sdks/python/apache_beam/ml/inference/base.py

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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.
#
# TODO: https://github.com/apache/beam/issues/21822
# mypy: ignore-errors

"""An extensible run inference transform.

Users of this module can extend the ModelHandler class for any machine learning
framework. A ModelHandler implementation is a required parameter of
RunInference.

The transform handles standard inference functionality, like metric
collection, sharing model between threads, and batching elements.
"""

import functools
import logging
import os
import pickle
import sys
import threading
import time
import uuid
from abc import ABC
from abc import abstractmethod
from collections import OrderedDict
from collections import defaultdict
from collections.abc import Callable
from collections.abc import Iterable
from collections.abc import Mapping
from collections.abc import Sequence
from copy import deepcopy
from dataclasses import dataclass
from datetime import datetime
from datetime import timedelta
from typing import Any
from typing import Generic
from typing import NamedTuple
from typing import Optional
from typing import TypeVar
from typing import Union

import apache_beam as beam
from apache_beam.io.components.adaptive_throttler import ReactiveThrottler
from apache_beam.utils import multi_process_shared
from apache_beam.utils import retry
from apache_beam.utils import shared

try:
  from apache_beam.io.components.rate_limiter import RateLimiter
except ImportError:
  RateLimiter = None

try:
  # pylint: disable=wrong-import-order, wrong-import-position
  import resource
except ImportError:
  resource = None  # type: ignore[assignment]

_NANOSECOND_TO_MILLISECOND = 1_000_000
_NANOSECOND_TO_MICROSECOND = 1_000
_MILLISECOND_TO_SECOND = 1_000

ModelT = TypeVar('ModelT')
ExampleT = TypeVar('ExampleT')
PreProcessT = TypeVar('PreProcessT')
PredictionT = TypeVar('PredictionT')
PostProcessT = TypeVar('PostProcessT')
_INPUT_TYPE = TypeVar('_INPUT_TYPE')
_OUTPUT_TYPE = TypeVar('_OUTPUT_TYPE')
KeyT = TypeVar('KeyT')


# We use NamedTuple to define the structure of the PredictionResult,
# however, as support for generic NamedTuples is not available in Python
# versions prior to 3.11, we use the __new__ method to provide default
# values for the fields while maintaining backwards compatibility.
class PredictionResult(NamedTuple('PredictionResult',
                                  [('example', _INPUT_TYPE),
                                   ('inference', _OUTPUT_TYPE),
                                   ('model_id', Optional[str])])):
  __slots__ = ()

  def __new__(cls, example, inference, model_id=None):
    return super().__new__(cls, example, inference, model_id)


PredictionResult.__doc__ = """A NamedTuple containing both input and output
  from the inference."""
PredictionResult.example.__doc__ = """The input example."""
PredictionResult.inference.__doc__ = """Results for the inference on the model
  for the given example."""
PredictionResult.model_id.__doc__ = """Model ID used to run the prediction."""


class RateLimitExceeded(RuntimeError):
  """RateLimit Exceeded to process a batch of requests."""
  pass


class ModelMetadata(NamedTuple):
  model_id: str
  model_name: str


class RunInferenceDLQ(NamedTuple):
  failed_inferences: beam.PCollection
  failed_preprocessing: Sequence[beam.PCollection]
  failed_postprocessing: Sequence[beam.PCollection]


class _ModelLoadStats(NamedTuple):
  model_tag: str
  load_latency: Optional[int]
  byte_size: Optional[int]


ModelMetadata.model_id.__doc__ = """Unique identifier for the model. This can be
    a file path or a URL where the model can be accessed. It is used to load
    the model for inference."""
ModelMetadata.model_name.__doc__ = """Human-readable name for the model. This
    can be used to identify the model in the metrics generated by the
    RunInference transform."""


def _to_milliseconds(time_ns: int) -> int:
  return int(time_ns / _NANOSECOND_TO_MILLISECOND)


def _to_microseconds(time_ns: int) -> int:
  return int(time_ns / _NANOSECOND_TO_MICROSECOND)


@dataclass(frozen=True)
class KeyModelPathMapping(Generic[KeyT]):
  """
  Dataclass for mapping 1 or more keys to 1 model path. This is used in
  conjunction with a KeyedModelHandler with many model handlers to update
  a set of keys' model handlers with the new path. Given
  `KeyModelPathMapping(keys: ['key1', 'key2'], update_path: 'updated/path',
  model_id: 'id1')`, all examples with keys `key1` or `key2` will have their
  corresponding model handler's update_model function called with
  'updated/path' and their metrics will correspond with 'id1'. For more
  information see the KeyedModelHandler documentation
  https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.KeyedModelHandler
  documentation and the website section on model updates
  https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh
  """
  keys: list[KeyT]
  update_path: str
  model_id: str = ''


class ModelHandler(Generic[ExampleT, PredictionT, ModelT]):
  """Has the ability to load and apply an ML model."""
  def __init__(
      self,
      *,
      min_batch_size: Optional[int] = None,
      max_batch_size: Optional[int] = None,
      max_batch_duration_secs: Optional[int] = None,
      max_batch_weight: Optional[int] = None,
      element_size_fn: Optional[Callable[[Any], int]] = None,
      large_model: bool = False,
      model_copies: Optional[int] = None,
      **kwargs):
    """Initializes the ModelHandler.

    Args:
      min_batch_size: the minimum batch size to use when batching inputs.
      max_batch_size: the maximum batch size to use when batching inputs.
      max_batch_duration_secs: the maximum amount of time to buffer a batch
        before emitting; used in streaming contexts.
      max_batch_weight: the maximum weight of a batch. Requires element_size_fn.
      element_size_fn: a function that returns the size (weight) of an element.
      large_model: set to true if your model is large enough to run into
        memory pressure if you load multiple copies.
      model_copies: The exact number of models that you would like loaded
        onto your machine.
      kwargs: 'env_vars' can be used to set environment variables
        before loading the model.
    """
    self._env_vars = kwargs.get('env_vars', {})
    self._batching_kwargs: dict[str, Any] = {}
    if min_batch_size is not None:
      self._batching_kwargs['min_batch_size'] = min_batch_size
    if max_batch_size is not None:
      self._batching_kwargs['max_batch_size'] = max_batch_size
    if max_batch_duration_secs is not None:
      self._batching_kwargs['max_batch_duration_secs'] = max_batch_duration_secs
    if max_batch_weight is not None:
      self._batching_kwargs['max_batch_weight'] = max_batch_weight
    if element_size_fn is not None:
      self._batching_kwargs['element_size_fn'] = element_size_fn
    self._large_model = large_model
    self._model_copies = model_copies
    self._share_across_processes = large_model or (model_copies is not None)

  def load_model(self) -> ModelT:
    """Loads and initializes a model for processing."""
    raise NotImplementedError(type(self))

  def run_inference(
      self,
      batch: Sequence[ExampleT],
      model: ModelT,
      inference_args: Optional[dict[str, Any]] = None) -> Iterable[PredictionT]:
    """Runs inferences on a batch of examples.

    Args:
      batch: A sequence of examples or features.
      model: The model used to make inferences.
      inference_args: Extra arguments for models whose inference call requires
        extra parameters.

    Returns:
      An Iterable of Predictions.
    """
    raise NotImplementedError(type(self))

  def get_num_bytes(self, batch: Sequence[ExampleT]) -> int:
    """
    Returns:
       The number of bytes of data for a batch.
    """
    return len(pickle.dumps(batch))

  def get_metrics_namespace(self) -> str:
    """
    Returns:
       A namespace for metrics collected by the RunInference transform.
    """
    return 'RunInference'

  def get_resource_hints(self) -> dict:
    """
    Returns:
       Resource hints for the transform.
    """
    return {}

  def batch_elements_kwargs(self) -> Mapping[str, Any]:
    """
    Returns:
       kwargs suitable for beam.BatchElements.
    """
    return getattr(self, '_batching_kwargs', {})

  def validate_inference_args(self, inference_args: Optional[dict[str, Any]]):
    """
    Allows model handlers to provide some validation to make sure passed in
    inference args are valid. Some ModelHandlers throw here to disallow
    inference args altogether.
    """
    pass

  def update_model_path(self, model_path: Optional[str] = None):
    """
    Update the model path produced by side inputs. update_model_path should be
    used when a ModelHandler represents a single model, not multiple models.
    This will be true in most cases. For more information see the website
    section on model updates
    https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh
    """
    pass

  def update_model_paths(
      self,
      model: ModelT,
      model_paths: Optional[Union[str, list[KeyModelPathMapping]]] = None):
    """
    Update the model paths produced by side inputs. update_model_paths should
    be used when updating multiple models at once (e.g. when using a
    KeyedModelHandler that holds multiple models).  For more information see
    the KeyedModelHandler documentation
    https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.KeyedModelHandler
    documentation and the website section on model updates
    https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh
    """
    pass

  def get_preprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
    """Gets all preprocessing functions to be run before batching/inference.
    Functions are in order that they should be applied."""
    return []

  def get_postprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
    """Gets all postprocessing functions to be run after inference.
    Functions are in order that they should be applied."""
    return []

  def should_skip_batching(self) -> bool:
    """Whether RunInference's batching should be skipped. Can be flipped to
    True by using `with_no_batching`"""
    return False

  def set_environment_vars(self):
    """Sets environment variables using a dictionary provided via kwargs.
    Keys are the env variable name, and values are the env variable value.
    Child ModelHandler classes should set _env_vars via kwargs in __init__,
    or else call super().__init__()."""
    env_vars = getattr(self, '_env_vars', {})
    for env_variable, env_value in env_vars.items():
      os.environ[env_variable] = env_value

  def with_preprocess_fn(
      self, fn: Callable[[PreProcessT], ExampleT]
  ) -> 'ModelHandler[PreProcessT, PredictionT, ModelT]':
    """Returns a new ModelHandler with a preprocessing function
    associated with it. The preprocessing function will be run
    before batching/inference and should map your input PCollection
    to the base ModelHandler's input type. If you apply multiple
    preprocessing functions, they will be run on your original
    PCollection in order from last applied to first applied."""
    return _PreProcessingModelHandler(self, fn)

  def with_postprocess_fn(
      self, fn: Callable[[PredictionT], PostProcessT]
  ) -> 'ModelHandler[ExampleT, PostProcessT, ModelT]':
    """Returns a new ModelHandler with a postprocessing function
    associated with it. The postprocessing function will be run
    after inference and should map the base ModelHandler's output
    type to your desired output type. If you apply multiple
    postprocessing functions, they will be run on your original
    inference result in order from first applied to last applied."""
    return _PostProcessingModelHandler(self, fn)

  def with_no_batching(
      self
  ) -> """ModelHandler[Union[
    ExampleT, Iterable[ExampleT]], PostProcessT, ModelT, PostProcessT]""":
    """Returns a new ModelHandler which does not require batching
    of inputs so that RunInference will skip this step.  RunInference will
    expect the input to be pre-batched and passed in as an Iterable of records.
    If you skip batching, any preprocessing functions should accept a batch of
    data, not just a single record.

    This option is only recommended if you want to do custom batching yourself.
    If you just want to pass in records without a batching dimension, it is
    recommended to (1) add `max_batch_size=1` to `batch_elements_kwargs` and
    (2) remove the batching dimension as part of your inference call (by
    calling `record=batch[0]`)"""
    return _PrebatchedModelHandler(self)

  def share_model_across_processes(self) -> bool:
    """Returns a boolean representing whether or not a model should
    be shared across multiple processes instead of being loaded per process.
    This is primary useful for large models that  can't fit multiple copies in
    memory. Multi-process support may vary by runner, but this will fallback to
    loading per process as necessary. See
    https://beam.apache.org/releases/pydoc/current/apache_beam.utils.multi_process_shared.html"""
    return getattr(self, '_share_across_processes', False)

  def model_copies(self) -> int:
    """Returns the maximum number of model copies that should be loaded at one
    time. This only impacts model handlers that are using
    share_model_across_processes to share their model across processes instead
    of being loaded per process."""
    return getattr(self, '_model_copies', None) or 1

  def override_metrics(self, metrics_namespace: str = '') -> bool:
    """Returns a boolean representing whether or not a model handler will
    override metrics reporting. If True, RunInference will not report any
    metrics."""
    return False

  def should_garbage_collect_on_timeout(self) -> bool:
    """Whether the model should be garbage collected if model loading or
    inference timeout, or if it should be left for future calls. Usually should
    not be overriden unless the model handler implements other mechanisms for
    garbage collection."""
    return self.share_model_across_processes()


class RemoteModelHandler(ABC, ModelHandler[ExampleT, PredictionT, ModelT]):
  """Has the ability to call a model at a remote endpoint."""
  def __init__(
      self,
      namespace: str = '',
      num_retries: int = 5,
      throttle_delay_secs: int = 5,
      retry_filter: Callable[[Exception], bool] = lambda x: True,
      *,
      window_ms: int = 1 * _MILLISECOND_TO_SECOND,
      bucket_ms: int = 1 * _MILLISECOND_TO_SECOND,
      overload_ratio: float = 2,
      rate_limiter: Optional[RateLimiter] = None):
    """Initializes a ReactiveThrottler class for enabling
    client-side throttling for remote calls to an inference service. Also wraps
    provided calls to the service with retry logic.
    
    See https://s.apache.org/beam-client-side-throttling for more details
    on the configuration of the throttling and retry
    mechanics.

    Args:
      namespace: the metrics and logging namespace
      num_retries: the maximum number of times to retry a request on retriable
        errors before failing
      throttle_delay_secs: the amount of time to throttle when the client-side
        elects to throttle
      retry_filter: a function accepting an exception as an argument and
        returning a boolean. On a true return, the run_inference call will
        be retried. Defaults to always retrying.
      window_ms: length of history to consider, in ms, to set throttling.
      bucket_ms: granularity of time buckets that we store data in, in ms.
      overload_ratio: the target ratio between requests sent and successful
        requests. This is "K" in the formula in
        https://landing.google.com/sre/book/chapters/handling-overload.html.
      rate_limiter: A RateLimiter object for setting a global rate limit.
    """
    # Configure ReactiveThrottler for client-side throttling behavior.
    self.throttler = ReactiveThrottler(
        window_ms=window_ms,
        bucket_ms=bucket_ms,
        overload_ratio=overload_ratio,
        namespace=namespace,
        throttle_delay_secs=throttle_delay_secs)
    self.logger = logging.getLogger(namespace)
    self.num_retries = num_retries
    self.retry_filter = retry_filter
    self._rate_limiter = rate_limiter
    self._shared_rate_limiter = None
    self._shared_handle = shared.Shared()

  def __init_subclass__(cls):
    if cls.load_model is not RemoteModelHandler.load_model:
      raise Exception(
          "Cannot override RemoteModelHandler.load_model, ",
          "implement create_client instead.")
    if cls.run_inference is not RemoteModelHandler.run_inference:
      raise Exception(
          "Cannot override RemoteModelHandler.run_inference, ",
          "implement request instead.")

  @abstractmethod
  def create_client(self) -> ModelT:
    """Creates the client that is used to make the remote inference request
    in request(). All relevant arguments should be passed to __init__().
    """
    raise NotImplementedError(type(self))

  def load_model(self) -> ModelT:
    return self.create_client()

  def retry_on_exception(func):
    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
      return retry.with_exponential_backoff(
          num_retries=self.num_retries,
          retry_filter=self.retry_filter)(func)(self, *args, **kwargs)

    return wrapper

  @retry_on_exception
  def run_inference(
      self,
      batch: Sequence[ExampleT],
      model: ModelT,
      inference_args: Optional[dict[str, Any]] = None) -> Iterable[PredictionT]:
    """Runs inferences on a batch of examples. Calls a remote model for
    predictions and will retry if a retryable exception is raised.

    Args:
      batch: A sequence of examples or features.
      model: The model used to make inferences.
      inference_args: Extra arguments for models whose inference call requires
        extra parameters.

    Returns:
      An Iterable of Predictions.
    """
    if self._rate_limiter:
      if self._shared_rate_limiter is None:

        def init_limiter():
          return self._rate_limiter

        self._shared_rate_limiter = self._shared_handle.acquire(init_limiter)

      if not self._shared_rate_limiter.allow(hits_added=len(batch)):
        raise RateLimitExceeded(
            "Rate Limit Exceeded, "
            "Could not process this batch.")

    self.throttler.throttle()

    try:
      req_time = time.time()
      predictions = self.request(batch, model, inference_args)
      self.throttler.successful_request(req_time * _MILLISECOND_TO_SECOND)
      return predictions
    except Exception as e:
      self.logger.error("exception raised as part of request, got %s", e)
      raise

  @abstractmethod
  def request(
      self,
      batch: Sequence[ExampleT],
      model: ModelT,
      inference_args: Optional[dict[str, Any]] = None) -> Iterable[PredictionT]:
    """Makes a request to a remote inference service and returns the response.
    Should raise an exception of some kind if there is an error to enable the
    retry and client-side throttling logic to work. Returns an iterable of the
    desired prediction type. This method should return the values directly, as
    handling return values as a generator can prevent the retry logic from
    functioning correctly.

    Args:
      batch: A sequence of examples or features.
      model: The model used to make inferences.
      inference_args: Extra arguments for models whose inference call requires
        extra parameters.

    Returns:
      An Iterable of Predictions.
    """
    raise NotImplementedError(type(self))


class _ModelManager:
  """
  A class for efficiently managing copies of multiple models. Will load a
  single copy of each model into a multi_process_shared object and then
  return a lookup key for that object.
  """
  def __init__(self, mh_map: dict[str, ModelHandler]):
    """
    Args:
      mh_map: A map from keys to model handlers which can be used to load a
        model.
    """
    self._max_models = None
    # Map keys to model handlers
    self._mh_map: dict[str, ModelHandler] = mh_map
    # Map keys to the last updated model path for that key
    self._key_to_last_update: dict[str, str] = defaultdict(str)
    # Map key for a model to a unique tag that will persist for the life of
    # that model in memory. A new tag will be generated if a model is swapped
    # out of memory and reloaded.
    self._tag_map: dict[str, str] = OrderedDict()
    # Map a tag to a multiprocessshared model object for that tag. Each entry
    # of this map should last as long as the corresponding entry in _tag_map.
    self._proxy_map: dict[str, multi_process_shared.MultiProcessShared] = {}

  def load(self, key: str) -> _ModelLoadStats:
    """
    Loads the appropriate model for the given key into memory.
    Args:
      key: the key associated with the model we'd like to load.
    Returns:
      _ModelLoadStats with tag, byte size, and latency to load the model. If
        the model was already loaded, byte size/latency will be None.
    """
    # Map the key for a model to a unique tag that will persist until the model
    # is released. This needs to be unique between releasing/reacquiring th
    # model because otherwise the ProxyManager will try to reuse the model that
    # has been released and deleted.
    if key in self._tag_map:
      self._tag_map.move_to_end(key)
      return _ModelLoadStats(self._tag_map[key], None, None)
    else:
      self._tag_map[key] = uuid.uuid4().hex

    tag = self._tag_map[key]
    mh = self._mh_map[key]

    if self._max_models is not None and self._max_models < len(self._tag_map):
      # If we're about to exceed our LRU size, release the last used model.
      tag_to_remove = self._tag_map.popitem(last=False)[1]
      shared_handle, model_to_remove = self._proxy_map[tag_to_remove]
      shared_handle.release(model_to_remove)
      del self._proxy_map[tag_to_remove]

    # Load the new model
    memory_before = _get_current_process_memory_in_bytes()
    start_time = _to_milliseconds(time.time_ns())
    shared_handle = multi_process_shared.MultiProcessShared(
        mh.load_model, tag=tag)
    model_reference = shared_handle.acquire()
    self._proxy_map[tag] = (shared_handle, model_reference)
    memory_after = _get_current_process_memory_in_bytes()
    end_time = _to_milliseconds(time.time_ns())

    return _ModelLoadStats(
        tag, end_time - start_time, memory_after - memory_before)

  def increment_max_models(self, increment: int):
    """
    Increments the number of models that this instance of a _ModelManager is
    able to hold. If it is never called, no limit is imposed.
    Args:
      increment: the amount by which we are incrementing the number of models.
    """
    if self._max_models is None:
      self._max_models = 0
    self._max_models += increment

  def update_model_handler(self, key: str, model_path: str, previous_key: str):
    """
    Updates the model path of this model handler and removes it from memory so
    that it can be reloaded with the updated path. No-ops if no model update
    needs to be applied.
    Args:
      key: the key associated with the model we'd like to update.
      model_path: the new path to the model we'd like to load.
      previous_key: the key that is associated with the old version of this
        model. This will often be the same as the current key, but sometimes
        we will want to keep both the old and new models to serve different
        cohorts. In that case, the keys should be different.
    """
    if self._key_to_last_update[key] == model_path:
      return
    self._key_to_last_update[key] = model_path
    if key not in self._mh_map:
      self._mh_map[key] = deepcopy(self._mh_map[previous_key])
    self._mh_map[key].update_model_path(model_path)
    if key in self._tag_map:
      tag_to_remove = self._tag_map[key]
      shared_handle, model_to_remove = self._proxy_map[tag_to_remove]
      shared_handle.release(model_to_remove)
      del self._tag_map[key]
      del self._proxy_map[tag_to_remove]


# Use a dataclass instead of named tuple because NamedTuples and generics don't
# mix well across the board for all versions:
# https://github.com/python/typing/issues/653
class KeyModelMapping(Generic[KeyT, ExampleT, PredictionT, ModelT]):
  """
  Dataclass for mapping 1 or more keys to 1 model handler. Given
  `KeyModelMapping(['key1', 'key2'], myMh)`, all examples with keys `key1`
  or `key2` will be run against the model defined by the `myMh` ModelHandler.
  """
  def __init__(
      self, keys: list[KeyT], mh: ModelHandler[ExampleT, PredictionT, ModelT]):
    self.keys = keys
    self.mh = mh


class KeyedModelHandler(Generic[KeyT, ExampleT, PredictionT, ModelT],
                        ModelHandler[tuple[KeyT, ExampleT],
                                     tuple[KeyT, PredictionT],
                                     Union[ModelT, _ModelManager]]):
  def __init__(
      self,
      unkeyed: Union[ModelHandler[ExampleT, PredictionT, ModelT],
                     list[KeyModelMapping[KeyT, ExampleT, PredictionT,
                                          ModelT]]],
      max_models_per_worker_hint: Optional[int] = None):
    """A ModelHandler that takes keyed examples and returns keyed predictions.

    For example, if the original model is used with RunInference to take a
    PCollection[E] to a PCollection[P], this ModelHandler would take a
    PCollection[tuple[K, E]] to a PCollection[tuple[K, P]], making it possible
    to use the key to associate the outputs with the inputs. KeyedModelHandler
    is able to accept either a single unkeyed ModelHandler or many different
    model handlers corresponding to the keys for which that ModelHandler should
    be used. For example, the following configuration could be used to map keys
    1-3 to ModelHandler1 and keys 4-5 to ModelHandler2:

        k1 = ['k1', 'k2', 'k3']
        k2 = ['k4', 'k5']
        KeyedModelHandler([KeyModelMapping(k1, mh1), KeyModelMapping(k2, mh2)])

    Note that a single copy of each of these models may all be held in memory
    at the same time; be careful not to load too many large models or your
    pipeline may cause Out of Memory exceptions.

    KeyedModelHandlers support Automatic Model Refresh to update your model
    to a newer version without stopping your streaming pipeline. For an
    overview of this feature, see
    https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh


    To use this feature with a KeyedModelHandler that has many models per key,
    you can pass in a list of KeyModelPathMapping objects to define your new
    model paths. For example, passing in the side input of

        [KeyModelPathMapping(keys=['k1', 'k2'], update_path='update/path/1'),
        KeyModelPathMapping(keys=['k3'], update_path='update/path/2')]

    will update the model corresponding to keys 'k1' and 'k2' with path
    'update/path/1' and the model corresponding to 'k3' with 'update/path/2'.
    In order to do a side input update: (1) all restrictions mentioned in
    https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh
    must be met, (2) all update_paths must be non-empty, even if they are not
    being updated from their original values, and (3) the set of keys
    originally defined cannot change. This means that if originally you have
    defined model handlers for 'key1', 'key2', and 'key3', all 3 of those keys
    must appear in your list of KeyModelPathMappings exactly once. No
    additional keys can be added.

    When using many models defined per key, metrics about inference and model
    loading will be gathered on an  aggregate basis for all keys. These will be
    reported with no prefix. Metrics will also be gathered on a per key basis.
    Since some keys can share the same model, only one set of metrics will be
    reported per key 'cohort'. These will be reported in the form:
    `<cohort_key>-<metric_name>`, where `<cohort_key>` can be any key selected
    from the cohort. When model updates occur, the metrics will be reported in
    the form `<cohort_key>-<model id>-<metric_name>`.

    Loading multiple models at the same time can increase the risk of an out of
    memory (OOM) exception. To avoid this issue, use the parameter
    `max_models_per_worker_hint` to limit the number of models that are loaded
    at the same time. For more information about memory management, see
    `Use a keyed `ModelHandler <https://beam.apache.org/documentation/ml/about-ml/#use-a-keyed-modelhandler-object>_`.  # pylint: disable=line-too-long


    Args:
      unkeyed: Either (a) an implementation of ModelHandler that does not
        require keys or (b) a list of KeyModelMappings mapping lists of keys to
        unkeyed ModelHandlers.
      max_models_per_worker_hint: A hint to the runner indicating how many
        models can be held in memory at one time per worker process. For
        example, if your worker has 8 GB of memory provisioned and your workers
        take up 1 GB each, you should set this to 7 to allow all models to sit
        in memory with some buffer. For more information about memory management,
        see `Use a keyed `ModelHandler <https://beam.apache.org/documentation/ml/about-ml/#use-a-keyed-modelhandler-object>_`.  # pylint: disable=line-too-long
    """
    self._metrics_collectors: dict[str, _MetricsCollector] = {}
    self._default_metrics_collector: _MetricsCollector = None
    self._metrics_namespace = ''
    self._single_model = not isinstance(unkeyed, list)
    if self._single_model:
      if len(unkeyed.get_preprocess_fns()) or len(
          unkeyed.get_postprocess_fns()):
        raise Exception(
            'Cannot make make an unkeyed model handler with pre or '
            'postprocessing functions defined into a keyed model handler. All '
            'pre/postprocessing functions must be defined on the outer model'
            'handler.')
      self._env_vars = getattr(unkeyed, '_env_vars', {})
      self._unkeyed = unkeyed
      return

    self._max_models_per_worker_hint = max_models_per_worker_hint
    # To maintain an efficient representation, we will map all keys in a given
    # KeyModelMapping to a single id (the first key in the KeyModelMapping
    # list). We will then map that key to a ModelHandler. This will allow us to
    # quickly look up the appropriate ModelHandler for any given key.
    self._id_to_mh_map: dict[str, ModelHandler[ExampleT, PredictionT,
                                               ModelT]] = {}
    self._key_to_id_map: dict[str, str] = {}
    for mh_tuple in unkeyed:
      mh = mh_tuple.mh
      keys = mh_tuple.keys
      if len(mh.get_preprocess_fns()) or len(mh.get_postprocess_fns()):
        raise ValueError(
            'Cannot use an unkeyed model handler with pre or '
            'postprocessing functions defined in a keyed model handler. All '
            'pre/postprocessing functions must be defined on the outer model'
            'handler.')
      hints = mh.get_resource_hints()
      if len(hints) > 0:
        logging.warning(
            'mh %s defines the following resource hints, which will be'
            'ignored: %s. Resource hints are not respected when more than one '
            'model handler is used in a KeyedModelHandler. If you would like '
            'to specify resource hints, you can do so by overriding the '
            'KeyedModelHandler.get_resource_hints() method.',
            mh,
            hints)
      batch_kwargs = mh.batch_elements_kwargs()
      if len(batch_kwargs) > 0:
        logging.warning(
            'mh %s defines the following batching kwargs which will be '
            'ignored %s. Batching kwargs are not respected when '
            'more than one model handler is used in a KeyedModelHandler. If '
            'you would like to specify resource hints, you can do so by '
            'overriding the KeyedModelHandler.batch_elements_kwargs() method.',
            hints,
            batch_kwargs)
      env_vars = getattr(mh, '_env_vars', {})
      if len(env_vars) > 0:
        logging.warning(
            'mh %s defines the following _env_vars which will be ignored %s. '
            '_env_vars are not respected when more than one model handler is '
            'used in a KeyedModelHandler. If you need env vars set at '
            'inference time, you can do so with '
            'a custom inference function.',
            mh,
            env_vars)

      if len(keys) == 0:
        raise ValueError(
            f'Empty list maps to model handler {mh}. All model handlers must '
            'have one or more associated keys.')
      self._id_to_mh_map[keys[0]] = mh
      for key in keys:
        if key in self._key_to_id_map:
          raise ValueError(
              f'key {key} maps to multiple model handlers. All keys must map '
              'to exactly one model handler.')
        self._key_to_id_map[key] = keys[0]

  def load_model(self) -> Union[ModelT, _ModelManager]:
    if self._single_model:
      return self._unkeyed.load_model()
    return _ModelManager(self._id_to_mh_map)

  def run_inference(
      self,
      batch: Sequence[tuple[KeyT, ExampleT]],
      model: Union[ModelT, _ModelManager],
      inference_args: Optional[dict[str, Any]] = None
  ) -> Iterable[tuple[KeyT, PredictionT]]:
    if self._single_model:
      keys, unkeyed_batch = zip(*batch)
      return zip(
          keys,
          self._unkeyed.run_inference(unkeyed_batch, model, inference_args))

    # The first time a MultiProcessShared ModelManager is used for inference
    # from this process, we should increment its max model count
    if self._max_models_per_worker_hint is not None:
      lock = threading.Lock()
      if lock.acquire(blocking=False):
        model.increment_max_models(self._max_models_per_worker_hint)
      self._max_models_per_worker_hint = None

    batch_by_key = defaultdict(list)
    key_by_id = defaultdict(set)
    for key, example in batch:
      batch_by_key[key].append(example)
      key_by_id[self._key_to_id_map[key]].add(key)

    predictions = []
    for id, keys in key_by_id.items():
      mh = self._id_to_mh_map[id]
      loaded_model = model.load(id)
      keyed_model_tag = loaded_model.model_tag
      if loaded_model.byte_size is not None:
        self._metrics_collectors[id].update_load_model_metrics(
            loaded_model.load_latency, loaded_model.byte_size)
        self._default_metrics_collector.update_load_model_metrics(
            loaded_model.load_latency, loaded_model.byte_size)
      keyed_model_shared_handle = multi_process_shared.MultiProcessShared(
          mh.load_model, tag=keyed_model_tag)
      keyed_model = keyed_model_shared_handle.acquire()
      start_time = _to_microseconds(time.time_ns())
      num_bytes = 0
      num_elements = 0
      try:
        for key in keys:
          unkeyed_batches = batch_by_key[key]
          try:
            for inf in mh.run_inference(unkeyed_batches,
                                        keyed_model,
                                        inference_args):
              predictions.append((key, inf))
          except BaseException as e:
            self._metrics_collectors[id].failed_batches_counter.inc()
            self._default_metrics_collector.failed_batches_counter.inc()
            raise e
          num_bytes += mh.get_num_bytes(unkeyed_batches)
          num_elements += len(unkeyed_batches)
      finally:
        keyed_model_shared_handle.release(keyed_model)
      end_time = _to_microseconds(time.time_ns())
      inference_latency = end_time - start_time
      self._metrics_collectors[id].update(
          num_elements, num_bytes, inference_latency)
      self._default_metrics_collector.update(
          num_elements, num_bytes, inference_latency)

    return predictions

  def get_num_bytes(self, batch: Sequence[tuple[KeyT, ExampleT]]) -> int:
    keys, unkeyed_batch = zip(*batch)
    batch_bytes = len(pickle.dumps(keys))
    if self._single_model:
      return batch_bytes + self._unkeyed.get_num_bytes(unkeyed_batch)

    batch_by_key = defaultdict(list)
    for key, examples in batch:
      batch_by_key[key].append(examples)

    for key, examples in batch_by_key.items():
      mh_id = self._key_to_id_map[key]
      batch_bytes += self._id_to_mh_map[mh_id].get_num_bytes(examples)
    return batch_bytes

  def get_metrics_namespace(self) -> str:
    if self._single_model:
      return self._unkeyed.get_metrics_namespace()
    return 'BeamML_KeyedModels'

  def get_resource_hints(self):
    if self._single_model:
      return self._unkeyed.get_resource_hints()
    return {}

  def batch_elements_kwargs(self):
    if self._single_model:
      return self._unkeyed.batch_elements_kwargs()
    return {}

  def validate_inference_args(self, inference_args: Optional[dict[str, Any]]):
    if self._single_model:
      return self._unkeyed.validate_inference_args(inference_args)
    for mh in self._id_to_mh_map.values():
      mh.validate_inference_args(inference_args)

  def update_model_paths(
      self,
      model: Union[ModelT, _ModelManager],
      model_paths: list[KeyModelPathMapping[KeyT]] = None):
    # When there are many models, the keyed model handler is responsible for
    # reorganizing the model handlers into cohorts and telling the model
    # manager to update every cohort's associated model handler. The model
    # manager is responsible for performing the updates and tracking which
    # updates have already been applied.
    if model_paths is None or len(model_paths) == 0 or model is None:
      return
    if self._single_model:
      raise RuntimeError(
          'Invalid model update: sent many model paths to '
          'update, but KeyedModelHandler is wrapping a single '
          'model.')
    # Map cohort ids to a dictionary mapping new model paths to the keys that
    # were originally in that cohort. We will use this to construct our new
    # cohorts.
    # cohort_path_mapping will be structured as follows:
    # {
    # original_cohort_id: {
    #    'update/path/1': ['key1FromOriginalCohort', key2FromOriginalCohort'],
    #    'update/path/2': ['key3FromOriginalCohort', key4FromOriginalCohort'],
    #    }
    # }
    cohort_path_mapping: dict[KeyT, dict[str, list[KeyT]]] = {}
    key_modelid_mapping: dict[KeyT, str] = {}
    seen_keys = set()
    for mp in model_paths:
      keys = mp.keys
      update_path = mp.update_path
      model_id = mp.model_id
      if len(update_path) == 0:
        raise ValueError(f'Invalid model update, path for {keys} is empty')
      for key in keys:
        if key in seen_keys:
          raise ValueError(
              f'Invalid model update: {key} appears in multiple '
              'update lists. A single model update must provide exactly one '
              'updated path per key.')
        seen_keys.add(key)
        if key not in self._key_to_id_map:
          raise ValueError(
              f'Invalid model update: {key} appears in '
              'update, but not in the original configuration.')
        key_modelid_mapping[key] = model_id
        cohort_id = self._key_to_id_map[key]
        if cohort_id not in cohort_path_mapping:
          cohort_path_mapping[cohort_id] = defaultdict(list)
        cohort_path_mapping[cohort_id][update_path].append(key)
    for key in self._key_to_id_map:
      if key not in seen_keys:
        raise ValueError(
            f'Invalid model update: {key} appears in the '
            'original configuration, but not the update.')

    # We now have our new set of cohorts. For each one, update our local model
    # handler configuration and send the results to the ModelManager
    for old_cohort_id, path_key_mapping in cohort_path_mapping.items():
      for updated_path, keys in path_key_mapping.items():
        cohort_id = old_cohort_id
        if old_cohort_id not in keys:
          # Create new cohort
          cohort_id = keys[0]
          for key in keys:
            self._key_to_id_map[key] = cohort_id
          mh = self._id_to_mh_map[old_cohort_id]
          self._id_to_mh_map[cohort_id] = deepcopy(mh)
        self._id_to_mh_map[cohort_id].update_model_path(updated_path)
        model.update_model_handler(cohort_id, updated_path, old_cohort_id)
        model_id = key_modelid_mapping[cohort_id]
        self._metrics_collectors[cohort_id] = _MetricsCollector(
            self._metrics_namespace, f'{cohort_id}-{model_id}-')

  def update_model_path(self, model_path: Optional[str] = None):
    if self._single_model:
      return self._unkeyed.update_model_path(model_path=model_path)
    if model_path is not None:
      raise RuntimeError(
          'Model updates are currently not supported for ' +
          'KeyedModelHandlers with multiple different per-key ' +
          'ModelHandlers.')

  def share_model_across_processes(self) -> bool:
    if self._single_model:
      return self._unkeyed.share_model_across_processes()
    return True

  def model_copies(self) -> int:
    if self._single_model:
      return self._unkeyed.model_copies()
    for mh in self._id_to_mh_map.values():
      if mh.model_copies() != 1:
        raise ValueError(
            'KeyedModelHandler cannot map records to multiple '
            'models if one or more of its ModelHandlers '
            'require multiple model copies (set via '
            'model_copies). To fix, verify that each '
            'ModelHandler is not set to load multiple copies of '
            'its model.')

    return 1

  def override_metrics(self, metrics_namespace: str = '') -> bool:
    if self._single_model:
      return self._unkeyed.override_metrics(metrics_namespace)

    self._metrics_namespace = metrics_namespace
    self._default_metrics_collector = _MetricsCollector(metrics_namespace)
    for cohort_id in self._id_to_mh_map:
      self._metrics_collectors[cohort_id] = _MetricsCollector(
          metrics_namespace, f'{cohort_id}-')

    return True

  def should_garbage_collect_on_timeout(self) -> bool:
    return self._single_model and self.share_model_across_processes()


class MaybeKeyedModelHandler(Generic[KeyT, ExampleT, PredictionT, ModelT],
                             ModelHandler[Union[ExampleT, tuple[KeyT,
                                                                ExampleT]],
                                          Union[PredictionT,
                                                tuple[KeyT, PredictionT]],
                                          ModelT]):
  def __init__(self, unkeyed: ModelHandler[ExampleT, PredictionT, ModelT]):
    """A ModelHandler that takes examples that might have keys and returns
    predictions that might have keys.

    For example, if the original model is used with RunInference to take a
    PCollection[E] to a PCollection[P], this ModelHandler would take either
    PCollection[E] to a PCollection[P] or PCollection[tuple[K, E]] to a
    PCollection[tuple[K, P]], depending on the whether the elements are
    tuples. This pattern makes it possible to associate the outputs with the
    inputs based on the key.

    Note that you cannot use this ModelHandler if E is a tuple type.
    In addition, either all examples should be keyed, or none of them.

    Args:
      unkeyed: An implementation of ModelHandler that does not require keys.
    """
    if len(unkeyed.get_preprocess_fns()) or len(unkeyed.get_postprocess_fns()):
      raise Exception(
          'Cannot make make an unkeyed model handler with pre or '
          'postprocessing functions defined into a keyed model handler. All '
          'pre/postprocessing functions must be defined on the outer model'
          'handler.')
    self._unkeyed = unkeyed
    self._env_vars = getattr(unkeyed, '_env_vars', {})

  def load_model(self) -> ModelT:
    return self._unkeyed.load_model()

  def run_inference(
      self,
      batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]],
      model: ModelT,
      inference_args: Optional[dict[str, Any]] = None
  ) -> Union[Iterable[PredictionT], Iterable[tuple[KeyT, PredictionT]]]:
    # Really the input should be
    #    Union[Sequence[ExampleT], Sequence[tuple[KeyT, ExampleT]]]
    # but there's not a good way to express (or check) that.
    if isinstance(batch[0], tuple):
      is_keyed = True
      keys, unkeyed_batch = zip(*batch)  # type: ignore[arg-type]
    else:
      is_keyed = False
      unkeyed_batch = batch  # type: ignore[assignment]
    unkeyed_results = self._unkeyed.run_inference(
        unkeyed_batch, model, inference_args)
    if is_keyed:
      return zip(keys, unkeyed_results)
    else:
      return unkeyed_results

  def get_num_bytes(
      self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]]) -> int:
    # MyPy can't follow the branching logic.
    if isinstance(batch[0], tuple):
      keys, unkeyed_batch = zip(*batch)  # type: ignore[arg-type]
      return len(
          pickle.dumps(keys)) + self._unkeyed.get_num_bytes(unkeyed_batch)
    else:
      return self._unkeyed.get_num_bytes(batch)  # type: ignore[arg-type]

  def get_metrics_namespace(self) -> str:
    return self._unkeyed.get_metrics_namespace()

  def get_resource_hints(self):
    return self._unkeyed.get_resource_hints()

  def batch_elements_kwargs(self):
    return self._unkeyed.batch_elements_kwargs()

  def validate_inference_args(self, inference_args: Optional[dict[str, Any]]):
    return self._unkeyed.validate_inference_args(inference_args)

  def update_model_path(self, model_path: Optional[str] = None):
    return self._unkeyed.update_model_path(model_path=model_path)

  def get_preprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
    return self._unkeyed.get_preprocess_fns()

  def get_postprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
    return self._unkeyed.get_postprocess_fns()

  def should_skip_batching(self) -> bool:
    return self._unkeyed.should_skip_batching()

  def share_model_across_processes(self) -> bool:
    return self._unkeyed.share_model_across_processes()

  def model_copies(self) -> int:
    return self._unkeyed.model_copies()


class _PrebatchedModelHandler(Generic[ExampleT, PredictionT, ModelT],
                              ModelHandler[Sequence[ExampleT],
                                           PredictionT,
                                           ModelT]):
  def __init__(self, base: ModelHandler[ExampleT, PredictionT, ModelT]):
    """A ModelHandler that skips batching in RunInference.

    Args:
      base: An implementation of the underlying model handler.
    """
    self._base = base
    self._env_vars = getattr(base, '_env_vars', {})

  def load_model(self) -> ModelT:
    return self._base.load_model()

  def run_inference(
      self,
      batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]],
      model: ModelT,
      inference_args: Optional[dict[str, Any]] = None
  ) -> Union[Iterable[PredictionT], Iterable[tuple[KeyT, PredictionT]]]:
    return self._base.run_inference(batch, model, inference_args)

  def get_num_bytes(
      self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]]) -> int:
    return self._base.get_num_bytes(batch)

  def get_metrics_namespace(self) -> str:
    return self._base.get_metrics_namespace()

  def get_resource_hints(self):
    return self._base.get_resource_hints()

  def batch_elements_kwargs(self):
    return self._base.batch_elements_kwargs()

  def validate_inference_args(self, inference_args: Optional[dict[str, Any]]):
    return self._base.validate_inference_args(inference_args)

  def update_model_path(self, model_path: Optional[str] = None):
    return self._base.update_model_path(model_path=model_path)

  def get_preprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
    return self._base.get_preprocess_fns()

  def should_skip_batching(self) -> bool:
    return True

  def share_model_across_processes(self) -> bool:
    return self._base.share_model_across_processes()

  def model_copies(self) -> int:
    return self._base.model_copies()

  def get_postprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
    return self._base.get_postprocess_fns()


class _PreProcessingModelHandler(Generic[ExampleT,
                                         PredictionT,
                                         ModelT,
                                         PreProcessT],
                                 ModelHandler[PreProcessT, PredictionT,
                                              ModelT]):
  def __init__(
      self,
      base: ModelHandler[ExampleT, PredictionT, ModelT],
      preprocess_fn: Callable[[PreProcessT], ExampleT]):
    """A ModelHandler that has a preprocessing function associated with it.

    Args:
      base: An implementation of the underlying model handler.
      preprocess_fn: the preprocessing function to use.
    """
    self._base = base
    self._env_vars = getattr(base, '_env_vars', {})
    self._preprocess_fn = preprocess_fn

  def load_model(self) -> ModelT:
    return self._base.load_model()

  def run_inference(
      self,
      batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]],
      model: ModelT,
      inference_args: Optional[dict[str, Any]] = None
  ) -> Union[Iterable[PredictionT], Iterable[tuple[KeyT, PredictionT]]]:
    return self._base.run_inference(batch, model, inference_args)

  def get_num_bytes(
      self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]]) -> int:
    return self._base.get_num_bytes(batch)

  def get_metrics_namespace(self) -> str:
    return self._base.get_metrics_namespace()

  def get_resource_hints(self):
    return self._base.get_resource_hints()

  def batch_elements_kwargs(self):
    return self._base.batch_elements_kwargs()

  def validate_inference_args(self, inference_args: Optional[dict[str, Any]]):
    return self._base.validate_inference_args(inference_args)

  def update_model_path(self, model_path: Optional[str] = None):
    return self._base.update_model_path(model_path=model_path)

  def get_preprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
    return [self._preprocess_fn] + self._base.get_preprocess_fns()

  def should_skip_batching(self) -> bool:
    return self._base.should_skip_batching()

  def share_model_across_processes(self) -> bool:
    return self._base.share_model_across_processes()

  def model_copies(self) -> int:
    return self._base.model_copies()

  def get_postprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
    return self._base.get_postprocess_fns()


class _PostProcessingModelHandler(Generic[ExampleT,
                                          PredictionT,
                                          ModelT,
                                          PostProcessT],
                                  ModelHandler[ExampleT, PostProcessT, ModelT]):
  def __init__(
      self,
      base: ModelHandler[ExampleT, PredictionT, ModelT],
      postprocess_fn: Callable[[PredictionT], PostProcessT]):
    """A ModelHandler that has a preprocessing function associated with it.

    Args:
      base: An implementation of the underlying model handler.
      postprocess_fn: the preprocessing function to use.
    """
    self._base = base
    self._env_vars = getattr(base, '_env_vars', {})
    self._postprocess_fn = postprocess_fn

  def load_model(self) -> ModelT:
    return self._base.load_model()

  def run_inference(
      self,
      batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]],
      model: ModelT,
      inference_args: Optional[dict[str, Any]] = None
  ) -> Union[Iterable[PredictionT], Iterable[tuple[KeyT, PredictionT]]]:
    return self._base.run_inference(batch, model, inference_args)

  def get_num_bytes(
      self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]]) -> int:
    return self._base.get_num_bytes(batch)

  def get_metrics_namespace(self) -> str:
    return self._base.get_metrics_namespace()

  def get_resource_hints(self):
    return self._base.get_resource_hints()

  def batch_elements_kwargs(self):
    return self._base.batch_elements_kwargs()

  def validate_inference_args(self, inference_args: Optional[dict[str, Any]]):
    return self._base.validate_inference_args(inference_args)

  def update_model_path(self, model_path: Optional[str] = None):
    return self._base.update_model_path(model_path=model_path)

  def get_preprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
    return self._base.get_preprocess_fns()

  def should_skip_batching(self) -> bool:
    return self._base.should_skip_batching()

  def share_model_across_processes(self) -> bool:
    return self._base.share_model_across_processes()

  def model_copies(self) -> int:
    return self._base.model_copies()

  def get_postprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
    return self._base.get_postprocess_fns() + [self._postprocess_fn]


class RunInference(beam.PTransform[beam.PCollection[Union[ExampleT,
                                                          Iterable[ExampleT]]],
                                   beam.PCollection[PredictionT]]):
  def __init__(
      self,
      model_handler: ModelHandler[ExampleT, PredictionT, Any],
      clock=time,
      inference_args: Optional[dict[str, Any]] = None,
      metrics_namespace: Optional[str] = None,
      *,
      model_metadata_pcoll: beam.PCollection[ModelMetadata] = None,
      watch_model_pattern: Optional[str] = None,
      model_identifier: Optional[str] = None,
      **kwargs):
    """
    A transform that takes a PCollection of examples (or features) for use
    on an ML model. The transform then outputs inferences (or predictions) for
    those examples in a PCollection of PredictionResults that contains the input
    examples and the output inferences.

    Models for supported frameworks can be loaded using a URI. Supported
    services can also be used.

    This transform attempts to batch examples using the beam.BatchElements
    transform. Batching can be configured using the ModelHandler.

    Args:
        model_handler: An implementation of ModelHandler.
        clock: A clock implementing time_ns. *Used for unit testing.*
        inference_args: Extra arguments for models whose inference call requires
          extra parameters.
        metrics_namespace: Namespace of the transform to collect metrics.
        model_metadata_pcoll: PCollection that emits Singleton ModelMetadata
          containing model path and model name, that is used as a side input
          to the _RunInferenceDoFn.
        watch_model_pattern: A glob pattern used to watch a directory
          for automatic model refresh.
        model_identifier: A string used to identify the model being loaded. You
          can set this if you want to reuse the same model across multiple
          RunInference steps and don't want to reload it twice. Note that using
          the same tag for different models will lead to non-deterministic
          results, so exercise caution when using this parameter. This only
          impacts models which are already being shared across processes.
    """
    self._model_handler = model_handler
    self._inference_args = inference_args
    self._clock = clock
    self._metrics_namespace = metrics_namespace
    self._model_metadata_pcoll = model_metadata_pcoll
    self._with_exception_handling = False
    self._exception_handling_timeout = None
    self._timeout = None
    self._watch_model_pattern = watch_model_pattern
    self._kwargs = kwargs
    # Generate a random tag to use for shared.py and multi_process_shared.py to
    # allow us to effectively disambiguate in multi-model settings. Only use
    # the same tag if the model being loaded across multiple steps is actually
    # the same.
    self._model_tag = model_identifier
    if model_identifier is None:
      self._model_tag = uuid.uuid4().hex

  def annotations(self):
    return {
        'model_handler': str(self._model_handler),
        'model_handler_type': (
            f'{self._model_handler.__class__.__module__}'
            f'.{self._model_handler.__class__.__qualname__}'),
        **super().annotations()
    }

  def _get_model_metadata_pcoll(self, pipeline):
    # avoid circular imports.
    # pylint: disable=wrong-import-position
    from apache_beam.ml.inference.utils import WatchFilePattern
    extra_params = {}
    if 'interval' in self._kwargs:
      extra_params['interval'] = self._kwargs['interval']
    if 'stop_timestamp' in self._kwargs:
      extra_params['stop_timestamp'] = self._kwargs['stop_timestamp']

    return (
        pipeline | WatchFilePattern(
            file_pattern=self._watch_model_pattern, **extra_params))

  # TODO(BEAM-14046): Add and link to help documentation.
  @classmethod
  def from_callable(cls, model_handler_provider, **kwargs):
    """Multi-language friendly constructor.

    Use this constructor with fully_qualified_named_transform to
    initialize the RunInference transform from PythonCallableSource provided
    by foreign SDKs.

    Args:
      model_handler_provider: A callable object that returns ModelHandler.
      kwargs: Keyword arguments for model_handler_provider.
    """
    return cls(model_handler_provider(**kwargs))

  def _apply_fns(
      self,
      pcoll: beam.PCollection,
      fns: Iterable[Callable[[Any], Any]],
      step_prefix: str) -> tuple[beam.PCollection, Iterable[beam.PCollection]]:
    bad_preprocessed = []
    for idx in range(len(fns)):
      fn = fns[idx]
      if self._with_exception_handling:
        pcoll, bad = (pcoll
        | f"{step_prefix}-{idx}" >> beam.Map(
          fn).with_exception_handling(
          exc_class=self._exc_class,
          use_subprocess=self._use_subprocess,
          threshold=self._threshold,
          timeout = self._timeout))
        bad_preprocessed.append(bad)
      else:
        pcoll = pcoll | f"{step_prefix}-{idx}" >> beam.Map(fn)

    return pcoll, bad_preprocessed

  # TODO(https://github.com/apache/beam/issues/21447): Add batch_size back off
  # in the case there are functional reasons large batch sizes cannot be
  # handled.
  def expand(
      self, pcoll: beam.PCollection[ExampleT]) -> beam.PCollection[PredictionT]:
    self._model_handler.validate_inference_args(self._inference_args)
    # DLQ pcollections
    bad_preprocessed = []
    bad_inference = None
    bad_postprocessed = []
    preprocess_fns = self._model_handler.get_preprocess_fns()
    postprocess_fns = self._model_handler.get_postprocess_fns()

    pcoll, bad_preprocessed = self._apply_fns(
      pcoll, preprocess_fns, 'BeamML_RunInference_Preprocess')

    resource_hints = self._model_handler.get_resource_hints()

    # check for the side input
    if self._watch_model_pattern:
      self._model_metadata_pcoll = self._get_model_metadata_pcoll(
          pcoll.pipeline)

    if self._model_handler.should_skip_batching():
      batched_elements_pcoll = pcoll
    else:
      batched_elements_pcoll = (
          pcoll
          # TODO(https://github.com/apache/beam/issues/21440): Hook into the
          # batching DoFn APIs.
          | beam.BatchElements(**self._model_handler.batch_elements_kwargs()))

    # Skip loading in setup if we are dependent on side inputs or we want to
    # enforce a timeout since neither of these are available in a helpful way
    # in setup.
    load_model_at_runtime = (
        self._model_metadata_pcoll is not None or self._timeout is not None)
    run_inference_pardo = beam.ParDo(
        _RunInferenceDoFn(
            self._model_handler,
            self._clock,
            self._metrics_namespace,
            load_model_at_runtime,
            self._model_tag),
        self._inference_args,
        beam.pvalue.AsSingleton(
            self._model_metadata_pcoll,
        ) if self._model_metadata_pcoll else None).with_resource_hints(
            **resource_hints)

    if self._with_exception_handling:
      # On timeouts, report back to the central model metadata
      # that the model is invalid
      model_tag = self._model_tag
      share_across_processes = self._model_handler.share_model_across_processes(
      )
      timeout = self._timeout

      def failure_callback(exception: Exception, element: Any):
        if type(exception) is not TimeoutError:
          return
        model_metadata = load_model_status(model_tag, share_across_processes)
        model_metadata.try_mark_current_model_invalid(timeout)
        logging.warning(
            "Inference failed with a timeout, marking the current " +
            "model for garbage collection")

      callback = None
      if (self._timeout is not None and
          self._model_handler.should_garbage_collect_on_timeout()):
        callback = failure_callback
      results, bad_inference = (
          batched_elements_pcoll
          | 'BeamML_RunInference' >>
          run_inference_pardo.with_exception_handling(
          exc_class=self._exc_class,
          use_subprocess=self._use_subprocess,
          threshold=self._threshold,
          timeout = self._timeout,
          on_failure_callback=callback))
    else:
      results = (
          batched_elements_pcoll
          | 'BeamML_RunInference' >> run_inference_pardo)

    results, bad_postprocessed = self._apply_fns(
      results, postprocess_fns, 'BeamML_RunInference_Postprocess')

    if self._with_exception_handling:
      dlq = RunInferenceDLQ(bad_inference, bad_preprocessed, bad_postprocessed)
      return results, dlq

    return results

  def with_exception_handling(
      self,
      *,
      exc_class=Exception,
      use_subprocess=False,
      threshold=1,
      timeout: Optional[int] = None):
    """Automatically provides a dead letter output for skipping bad records.
    This can allow a pipeline to continue successfully rather than fail or
    continuously throw errors on retry when bad elements are encountered.

    This returns a tagged output with two PCollections, the first being the
    results of successfully processing the input PCollection, and the second
    being the set of bad batches of records (those which threw exceptions
    during processing) along with information about the errors raised.

    For example, one would write::

        main, other = RunInference(
          maybe_error_raising_model_handler
        ).with_exception_handling()

    and `main` will be a PCollection of PredictionResults and `other` will
    contain a `RunInferenceDLQ` object with PCollections containing failed
    records for each failed inference, preprocess operation, or postprocess
    operation. To access each collection of failed records, one would write:

        failed_inferences = other.failed_inferences
        failed_preprocessing = other.failed_preprocessing
        failed_postprocessing = other.failed_postprocessing

    failed_inferences is in the form
    PCollection[tuple[failed batch, exception]].

    failed_preprocessing is in the form
    list[PCollection[tuple[failed record, exception]]]], where each element of
    the list corresponds to a preprocess function. These PCollections are
    in the same order that the preprocess functions are applied.

    failed_postprocessing is in the form
    list[PCollection[tuple[failed record, exception]]]], where each element of
    the list corresponds to a postprocess function. These PCollections are
    in the same order that the postprocess functions are applied.


    Args:
      exc_class: An exception class, or tuple of exception classes, to catch.
          Optional, defaults to 'Exception'.
      use_subprocess: Whether to execute the DoFn logic in a subprocess. This
          allows one to recover from errors that can crash the calling process
          (e.g. from an underlying library causing a segfault), but is
          slower as elements and results must cross a process boundary.  Note
          that this starts up a long-running process that is used to handle
          all the elements (until hard failure, which should be rare) rather
          than a new process per element, so the overhead should be minimal
          (and can be amortized if there's any per-process or per-bundle
          initialization that needs to be done). Optional, defaults to False.
      threshold: An upper bound on the ratio of records that can be bad before
          aborting the entire pipeline. Optional, defaults to 1.0 (meaning
          up to 100% of records can be bad and the pipeline will still succeed).
      timeout: The maximum amount of time in seconds given to load a model, run
          inference on a batch of elements and perform and pre/postprocessing
          operations. Since the timeout applies in multiple places, it should
          be equal to the maximum possible timeout for any of these operations.
          Note in particular that model load and inference on a single batch
          count to the same timeout value. When an inference fails, all related
          resources, including the model, will be deleted and reloaded. As a
          result, it is recommended to leave significant buffer and set the
          timeout to at least `2 * (time to load model + time to run
          inference on a batch of data)`.
    """
    self._with_exception_handling = True
    self._exc_class = exc_class
    self._use_subprocess = use_subprocess
    self._threshold = threshold
    self._timeout = timeout
    return self


class _MetricsCollector:
  """
  A metrics collector that tracks ML related performance and memory usage.
  """
  def __init__(self, namespace: str, prefix: str = ''):
    """
    Args:
     namespace: Namespace for the metrics.
     prefix: Unique identifier for metrics, used when models
      are updated using side input.
    """
    # Metrics
    if prefix:
      prefix = f'{prefix}_'
    self._inference_counter = beam.metrics.Metrics.counter(
        namespace, prefix + 'num_inferences')
    self.failed_batches_counter = beam.metrics.Metrics.counter(
        namespace, prefix + 'failed_batches_counter')
    self._inference_request_batch_size = beam.metrics.Metrics.distribution(
        namespace, prefix + 'inference_request_batch_size')
    self._inference_request_batch_byte_size = (
        beam.metrics.Metrics.distribution(
            namespace, prefix + 'inference_request_batch_byte_size'))
    # Batch inference latency in microseconds.
    self._inference_batch_latency_micro_secs = (
        beam.metrics.Metrics.distribution(
            namespace, prefix + 'inference_batch_latency_micro_secs'))
    self._model_byte_size = beam.metrics.Metrics.distribution(
        namespace, prefix + 'model_byte_size')
    # Model load latency in milliseconds.
    self._load_model_latency_milli_secs = beam.metrics.Metrics.distribution(
        namespace, prefix + 'load_model_latency_milli_secs')

    # Metrics cache
    self._load_model_latency_milli_secs_cache = None
    self._model_byte_size_cache = None

  def update_metrics_with_cache(self):
    if self._load_model_latency_milli_secs_cache is not None:
      self._load_model_latency_milli_secs.update(
          self._load_model_latency_milli_secs_cache)
      self._load_model_latency_milli_secs_cache = None
    if self._model_byte_size_cache is not None:
      self._model_byte_size.update(self._model_byte_size_cache)
      self._model_byte_size_cache = None

  def cache_load_model_metrics(self, load_model_latency_ms, model_byte_size):
    self._load_model_latency_milli_secs_cache = load_model_latency_ms
    self._model_byte_size_cache = model_byte_size

  def update_load_model_metrics(self, load_model_latency_ms, model_byte_size):
    self._load_model_latency_milli_secs.update(load_model_latency_ms)
    self._model_byte_size.update(model_byte_size)

  def update(
      self,
      examples_count: int,
      examples_byte_size: int,
      latency_micro_secs: int):
    self._inference_batch_latency_micro_secs.update(latency_micro_secs)
    self._inference_counter.inc(examples_count)
    self._inference_request_batch_size.update(examples_count)
    self._inference_request_batch_byte_size.update(examples_byte_size)


class _ModelRoutingStrategy():
  """A class meant to sit in a shared location for mapping incoming batches to
  different models. Currently only supports round-robin, but can be extended
  to support other protocols if needed.
  """
  def __init__(self):
    self._cur_index = 0

  def next_model_index(self, num_models):
    self._cur_index = (self._cur_index + 1) % num_models
    return self._cur_index


class _ModelStatus():
  """A class holding any metadata about a model required by RunInference.

    Currently, this only includes whether or not the model is valid. Uses the
    model tag to map models to metadata.
  """
  def __init__(self, share_model_across_processes: bool):
    self._active_tags = set()
    self._invalid_tags = set()
    self._tag_mapping = {}
    self._model_first_seen = {}
    self._pending_hard_delete = []
    self._share_model_across_process = share_model_across_processes

  def try_mark_current_model_invalid(self, min_model_life_seconds):
    """Mark the current model invalid.

      Since we don't have sufficient information to say which model is being
      marked invalid, but there may be multiple active models, we will mark all
      models currently in use as inactive so that they all get reloaded. To
      avoid thrashing, however, we will only mark models as invalid if they've
      been active at least min_model_life_seconds seconds.
    """
    cutoff_time = datetime.now() - timedelta(seconds=min_model_life_seconds)
    for tag in list(self._active_tags):
      if cutoff_time >= self._model_first_seen[tag]:
        self._invalid_tags.add(tag)
        # Delete old models after a grace period of 2 * the model life.
        # This already happens automatically for shared.Shared models, so
        # cleanup is only necessary for multi_process_shared models.
        if self._share_model_across_process:
          self._pending_hard_delete.append((
              tag,
              datetime.now() + 2 * timedelta(seconds=min_model_life_seconds)))
        self._active_tags.remove(tag)

  def get_valid_tag(self, tag: str) -> str:
    """Takes in a proposed valid tag and returns a valid one.

      Will always return a valid tag. If the passed in tag is valid, this
      function will simply return it, otherwise it will deterministically
      generate a new tag to use instead. The new tag will be the original tag
      with an incrementing suffix (e.g. `my_tag_reload_1`, `my_tag_reload_2`)
      for each reload
    """
    if tag not in self._invalid_tags:
      if tag not in self._model_first_seen:
        self._model_first_seen[tag] = datetime.now()
      self._active_tags.add(tag)
      return tag
    if (tag in self._tag_mapping and
        self._tag_mapping[tag] not in self._invalid_tags):
      return self._tag_mapping[tag]
    i = 1
    new_tag = f'{tag}_reload_{i}'
    while new_tag in self._invalid_tags:
      i += 1
      new_tag = f'{tag}_reload_{i}'
    self._tag_mapping[tag] = new_tag
    self._model_first_seen[new_tag] = datetime.now()
    self._active_tags.add(new_tag)
    return new_tag

  def is_valid_tag(self, tag: str) -> bool:
    return tag == self.get_valid_tag(tag)

  def get_tags_for_garbage_collection(self) -> list[str]:
    # Since this function may be in multi_process_shared space, delegate model
    # deletion to the calling process which is not to avoid having a
    # multi_process_shared reference in multi_process_shared space, which
    # can create issues with python's multiprocessing module.
    # We will rely on the calling process to report back deleted models so that
    # we can confirm deletion.
    to_delete = []
    cur_time = datetime.now()
    for i in range(len(self._pending_hard_delete)):
      delete_time = self._pending_hard_delete[i][1]
      tag = self._pending_hard_delete[i][0]
      if delete_time <= cur_time:
        to_delete.append(tag)
      else:
        # early return once we hit a model which was added later since models
        # are added in order.
        return to_delete

    return to_delete

  def mark_tags_deleted(self, deleted_tags: set[str]):
    while len(self._pending_hard_delete) > 0:
      tag = self._pending_hard_delete[0][0]
      if tag in deleted_tags:
        self._pending_hard_delete.pop(0)
      else:
        return


def load_model_status(
    model_tag: str, share_across_processes: bool) -> _ModelStatus:
  tag = f'{model_tag}_model_status'
  if share_across_processes:
    return multi_process_shared.MultiProcessShared(
        lambda: _ModelStatus(True), tag=tag, always_proxy=True).acquire()
  return shared.Shared().acquire(lambda: _ModelStatus(False), tag=tag)


class _SharedModelWrapper():
  """A router class to map incoming calls to the correct model.

    This allows us to round robin calls to models sitting in different
    processes so that we can more efficiently use resources (e.g. GPUs).
  """
  def __init__(self, models: list[Any], model_tag: str):
    self.models = models
    if len(models) > 1:
      self.model_router = multi_process_shared.MultiProcessShared(
          lambda: _ModelRoutingStrategy(),
          tag=f'{model_tag}_counter',
          always_proxy=True).acquire()

  def next_model(self):
    if len(self.models) == 1:
      # Short circuit if there's no routing strategy needed in order to
      # avoid the cross-process call
      return self.models[0]

    return self.models[self.model_router.next_model_index(len(self.models))]

  def all_models(self):
    return self.models


class _RunInferenceDoFn(beam.DoFn, Generic[ExampleT, PredictionT]):
  def __init__(
      self,
      model_handler: ModelHandler[ExampleT, PredictionT, Any],
      clock,
      metrics_namespace,
      load_model_at_runtime: bool = False,
      model_tag: str = "RunInference"):
    """A DoFn implementation generic to frameworks.

      Args:
        model_handler: An implementation of ModelHandler.
        clock: A clock implementing time_ns. *Used for unit testing.*
        metrics_namespace: Namespace of the transform to collect metrics.
        load_model_at_runtime: Bool to indicate if model loading should be
            deferred to runtime - for example if we are depending on side
            inputs to get the model path or we want to enforce a timeout on
            model loading.
        model_tag: Tag to use to disambiguate models in multi-model settings.
    """
    self._model_handler = model_handler
    self._shared_model_handle = shared.Shared()
    self._clock = clock
    self._model = None
    self._metrics_namespace = metrics_namespace
    self._load_model_at_runtime = load_model_at_runtime
    self._side_input_path = None
    # _model_tag is the original tag passed in.
    # _cur_tag is the tag of the actually loaded model
    self._model_tag = model_tag
    self._cur_tag = model_tag

  def _load_model(
      self,
      side_input_model_path: Optional[Union[str,
                                            list[KeyModelPathMapping]]] = None
  ) -> _SharedModelWrapper:
    def load():
      """Function for constructing shared LoadedModel."""
      memory_before = _get_current_process_memory_in_bytes()
      start_time = _to_milliseconds(self._clock.time_ns())
      if isinstance(side_input_model_path, str):
        self._model_handler.update_model_path(side_input_model_path)
      else:
        if self._model is not None:
          models = self._model.all_models()
          for m in models:
            self._model_handler.update_model_paths(m, side_input_model_path)
      model = self._model_handler.load_model()
      end_time = _to_milliseconds(self._clock.time_ns())
      memory_after = _get_current_process_memory_in_bytes()
      load_model_latency_ms = end_time - start_time
      model_byte_size = memory_after - memory_before
      if self._metrics_collector:
        self._metrics_collector.cache_load_model_metrics(
            load_model_latency_ms, model_byte_size)
      return model

    # TODO(https://github.com/apache/beam/issues/21443): Investigate releasing
    # model.
    model_tag = self._model_tag
    if isinstance(side_input_model_path, str) and side_input_model_path != '':
      model_tag = side_input_model_path
    # Ensure the tag we're loading is valid, if not replace it with a valid tag
    self._cur_tag = self._model_metadata.get_valid_tag(model_tag)
    if self._model_handler.share_model_across_processes():
      models = []
      for copy_tag in _get_tags_for_copies(self._cur_tag,
                                           self._model_handler.model_copies()):
        models.append(
            multi_process_shared.MultiProcessShared(
                load, tag=copy_tag, always_proxy=True).acquire())
      model_wrapper = _SharedModelWrapper(models, self._cur_tag)
    else:
      model = self._shared_model_handle.acquire(load, tag=self._cur_tag)
      model_wrapper = _SharedModelWrapper([model], self._cur_tag)
    # since shared_model_handle is shared across threads, the model path
    # might not get updated in the model handler
    # because we directly get cached weak ref model from shared cache, instead
    # of calling load(). For sanity check, call update_model_path again.
    if isinstance(side_input_model_path, str):
      self._model_handler.update_model_path(side_input_model_path)
    else:
      if self._model is not None:
        models = self._model.all_models()
        for m in models:
          self._model_handler.update_model_paths(m, side_input_model_path)
    return model_wrapper

  def get_metrics_collector(self, prefix: str = ''):
    """
    Args:
      prefix: Unique identifier for metrics, used when models
      are updated using side input.
    """
    metrics_namespace = (
        self._metrics_namespace) if self._metrics_namespace else (
            self._model_handler.get_metrics_namespace())
    if self._model_handler.override_metrics(metrics_namespace):
      return None
    return _MetricsCollector(metrics_namespace, prefix=prefix)

  def setup(self):
    self._metrics_collector = self.get_metrics_collector()
    self._model_handler.set_environment_vars()
    self._model_metadata = load_model_status(
        self._model_tag, self._model_handler.share_model_across_processes())
    if not self._load_model_at_runtime:
      self._model = self._load_model()

  def update_model(
      self,
      side_input_model_path: Optional[Union[str,
                                            list[KeyModelPathMapping]]] = None):
    self._model = self._load_model(side_input_model_path=side_input_model_path)

  def _run_inference(self, batch, inference_args):
    start_time = _to_microseconds(self._clock.time_ns())
    try:
      model = self._model.next_model()
      result_generator = self._model_handler.run_inference(
          batch, model, inference_args)
    except BaseException as e:
      if self._metrics_collector:
        self._metrics_collector.failed_batches_counter.inc()
      if (e is pickle.PickleError and
          self._model_handler.share_model_across_processes()):
        raise TypeError(
            'Pickling error encountered while running inference. '
            'This may be caused by trying to send unpickleable '
            'data to a model which is shared across processes. '
            'For more information, see '
            'https://beam.apache.org/documentation/ml/large-language-modeling/#pickling-errors'  # pylint: disable=line-too-long
        ) from e
      raise e
    predictions = list(result_generator)

    end_time = _to_microseconds(self._clock.time_ns())
    inference_latency = end_time - start_time
    num_bytes = self._model_handler.get_num_bytes(batch)
    num_elements = len(batch)
    if self._metrics_collector:
      self._metrics_collector.update(num_elements, num_bytes, inference_latency)

    return predictions

  def process(
      self,
      batch,
      inference_args,
      si_model_metadata: Optional[Union[ModelMetadata,
                                        list[ModelMetadata],
                                        list[KeyModelPathMapping]]]):
    """
    When side input is enabled:
      The method checks if the side input model has been updated, and if so,
      updates the model and runs inference on the batch of data. If the
      side input is empty or the model has not been updated, the method
      simply runs inference on the batch of data.
    """
    if not si_model_metadata:
      if (not self._model_metadata.is_valid_tag(self._cur_tag) or
          self._model is None):
        self.update_model(side_input_model_path=None)
      return self._run_inference(batch, inference_args)

    if isinstance(si_model_metadata, beam.pvalue.EmptySideInput):
      self.update_model(side_input_model_path=None)
    elif isinstance(si_model_metadata, list) and hasattr(si_model_metadata[0],
                                                         'keys'):
      # TODO(https://github.com/apache/beam/issues/27628): Update metrics here
      self.update_model(si_model_metadata)
    elif self._side_input_path != si_model_metadata.model_id:
      self._side_input_path = si_model_metadata.model_id
      self._metrics_collector = self.get_metrics_collector(
          prefix=si_model_metadata.model_name)
      lock = threading.Lock()
      with lock:
        self.update_model(si_model_metadata.model_id)
        return self._run_inference(batch, inference_args)

    return self._run_inference(batch, inference_args)

  def finish_bundle(self):
    # TODO(https://github.com/apache/beam/issues/21435): Figure out why there
    # is a cache.
    if self._metrics_collector:
      self._metrics_collector.update_metrics_with_cache()

    # Do garbage collection of old models
    tags_to_gc = self._model_metadata.get_tags_for_garbage_collection()
    if len(tags_to_gc) > 0:
      for unprefixed_tag in tags_to_gc:
        for tag in _get_tags_for_copies(unprefixed_tag,
                                        self._model_handler.model_copies()):
          multi_process_shared.MultiProcessShared(lambda: None,
                                                  tag).unsafe_hard_delete()
      self._model_metadata.mark_tags_deleted(tags_to_gc)


def _is_darwin() -> bool:
  return sys.platform == 'darwin'


def _get_current_process_memory_in_bytes():
  """
  Returns:
    memory usage in bytes.
  """

  if resource is not None:
    usage = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
    if _is_darwin():
      return usage
    return usage * 1024
  else:
    logging.warning(
        'Resource module is not available for current platform, '
        'memory usage cannot be fetched.')
  return 0


def _get_tags_for_copies(base_tag, num_copies):
  tags = []
  for i in range(num_copies):
    tags.append(f'{base_tag}{i}')
  return tags