# Path: tensorflow_transform/annotators.py
# _ASSET_KEY_COLLECTION = 'tft_asset_key_collection'
# _ASSET_FILENAME_COLLECTION = 'tft_asset_filename_collection'
# _OBJECT_TRACKER = None
# VOCABULARY_SIZE_BY_NAME_COLLECTION = 'tft_vocabulary_size_by_name_collection'
#   _OBJECT_TRACKER = object_tracker
#     _OBJECT_TRACKER = None
# class ObjectTracker:
#   def __init__(self):
#   def trackable_objects(self) -> List[base.Trackable]:
#   def add_trackable_object(self, trackable_object: base.Trackable,
#                            name: Optional[str]):
# def object_tracker_scope(object_tracker: ObjectTracker):
# def _get_object(name: str) -> Optional[base.Trackable]:
# def track_object(trackable: base.Trackable, name: Optional[str]):
# def make_and_track_object(trackable_factory_callable: Callable[[],
#                                                                base.Trackable],
#                           name: Optional[str] = None) -> base.Trackable:
# def get_asset_annotations(graph: tf.Graph):
# def clear_asset_annotations(graph: tf.Graph):
# def annotate_asset(asset_key: str, asset_filename: str):
# def annotate_vocab_size(vocab_filename: str, vocab_size: tf.Tensor):


import tensorflow as tf
from tensorflow_transform import annotators
from tensorflow.python.framework import ops  # pylint: disable=g-direct-tensorflow-import
  ...   x_int = tft.compute_and_apply_vocabulary(
  ...     inputs['x'], vocab_filename='my_vocab',
  ...     num_oov_buckets=num_oov_buckets)
  ...   depth = (
  ...     tft.experimental.get_vocabulary_size_by_name('my_vocab') +
  ...     num_oov_buckets)
  ...   x_encoded = tf.one_hot(
  ...     x_int, depth=tf.cast(depth, tf.int32), dtype=tf.int64)
  ...   return {'x_encoded': x_encoded}
  >>> raw_data = [dict(x='foo'), dict(x='foo'), dict(x='bar')]
  >>> feature_spec = dict(x=tf.io.FixedLenFeature([], tf.string))
  >>> raw_data_metadata = tft.DatasetMetadata.from_feature_spec(feature_spec)
  >>> with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
  ...   transformed_dataset, transform_fn = (
  ...       (raw_data, raw_data_metadata)
  ...       | tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
  >>> transformed_data, transformed_metadata = transformed_dataset
  >>> transformed_data
  [{'x_encoded': array([1, 0, 0])}, {'x_encoded': array([1, 0, 0])},
  {'x_encoded': array([0, 1, 0])}]

  Returns:
    An integer tensor containing the size of the requested vocabulary.

  Raises:
    ValueError: if no vocabulary size found for the given `vocab_filename`.

  """
  # pyformat: enable
  vocabulary_sizes_coll = ops.get_default_graph().get_collection(

# Path: tensorflow_transform/annotators.py
# _ASSET_KEY_COLLECTION = 'tft_asset_key_collection'
# _ASSET_FILENAME_COLLECTION = 'tft_asset_filename_collection'
# _OBJECT_TRACKER = None
# VOCABULARY_SIZE_BY_NAME_COLLECTION = 'tft_vocabulary_size_by_name_collection'
#   _OBJECT_TRACKER = object_tracker
#     _OBJECT_TRACKER = None
# class ObjectTracker:
#   def __init__(self):
#   def trackable_objects(self) -> List[base.Trackable]:
#   def add_trackable_object(self, trackable_object: base.Trackable,
#                            name: Optional[str]):
# def object_tracker_scope(object_tracker: ObjectTracker):
# def _get_object(name: str) -> Optional[base.Trackable]:
# def track_object(trackable: base.Trackable, name: Optional[str]):
# def make_and_track_object(trackable_factory_callable: Callable[[],
#                                                                base.Trackable],
#                           name: Optional[str] = None) -> base.Trackable:
# def get_asset_annotations(graph: tf.Graph):
# def clear_asset_annotations(graph: tf.Graph):
# def annotate_asset(asset_key: str, asset_filename: str):
# def annotate_vocab_size(vocab_filename: str, vocab_size: tf.Tensor):


# Path: tensorflow_transform/experimental/annotators.py
import tensorflow as tf
from tensorflow_transform import annotators
from tensorflow.python.framework import ops  # pylint: disable=g-direct-tensorflow-import

  ...   x_int = tft.compute_and_apply_vocabulary(
  ...     inputs['x'], vocab_filename='my_vocab',
  ...     num_oov_buckets=num_oov_buckets)
  ...   depth = (
  ...     tft.experimental.get_vocabulary_size_by_name('my_vocab') +
  ...     num_oov_buckets)
  ...   x_encoded = tf.one_hot(
  ...     x_int, depth=tf.cast(depth, tf.int32), dtype=tf.int64)
  ...   return {'x_encoded': x_encoded}
  >>> raw_data = [dict(x='foo'), dict(x='foo'), dict(x='bar')]
  >>> feature_spec = dict(x=tf.io.FixedLenFeature([], tf.string))
  >>> raw_data_metadata = tft.DatasetMetadata.from_feature_spec(feature_spec)
  >>> with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
  ...   transformed_dataset, transform_fn = (
  ...       (raw_data, raw_data_metadata)
  ...       | tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
  >>> transformed_data, transformed_metadata = transformed_dataset
  >>> transformed_data
  [{'x_encoded': array([1, 0, 0])}, {'x_encoded': array([1, 0, 0])},
  {'x_encoded': array([0, 1, 0])}]

  Returns:
    An integer tensor containing the size of the requested vocabulary.

  Raises:
    ValueError: if no vocabulary size found for the given `vocab_filename`.

  """
  # pyformat: enable
  vocabulary_sizes_coll = ops.get_default_graph().get_collection(

      annotators.VOCABULARY_SIZE_BY_NAME_COLLECTION)