text: # Lint as python3
  # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  # pylint: disable=g-import-not-at-top
  """Utilities for file download and caching."""
  
  import tensorflow.compat.v2 as tf
  
  from abc import abstractmethod
  from contextlib import closing
  import functools
  import hashlib
  import multiprocessing.dummy
  import os
  import pathlib
  import queue
  import random
  import shutil
  import tarfile
  import threading
  import time
  import typing
  import urllib
  import weakref
  import zipfile
  from six.moves.urllib.parse import urlsplit
  
  import numpy as np
  from six.moves.urllib.request import urlopen
  from keras.utils import tf_inspect
  from keras.utils.generic_utils import Progbar
  from keras.utils.io_utils import path_to_string
  from tensorflow.python.util.tf_export import keras_export
  
  # Required to support google internal urlretrieve
  if True:  # This gets transformed to `if sys.version_info[0] == 2:` in OSS.  # pylint: disable=using-constant-test
  
    def urlretrieve(url, filename, reporthook=None, data=None):
      """Replacement for `urlretrieve` for Python 2.
  
      Under Python 2, `urlretrieve` relies on `FancyURLopener` from legacy
      `urllib` module, known to have issues with proxy management.
  
      Args:
          url: url to retrieve.
          filename: where to store the retrieved data locally.
          reporthook: a hook function that will be called once on establishment of
            the network connection and once after each block read thereafter. The
            hook will be passed three arguments; a count of blocks transferred so
            far, a block size in bytes, and the total size of the file.
          data: `data` argument passed to `urlopen`.
      """
  
      def chunk_read(response, chunk_size=8192, reporthook=None):
        content_type = response.info().get('Content-Length')
        total_size = -1
        if content_type is not None:
          total_size = int(content_type.strip())
        count = 0
        while True:
          chunk = response.read(chunk_size)
          count += 1
          if reporthook is not None:
            reporthook(count, chunk_size, total_size)
          if chunk:
            yield chunk
          else:
            break
  
      response = urlopen(url, data)
      with open(filename, 'wb') as fd:
        for chunk in chunk_read(response, reporthook=reporthook):
          fd.write(chunk)
  else:
    from urllib.request import urlretrieve  # pylint: disable=g-importing-member
  
  
  def is_generator_or_sequence(x):
    """Check if `x` is a Keras generator type."""
    builtin_iterators = (str, list, tuple, dict, set, frozenset)
    if isinstance(x, (tf.Tensor, np.ndarray) + builtin_iterators):
      return False
    return (tf_inspect.isgenerator(x) or
            isinstance(x, Sequence) or
            isinstance(x, typing.Iterator))
  
  
  def _extract_archive(file_path, path='.', archive_format='auto'):
    """Extracts an archive if it matches tar, tar.gz, tar.bz, or zip formats.
  
    Args:
        file_path: path to the archive file
        path: path to extract the archive file
        archive_format: Archive format to try for extracting the file.
            Options are 'auto', 'tar', 'zip', and None.
            'tar' includes tar, tar.gz, and tar.bz files.
            The default 'auto' is ['tar', 'zip'].
            None or an empty list will return no matches found.
  
    Returns:
        True if a match was found and an archive extraction was completed,
        False otherwise.
    """
    if archive_format is None:
      return False
    if archive_format == 'auto':
      archive_format = ['tar', 'zip']
    if isinstance(archive_format, str):
      archive_format = [archive_format]
  
    file_path = path_to_string(file_path)
    path = path_to_string(path)
  
    for archive_type in archive_format:
      if archive_type == 'tar':
        open_fn = tarfile.open
        is_match_fn = tarfile.is_tarfile
      if archive_type == 'zip':
        open_fn = zipfile.ZipFile
        is_match_fn = zipfile.is_zipfile
  
      if is_match_fn(file_path):
        with open_fn(file_path) as archive:
          try:
            archive.extractall(path)
          except (tarfile.TarError, RuntimeError, KeyboardInterrupt):
            if os.path.exists(path):
              if os.path.isfile(path):
                os.remove(path)
              else:
                shutil.rmtree(path)
            raise
        return True
    return False
  
  
  @keras_export('keras.utils.get_file')
  def get_file(fname=None,
               origin=None,
               untar=False,
               md5_hash=None,
               file_hash=None,
               cache_subdir='datasets',
               hash_algorithm='auto',
               extract=False,
               archive_format='auto',
               cache_dir=None):
    """Downloads a file from a URL if it not already in the cache.
  
    By default the file at the url `origin` is downloaded to the
    cache_dir `~/.keras`, placed in the cache_subdir `datasets`,
    and given the filename `fname`. The final location of a file
    `example.txt` would therefore be `~/.keras/datasets/example.txt`.
  
    Files in tar, tar.gz, tar.bz, and zip formats can also be extracted.
    Passing a hash will verify the file after download. The command line
    programs `shasum` and `sha256sum` can compute the hash.
  
    Example:
  
    ```python
    path_to_downloaded_file = tf.keras.utils.get_file(
        "flower_photos",
        "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz",
        untar=True)
    ```
  
    Args:
        fname: Name of the file. If an absolute path `/path/to/file.txt` is
            specified the file will be saved at that location. If `None`, the
            name of the file at `origin` will be used.
        origin: Original URL of the file.
        untar: Deprecated in favor of `extract` argument.
            boolean, whether the file should be decompressed
        md5_hash: Deprecated in favor of `file_hash` argument.
            md5 hash of the file for verification
        file_hash: The expected hash string of the file after download.
            The sha256 and md5 hash algorithms are both supported.
        cache_subdir: Subdirectory under the Keras cache dir where the file is
            saved. If an absolute path `/path/to/folder` is
            specified the file will be saved at that location.
        hash_algorithm: Select the hash algorithm to verify the file.
            options are `'md5'`, `'sha256'`, and `'auto'`.
            The default 'auto' detects the hash algorithm in use.
        extract: True tries extracting the file as an Archive, like tar or zip.
        archive_format: Archive format to try for extracting the file.
            Options are `'auto'`, `'tar'`, `'zip'`, and `None`.
            `'tar'` includes tar, tar.gz, and tar.bz files.
            The default `'auto'` corresponds to `['tar', 'zip']`.
            None or an empty list will return no matches found.
        cache_dir: Location to store cached files, when None it
            defaults to the default directory `~/.keras/`.
  
    Returns:
        Path to the downloaded file
    """
    if origin is None:
      raise ValueError('Please specify the "origin" argument (URL of the file '
                       'to download).')
  
    if cache_dir is None:
      cache_dir = os.path.join(os.path.expanduser('~'), '.keras')
    if md5_hash is not None and file_hash is None:
      file_hash = md5_hash
      hash_algorithm = 'md5'
    datadir_base = os.path.expanduser(cache_dir)
    if not os.access(datadir_base, os.W_OK):
      datadir_base = os.path.join('/tmp', '.keras')
    datadir = os.path.join(datadir_base, cache_subdir)
    _makedirs_exist_ok(datadir)
  
    fname = path_to_string(fname)
    if not fname:
      fname = os.path.basename(urlsplit(origin).path)
      if not fname:
        raise ValueError("Invalid origin '{}'".format(origin))
  
    if untar:
      if fname.endswith('.tar.gz'):
        fname = pathlib.Path(fname)
        # The 2 `.with_suffix()` are because of `.tar.gz` as pathlib
        # considers it as 2 suffixes.
        fname = fname.with_suffix('').with_suffix('')
        fname = str(fname)
      untar_fpath = os.path.join(datadir, fname)
      fpath = untar_fpath + '.tar.gz'
    else:
      fpath = os.path.join(datadir, fname)
  
    download = False
    if os.path.exists(fpath):
      # File found; verify integrity if a hash was provided.
      if file_hash is not None:
        if not validate_file(fpath, file_hash, algorithm=hash_algorithm):
          print('A local file was found, but it seems to be '
                'incomplete or outdated because the ' + hash_algorithm +
                ' file hash does not match the original value of ' + file_hash +
                ' so we will re-download the data.')
          download = True
    else:
      download = True
  
    if download:
      print('Downloading data from', origin)
  
      class ProgressTracker:
        # Maintain progbar for the lifetime of download.
        # This design was chosen for Python 2.7 compatibility.
        progbar = None
  
      def dl_progress(count, block_size, total_size):
        if ProgressTracker.progbar is None:
          if total_size == -1:
            total_size = None
          ProgressTracker.progbar = Progbar(total_size)
        else:
          ProgressTracker.progbar.update(count * block_size)
  
      error_msg = 'URL fetch failure on {}: {} -- {}'
      try:
        try:
          urlretrieve(origin, fpath, dl_progress)
        except urllib.error.HTTPError as e:
          raise Exception(error_msg.format(origin, e.code, e.msg))
        except urllib.error.URLError as e:
          raise Exception(error_msg.format(origin, e.errno, e.reason))
      except (Exception, KeyboardInterrupt) as e:
        if os.path.exists(fpath):
          os.remove(fpath)
        raise
      ProgressTracker.progbar = None
  
    if untar:
      if not os.path.exists(untar_fpath):
        _extract_archive(fpath, datadir, archive_format='tar')
      return untar_fpath
  
    if extract:
      _extract_archive(fpath, datadir, archive_format)
  
    return fpath
  
  
  def _makedirs_exist_ok(datadir):
    os.makedirs(datadir, exist_ok=True)  # pylint: disable=unexpected-keyword-arg
  
  
  def _resolve_hasher(algorithm, file_hash=None):
    """Returns hash algorithm as hashlib function."""
    if algorithm == 'sha256':
      return hashlib.sha256()
  
    if algorithm == 'auto' and file_hash is not None and len(file_hash) == 64:
      return hashlib.sha256()
  
    # This is used only for legacy purposes.
    return hashlib.md5()
  
  
  def _hash_file(fpath, algorithm='sha256', chunk_size=65535):
    """Calculates a file sha256 or md5 hash.
  
    Example:
  
    ```python
    _hash_file('/path/to/file.zip')
    'e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855'
    ```
  
    Args:
        fpath: path to the file being validated
        algorithm: hash algorithm, one of `'auto'`, `'sha256'`, or `'md5'`.
            The default `'auto'` detects the hash algorithm in use.
        chunk_size: Bytes to read at a time, important for large files.
  
    Returns:
        The file hash
    """
    if isinstance(algorithm, str):
      hasher = _resolve_hasher(algorithm)
    else:
      hasher = algorithm
  
    with open(fpath, 'rb') as fpath_file:
      for chunk in iter(lambda: fpath_file.read(chunk_size), b''):
        hasher.update(chunk)
  
    return hasher.hexdigest()
  
  
  def validate_file(fpath, file_hash, algorithm='auto', chunk_size=65535):
    """Validates a file against a sha256 or md5 hash.
  
    Args:
        fpath: path to the file being validated
        file_hash:  The expected hash string of the file.
            The sha256 and md5 hash algorithms are both supported.
        algorithm: Hash algorithm, one of 'auto', 'sha256', or 'md5'.
            The default 'auto' detects the hash algorithm in use.
        chunk_size: Bytes to read at a time, important for large files.
  
    Returns:
        Whether the file is valid
    """
    hasher = _resolve_hasher(algorithm, file_hash)
  
    if str(_hash_file(fpath, hasher, chunk_size)) == str(file_hash):
      return True
    else:
      return False
  
  
  class ThreadsafeIter:
    """Wrap an iterator with a lock and propagate exceptions to all threads."""
  
    def __init__(self, it):
      self.it = it
      self.lock = threading.Lock()
  
      # After a generator throws an exception all subsequent next() calls raise a
      # StopIteration Exception. This, however, presents an issue when mixing
      # generators and threading because it means the order of retrieval need not
      # match the order in which the generator was called. This can make it appear
      # that a generator exited normally when in fact the terminating exception is
      # just in a different thread. In order to provide thread safety, once
      # self.it has thrown an exception we continue to throw the same exception.
      self._exception = None
  
    def __iter__(self):
      return self
  
    def next(self):
      return self.__next__()
  
    def __next__(self):
      with self.lock:
        if self._exception:
          raise self._exception  # pylint: disable=raising-bad-type
  
        try:
          return next(self.it)
        except Exception as e:
          self._exception = e
          raise
  
  
  def threadsafe_generator(f):
  
    @functools.wraps(f)
    def g(*a, **kw):
      return ThreadsafeIter(f(*a, **kw))
  
    return g
  
  
  @keras_export('keras.utils.Sequence')
  class Sequence:
    """Base object for fitting to a sequence of data, such as a dataset.
  
    Every `Sequence` must implement the `__getitem__` and the `__len__` methods.
    If you want to modify your dataset between epochs you may implement
    `on_epoch_end`.
    The method `__getitem__` should return a complete batch.
  
    Notes:
  
    `Sequence` are a safer way to do multiprocessing. This structure guarantees
    that the network will only train once
     on each sample per epoch which is not the case with generators.
  
    Examples:
  
    ```python
    from skimage.io import imread
    from skimage.transform import resize
    import numpy as np
    import math
  
    # Here, `x_set` is list of path to the images
    # and `y_set` are the associated classes.
  
    class CIFAR10Sequence(Sequence):
  
        def __init__(self, x_set, y_set, batch_size):
            self.x, self.y = x_set, y_set
            self.batch_size = batch_size
  
        def __len__(self):
            return math.ceil(len(self.x) / self.batch_size)
  
        def __getitem__(self, idx):
            batch_x = self.x[idx * self.batch_size:(idx + 1) *
            self.batch_size]
            batch_y = self.y[idx * self.batch_size:(idx + 1) *
            self.batch_size]
  
            return np.array([
                resize(imread(file_name), (200, 200))
                   for file_name in batch_x]), np.array(batch_y)
    ```
    """
  
    @abstractmethod
    def __getitem__(self, index):
      """Gets batch at position `index`.
  
      Args:
          index: position of the batch in the Sequence.
  
      Returns:
          A batch
      """
      raise NotImplementedError
  
    @abstractmethod
    def __len__(self):
      """Number of batch in the Sequence.
  
      Returns:
          The number of batches in the Sequence.
      """
      raise NotImplementedError
  
    def on_epoch_end(self):
      """Method called at the end of every epoch.
      """
      pass
  
    def __iter__(self):
      """Create a generator that iterate over the Sequence."""
      for item in (self[i] for i in range(len(self))):
        yield item
  
  
  def iter_sequence_infinite(seq):
    """Iterates indefinitely over a Sequence.
  
    Args:
      seq: `Sequence` instance.
  
    Yields:
      Batches of data from the `Sequence`.
    """
    while True:
      for item in seq:
        yield item
  
  
  # Global variables to be shared across processes
  _SHARED_SEQUENCES = {}
  # We use a Value to provide unique id to different processes.
  _SEQUENCE_COUNTER = None
  
  
  # Because multiprocessing pools are inherently unsafe, starting from a clean
  # state can be essential to avoiding deadlocks. In order to accomplish this, we
  # need to be able to check on the status of Pools that we create.
  _DATA_POOLS = weakref.WeakSet()
  _WORKER_ID_QUEUE = None  # Only created if needed.
  _WORKER_IDS = set()
  _FORCE_THREADPOOL = False
  _FORCE_THREADPOOL_LOCK = threading.RLock()
  
  
  def dont_use_multiprocessing_pool(f):
    @functools.wraps(f)
    def wrapped(*args, **kwargs):
      with _FORCE_THREADPOOL_LOCK:
        global _FORCE_THREADPOOL
        old_force_threadpool, _FORCE_THREADPOOL = _FORCE_THREADPOOL, True
        out = f(*args, **kwargs)
        _FORCE_THREADPOOL = old_force_threadpool
        return out
    return wrapped
  
  
  def get_pool_class(use_multiprocessing):
    global _FORCE_THREADPOOL
    if not use_multiprocessing or _FORCE_THREADPOOL:
      return multiprocessing.dummy.Pool  # ThreadPool
    return multiprocessing.Pool
  
  
  def get_worker_id_queue():
    """Lazily create the queue to track worker ids."""
    global _WORKER_ID_QUEUE
    if _WORKER_ID_QUEUE is None:
      _WORKER_ID_QUEUE = multiprocessing.Queue()
    return _WORKER_ID_QUEUE
  
  
  def init_pool(seqs):
    global _SHARED_SEQUENCES
    _SHARED_SEQUENCES = seqs
  
  
  def get_index(uid, i):
    """Get the value from the Sequence `uid` at index `i`.
  
    To allow multiple Sequences to be used at the same time, we use `uid` to
    get a specific one. A single Sequence would cause the validation to
    overwrite the training Sequence.
  
    Args:
        uid: int, Sequence identifier
        i: index
  
    Returns:
        The value at index `i`.
    """
    return _SHARED_SEQUENCES[uid][i]
  
  
  @keras_export('keras.utils.SequenceEnqueuer')
  class SequenceEnqueuer:
    """Base class to enqueue inputs.
  
    The task of an Enqueuer is to use parallelism to speed up preprocessing.
    This is done with processes or threads.
  
    Example:
  
    ```python
        enqueuer = SequenceEnqueuer(...)
        enqueuer.start()
        datas = enqueuer.get()
        for data in datas:
            # Use the inputs; training, evaluating, predicting.
            # ... stop sometime.
        enqueuer.stop()
    ```
  
    The `enqueuer.get()` should be an infinite stream of datas.
    """
  
    def __init__(self, sequence,
                 use_multiprocessing=False):
      self.sequence = sequence
      self.use_multiprocessing = use_multiprocessing
  
      global _SEQUENCE_COUNTER
      if _SEQUENCE_COUNTER is None:
        try:
          _SEQUENCE_COUNTER = multiprocessing.Value('i', 0)
        except OSError:
          # In this case the OS does not allow us to use
          # multiprocessing. We resort to an int
          # for enqueuer indexing.
          _SEQUENCE_COUNTER = 0
  
      if isinstance(_SEQUENCE_COUNTER, int):
        self.uid = _SEQUENCE_COUNTER
        _SEQUENCE_COUNTER += 1
      else:
        # Doing Multiprocessing.Value += x is not process-safe.
        with _SEQUENCE_COUNTER.get_lock():
          self.uid = _SEQUENCE_COUNTER.value
          _SEQUENCE_COUNTER.value += 1
  
      self.workers = 0
      self.executor_fn = None
      self.queue = None
      self.run_thread = None
      self.stop_signal = None
  
    def is_running(self):
      return self.stop_signal is not None and not self.stop_signal.is_set()
  
    def start(self, workers=1, max_queue_size=10):
      """Starts the handler's workers.
  
      Args:
          workers: Number of workers.
          max_queue_size: queue size
              (when full, workers could block on `put()`)
      """
      if self.use_multiprocessing:
        self.executor_fn = self._get_executor_init(workers)
      else:
        # We do not need the init since it's threads.
        self.executor_fn = lambda _: get_pool_class(False)(workers)
      self.workers = workers
      self.queue = queue.Queue(max_queue_size)
      self.stop_signal = threading.Event()
      self.run_thread = threading.Thread(target=self._run)
      self.run_thread.daemon = True
      self.run_thread.start()
  
    def _send_sequence(self):
      """Sends current Iterable to all workers."""
      # For new processes that may spawn
      _SHARED_SEQUENCES[self.uid] = self.sequence
  
    def stop(self, timeout=None):
      """Stops running threads and wait for them to exit, if necessary.
  
      Should be called by the same thread which called `start()`.
  
      Args:
          timeout: maximum time to wait on `thread.join()`
      """
      self.stop_signal.set()
      with self.queue.mutex:
        self.queue.queue.clear()
        self.queue.unfinished_tasks = 0
        self.queue.not_full.notify()
      self.run_thread.join(timeout)
      _SHARED_SEQUENCES[self.uid] = None
  
    def __del__(self):
      if self.is_running():
        self.stop()
  
    @abstractmethod
    def _run(self):
      """Submits request to the executor and queue the `Future` objects."""
      raise NotImplementedError
  
    @abstractmethod
    def _get_executor_init(self, workers):
      """Gets the Pool initializer for multiprocessing.
  
      Args:
          workers: Number of workers.
  
      Returns:
          Function, a Function to initialize the pool
      """
      raise NotImplementedError
  
    @abstractmethod
    def get(self):
      """Creates a generator to extract data from the queue.
  
      Skip the data if it is `None`.
      # Returns
          Generator yielding tuples `(inputs, targets)`
              or `(inputs, targets, sample_weights)`.
      """
      raise NotImplementedError
  
  
  @keras_export('keras.utils.OrderedEnqueuer')
  class OrderedEnqueuer(SequenceEnqueuer):
    """Builds a Enqueuer from a Sequence.
  
    Args:
        sequence: A `tf.keras.utils.data_utils.Sequence` object.
        use_multiprocessing: use multiprocessing if True, otherwise threading
        shuffle: whether to shuffle the data at the beginning of each epoch
    """
  
    def __init__(self, sequence, use_multiprocessing=False, shuffle=False):
      super(OrderedEnqueuer, self).__init__(sequence, use_multiprocessing)
      self.shuffle = shuffle
  
    def _get_executor_init(self, workers):
      """Gets the Pool initializer for multiprocessing.
  
      Args:
          workers: Number of workers.
  
      Returns:
          Function, a Function to initialize the pool
      """
      def pool_fn(seqs):
        pool = get_pool_class(True)(
            workers, initializer=init_pool_generator,
            initargs=(seqs, None, get_worker_id_queue()))
        _DATA_POOLS.add(pool)
        return pool
  
      return pool_fn
  
    def _wait_queue(self):
      """Wait for the queue to be empty."""
      while True:
        time.sleep(0.1)
        if self.queue.unfinished_tasks == 0 or self.stop_signal.is_set():
          return
  
    def _run(self):
      """Submits request to the executor and queue the `Future` objects."""
      sequence = list(range(len(self.sequence)))
      self._send_sequence()  # Share the initial sequence
      while True:
        if self.shuffle:
          random.shuffle(sequence)
  
        with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:
          for i in sequence:
            if self.stop_signal.is_set():
              return
  
            self.queue.put(
                executor.apply_async(get_index, (self.uid, i)), block=True)
  
          # Done with the current epoch, waiting for the final batches
          self._wait_queue()
  
          if self.stop_signal.is_set():
            # We're done
            return
  
        # Call the internal on epoch end.
        self.sequence.on_epoch_end()
        self._send_sequence()  # Update the pool
  
    def get(self):
      """Creates a generator to extract data from the queue.
  
      Skip the data if it is `None`.
  
      Yields:
          The next element in the queue, i.e. a tuple
          `(inputs, targets)` or
          `(inputs, targets, sample_weights)`.
      """
      while self.is_running():
        try:
          inputs = self.queue.get(block=True, timeout=5).get()
          if self.is_running():
            self.queue.task_done()
          if inputs is not None:
            yield inputs
        except queue.Empty:
          pass
        except Exception as e:  # pylint: disable=broad-except
          self.stop()
          raise e
  
  
  def init_pool_generator(gens, random_seed=None, id_queue=None):
    """Initializer function for pool workers.
  
    Args:
      gens: State which should be made available to worker processes.
      random_seed: An optional value with which to seed child processes.
      id_queue: A multiprocessing Queue of worker ids. This is used to indicate
        that a worker process was created by Keras and can be terminated using
        the cleanup_all_keras_forkpools utility.
    """
    global _SHARED_SEQUENCES
    _SHARED_SEQUENCES = gens
  
    worker_proc = multiprocessing.current_process()
  
    # name isn't used for anything, but setting a more descriptive name is helpful
    # when diagnosing orphaned processes.
    worker_proc.name = 'Keras_worker_{}'.format(worker_proc.name)
  
    if random_seed is not None:
      np.random.seed(random_seed + worker_proc.ident)
  
    if id_queue is not None:
      # If a worker dies during init, the pool will just create a replacement.
      id_queue.put(worker_proc.ident, block=True, timeout=0.1)
  
  
  def next_sample(uid):
    """Gets the next value from the generator `uid`.
  
    To allow multiple generators to be used at the same time, we use `uid` to
    get a specific one. A single generator would cause the validation to
    overwrite the training generator.
  
    Args:
        uid: int, generator identifier
  
    Returns:
        The next value of generator `uid`.
    """
    return next(_SHARED_SEQUENCES[uid])
  
  
  @keras_export('keras.utils.GeneratorEnqueuer')
  class GeneratorEnqueuer(SequenceEnqueuer):
    """Builds a queue out of a data generator.
  
    The provided generator can be finite in which case the class will throw
    a `StopIteration` exception.
  
    Args:
        generator: a generator function which yields data
        use_multiprocessing: use multiprocessing if True, otherwise threading
        random_seed: Initial seed for workers,
            will be incremented by one for each worker.
    """
  
    def __init__(self, generator,
                 use_multiprocessing=False,
                 random_seed=None):
      super(GeneratorEnqueuer, self).__init__(generator, use_multiprocessing)
      self.random_seed = random_seed
  
    def _get_executor_init(self, workers):
      """Gets the Pool initializer for multiprocessing.
  
      Args:
        workers: Number of works.
  
      Returns:
          A Function to initialize the pool
      """
      def pool_fn(seqs):
        pool = get_pool_class(True)(
            workers, initializer=init_pool_generator,
            initargs=(seqs, self.random_seed, get_worker_id_queue()))
        _DATA_POOLS.add(pool)
        return pool
      return pool_fn
  
    def _run(self):
      """Submits request to the executor and queue the `Future` objects."""
      self._send_sequence()  # Share the initial generator
      with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:
        while True:
          if self.stop_signal.is_set():
            return
  
          self.queue.put(
              executor.apply_async(next_sample, (self.uid,)), block=True)
  
    def get(self):
      """Creates a generator to extract data from the queue.
  
      Skip the data if it is `None`.
  
      Yields:
          The next element in the queue, i.e. a tuple
          `(inputs, targets)` or
          `(inputs, targets, sample_weights)`.
      """
      try:
        while self.is_running():
          inputs = self.queue.get(block=True).get()
          self.queue.task_done()
          if inputs is not None:
            yield inputs
      except StopIteration:
        # Special case for finite generators
        last_ones = []
        while self.queue.qsize() > 0:
          last_ones.append(self.queue.get(block=True))
        # Wait for them to complete
        for f in last_ones:
          f.wait()
        # Keep the good ones
        last_ones = [future.get() for future in last_ones if future.successful()]
        for inputs in last_ones:
          if inputs is not None:
            yield inputs
      except Exception as e:  # pylint: disable=broad-except
        self.stop()
        if 'generator already executing' in str(e):
          raise RuntimeError(
              'Your generator is NOT thread-safe. '
              'Keras requires a thread-safe generator when '
              '`use_multiprocessing=False, workers > 1`. ')
        raise e
  