
      def reset(self) -> None:
          """Method to reset saved checkpoint names.
  
          Use this method if the engine will independently run multiple times:
  
          .. code-block:: python
  
              from ignite.handlers import Checkpoint
  
              trainer = ...
              checkpointer = Checkpoint(...)
  
              trainer.add_event_handler(Events.COMPLETED, checkpointer)
              trainer.add_event_handler(Events.STARTED, checkpointer.reset)
  
              # fold 0
              trainer.run(data0, max_epochs=max_epochs)
              print("Last checkpoint:", checkpointer.last_checkpoint)
  
              # fold 1
              trainer.run(data1, max_epochs=max_epochs)
              print("Last checkpoint:", checkpointer.last_checkpoint)
  
          .. versionadded:: 0.4.3
          """
          self._saved = []
  
      @property
      def last_checkpoint(self) -> Optional[str]:
          if len(self._saved) < 1:
              return None
          return self._saved[-1].filename
  
      def _check_lt_n_saved(self, or_equal: bool = False) -> bool:
          if self.n_saved is None:
              return True
          return len(self._saved) < self.n_saved + int(or_equal)
  
      def _compare_fn(self, new: Union[int, float]) -> bool:
          if self.greater_or_equal:
              return new >= self._saved[0].priority
          else:
              return new > self._saved[0].priority
  
      def __call__(self, engine: Engine) -> None:
  
          global_step = None
          if self.global_step_transform is not None:
              global_step = self.global_step_transform(engine, engine.last_event_name)
  
          if self.score_function is not None:
              priority = self.score_function(engine)
              if not isinstance(priority, numbers.Number):
                  raise ValueError("Output of score_function should be a number")
          else:
              if global_step is None:
                  global_step = engine.state.get_event_attrib_value(Events.ITERATION_COMPLETED)
              priority = global_step
  
          if self._check_lt_n_saved() or self._compare_fn(priority):
  
              priority_str = f"{priority}" if isinstance(priority, numbers.Integral) else f"{priority:.4f}"
  
              checkpoint = self._setup_checkpoint()
  
              name = "checkpoint"
              if len(checkpoint) == 1:
                  for k in checkpoint:
                      name = k
                  checkpoint = checkpoint[name]
  
              if self.filename_pattern is None:
                  filename_pattern = self.setup_filename_pattern(
                      with_prefix=len(self.filename_prefix) > 0,
                      with_score=self.score_function is not None,
                      with_score_name=self.score_name is not None,
                      with_global_step=global_step is not None,
                  )
              else:
                  filename_pattern = self.filename_pattern
  
              filename_dict = {
                  "filename_prefix": self.filename_prefix,
                  "ext": self.ext,
                  "name": name,
                  "score_name": self.score_name,
                  "score": priority_str if (self.score_function is not None) else None,
                  "global_step": global_step,
              }
              filename = filename_pattern.format(**filename_dict)
  
              metadata = {
                  "basename": f"{self.filename_prefix}{'_' * int(len(self.filename_prefix) > 0)}{name}",
                  "score_name": self.score_name,
                  "priority": priority,
              }
  
              try:
                  index = list(map(lambda it: it.filename == filename, self._saved)).index(True)
                  to_remove = True
              except ValueError:
                  index = 0
                  to_remove = not self._check_lt_n_saved()
  
              if to_remove:
                  item = self._saved.pop(index)
                  if isinstance(self.save_handler, BaseSaveHandler):
                      self.save_handler.remove(item.filename)
  
              self._saved.append(Checkpoint.Item(priority, filename))
              self._saved.sort(key=lambda it: it[0])
  
              if self.include_self:
                  # Now that we've updated _saved, we can add our own state_dict.
                  checkpoint["checkpointer"] = self.state_dict()
  
              try:
                  self.save_handler(checkpoint, filename, metadata)
              except TypeError:
                  self.save_handler(checkpoint, filename)
  
      def _setup_checkpoint(self) -> Dict[str, Dict[Any, Any]]:
          checkpoint = {}
          if self.to_save is not None:
              for k, obj in self.to_save.items():
                  if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)):
                      obj = obj.module
                  checkpoint[k] = obj.state_dict()
          return checkpoint
  
      @staticmethod
      def setup_filename_pattern(
          with_prefix: bool = True, with_score: bool = True, with_score_name: bool = True, with_global_step: bool = True
      ) -> str:
          """Helper method to get the default filename pattern for a checkpoint.
  
          Args:
              with_prefix: If True, the ``filename_prefix`` is added to the filename pattern:
                  ``{filename_prefix}_{name}...``. Default, True.
              with_score: If True, ``score`` is added to the filename pattern: ``..._{score}.{ext}``.
                  Default, True. At least one of ``with_score`` and ``with_global_step`` should be True.
              with_score_name: If True, ``score_name`` is added to the filename pattern:
                  ``..._{score_name}={score}.{ext}``. If activated, argument ``with_score`` should be
                  also True, otherwise an error is raised. Default, True.
              with_global_step: If True, ``{global_step}`` is added to the
                  filename pattern: ``...{name}_{global_step}...``.
                  At least one of ``with_score`` and ``with_global_step`` should be True.
  
          Examples:
              .. code-block:: python
  
                  from ignite.handlers import Checkpoint
  
                  filename_pattern = Checkpoint.setup_filename_pattern()
  
                  print(filename_pattern)
                  > "{filename_prefix}_{name}_{global_step}_{score_name}={score}.{ext}"
  
          .. versionadded:: 0.4.3
          """
          filename_pattern = "{name}"
  
          if not (with_global_step or with_score):
              raise ValueError("At least one of with_score and with_global_step should be True.")
  
          if with_global_step:
              filename_pattern += "_{global_step}"
  
          if with_score_name and with_score:
              filename_pattern += "_{score_name}={score}"
          elif with_score:
              filename_pattern += "_{score}"
          elif with_score_name:
              raise ValueError("If with_score_name is True, with_score should be also True")
  
          if with_prefix:
              filename_pattern = "{filename_prefix}_" + filename_pattern
  
          filename_pattern += ".{ext}"
          return filename_pattern
  
      @staticmethod
      def _check_objects(objs: Mapping, attr: str) -> None:
          for k, obj in objs.items():
              if not hasattr(obj, attr):
                  raise TypeError(f"Object {type(obj)} should have `{attr}` method")
  
      @staticmethod
      def load_objects(to_load: Mapping, checkpoint: Union[str, Mapping], **kwargs: Any) -> None:
          """Helper method to apply ``load_state_dict`` on the objects from ``to_load`` using states from ``checkpoint``.
  
          Args:
              to_load: a dictionary with objects, e.g. `{"model": model, "optimizer": optimizer, ...}`
              checkpoint: a string filepath or a dictionary with state_dicts to load, e.g. `{"model": model_state_dict,
                  "optimizer": opt_state_dict}`. If `to_load` contains a single key, then checkpoint can contain
                  directly corresponding state_dict.
              kwargs: Keyword arguments accepted for `nn.Module.load_state_dict()`. Passing `strict=False` enables
                  the user to load part of the pretrained model (useful for example, in Transfer Learning)
  
          Examples:
              .. code-block:: python
  
                  import torch
                  from ignite.engine import Engine, Events
                  from ignite.handlers import ModelCheckpoint, Checkpoint
                  trainer = Engine(lambda engine, batch: None)
                  handler = ModelCheckpoint('/tmp/models', 'myprefix', n_saved=None, create_dir=True)
                  model = torch.nn.Linear(3, 3)
                  optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
                  to_save = {"weights": model, "optimizer": optimizer}
                  trainer.add_event_handler(Events.EPOCH_COMPLETED(every=2), handler, to_save)
                  trainer.run(torch.randn(10, 1), 5)
  
                  to_load = to_save
                  checkpoint_fp = "/tmp/models/myprefix_checkpoint_40.pth"
                  checkpoint = torch.load(checkpoint_fp)
                  Checkpoint.load_objects(to_load=to_load, checkpoint=checkpoint)
  
                  # or using a string for checkpoint filepath
  
                  to_load = to_save
                  checkpoint_fp = "/tmp/models/myprefix_checkpoint_40.pth"
                  Checkpoint.load_objects(to_load=to_load, checkpoint=checkpoint_fp)
  
          Note:
              If ``to_load`` contains objects of type torch `DistributedDataParallel`_ or
              `DataParallel`_, method ``load_state_dict`` will applied to their internal wrapped model (``obj.module``).
  
          .. _DistributedDataParallel: https://pytorch.org/docs/stable/generated/
              torch.nn.parallel.DistributedDataParallel.html
          .. _DataParallel: https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html
          """
  
          if isinstance(checkpoint, str):
              checkpoint_obj = torch.load(checkpoint)
          else:
              checkpoint_obj = checkpoint
  
          Checkpoint._check_objects(to_load, "load_state_dict")
          if not isinstance(checkpoint, (collections.Mapping, str)):
              raise TypeError(f"Argument checkpoint should be a string or a dictionary, but given {type(checkpoint)}")
  
          if len(kwargs) > 1 or any(k for k in kwargs if k not in ["strict"]):
              warnings.warn("kwargs contains keys other than strict and these will be ignored")
  
          is_state_dict_strict = kwargs.get("strict", True)
          if len(to_load) == 1:
              # single object and checkpoint is directly a state_dict
              key, obj = list(to_load.items())[0]
              if key not in checkpoint_obj:
                  if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)):
                      obj = obj.module
                  obj.load_state_dict(checkpoint_obj, strict=is_state_dict_strict)
                  return
  
          # multiple objects to load
          for k, obj in to_load.items():
              if k not in checkpoint_obj:
                  raise ValueError(f"Object labeled by '{k}' from `to_load` is not found in the checkpoint")
              if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)):
                  obj = obj.module
              if isinstance(obj, torch.nn.Module):
                  obj.load_state_dict(checkpoint_obj[k], strict=is_state_dict_strict)
              else:
                  obj.load_state_dict(checkpoint_obj[k])
  
      def state_dict(self) -> "OrderedDict[str, List[Tuple[int, str]]]":
          """Method returns state dict with saved items: list of ``(priority, filename)`` pairs.
          Can be used to save internal state of the class.
          """
          return OrderedDict([("saved", [(p, f) for p, f in self._saved])])
  
      def load_state_dict(self, state_dict: Mapping) -> None:
          """Method replace internal state of the class with provided state dict data.
  
          Args:
              state_dict: a dict with "saved" key and list of ``(priority, filename)`` pairs as values.
          """
          super().load_state_dict(state_dict)
          self._saved = [Checkpoint.Item(p, f) for p, f in state_dict["saved"]]
  
      @staticmethod
      def get_default_score_fn(metric_name: str, score_sign: float = 1.0) -> Callable:
          """Helper method to get default score function based on the metric name.
  
          Args:
              metric_name: metric name to get the value from ``engine.state.metrics``.
                  Engine is the one to which :class:`~ignite.handlers.checkpoint.Checkpoint` handler is added.
              score_sign: sign of the score: 1.0 or -1.0. For error-like metrics, e.g. smaller is better,
                  a negative score sign should be used (objects with larger score are retained). Default, 1.0.
  
          Examples:
              .. code-block:: python
  
                  from ignite.handlers import Checkpoint
  
                  best_acc_score = Checkpoint.get_default_score_fn("accuracy")
  
                  best_model_handler = Checkpoint(
                      to_save, save_handler, score_name="val_accuracy", score_function=best_acc_score
                  )
                  evaluator.add_event_handler(Events.COMPLETED, best_model_handler)
  
              Usage with error-like metric:
  
              .. code-block:: python
  
                  from ignite.handlers import Checkpoint
  
                  neg_loss_score = Checkpoint.get_default_score_fn("loss", -1.0)
  
                  best_model_handler = Checkpoint(
                      to_save, save_handler, score_name="val_neg_loss", score_function=neg_loss_score
                  )
                  evaluator.add_event_handler(Events.COMPLETED, best_model_handler)
  
          .. versionadded:: 0.4.3
          """
          if score_sign not in (1.0, -1.0):
              raise ValueError("Argument score_sign should be 1 or -1")
  
          def wrapper(engine: Engine) -> float:
              return score_sign * engine.state.metrics[metric_name]
  
          return wrapper
  
  
  class DiskSaver(BaseSaveHandler):
      """Handler that saves input checkpoint on a disk.
  
      Args:
          dirname: Directory path where the checkpoint will be saved
          atomic: if True, checkpoint is serialized to a temporary file, and then
              moved to final destination, so that files are guaranteed to not be damaged
              (for example if exception occurs during saving).
          create_dir: if True, will create directory ``dirname`` if it doesnt exist.
          require_empty: If True, will raise exception if there are any files in the
              directory ``dirname``.
          kwargs: Accepted keyword arguments for `torch.save` or `xm.save`.
  
      .. versionchanged:: 0.4.2
          Accept ``kwargs`` for `torch.save` or `xm.save`.
      """
  
      def __init__(
          self, dirname: Union[str, Path], atomic: bool = True, create_dir: bool = True, require_empty: bool = True, **kwargs: Any
      ):
          self.dirname = os.path.expanduser(dirname)
          self._atomic = atomic
          self._check_and_setup(dirname, create_dir, require_empty)
          self.kwargs = kwargs
  
      @staticmethod
      @idist.one_rank_only()
      def _check_and_setup(dirname: Union[str, Path], create_dir: bool, require_empty: bool) -> None:
          if create_dir:
              if not Path(dirname).exists():
                  Path.mkdir(dirname, parents=True)
          # Ensure that dirname exists
          if not Path(dirname).exists():
              raise ValueError(f"Directory path '{dirname}' is not found")
  
          if require_empty:
              matched = [fname for fname in os.listdir(dirname) if fname.endswith(".pt")]
              if len(matched) > 0:
                  raise ValueError(
                      f"Files {matched} with extension '.pt' are already present "
                      f"in the directory {dirname}. If you want to use this "
                      "directory anyway, pass `require_empty=False`."
                      ""
                  )
  
      def __call__(self, checkpoint: Mapping, x: str, metadata: Optional[Mapping] = None) -> None:
          path = Path(self.dirname, filename).exists()
  
          if idist.has_xla_support:
              self._save_xla(checkpoint, path)
          else:
              self._save_native(checkpoint, path)
  
      @idist.one_rank_only()
      def _save_native(self, checkpoint: Mapping, path: str) -> None:
          self._save_func(checkpoint, path, torch.save)
  
      def _save_xla(self, checkpoint: Mapping, path: str) -> None:
          import torch_xla.core.xla_model as xm
  
          # all tpu procs should enter here as internally performs sync across device
          self._save_func(checkpoint, path, xm.save, rank=idist.get_rank())
  
      def _save_func(self, checkpoint: Mapping, path: str, func: Callable, rank: int = 0) -> None:
          if not self._atomic:
              func(checkpoint, path, **self.kwargs)
          else:
              tmp_file = None
              tmp_name = ""
              tmp: Optional[IO[bytes]] = None
              if rank == 0:
                  tmp = tempfile.NamedTemporaryFile(delete=False, dir=self.dirname)
                  tmp_file = tmp.file  # type: ignore
                  tmp_name = tmp.name
              try:
                  func(checkpoint, tmp_file, **self.kwargs)
              except BaseException:
                  if tmp is not None:
                      tmp.close()
                      os.remove(tmp_name)
                      raise
              else:
                  if tmp is not None:
                      tmp.close()
                      os.replace(tmp.name, path)
                      # append group/others read mode
                      os.chmod(path, os.stat(path).st_mode | stat.S_IRGRP | stat.S_IROTH)
  
      @idist.one_rank_only()
      def remove(self, filename: str) -> None:
          path = Path(self.dirname, filename).exists()
          os.remove(path)
  
  
  class ModelCheckpoint(Checkpoint):
      """ModelCheckpoint handler can be used to periodically save objects to disk only. If needed to store checkpoints to
      another storage type, please consider :class:`~ignite.handlers.checkpoint.Checkpoint`.
  
      This handler expects two arguments:
  
          - an :class:`~ignite.engine.engine.Engine` object
          - a `dict` mapping names (`str`) to objects that should be saved to disk.
  
      See Examples for further details.
  
      .. warning::
  
          Behaviour of this class has been changed since v0.3.0.
  
          There is no more internal counter that has been used to indicate the number of save actions. User could
          see its value `step_number` in the filename, e.g. `{filename_prefix}_{name}_{step_number}.pt`. Actually,
          `step_number` is replaced by current engine's epoch if `score_function` is specified and current iteration
          otherwise.
  
          A single `pt` file is created instead of multiple files.
  
      Args:
          dirname: Directory path where objects will be saved.
          filename_prefix: Prefix for the file names to which objects will be saved. See Notes of
              :class:`~ignite.handlers.checkpoint.Checkpoint` for more details.
          score_function: if not None, it should be a function taking a single argument, an
              :class:`~ignite.engine.engine.Engine` object, and return a score (`float`). Objects with highest scores
              will be retained.
          score_name: if ``score_function`` not None, it is possible to store its value using
              `score_name`. See Notes for more details.
          n_saved: Number of objects that should be kept on disk. Older files will be removed. If set to
              `None`, all objects are kept.
          atomic: If True, objects are serialized to a temporary file, and then moved to final
              destination, so that files are guaranteed to not be damaged (for example if exception
              occurs during saving).
          require_empty: If True, will raise exception if there are any files starting with
              ``filename_prefix`` in the directory ``dirname``.
          create_dir: If True, will create directory ``dirname`` if it does not exist.
          global_step_transform: global step transform function to output a desired global step.
              Input of the function is `(engine, event_name)`. Output of function should be an integer.
              Default is None, global_step based on attached engine. If provided, uses function output as global_step.
              To setup global step from another engine, please use :meth:`~ignite.handlers.global_step_from_engine`.
          include_self: Whether to include the `state_dict` of this object in the checkpoint. If `True`, then
              there must not be another object in ``to_save`` with key ``checkpointer``.
          kwargs: Accepted keyword arguments for `torch.save` or `xm.save` in `DiskSaver`.
  
      .. versionchanged:: 0.4.2
          Accept ``kwargs`` for `torch.save` or `xm.save`
  
      Examples:
          .. code-block:: python
  
              import os
              from ignite.engine import Engine, Events
              from ignite.handlers import ModelCheckpoint
              from torch import nn
              trainer = Engine(lambda engine, batch: None)
              handler = ModelCheckpoint('/tmp/models', 'myprefix', n_saved=2, create_dir=True)
              model = nn.Linear(3, 3)
              trainer.add_event_handler(Events.EPOCH_COMPLETED(every=2), handler, {'mymodel': model})
              trainer.run([0, 1, 2, 3, 4], max_epochs=6)
              os.listdir('/tmp/models')
              # ['myprefix_mymodel_20.pt', 'myprefix_mymodel_30.pt']
              handler.last_checkpoint
              # ['/tmp/models/myprefix_mymodel_30.pt']
      """
  
      def __init__(
          self,
          dirname: str,
          filename_prefix: str,
          score_function: Optional[Callable] = None,
          score_name: Optional[str] = None,
          n_saved: Union[int, None] = 1,
          atomic: bool = True,
          require_empty: bool = True,
          create_dir: bool = True,
          global_step_transform: Optional[Callable] = None,
          include_self: bool = False,
          **kwargs: Any,
      ):
  
          disk_saver = DiskSaver(dirname, atomic=atomic, create_dir=create_dir, require_empty=require_empty, **kwargs)
  
          super(ModelCheckpoint, self).__init__(
              to_save={},
              save_handler=disk_saver,
              filename_prefix=filename_prefix,
              score_function=score_function,
              score_name=score_name,
              n_saved=n_saved,
              global_step_transform=global_step_transform,
              include_self=include_self,
          )
  
      @property
      def last_checkpoint(self) -> Union[str, None]:
          if len(self._saved) < 1:
              return None
  
          if not isinstance(self.save_handler, DiskSaver):
              raise RuntimeError(
                  f"Unable to save checkpoint, save_handler should be DiskSaver, got {type(self.save_handler)}."
              )
  
          return Path(self.save_handler.dirname, self._saved[-1].filename).exists()
  
      def __call__(self, engine: Engine, to_save: Mapping):  # type: ignore
  
          if len(to_save) == 0:
              raise RuntimeError("No objects to checkpoint found.")
  
          self._check_objects(to_save, "state_dict")
          self.to_save = to_save
          super(ModelCheckpoint, self).__call__(engine)
  