Merge branch 'hand-landmarker-python' of https://github.com/kinaryml/mediapipe into hand-landmarker-python

mediapipe/framework/api2/builder.h

@@ -106,6 +106,13 @@ class MultiPort : public Single {
     return Single{&GetWithAutoGrow(&vec_, index)};
   }
 
+  template <typename U>
+  auto Cast() {
+    using SingleCastT =
+        std::invoke_result_t<decltype(&Single::template Cast<U>), Single*>;
+    return MultiPort<SingleCastT>(&vec_);
+  }
+
  private:
   std::vector<std::unique_ptr<Base>>& vec_;
 };

mediapipe/framework/api2/builder_test.cc

@@ -445,6 +445,57 @@ TEST(BuilderTest, AnyTypeCanBeCast) {
   EXPECT_THAT(graph.GetConfig(), EqualsProto(expected));
 }
 
+TEST(BuilderTest, MultiPortIsCastToMultiPort) {
+  builder::Graph graph;
+  builder::MultiSource<AnyType> any_input = graph.In("ANY_INPUT");
+  builder::MultiSource<int> int_input = any_input.Cast<int>();
+  builder::MultiDestination<AnyType> any_output = graph.Out("ANY_OUTPUT");
+  builder::MultiDestination<int> int_output = any_output.Cast<int>();
+  int_input >> int_output;
+
+  CalculatorGraphConfig expected =
+      mediapipe::ParseTextProtoOrDie<CalculatorGraphConfig>(R"pb(
+        input_stream: "ANY_INPUT:__stream_0"
+        output_stream: "ANY_OUTPUT:__stream_0"
+      )pb");
+  EXPECT_THAT(graph.GetConfig(), EqualsProto(expected));
+}
+
+TEST(BuilderTest, MultiPortCanBeSlicedToSinglePort) {
+  builder::Graph graph;
+  builder::MultiSource<AnyType> any_multi_input = graph.In("ANY_INPUT");
+  builder::Source<AnyType> any_input = any_multi_input;
+  builder::MultiDestination<AnyType> any_multi_output = graph.Out("ANY_OUTPUT");
+  builder::Destination<AnyType> any_output = any_multi_output;
+  any_input >> any_output;
+
+  CalculatorGraphConfig expected =
+      mediapipe::ParseTextProtoOrDie<CalculatorGraphConfig>(R"pb(
+        input_stream: "ANY_INPUT:__stream_0"
+        output_stream: "ANY_OUTPUT:__stream_0"
+      )pb");
+  EXPECT_THAT(graph.GetConfig(), EqualsProto(expected));
+}
+
+TEST(BuilderTest, SinglePortAccessWorksThroughSlicing) {
+  builder::Graph graph;
+  builder::Source<int> int_input = graph.In("INT_INPUT").Cast<int>();
+  builder::Source<AnyType> any_input = graph.In("ANY_OUTPUT");
+  builder::Destination<int> int_output = graph.Out("INT_OUTPUT").Cast<int>();
+  builder::Destination<AnyType> any_output = graph.Out("ANY_OUTPUT");
+  int_input >> int_output;
+  any_input >> any_output;
+
+  CalculatorGraphConfig expected =
+      mediapipe::ParseTextProtoOrDie<CalculatorGraphConfig>(R"pb(
+        input_stream: "ANY_OUTPUT:__stream_0"
+        input_stream: "INT_INPUT:__stream_1"
+        output_stream: "ANY_OUTPUT:__stream_0"
+        output_stream: "INT_OUTPUT:__stream_1"
+      )pb");
+  EXPECT_THAT(graph.GetConfig(), EqualsProto(expected));
+}
+
 }  // namespace test
 }  // namespace api2
 }  // namespace mediapipe

mediapipe/model_maker/python/core/tasks/BUILD

@@ -45,7 +45,10 @@ py_library(
     srcs = ["classifier.py"],
     deps = [
         ":custom_model",
+        "//mediapipe/model_maker/python/core:hyperparameters",
+        "//mediapipe/model_maker/python/core/data:classification_dataset",
         "//mediapipe/model_maker/python/core/data:dataset",
+        "//mediapipe/model_maker/python/core/utils:model_util",
     ],
 )
 

mediapipe/model_maker/python/core/tasks/classifier.py

@@ -13,24 +13,24 @@
 # limitations under the License.
 """Custom classifier."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import os
-from typing import Any, List
+from typing import Any, Callable, Optional, Sequence, Union
 
 import tensorflow as tf
 
+from mediapipe.model_maker.python.core import hyperparameters as hp
+from mediapipe.model_maker.python.core.data import classification_dataset as classification_ds
 from mediapipe.model_maker.python.core.data import dataset
 from mediapipe.model_maker.python.core.tasks import custom_model
+from mediapipe.model_maker.python.core.utils import model_util
 
 
 class Classifier(custom_model.CustomModel):
   """An abstract base class that represents a TensorFlow classifier."""
 
-  def __init__(self, model_spec: Any, label_names: List[str], shuffle: bool):
+  def __init__(self, model_spec: Any, label_names: Sequence[str],
-    """Initilizes a classifier with its specifications.
+               shuffle: bool):
+    """Initializes a classifier with its specifications.
 
     Args:
         model_spec: Specification for the model.
@@ -40,6 +40,59 @@ class Classifier(custom_model.CustomModel):
     super(Classifier, self).__init__(model_spec, shuffle)
     self._label_names = label_names
     self._num_classes = len(label_names)
+    self._model: tf.keras.Model = None
+    self._optimizer: Union[str, tf.keras.optimizers.Optimizer] = None
+    self._loss_function: Union[str, tf.keras.losses.Loss] = None
+    self._metric_function: Union[str, tf.keras.metrics.Metric] = None
+    self._callbacks: Sequence[tf.keras.callbacks.Callback] = None
+    self._hparams: hp.BaseHParams = None
+    self._history: tf.keras.callbacks.History = None
+
+  # TODO: Integrate this into all Model Maker tasks.
+  def _train_model(self,
+                   train_data: classification_ds.ClassificationDataset,
+                   validation_data: classification_ds.ClassificationDataset,
+                   preprocessor: Optional[Callable[..., bool]] = None):
+    """Trains the classifier model.
+
+    Compiles and fits the tf.keras `_model` and records the `_history`.
+
+    Args:
+      train_data: Training data.
+      validation_data: Validation data.
+      preprocessor: An optional data preprocessor that can be used when
+        generating a tf.data.Dataset.
+    """
+    tf.compat.v1.logging.info('Training the models...')
+    if len(train_data) < self._hparams.batch_size:
+      raise ValueError(
+          f'The size of the train_data {len(train_data)} can\'t be smaller than'
+          f' batch_size {self._hparams.batch_size}. To solve this problem, set'
+          ' the batch_size smaller or increase the size of the train_data.')
+
+    train_dataset = train_data.gen_tf_dataset(
+        batch_size=self._hparams.batch_size,
+        is_training=True,
+        shuffle=self._shuffle,
+        preprocess=preprocessor)
+    self._hparams.steps_per_epoch = model_util.get_steps_per_epoch(
+        steps_per_epoch=self._hparams.steps_per_epoch,
+        batch_size=self._hparams.batch_size,
+        train_data=train_data)
+    train_dataset = train_dataset.take(count=self._hparams.steps_per_epoch)
+    validation_dataset = validation_data.gen_tf_dataset(
+        batch_size=self._hparams.batch_size,
+        is_training=False,
+        preprocess=preprocessor)
+    self._model.compile(
+        optimizer=self._optimizer,
+        loss=self._loss_function,
+        metrics=[self._metric_function])
+    self._history = self._model.fit(
+        x=train_dataset,
+        epochs=self._hparams.epochs,
+        validation_data=validation_dataset,
+        callbacks=self._callbacks)
 
   def evaluate(self, data: dataset.Dataset, batch_size: int = 32) -> Any:
     """Evaluates the classifier with the provided evaluation dataset.

mediapipe/model_maker/python/core/utils/BUILD

@@ -35,6 +35,7 @@ py_library(
     name = "model_util",
     srcs = ["model_util.py"],
     deps = [
+        ":file_util",
         ":quantization",
         "//mediapipe/model_maker/python/core/data:dataset",
     ],
@@ -50,6 +51,18 @@ py_test(
     ],
 )
 
+py_library(
+    name = "file_util",
+    srcs = ["file_util.py"],
+)
+
+py_test(
+    name = "file_util_test",
+    srcs = ["file_util_test.py"],
+    data = ["//mediapipe/model_maker/python/core/utils/testdata"],
+    deps = [":file_util"],
+)
+
 py_library(
     name = "loss_functions",
     srcs = ["loss_functions.py"],

mediapipe/model_maker/python/core/utils/file_util.py

@@ -0,0 +1,36 @@
+# Copyright 2022 The MediaPipe 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.
+"""Utilities for files."""
+
+import os
+
+# resources dependency
+
+
+def get_absolute_path(file_path: str) -> str:
+  """Gets the absolute path of a file.
+
+  Args:
+    file_path: The path to a file relative to the `mediapipe` dir
+
+  Returns:
+   The full path of the file
+  """
+  # Extract the file path before mediapipe/ as the `base_dir`. By joining it
+  # with the `path` which defines the relative path under mediapipe/, it
+  # yields to the absolute path of the model files directory.
+  cwd = os.path.dirname(__file__)
+  base_dir = cwd[:cwd.rfind('mediapipe')]
+  absolute_path = os.path.join(base_dir, file_path)
+  return absolute_path

mediapipe/model_maker/python/core/utils/file_util_test.py

@@ -0,0 +1,29 @@
+# Copyright 2022 The MediaPipe 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.
+import os
+
+from absl.testing import absltest
+from mediapipe.model_maker.python.core.utils import file_util
+
+
+class FileUtilTest(absltest.TestCase):
+
+  def test_get_absolute_path(self):
+    test_file = 'mediapipe/model_maker/python/core/utils/testdata/test.txt'
+    absolute_path = file_util.get_absolute_path(test_file)
+    self.assertTrue(os.path.exists(absolute_path))
+
+
+if __name__ == '__main__':
+  absltest.main()

mediapipe/model_maker/python/core/utils/model_util.py

@@ -11,7 +11,7 @@
 # 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.
-"""Utilities for keras models."""
+"""Utilities for models."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -26,8 +26,8 @@ from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 import numpy as np
 import tensorflow as tf
 
-# resources dependency
 from mediapipe.model_maker.python.core.data import dataset
+from mediapipe.model_maker.python.core.utils import file_util
 from mediapipe.model_maker.python.core.utils import quantization
 
 DEFAULT_SCALE, DEFAULT_ZERO_POINT = 0, 0
@@ -39,11 +39,10 @@ def get_default_callbacks(
   """Gets default callbacks."""
   summary_dir = os.path.join(export_dir, 'summaries')
   summary_callback = tf.keras.callbacks.TensorBoard(summary_dir)
-  # Save checkpoint every 20 epochs.
 
   checkpoint_path = os.path.join(export_dir, 'checkpoint')
   checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
-      checkpoint_path, save_weights_only=True, period=20)
+      checkpoint_path, save_weights_only=True)
   return [summary_callback, checkpoint_callback]
 
 
@@ -62,16 +61,26 @@ def load_keras_model(model_path: str,
   Returns:
     A tensorflow Keras model.
   """
-  # Extract the file path before mediapipe/ as the `base_dir`. By joining it
+  absolute_path = file_util.get_absolute_path(model_path)
-  # with the `model_path` which defines the relative path under mediapipe/, it
-  # yields to the aboslution path of the model files directory.
-  cwd = os.path.dirname(__file__)
-  base_dir = cwd[:cwd.rfind('mediapipe')]
-  absolute_path = os.path.join(base_dir, model_path)
   return tf.keras.models.load_model(
       absolute_path, custom_objects={'tf': tf}, compile=compile_on_load)
 
 
+def load_tflite_model_buffer(model_path: str) -> bytearray:
+  """Loads a TFLite model buffer from file.
+
+  Args:
+    model_path: Relative path to a TFLite file
+
+  Returns:
+    A TFLite model buffer
+  """
+  absolute_path = file_util.get_absolute_path(model_path)
+  with tf.io.gfile.GFile(absolute_path, 'rb') as f:
+    tflite_model_buffer = f.read()
+  return tflite_model_buffer
+
+
 def get_steps_per_epoch(steps_per_epoch: Optional[int] = None,
                         batch_size: Optional[int] = None,
                         train_data: Optional[dataset.Dataset] = None) -> int:

mediapipe/model_maker/python/core/utils/model_util_test.py

@@ -24,7 +24,7 @@ from mediapipe.model_maker.python.core.utils import test_util
 
 class ModelUtilTest(tf.test.TestCase, parameterized.TestCase):
 
-  def test_load_model(self):
+  def test_load_keras_model(self):
     input_dim = 4
     model = test_util.build_model(input_shape=[input_dim], num_classes=2)
     saved_model_path = os.path.join(self.get_temp_dir(), 'saved_model')
@@ -36,6 +36,19 @@ class ModelUtilTest(tf.test.TestCase, parameterized.TestCase):
     loaded_model_output = loaded_model.predict_on_batch(input_tensors)
     self.assertTrue((model_output == loaded_model_output).all())
 
+  def test_load_tflite_model_buffer(self):
+    input_dim = 4
+    model = test_util.build_model(input_shape=[input_dim], num_classes=2)
+    tflite_model = model_util.convert_to_tflite(model)
+    tflite_file = os.path.join(self.get_temp_dir(), 'model.tflite')
+    model_util.save_tflite(tflite_model=tflite_model, tflite_file=tflite_file)
+
+    tflite_model_buffer = model_util.load_tflite_model_buffer(tflite_file)
+    test_util.test_tflite(
+        keras_model=model,
+        tflite_model=tflite_model_buffer,
+        size=[1, input_dim])
+
   @parameterized.named_parameters(
       dict(
           testcase_name='input_only_steps_per_epoch',

mediapipe/model_maker/python/core/utils/testdata/BUILD

@@ -0,0 +1,23 @@
+# Copyright 2022 The MediaPipe 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.
+
+package(
+    default_visibility = ["//mediapipe/model_maker/python/core/utils:__subpackages__"],
+    licenses = ["notice"],  # Apache 2.0
+)
+
+filegroup(
+    name = "testdata",
+    srcs = ["test.txt"],
+)

mediapipe/model_maker/python/vision/image_classifier/image_classifier.py

@@ -68,7 +68,10 @@ class ImageClassifier(classifier.Classifier):
   ) -> 'ImageClassifier':
     """Creates and trains an image classifier.
 
-    Loads data and trains the model based on data for image classification.
+    Loads data and trains the model based on data for image classification. If a
+    checkpoint file exists in the {options.hparams.export_dir}/checkpoint/
+    directory, the training process will load the weight from the checkpoint
+    file for continual training.
 
     Args:
       train_data: Training data.
@@ -190,7 +193,7 @@ class ImageClassifier(classifier.Classifier):
         tflite_model,
         self._model_spec.mean_rgb,
         self._model_spec.stddev_rgb,
-        labels=metadata_writer.Labels().add(self._label_names))
+        labels=metadata_writer.Labels().add(list(self._label_names)))
     tflite_model_with_metadata, metadata_json = writer.populate()
     model_util.save_tflite(tflite_model_with_metadata, tflite_file)
     with open(metadata_file, 'w') as f:

mediapipe/model_maker/python/vision/image_classifier/image_classifier_test.py

@@ -13,10 +13,13 @@
 # limitations under the License.
 
 import filecmp
+import io
 import os
+import tempfile
 
-from unittest import mock
+from unittest import mock as unittest_mock
 from absl.testing import parameterized
+import mock
 import numpy as np
 import tensorflow as tf
 
@@ -63,14 +66,20 @@ class ImageClassifierTest(tf.test.TestCase, parameterized.TestCase):
           options=image_classifier.ImageClassifierOptions(
               supported_model=image_classifier.SupportedModels.MOBILENET_V2,
               hparams=image_classifier.HParams(
-                  epochs=1, batch_size=1, shuffle=True))),
+                  epochs=1,
+                  batch_size=1,
+                  shuffle=True,
+                  export_dir=tempfile.mkdtemp()))),
       dict(
           testcase_name='efficientnet_lite0',
           options=image_classifier.ImageClassifierOptions(
               supported_model=(
                   image_classifier.SupportedModels.EFFICIENTNET_LITE0),
               hparams=image_classifier.HParams(
-                  epochs=1, batch_size=1, shuffle=True))),
+                  epochs=1,
+                  batch_size=1,
+                  shuffle=True,
+                  export_dir=tempfile.mkdtemp()))),
       dict(
           testcase_name='efficientnet_lite0_change_dropout_rate',
           options=image_classifier.ImageClassifierOptions(
@@ -78,21 +87,30 @@ class ImageClassifierTest(tf.test.TestCase, parameterized.TestCase):
                   image_classifier.SupportedModels.EFFICIENTNET_LITE0),
               model_options=image_classifier.ModelOptions(dropout_rate=0.1),
               hparams=image_classifier.HParams(
-                  epochs=1, batch_size=1, shuffle=True))),
+                  epochs=1,
+                  batch_size=1,
+                  shuffle=True,
+                  export_dir=tempfile.mkdtemp()))),
       dict(
           testcase_name='efficientnet_lite2',
           options=image_classifier.ImageClassifierOptions(
               supported_model=(
                   image_classifier.SupportedModels.EFFICIENTNET_LITE2),
               hparams=image_classifier.HParams(
-                  epochs=1, batch_size=1, shuffle=True))),
+                  epochs=1,
+                  batch_size=1,
+                  shuffle=True,
+                  export_dir=tempfile.mkdtemp()))),
       dict(
           testcase_name='efficientnet_lite4',
           options=image_classifier.ImageClassifierOptions(
               supported_model=(
                   image_classifier.SupportedModels.EFFICIENTNET_LITE4),
               hparams=image_classifier.HParams(
-                  epochs=1, batch_size=1, shuffle=True))),
+                  epochs=1,
+                  batch_size=1,
+                  shuffle=True,
+                  export_dir=tempfile.mkdtemp()))),
   )
   def test_create_and_train_model(
       self, options: image_classifier.ImageClassifierOptions):
@@ -117,16 +135,35 @@ class ImageClassifierTest(tf.test.TestCase, parameterized.TestCase):
     self.assertGreater(os.path.getsize(output_metadata_file), 0)
     self.assertTrue(filecmp.cmp(output_metadata_file, expected_metadata_file))
 
+  def test_continual_training_by_loading_checkpoint(self):
+    mock_stdout = io.StringIO()
+    with mock.patch('sys.stdout', mock_stdout):
+      options = image_classifier.ImageClassifierOptions(
+          supported_model=image_classifier.SupportedModels.EFFICIENTNET_LITE0,
+          hparams=image_classifier.HParams(
+              epochs=5, batch_size=1, shuffle=True))
+      model = image_classifier.ImageClassifier.create(
+          train_data=self._train_data,
+          validation_data=self._test_data,
+          options=options)
+      model = image_classifier.ImageClassifier.create(
+          train_data=self._train_data,
+          validation_data=self._test_data,
+          options=options)
+      self._test_accuracy(model)
+
+    self.assertRegex(mock_stdout.getvalue(), 'Resuming from')
+
   def _test_accuracy(self, model, threshold=0.0):
     _, accuracy = model.evaluate(self._test_data)
     self.assertGreaterEqual(accuracy, threshold)
 
-  @mock.patch.object(
+  @unittest_mock.patch.object(
       image_classifier.hyperparameters,
       'HParams',
       autospec=True,
       return_value=image_classifier.HParams(epochs=1))
-  @mock.patch.object(
+  @unittest_mock.patch.object(
       image_classifier.model_options,
       'ImageClassifierModelOptions',
       autospec=True,

mediapipe/model_maker/python/vision/image_classifier/train_image_classifier_lib.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 """Library to train model."""
 
+import os
 import tensorflow as tf
 
 from mediapipe.model_maker.python.core.utils import model_util
@@ -78,11 +79,24 @@ def train_model(model: tf.keras.Model, hparams: hp.HParams,
   loss = tf.keras.losses.CategoricalCrossentropy(
       label_smoothing=hparams.label_smoothing)
   model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
-  callbacks = model_util.get_default_callbacks(export_dir=hparams.export_dir)
+
+  summary_dir = os.path.join(hparams.export_dir, 'summaries')
+  summary_callback = tf.keras.callbacks.TensorBoard(summary_dir)
+  # Save checkpoint every 5 epochs.
+  checkpoint_path = os.path.join(hparams.export_dir, 'checkpoint')
+  checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
+      os.path.join(checkpoint_path, 'model-{epoch:04d}'),
+      save_weights_only=True,
+      period=5)
+
+  latest_checkpoint = tf.train.latest_checkpoint(checkpoint_path)
+  if latest_checkpoint:
+    print(f'Resuming from {latest_checkpoint}')
+    model.load_weights(latest_checkpoint)
 
   # Train the model.
   return model.fit(
       x=train_ds,
       epochs=hparams.epochs,
       validation_data=validation_ds,
-      callbacks=callbacks)
+      callbacks=[summary_callback, checkpoint_callback])

mediapipe/tasks/cc/text/text_embedder/BUILD

@@ -0,0 +1,87 @@
+# Copyright 2022 The MediaPipe 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.
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+cc_library(
+    name = "text_embedder",
+    srcs = ["text_embedder.cc"],
+    hdrs = ["text_embedder.h"],
+    deps = [
+        ":text_embedder_graph",
+        "//mediapipe/calculators/tensor:inference_calculator_cc_proto",
+        "//mediapipe/framework:calculator_cc_proto",
+        "//mediapipe/framework/api2:builder",
+        "//mediapipe/tasks/cc/components/containers:embedding_result",
+        "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
+        "//mediapipe/tasks/cc/components/processors:embedder_options",
+        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_cc_proto",
+        "//mediapipe/tasks/cc/components/utils:cosine_similarity",
+        "//mediapipe/tasks/cc/core:base_options",
+        "//mediapipe/tasks/cc/core:base_task_api",
+        "//mediapipe/tasks/cc/core:task_api_factory",
+        "//mediapipe/tasks/cc/core/proto:base_options_cc_proto",
+        "//mediapipe/tasks/cc/text/text_embedder/proto:text_embedder_graph_options_cc_proto",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/status:statusor",
+        "@com_google_absl//absl/strings",
+    ],
+)
+
+cc_library(
+    name = "text_embedder_graph",
+    srcs = ["text_embedder_graph.cc"],
+    deps = [
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator_cc_proto",
+        "//mediapipe/framework:calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/api2:builder",
+        "//mediapipe/framework/api2:port",
+        "//mediapipe/tasks/cc/components:text_preprocessing_graph",
+        "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
+        "//mediapipe/tasks/cc/components/processors:embedding_postprocessing_graph",
+        "//mediapipe/tasks/cc/components/processors/proto:embedding_postprocessing_graph_options_cc_proto",
+        "//mediapipe/tasks/cc/components/proto:text_preprocessing_graph_options_cc_proto",
+        "//mediapipe/tasks/cc/core:model_resources",
+        "//mediapipe/tasks/cc/core:model_task_graph",
+        "//mediapipe/tasks/cc/core/proto:model_resources_calculator_cc_proto",
+        "//mediapipe/tasks/cc/text/text_embedder/proto:text_embedder_graph_options_cc_proto",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/status:statusor",
+    ],
+    alwayslink = 1,
+)
+
+cc_test(
+    name = "text_embedder_test",
+    srcs = ["text_embedder_test.cc"],
+    data = [
+        "//mediapipe/tasks/testdata/text:mobilebert_embedding_model",
+        "//mediapipe/tasks/testdata/text:regex_embedding_with_metadata",
+    ],
+    deps = [
+        ":text_embedder",
+        "//mediapipe/framework/deps:file_path",
+        "//mediapipe/framework/port:gtest_main",
+        "//mediapipe/tasks/cc:common",
+        "//mediapipe/tasks/cc/components/containers:embedding_result",
+        "@com_google_absl//absl/flags:flag",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/status:statusor",
+        "@org_tensorflow//tensorflow/lite/core/shims:cc_shims_test_util",
+    ],
+)

mediapipe/tasks/cc/text/text_embedder/proto/BUILD

@@ -0,0 +1,30 @@
+# Copyright 2022 The MediaPipe 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.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+mediapipe_proto_library(
+    name = "text_embedder_graph_options_proto",
+    srcs = ["text_embedder_graph_options.proto"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_proto",
+        "//mediapipe/tasks/cc/core/proto:base_options_proto",
+    ],
+)

mediapipe/tasks/cc/text/text_embedder/proto/text_embedder_graph_options.proto

@@ -0,0 +1,36 @@
+/* Copyright 2022 The MediaPipe 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.
+==============================================================================*/
+
+syntax = "proto2";
+
+package mediapipe.tasks.text.text_embedder.proto;
+
+import "mediapipe/framework/calculator.proto";
+import "mediapipe/tasks/cc/components/processors/proto/embedder_options.proto";
+import "mediapipe/tasks/cc/core/proto/base_options.proto";
+
+message TextEmbedderGraphOptions {
+  extend mediapipe.CalculatorOptions {
+    optional TextEmbedderGraphOptions ext = 477589892;
+  }
+
+  // Base options for configuring MediaPipe Tasks, such as specifying the TfLite
+  // model file with metadata, accelerator options, etc.
+  optional core.proto.BaseOptions base_options = 1;
+
+  // Options for configuring the embedder behavior, such as normalization or
+  // quantization.
+  optional components.processors.proto.EmbedderOptions embedder_options = 2;
+}

mediapipe/tasks/cc/text/text_embedder/text_embedder.cc

@@ -0,0 +1,104 @@
+/* Copyright 2022 The MediaPipe 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.
+==============================================================================*/
+
+#include "mediapipe/tasks/cc/text/text_embedder/text_embedder.h"
+
+#include <memory>
+
+#include "absl/status/statusor.h"
+#include "mediapipe/calculators/tensor/inference_calculator.pb.h"
+#include "mediapipe/framework/api2/builder.h"
+#include "mediapipe/framework/calculator.pb.h"
+#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
+#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
+#include "mediapipe/tasks/cc/components/processors/embedder_options.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedder_options.pb.h"
+#include "mediapipe/tasks/cc/components/utils/cosine_similarity.h"
+#include "mediapipe/tasks/cc/core/base_options.h"
+#include "mediapipe/tasks/cc/core/proto/base_options.pb.h"
+#include "mediapipe/tasks/cc/core/task_api_factory.h"
+#include "mediapipe/tasks/cc/text/text_embedder/proto/text_embedder_graph_options.pb.h"
+
+namespace mediapipe::tasks::text::text_embedder {
+namespace {
+
+constexpr char kTextTag[] = "TEXT";
+constexpr char kEmbeddingsTag[] = "EMBEDDINGS";
+constexpr char kTextInStreamName[] = "text_in";
+constexpr char kEmbeddingsStreamName[] = "embeddings_out";
+constexpr char kGraphTypeName[] =
+    "mediapipe.tasks.text.text_embedder.TextEmbedderGraph";
+
+using ::mediapipe::tasks::components::containers::ConvertToEmbeddingResult;
+using ::mediapipe::tasks::components::containers::proto::EmbeddingResult;
+
+// Creates a MediaPipe graph config that contains a single node of type
+// "mediapipe.tasks.text.text_embedder.TextEmbedderGraph".
+CalculatorGraphConfig CreateGraphConfig(
+    std::unique_ptr<proto::TextEmbedderGraphOptions> options_proto) {
+  api2::builder::Graph graph;
+  auto& task_graph = graph.AddNode(kGraphTypeName);
+  task_graph.GetOptions<proto::TextEmbedderGraphOptions>().Swap(
+      options_proto.get());
+  graph.In(kTextTag).SetName(kTextInStreamName) >> task_graph.In(kTextTag);
+  task_graph.Out(kEmbeddingsTag).SetName(kEmbeddingsStreamName) >>
+      graph.Out(kEmbeddingsTag);
+  return graph.GetConfig();
+}
+
+// Converts the user-facing TextEmbedderOptions struct to the internal
+// TextEmbedderGraphOptions proto.
+std::unique_ptr<proto::TextEmbedderGraphOptions>
+ConvertTextEmbedderOptionsToProto(TextEmbedderOptions* options) {
+  auto options_proto = std::make_unique<proto::TextEmbedderGraphOptions>();
+  auto base_options_proto = std::make_unique<tasks::core::proto::BaseOptions>(
+      tasks::core::ConvertBaseOptionsToProto(&(options->base_options)));
+  options_proto->mutable_base_options()->Swap(base_options_proto.get());
+  auto embedder_options_proto =
+      std::make_unique<components::processors::proto::EmbedderOptions>(
+          components::processors::ConvertEmbedderOptionsToProto(
+              &(options->embedder_options)));
+  options_proto->mutable_embedder_options()->Swap(embedder_options_proto.get());
+  return options_proto;
+}
+
+}  // namespace
+
+absl::StatusOr<std::unique_ptr<TextEmbedder>> TextEmbedder::Create(
+    std::unique_ptr<TextEmbedderOptions> options) {
+  std::unique_ptr<proto::TextEmbedderGraphOptions> options_proto =
+      ConvertTextEmbedderOptionsToProto(options.get());
+  return core::TaskApiFactory::Create<TextEmbedder,
+                                      proto::TextEmbedderGraphOptions>(
+      CreateGraphConfig(std::move(options_proto)),
+      std::move(options->base_options.op_resolver));
+}
+
+absl::StatusOr<TextEmbedderResult> TextEmbedder::Embed(absl::string_view text) {
+  ASSIGN_OR_RETURN(
+      auto output_packets,
+      runner_->Process(
+          {{kTextInStreamName, MakePacket<std::string>(std::string(text))}}));
+  return ConvertToEmbeddingResult(
+      output_packets[kEmbeddingsStreamName].Get<EmbeddingResult>());
+}
+
+absl::StatusOr<double> TextEmbedder::CosineSimilarity(
+    const components::containers::Embedding& u,
+    const components::containers::Embedding& v) {
+  return components::utils::CosineSimilarity(u, v);
+}
+
+}  // namespace mediapipe::tasks::text::text_embedder

mediapipe/tasks/cc/text/text_embedder/text_embedder.h

@@ -0,0 +1,96 @@
+/* Copyright 2022 The MediaPipe 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.
+==============================================================================*/
+
+#ifndef MEDIAPIPE_TASKS_CC_TEXT_TEXT_EMBEDDER_TEXT_EMBEDDER_H_
+#define MEDIAPIPE_TASKS_CC_TEXT_TEXT_EMBEDDER_TEXT_EMBEDDER_H_
+
+#include <memory>
+
+#include "absl/status/status.h"
+#include "absl/status/statusor.h"
+#include "absl/strings/string_view.h"
+#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
+#include "mediapipe/tasks/cc/components/processors/embedder_options.h"
+#include "mediapipe/tasks/cc/core/base_options.h"
+#include "mediapipe/tasks/cc/core/base_task_api.h"
+
+namespace mediapipe::tasks::text::text_embedder {
+
+// Alias the shared EmbeddingResult struct as result typo.
+using TextEmbedderResult =
+    ::mediapipe::tasks::components::containers::EmbeddingResult;
+
+// Options for configuring a MediaPipe text embedder task.
+struct TextEmbedderOptions {
+  // Base options for configuring MediaPipe Tasks, such as specifying the model
+  // file with metadata, accelerator options, op resolver, etc.
+  tasks::core::BaseOptions base_options;
+
+  // Options for configuring the embedder behavior, such as L2-normalization or
+  // scalar-quantization.
+  components::processors::EmbedderOptions embedder_options;
+};
+
+// Performs embedding extraction on text.
+//
+// This API expects a TFLite model with TFLite Model Metadata that contains the
+// mandatory (described below) input tensors and output tensors. Metadata should
+// contain the input process unit for the model's Tokenizer as well as input /
+// output tensor metadata.
+//
+// TODO: Support Universal Sentence Encoder.
+// Input tensors:
+//   (kTfLiteInt32)
+//    - 3 input tensors of size `[batch_size x bert_max_seq_len]` with names
+//    "ids", "mask", and "segment_ids" representing the input ids, mask ids, and
+//    segment ids respectively
+//    - or 1 input tensor of size `[batch_size x max_seq_len]` representing the
+//      input ids
+//
+// At least one output tensor with:
+//   (kTfLiteFloat32)
+//    - `N` components corresponding to the `N` dimensions of the returned
+//      feature vector for this output layer.
+//    - Either 2 or 4 dimensions, i.e. `[1 x N]` or `[1 x 1 x 1 x N]`.
+class TextEmbedder : core::BaseTaskApi {
+ public:
+  using BaseTaskApi::BaseTaskApi;
+
+  // Creates a TextEmbedder from the provided `options`. A non-default
+  // OpResolver can be specified in the BaseOptions in order to support custom
+  // Ops or specify a subset of built-in Ops.
+  static absl::StatusOr<std::unique_ptr<TextEmbedder>> Create(
+      std::unique_ptr<TextEmbedderOptions> options);
+
+  // Performs embedding extraction on the input `text`.
+  absl::StatusOr<TextEmbedderResult> Embed(absl::string_view text);
+
+  // Shuts down the TextEmbedder when all the work is done.
+  absl::Status Close() { return runner_->Close(); }
+
+  // Utility function to compute cosine similarity [1] between two embeddings.
+  // May return an InvalidArgumentError if e.g. the embeddings are of different
+  // types (quantized vs. float), have different sizes, or have a an L2-norm of
+  // 0.
+  //
+  // [1]: https://en.wikipedia.org/wiki/Cosine_similarity
+  static absl::StatusOr<double> CosineSimilarity(
+      const components::containers::Embedding& u,
+      const components::containers::Embedding& v);
+};
+
+}  // namespace mediapipe::tasks::text::text_embedder
+
+#endif  // MEDIAPIPE_TASKS_CC_TEXT_TEXT_EMBEDDER_TEXT_EMBEDDER_H_

mediapipe/tasks/cc/text/text_embedder/text_embedder_graph.cc

@@ -0,0 +1,145 @@
+/* Copyright 2022 The MediaPipe 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.
+==============================================================================*/
+
+#include "absl/status/status.h"
+#include "absl/status/statusor.h"
+#include "mediapipe/calculators/tensor/inference_calculator.pb.h"
+#include "mediapipe/framework/api2/builder.h"
+#include "mediapipe/framework/api2/port.h"
+#include "mediapipe/framework/calculator.pb.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
+#include "mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.pb.h"
+#include "mediapipe/tasks/cc/components/proto/text_preprocessing_graph_options.pb.h"
+#include "mediapipe/tasks/cc/components/text_preprocessing_graph.h"
+#include "mediapipe/tasks/cc/core/model_resources.h"
+#include "mediapipe/tasks/cc/core/model_task_graph.h"
+#include "mediapipe/tasks/cc/core/proto/model_resources_calculator.pb.h"
+#include "mediapipe/tasks/cc/text/text_embedder/proto/text_embedder_graph_options.pb.h"
+
+namespace mediapipe::tasks::text::text_embedder {
+namespace {
+
+using ::mediapipe::api2::Input;
+using ::mediapipe::api2::Output;
+using ::mediapipe::api2::builder::Graph;
+using ::mediapipe::api2::builder::Source;
+using ::mediapipe::tasks::components::containers::proto::EmbeddingResult;
+using ::mediapipe::tasks::core::ModelResources;
+
+constexpr char kEmbeddingsTag[] = "EMBEDDINGS";
+constexpr char kTextTag[] = "TEXT";
+constexpr char kMetadataExtractorTag[] = "METADATA_EXTRACTOR";
+constexpr char kTensorsTag[] = "TENSORS";
+
+}  // namespace
+
+// A "mediapipe.tasks.text.TextEmbedderGraph" performs text embedding
+// extraction.
+// - Accepts input text and outputs embeddings on CPU.
+//
+// Inputs:
+//   TEXT - std::string
+//     Input text to perform embedding extraction on.
+//
+// Outputs:
+//   EMBEDDINGS - EmbeddingResult
+//     The embedding result.
+//
+// Example:
+// node {
+//   calculator: "mediapipe.tasks.text.TextEmbedderGraph"
+//   input_stream: "TEXT:text_in"
+//   output_stream: "EMBEDDINGS:embedding_result_out"
+//   options {
+//     [mediapipe.tasks.text.text_embedder.proto.TextEmbedderGraphOptions.ext] {
+//       base_options {
+//         model_asset {
+//           file_name: "/path/to/model.tflite"
+//         }
+//       }
+//     }
+//   }
+// }
+class TextEmbedderGraph : public core::ModelTaskGraph {
+ public:
+  absl::StatusOr<CalculatorGraphConfig> GetConfig(
+      SubgraphContext* sc) override {
+    CHECK(sc != nullptr);
+    ASSIGN_OR_RETURN(const ModelResources* model_resources,
+                     CreateModelResources<proto::TextEmbedderGraphOptions>(sc));
+    Graph graph;
+    ASSIGN_OR_RETURN(
+        Source<EmbeddingResult> embedding_result_out,
+        BuildTextEmbedderTask(sc->Options<proto::TextEmbedderGraphOptions>(),
+                              *model_resources,
+                              graph[Input<std::string>(kTextTag)], graph));
+    embedding_result_out >> graph[Output<EmbeddingResult>(kEmbeddingsTag)];
+    return graph.GetConfig();
+  }
+
+ private:
+  // Adds a mediapipe TextEmbedder task graph into the provided
+  // builder::Graph instance. The TextEmbedder task takes an input
+  // text (std::string) and returns an embedding result.
+  //
+  // task_options: the mediapipe tasks TextEmbedderGraphOptions proto.
+  // model_resources: the ModelResources object initialized from a
+  //   TextEmbedder model file with model metadata.
+  // text_in: (std::string) stream to run embedding extraction on.
+  // graph: the mediapipe builder::Graph instance to be updated.
+  absl::StatusOr<Source<EmbeddingResult>> BuildTextEmbedderTask(
+      const proto::TextEmbedderGraphOptions& task_options,
+      const ModelResources& model_resources, Source<std::string> text_in,
+      Graph& graph) {
+    // Adds preprocessing calculators and connects them to the text input
+    // stream.
+    auto& preprocessing =
+        graph.AddNode("mediapipe.tasks.components.TextPreprocessingSubgraph");
+    MP_RETURN_IF_ERROR(components::ConfigureTextPreprocessingSubgraph(
+        model_resources,
+        preprocessing.GetOptions<
+            tasks::components::proto::TextPreprocessingGraphOptions>()));
+    text_in >> preprocessing.In(kTextTag);
+
+    // Adds both InferenceCalculator and ModelResourcesCalculator.
+    auto& inference = AddInference(
+        model_resources, task_options.base_options().acceleration(), graph);
+    // The metadata extractor side-output comes from the
+    // ModelResourcesCalculator.
+    inference.SideOut(kMetadataExtractorTag) >>
+        preprocessing.SideIn(kMetadataExtractorTag);
+    preprocessing.Out(kTensorsTag) >> inference.In(kTensorsTag);
+
+    // Adds postprocessing calculators and connects its input stream to the
+    // inference results.
+    auto& postprocessing = graph.AddNode(
+        "mediapipe.tasks.components.processors.EmbeddingPostprocessingGraph");
+    MP_RETURN_IF_ERROR(components::processors::ConfigureEmbeddingPostprocessing(
+        model_resources, task_options.embedder_options(),
+        &postprocessing.GetOptions<components::processors::proto::
+                                       EmbeddingPostprocessingGraphOptions>()));
+    inference.Out(kTensorsTag) >> postprocessing.In(kTensorsTag);
+
+    // Outputs the embedding result.
+    return postprocessing[Output<EmbeddingResult>(kEmbeddingsTag)];
+  }
+};
+
+REGISTER_MEDIAPIPE_GRAPH(
+    ::mediapipe::tasks::text::text_embedder::TextEmbedderGraph);
+
+}  // namespace mediapipe::tasks::text::text_embedder

mediapipe/tasks/cc/text/text_embedder/text_embedder_test.cc

@@ -0,0 +1,143 @@
+/* Copyright 2022 The MediaPipe 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.
+==============================================================================*/
+
+#include "mediapipe/tasks/cc/text/text_embedder/text_embedder.h"
+
+#include <memory>
+
+#include "absl/flags/flag.h"
+#include "absl/status/status.h"
+#include "absl/status/statusor.h"
+#include "mediapipe/framework/deps/file_path.h"
+#include "mediapipe/framework/port/gmock.h"
+#include "mediapipe/framework/port/gtest.h"
+#include "mediapipe/framework/port/status_matchers.h"
+#include "mediapipe/tasks/cc/common.h"
+#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
+#include "tensorflow/lite/core/shims/cc/shims_test_util.h"
+
+namespace mediapipe::tasks::text::text_embedder {
+namespace {
+
+constexpr char kTestDataDirectory[] = "/mediapipe/tasks/testdata/text/";
+
+// Note that these models use dynamic-sized tensors.
+// Embedding model with BERT preprocessing.
+constexpr char kMobileBert[] = "mobilebert_embedding_with_metadata.tflite";
+// Embedding model with regex preprocessing.
+constexpr char kRegexOneEmbeddingModel[] =
+    "regex_one_embedding_with_metadata.tflite";
+
+// Tolerance for embedding vector coordinate values.
+constexpr float kEpsilon = 1e-4;
+// Tolerancy for cosine similarity evaluation.
+constexpr double kSimilarityTolerancy = 1e-6;
+
+using ::mediapipe::file::JoinPath;
+using ::testing::HasSubstr;
+using ::testing::Optional;
+
+class EmbedderTest : public tflite_shims::testing::Test {};
+
+TEST_F(EmbedderTest, FailsWithMissingModel) {
+  auto text_embedder =
+      TextEmbedder::Create(std::make_unique<TextEmbedderOptions>());
+  ASSERT_EQ(text_embedder.status().code(), absl::StatusCode::kInvalidArgument);
+  ASSERT_THAT(
+      text_embedder.status().message(),
+      HasSubstr("ExternalFile must specify at least one of 'file_content', "
+                "'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."));
+  ASSERT_THAT(text_embedder.status().GetPayload(kMediaPipeTasksPayload),
+              Optional(absl::Cord(absl::StrCat(
+                  MediaPipeTasksStatus::kRunnerInitializationError))));
+}
+
+TEST_F(EmbedderTest, SucceedsWithMobileBert) {
+  auto options = std::make_unique<TextEmbedderOptions>();
+  options->base_options.model_asset_path =
+      JoinPath("./", kTestDataDirectory, kMobileBert);
+  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextEmbedder> text_embedder,
+                          TextEmbedder::Create(std::move(options)));
+  MP_ASSERT_OK_AND_ASSIGN(
+      TextEmbedderResult result0,
+      text_embedder->Embed("it's a charming and often affecting journey"));
+  ASSERT_EQ(result0.embeddings.size(), 1);
+  ASSERT_EQ(result0.embeddings[0].float_embedding.size(), 512);
+  ASSERT_NEAR(result0.embeddings[0].float_embedding[0], 19.9016f, kEpsilon);
+
+  MP_ASSERT_OK_AND_ASSIGN(
+      auto result1, text_embedder->Embed("what a great and fantastic trip"));
+  ASSERT_EQ(result1.embeddings.size(), 1);
+  ASSERT_EQ(result1.embeddings[0].float_embedding.size(), 512);
+  ASSERT_NEAR(result1.embeddings[0].float_embedding[0], 22.626251f, kEpsilon);
+
+  // Check cosine similarity.
+  MP_ASSERT_OK_AND_ASSIGN(
+      double similarity, TextEmbedder::CosineSimilarity(result0.embeddings[0],
+                                                        result1.embeddings[0]));
+  EXPECT_NEAR(similarity, 0.969514, kSimilarityTolerancy);
+
+  MP_ASSERT_OK(text_embedder->Close());
+}
+
+TEST(EmbedTest, SucceedsWithRegexOneEmbeddingModel) {
+  auto options = std::make_unique<TextEmbedderOptions>();
+  options->base_options.model_asset_path =
+      JoinPath("./", kTestDataDirectory, kRegexOneEmbeddingModel);
+  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextEmbedder> text_embedder,
+                          TextEmbedder::Create(std::move(options)));
+
+  MP_ASSERT_OK_AND_ASSIGN(
+      auto result0,
+      text_embedder->Embed("it's a charming and often affecting journey"));
+  EXPECT_EQ(result0.embeddings.size(), 1);
+  EXPECT_EQ(result0.embeddings[0].float_embedding.size(), 16);
+
+  EXPECT_NEAR(result0.embeddings[0].float_embedding[0], 0.0309356f, kEpsilon);
+
+  MP_ASSERT_OK_AND_ASSIGN(
+      auto result1, text_embedder->Embed("what a great and fantastic trip"));
+  EXPECT_EQ(result1.embeddings.size(), 1);
+  EXPECT_EQ(result1.embeddings[0].float_embedding.size(), 16);
+
+  EXPECT_NEAR(result1.embeddings[0].float_embedding[0], 0.0312863f, kEpsilon);
+
+  // Check cosine similarity.
+  MP_ASSERT_OK_AND_ASSIGN(
+      double similarity, TextEmbedder::CosineSimilarity(result0.embeddings[0],
+                                                        result1.embeddings[0]));
+  EXPECT_NEAR(similarity, 0.999937, kSimilarityTolerancy);
+
+  MP_ASSERT_OK(text_embedder->Close());
+}
+
+TEST_F(EmbedderTest, SucceedsWithQuantization) {
+  auto options = std::make_unique<TextEmbedderOptions>();
+  options->base_options.model_asset_path =
+      JoinPath("./", kTestDataDirectory, kMobileBert);
+  options->embedder_options.quantize = true;
+  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextEmbedder> text_embedder,
+                          TextEmbedder::Create(std::move(options)));
+  MP_ASSERT_OK_AND_ASSIGN(
+      TextEmbedderResult result,
+      text_embedder->Embed("it's a charming and often affecting journey"));
+  ASSERT_EQ(result.embeddings.size(), 1);
+  ASSERT_EQ(result.embeddings[0].quantized_embedding.size(), 512);
+
+  MP_ASSERT_OK(text_embedder->Close());
+}
+
+}  // namespace
+}  // namespace mediapipe::tasks::text::text_embedder

mediapipe/tasks/java/com/google/mediapipe/tasks/core/BaseOptions.java

@@ -26,22 +26,23 @@ public abstract class BaseOptions {
   @AutoValue.Builder
   public abstract static class Builder {
     /**
-     * Sets the model path to a tflite model with metadata in the assets.
+     * Sets the model path to a model asset file (a tflite model or a model asset bundle file) in
+     * the Android app assets folder.
      *
      * <p>Note: when model path is set, both model file descriptor and model buffer should be empty.
      */
     public abstract Builder setModelAssetPath(String value);
 
     /**
-     * Sets the native fd int of a tflite model with metadata.
+     * Sets the native fd int of a model asset file (a tflite model or a model asset bundle file).
      *
      * <p>Note: when model file descriptor is set, both model path and model buffer should be empty.
      */
     public abstract Builder setModelAssetFileDescriptor(Integer value);
 
     /**
-     * Sets either the direct {@link ByteBuffer} or the {@link MappedByteBuffer} of a tflite model
+     * Sets either the direct {@link ByteBuffer} or the {@link MappedByteBuffer} of a model asset
-     * with metadata.
+     * file (a tflite model or a model asset bundle file).
      *
      * <p>Note: when model buffer is set, both model file and model file descriptor should be empty.
      */

mediapipe/tasks/python/components/containers/landmark_detection_result.py

@@ -97,8 +97,7 @@ class LandmarksDetectionResult:
       landmarks.append(_NormalizedLandmark.create_from_pb2(landmark))
 
     for landmark in pb2_obj.world_landmarks.landmark:
-      world_landmarks.append(_Landmark.create_from_pb2(landmark)
+      world_landmarks.append(_Landmark.create_from_pb2(landmark))
-                             )
     return LandmarksDetectionResult(
         landmarks=landmarks,
         categories=categories,

mediapipe/tasks/testdata/vision/BUILD

@@ -144,8 +144,13 @@ filegroup(
 )
 
 # Gestures related models. Visible to model_maker.
+# TODO: Upload canned gesture model and gesture embedding model to GCS after Model Card approval
 filegroup(
     name = "test_gesture_models",
+    srcs = [
+        "hand_landmark_full.tflite",
+        "palm_detection_full.tflite",
+    ],
     visibility = [
         "//mediapipe/model_maker:__subpackages__",
         "//mediapipe/tasks:internal",

mediapipe/tasks/web/BUILD

@@ -0,0 +1,106 @@
+# This contains the MediaPipe Tasks NPM package definitions.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
+load("@build_bazel_rules_nodejs//:index.bzl", "pkg_npm")
+load("@npm//@bazel/rollup:index.bzl", "rollup_bundle")
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+# Audio
+
+mediapipe_ts_library(
+    name = "audio_lib",
+    srcs = ["audio.ts"],
+    deps = ["//mediapipe/tasks/web/audio:audio_lib"],
+)
+
+rollup_bundle(
+    name = "audio_bundle",
+    config_file = "rollup.config.mjs",
+    entry_point = "audio.ts",
+    output_dir = False,
+    deps = [
+        ":audio_lib",
+        "@npm//@rollup/plugin-commonjs",
+        "@npm//@rollup/plugin-node-resolve",
+    ],
+)
+
+pkg_npm(
+    name = "audio_pkg",
+    package_name = "__PACKAGE_NAME__",
+    srcs = ["package.json"],
+    substitutions = {
+        "__PACKAGE_NAME__": "@mediapipe/tasks-audio",
+        "__DESCRIPTION__": "MediaPipe Audio Tasks",
+        "__BUNDLE__": "audio_bundle.js",
+    },
+    tgz = "audio.tgz",
+    deps = [":audio_bundle"],
+)
+
+# Text
+
+mediapipe_ts_library(
+    name = "text_lib",
+    srcs = ["text.ts"],
+    deps = ["//mediapipe/tasks/web/text:text_lib"],
+)
+
+rollup_bundle(
+    name = "text_bundle",
+    config_file = "rollup.config.mjs",
+    entry_point = "text.ts",
+    output_dir = False,
+    deps = [
+        ":text_lib",
+        "@npm//@rollup/plugin-commonjs",
+        "@npm//@rollup/plugin-node-resolve",
+    ],
+)
+
+pkg_npm(
+    name = "text_pkg",
+    package_name = "__PACKAGE_NAME__",
+    srcs = ["package.json"],
+    substitutions = {
+        "__PACKAGE_NAME__": "@mediapipe/tasks-text",
+        "__DESCRIPTION__": "MediaPipe Text Tasks",
+        "__BUNDLE__": "text_bundle.js",
+    },
+    tgz = "text.tgz",
+    deps = [":text_bundle"],
+)
+
+# Vision
+
+mediapipe_ts_library(
+    name = "vision_lib",
+    srcs = ["vision.ts"],
+    deps = ["//mediapipe/tasks/web/vision:vision_lib"],
+)
+
+rollup_bundle(
+    name = "vision_bundle",
+    config_file = "rollup.config.mjs",
+    entry_point = "vision.ts",
+    output_dir = False,
+    deps = [
+        ":vision_lib",
+        "@npm//@rollup/plugin-commonjs",
+        "@npm//@rollup/plugin-node-resolve",
+    ],
+)
+
+pkg_npm(
+    name = "vision_pkg",
+    package_name = "__PACKAGE_NAME__",
+    srcs = ["package.json"],
+    substitutions = {
+        "__PACKAGE_NAME__": "@mediapipe/tasks-vision",
+        "__DESCRIPTION__": "MediaPipe Vision Tasks",
+        "__BUNDLE__": "vision_bundle.js",
+    },
+    tgz = "vision.tgz",
+    deps = [":vision_bundle"],
+)

mediapipe/tasks/web/audio.ts

@@ -0,0 +1,17 @@
+/**
+ * Copyright 2022 The MediaPipe 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.
+ */
+
+export * from '../../tasks/web/audio/index';

mediapipe/tasks/web/audio/BUILD

@@ -2,6 +2,8 @@
 
 load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
 
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
 mediapipe_ts_library(
     name = "audio_lib",
     srcs = ["index.ts"],

mediapipe/tasks/web/audio/audio_classifier/audio_classifier.ts

@@ -198,7 +198,7 @@ export class AudioClassifier extends TaskRunner {
     classifierNode.addInputStream('AUDIO:' + AUDIO_STREAM);
     classifierNode.addInputStream('SAMPLE_RATE:' + SAMPLE_RATE_STREAM);
     classifierNode.addOutputStream(
-        'CLASSIFICATION_RESULT:' + CLASSIFICATION_RESULT_STREAM);
+        'CLASSIFICATIONS:' + CLASSIFICATION_RESULT_STREAM);
     classifierNode.setOptions(calculatorOptions);
 
     graphConfig.addNode(classifierNode);

mediapipe/tasks/web/package.json

@@ -0,0 +1,15 @@
+{
+  "name": "__PACKAGE_NAME__",
+  "version": "__VERSION__",
+  "description": "__DESCRIPTION__",
+  "main": "__BUNDLE__",
+  "module": "__BUNDLE__",
+  "author": "mediapipe@google.com",
+  "license": "Apache-2.0",
+  "type": "module",
+  "dependencies": {
+    "google-protobuf": "^3.21.2"
+  },
+  "homepage": "http://mediapipe.dev",
+  "keywords": [ "AR", "ML", "Augmented", "MediaPipe", "MediaPipe Tasks" ]
+}

mediapipe/tasks/web/rollup.config.mjs

@@ -0,0 +1,9 @@
+import resolve from '@rollup/plugin-node-resolve';
+import commonjs from '@rollup/plugin-commonjs';
+
+export default {
+  plugins: [
+    resolve(),
+    commonjs()
+  ]
+}

mediapipe/tasks/web/text.ts

@@ -0,0 +1,17 @@
+/**
+ * Copyright 2022 The MediaPipe 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.
+ */
+
+export * from '../../tasks/web/text/index';

mediapipe/tasks/web/text/BUILD

@@ -2,6 +2,8 @@
 
 load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
 
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
 mediapipe_ts_library(
     name = "text_lib",
     srcs = ["index.ts"],

mediapipe/tasks/web/text/text_classifier/BUILD

@@ -13,8 +13,8 @@ mediapipe_ts_library(
     name = "text_classifier",
     srcs = [
         "text_classifier.ts",
-        "text_classifier_options.d.ts",
+        "text_classifier_options.ts",
-        "text_classifier_result.d.ts",
+        "text_classifier_result.ts",
     ],
     deps = [
         "//mediapipe/framework:calculator_jspb_proto",

mediapipe/tasks/web/vision.ts

@@ -0,0 +1,17 @@
+/**
+ * Copyright 2022 The MediaPipe 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.
+ */
+
+export * from '../../tasks/web/vision/index';

mediapipe/tasks/web/vision/BUILD

@@ -2,6 +2,8 @@
 
 load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
 
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
 mediapipe_ts_library(
     name = "vision_lib",
     srcs = ["index.ts"],

mediapipe/tasks/web/vision/gesture_recognizer/BUILD

@@ -13,8 +13,8 @@ mediapipe_ts_library(
     name = "gesture_recognizer",
     srcs = [
         "gesture_recognizer.ts",
-        "gesture_recognizer_options.d.ts",
+        "gesture_recognizer_options.ts",
-        "gesture_recognizer_result.d.ts",
+        "gesture_recognizer_result.ts",
     ],
     deps = [
         "//mediapipe/framework:calculator_jspb_proto",

mediapipe/tasks/web/vision/object_detector/BUILD

@@ -13,8 +13,8 @@ mediapipe_ts_library(
     name = "object_detector",
     srcs = [
         "object_detector.ts",
-        "object_detector_options.d.ts",
+        "object_detector_options.ts",
-        "object_detector_result.d.ts",
+        "object_detector_result.ts",
     ],
     deps = [
         "//mediapipe/framework:calculator_jspb_proto",

package.json

@@ -3,12 +3,16 @@
   "version": "0.0.0-alphga",
   "description": "MediaPipe GitHub repo",
   "devDependencies": {
+    "@bazel/rollup": "^5.7.1",
     "@bazel/typescript": "^5.7.1",
+    "@rollup/plugin-commonjs": "^23.0.2",
+    "@rollup/plugin-node-resolve": "^15.0.1",
     "@types/google-protobuf": "^3.15.6",
     "@types/offscreencanvas": "^2019.7.0",
     "google-protobuf": "^3.21.2",
     "protobufjs": "^7.1.2",
     "protobufjs-cli": "^1.0.2",
+    "rollup": "^2.3.0",
     "ts-protoc-gen": "^0.15.0",
     "typescript": "^4.8.4"
   }

yarn.lock

@@ -3,9 +3,16 @@
 
 
 "@babel/parser@^7.9.4":
-  version "7.20.1"
+  version "7.20.3"
-  resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.20.1.tgz#3e045a92f7b4623cafc2425eddcb8cf2e54f9cc5"
+  resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.20.3.tgz#5358cf62e380cf69efcb87a7bb922ff88bfac6e2"
-  integrity sha512-hp0AYxaZJhxULfM1zyp7Wgr+pSUKBcP3M+PHnSzWGdXOzg/kHWIgiUWARvubhUKGOEw3xqY4x+lyZ9ytBVcELw==
+  integrity sha512-OP/s5a94frIPXwjzEcv5S/tpQfc6XhxYUnmWpgdqMWGgYCuErA3SzozaRAMQgSZWKeTJxht9aWAkUY+0UzvOFg==
+
+"@bazel/rollup@^5.7.1":
+  version "5.7.1"
+  resolved "https://registry.yarnpkg.com/@bazel/rollup/-/rollup-5.7.1.tgz#6f644c2d493a5bd9cd3724a6f239e609585c6e37"
+  integrity sha512-LLNogoK2Qx9GIJVywQ+V/czjud8236mnaRX//g7qbOyXoWZDQvAEgsxRHq+lS/XX9USbh+zJJlfb+Dfp/PXx4A==
+  dependencies:
+    "@bazel/worker" "5.7.1"
 
 "@bazel/typescript@^5.7.1":
   version "5.7.1"
@@ -77,6 +84,44 @@
   resolved "https://registry.yarnpkg.com/@protobufjs/utf8/-/utf8-1.1.0.tgz#a777360b5b39a1a2e5106f8e858f2fd2d060c570"
   integrity sha512-Vvn3zZrhQZkkBE8LSuW3em98c0FwgO4nxzv6OdSxPKJIEKY2bGbHn+mhGIPerzI4twdxaP8/0+06HBpwf345Lw==
 
+"@rollup/plugin-commonjs@^23.0.2":
+  version "23.0.2"
+  resolved "https://registry.yarnpkg.com/@rollup/plugin-commonjs/-/plugin-commonjs-23.0.2.tgz#3a3a5b7b1b1cb29037eb4992edcaae997d7ebd92"
+  integrity sha512-e9ThuiRf93YlVxc4qNIurvv+Hp9dnD+4PjOqQs5vAYfcZ3+AXSrcdzXnVjWxcGQOa6KGJFcRZyUI3ktWLavFjg==
+  dependencies:
+    "@rollup/pluginutils" "^5.0.1"
+    commondir "^1.0.1"
+    estree-walker "^2.0.2"
+    glob "^8.0.3"
+    is-reference "1.2.1"
+    magic-string "^0.26.4"
+
+"@rollup/plugin-node-resolve@^15.0.1":
+  version "15.0.1"
+  resolved "https://registry.yarnpkg.com/@rollup/plugin-node-resolve/-/plugin-node-resolve-15.0.1.tgz#72be449b8e06f6367168d5b3cd5e2802e0248971"
+  integrity sha512-ReY88T7JhJjeRVbfCyNj+NXAG3IIsVMsX9b5/9jC98dRP8/yxlZdz7mHZbHk5zHr24wZZICS5AcXsFZAXYUQEg==
+  dependencies:
+    "@rollup/pluginutils" "^5.0.1"
+    "@types/resolve" "1.20.2"
+    deepmerge "^4.2.2"
+    is-builtin-module "^3.2.0"
+    is-module "^1.0.0"
+    resolve "^1.22.1"
+
+"@rollup/pluginutils@^5.0.1":
+  version "5.0.2"
+  resolved "https://registry.yarnpkg.com/@rollup/pluginutils/-/pluginutils-5.0.2.tgz#012b8f53c71e4f6f9cb317e311df1404f56e7a33"
+  integrity sha512-pTd9rIsP92h+B6wWwFbW8RkZv4hiR/xKsqre4SIuAOaOEQRxi0lqLke9k2/7WegC85GgUs9pjmOjCUi3In4vwA==
+  dependencies:
+    "@types/estree" "^1.0.0"
+    estree-walker "^2.0.2"
+    picomatch "^2.3.1"
+
+"@types/estree@*", "@types/estree@^1.0.0":
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/@types/estree/-/estree-1.0.0.tgz#5fb2e536c1ae9bf35366eed879e827fa59ca41c2"
+  integrity sha512-WulqXMDUTYAXCjZnk6JtIHPigp55cVtDgDrO2gHRwhyJto21+1zbVCtOYB2L1F9w4qCQ0rOGWBnBe0FNTiEJIQ==
+
 "@types/google-protobuf@^3.15.6":
   version "3.15.6"
   resolved "https://registry.yarnpkg.com/@types/google-protobuf/-/google-protobuf-3.15.6.tgz#674a69493ef2c849b95eafe69167ea59079eb504"
@@ -110,6 +155,11 @@
   resolved "https://registry.yarnpkg.com/@types/offscreencanvas/-/offscreencanvas-2019.7.0.tgz#e4a932069db47bb3eabeb0b305502d01586fa90d"
   integrity sha512-PGcyveRIpL1XIqK8eBsmRBt76eFgtzuPiSTyKHZxnGemp2yzGzWpjYKAfK3wIMiU7eH+851yEpiuP8JZerTmWg==
 
+"@types/resolve@1.20.2":
+  version "1.20.2"
+  resolved "https://registry.yarnpkg.com/@types/resolve/-/resolve-1.20.2.tgz#97d26e00cd4a0423b4af620abecf3e6f442b7975"
+  integrity sha512-60BCwRFOZCQhDncwQdxxeOEEkbc5dIMccYLwbxsS4TUNeVECQ/pBJ0j09mrHOl/JJvpRPGwO9SvE4nR2Nb/a4Q==
+
 acorn-jsx@^5.3.2:
   version "5.3.2"
   resolved "https://registry.yarnpkg.com/acorn-jsx/-/acorn-jsx-5.3.2.tgz#7ed5bb55908b3b2f1bc55c6af1653bada7f07937"
@@ -162,6 +212,11 @@ buffer-from@^1.0.0:
   resolved "https://registry.yarnpkg.com/buffer-from/-/buffer-from-1.1.2.tgz#2b146a6fd72e80b4f55d255f35ed59a3a9a41bd5"
   integrity sha512-E+XQCRwSbaaiChtv6k6Dwgc+bx+Bs6vuKJHHl5kox/BaKbhiXzqQOwK4cO22yElGp2OCmjwVhT3HmxgyPGnJfQ==
 
+builtin-modules@^3.3.0:
+  version "3.3.0"
+  resolved "https://registry.yarnpkg.com/builtin-modules/-/builtin-modules-3.3.0.tgz#cae62812b89801e9656336e46223e030386be7b6"
+  integrity sha512-zhaCDicdLuWN5UbN5IMnFqNMhNfo919sH85y2/ea+5Yg9TsTkeZxpL+JLbp6cgYFS4sRLp3YV4S6yDuqVWHYOw==
+
 catharsis@^0.9.0:
   version "0.9.0"
   resolved "https://registry.yarnpkg.com/catharsis/-/catharsis-0.9.0.tgz#40382a168be0e6da308c277d3a2b3eb40c7d2121"
@@ -189,6 +244,11 @@ color-name@~1.1.4:
   resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
   integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
 
+commondir@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/commondir/-/commondir-1.0.1.tgz#ddd800da0c66127393cca5950ea968a3aaf1253b"
+  integrity sha512-W9pAhw0ja1Edb5GVdIF1mjZw/ASI0AlShXM83UUGe2DVr5TdAPEA1OA8m/g8zWp9x6On7gqufY+FatDbC3MDQg==
+
 concat-map@0.0.1:
   version "0.0.1"
   resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
@@ -199,6 +259,11 @@ deep-is@~0.1.3:
   resolved "https://registry.yarnpkg.com/deep-is/-/deep-is-0.1.4.tgz#a6f2dce612fadd2ef1f519b73551f17e85199831"
   integrity sha512-oIPzksmTg4/MriiaYGO+okXDT7ztn/w3Eptv/+gSIdMdKsJo0u4CfYNFJPy+4SKMuCqGw2wxnA+URMg3t8a/bQ==
 
+deepmerge@^4.2.2:
+  version "4.2.2"
+  resolved "https://registry.yarnpkg.com/deepmerge/-/deepmerge-4.2.2.tgz#44d2ea3679b8f4d4ffba33f03d865fc1e7bf4955"
+  integrity sha512-FJ3UgI4gIl+PHZm53knsuSFpE+nESMr7M4v9QcgB7S63Kj/6WqMiFQJpBBYz1Pt+66bZpP3Q7Lye0Oo9MPKEdg==
+
 entities@~2.1.0:
   version "2.1.0"
   resolved "https://registry.yarnpkg.com/entities/-/entities-2.1.0.tgz#992d3129cf7df6870b96c57858c249a120f8b8b5"
@@ -227,9 +292,9 @@ eslint-visitor-keys@^3.3.0:
   integrity sha512-mQ+suqKJVyeuwGYHAdjMFqjCyfl8+Ldnxuyp3ldiMBFKkvytrXUZWaiPCEav8qDHKty44bD+qV1IP4T+w+xXRA==
 
 espree@^9.0.0:
-  version "9.4.0"
+  version "9.4.1"
-  resolved "https://registry.yarnpkg.com/espree/-/espree-9.4.0.tgz#cd4bc3d6e9336c433265fc0aa016fc1aaf182f8a"
+  resolved "https://registry.yarnpkg.com/espree/-/espree-9.4.1.tgz#51d6092615567a2c2cff7833445e37c28c0065bd"
-  integrity sha512-DQmnRpLj7f6TgN/NYb0MTzJXL+vJF9h3pHy4JhCIs3zwcgez8xmGg3sXHcEO97BrmO2OSvCwMdfdlyl+E9KjOw==
+  integrity sha512-XwctdmTO6SIvCzd9810yyNzIrOrqNYV9Koizx4C/mRhf9uq0o4yHoCEU/670pOxOL/MSraektvSAji79kX90Vg==
   dependencies:
     acorn "^8.8.0"
     acorn-jsx "^5.3.2"
@@ -250,6 +315,11 @@ estraverse@^5.1.0:
   resolved "https://registry.yarnpkg.com/estraverse/-/estraverse-5.3.0.tgz#2eea5290702f26ab8fe5370370ff86c965d21123"
   integrity sha512-MMdARuVEQziNTeJD8DgMqmhwR11BRQ/cBP+pLtYdSTnf3MIO8fFeiINEbX36ZdNlfU/7A9f3gUw49B3oQsvwBA==
 
+estree-walker@^2.0.2:
+  version "2.0.2"
+  resolved "https://registry.yarnpkg.com/estree-walker/-/estree-walker-2.0.2.tgz#52f010178c2a4c117a7757cfe942adb7d2da4cac"
+  integrity sha512-Rfkk/Mp/DL7JVje3u18FxFujQlTNR2q6QfMSMB7AvCBx91NGj/ba3kCfza0f6dVDbw7YlRf/nDrn7pQrCCyQ/w==
+
 esutils@^2.0.2:
   version "2.0.3"
   resolved "https://registry.yarnpkg.com/esutils/-/esutils-2.0.3.tgz#74d2eb4de0b8da1293711910d50775b9b710ef64"
@@ -265,6 +335,16 @@ fs.realpath@^1.0.0:
   resolved "https://registry.yarnpkg.com/fs.realpath/-/fs.realpath-1.0.0.tgz#1504ad2523158caa40db4a2787cb01411994ea4f"
   integrity sha512-OO0pH2lK6a0hZnAdau5ItzHPI6pUlvI7jMVnxUQRtw4owF2wk8lOSabtGDCTP4Ggrg2MbGnWO9X8K1t4+fGMDw==
 
+fsevents@~2.3.2:
+  version "2.3.2"
+  resolved "https://registry.yarnpkg.com/fsevents/-/fsevents-2.3.2.tgz#8a526f78b8fdf4623b709e0b975c52c24c02fd1a"
+  integrity sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA==
+
+function-bind@^1.1.1:
+  version "1.1.1"
+  resolved "https://registry.yarnpkg.com/function-bind/-/function-bind-1.1.1.tgz#a56899d3ea3c9bab874bb9773b7c5ede92f4895d"
+  integrity sha512-yIovAzMX49sF8Yl58fSCWJ5svSLuaibPxXQJFLmBObTuCr0Mf1KiPopGM9NiFjiYBCbfaa2Fh6breQ6ANVTI0A==
+
 glob@^7.1.3:
   version "7.2.3"
   resolved "https://registry.yarnpkg.com/glob/-/glob-7.2.3.tgz#b8df0fb802bbfa8e89bd1d938b4e16578ed44f2b"
@@ -277,7 +357,7 @@ glob@^7.1.3:
     once "^1.3.0"
     path-is-absolute "^1.0.0"
 
-glob@^8.0.0:
+glob@^8.0.0, glob@^8.0.3:
   version "8.0.3"
   resolved "https://registry.yarnpkg.com/glob/-/glob-8.0.3.tgz#415c6eb2deed9e502c68fa44a272e6da6eeca42e"
   integrity sha512-ull455NHSHI/Y1FqGaaYFaLGkNMMJbavMrEGFXG/PGrg6y7sutWHUHrz6gy6WEBH6akM1M414dWKCNs+IhKdiQ==
@@ -303,6 +383,13 @@ has-flag@^4.0.0:
   resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-4.0.0.tgz#944771fd9c81c81265c4d6941860da06bb59479b"
   integrity sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==
 
+has@^1.0.3:
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/has/-/has-1.0.3.tgz#722d7cbfc1f6aa8241f16dd814e011e1f41e8796"
+  integrity sha512-f2dvO0VU6Oej7RkWJGrehjbzMAjFp5/VKPp5tTpWIV4JHHZK1/BxbFRtf/siA2SWTe09caDmVtYYzWEIbBS4zw==
+  dependencies:
+    function-bind "^1.1.1"
+
 inflight@^1.0.4:
   version "1.0.6"
   resolved "https://registry.yarnpkg.com/inflight/-/inflight-1.0.6.tgz#49bd6331d7d02d0c09bc910a1075ba8165b56df9"
@@ -316,6 +403,32 @@ inherits@2:
   resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.4.tgz#0fa2c64f932917c3433a0ded55363aae37416b7c"
   integrity sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==
 
+is-builtin-module@^3.2.0:
+  version "3.2.0"
+  resolved "https://registry.yarnpkg.com/is-builtin-module/-/is-builtin-module-3.2.0.tgz#bb0310dfe881f144ca83f30100ceb10cf58835e0"
+  integrity sha512-phDA4oSGt7vl1n5tJvTWooWWAsXLY+2xCnxNqvKhGEzujg+A43wPlPOyDg3C8XQHN+6k/JTQWJ/j0dQh/qr+Hw==
+  dependencies:
+    builtin-modules "^3.3.0"
+
+is-core-module@^2.9.0:
+  version "2.11.0"
+  resolved "https://registry.yarnpkg.com/is-core-module/-/is-core-module-2.11.0.tgz#ad4cb3e3863e814523c96f3f58d26cc570ff0144"
+  integrity sha512-RRjxlvLDkD1YJwDbroBHMb+cukurkDWNyHx7D3oNB5x9rb5ogcksMC5wHCadcXoo67gVr/+3GFySh3134zi6rw==
+  dependencies:
+    has "^1.0.3"
+
+is-module@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/is-module/-/is-module-1.0.0.tgz#3258fb69f78c14d5b815d664336b4cffb6441591"
+  integrity sha512-51ypPSPCoTEIN9dy5Oy+h4pShgJmPCygKfyRCISBI+JoWT/2oJvK8QPxmwv7b/p239jXrm9M1mlQbyKJ5A152g==
+
+is-reference@1.2.1:
+  version "1.2.1"
+  resolved "https://registry.yarnpkg.com/is-reference/-/is-reference-1.2.1.tgz#8b2dac0b371f4bc994fdeaba9eb542d03002d0b7"
+  integrity sha512-U82MsXXiFIrjCK4otLT+o2NA2Cd2g5MLoOVXUZjIOhLurrRxpEXzI8O0KZHr3IjLvlAH1kTPYSuqer5T9ZVBKQ==
+  dependencies:
+    "@types/estree" "*"
+
 js2xmlparser@^4.0.2:
   version "4.0.2"
   resolved "https://registry.yarnpkg.com/js2xmlparser/-/js2xmlparser-4.0.2.tgz#2a1fdf01e90585ef2ae872a01bc169c6a8d5e60a"
@@ -372,9 +485,9 @@ lodash@^4.17.14, lodash@^4.17.15:
   integrity sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==
 
 long@^5.0.0:
-  version "5.2.0"
+  version "5.2.1"
-  resolved "https://registry.yarnpkg.com/long/-/long-5.2.0.tgz#2696dadf4b4da2ce3f6f6b89186085d94d52fd61"
+  resolved "https://registry.yarnpkg.com/long/-/long-5.2.1.tgz#e27595d0083d103d2fa2c20c7699f8e0c92b897f"
-  integrity sha512-9RTUNjK60eJbx3uz+TEGF7fUr29ZDxR5QzXcyDpeSfeH28S9ycINflOgOlppit5U+4kNTe83KQnMEerw7GmE8w==
+  integrity sha512-GKSNGeNAtw8IryjjkhZxuKB3JzlcLTwjtiQCHKvqQet81I93kXslhDQruGI/QsddO83mcDToBVy7GqGS/zYf/A==
 
 lru-cache@^6.0.0:
   version "6.0.0"
@@ -383,6 +496,13 @@ lru-cache@^6.0.0:
   dependencies:
     yallist "^4.0.0"
 
+magic-string@^0.26.4:
+  version "0.26.7"
+  resolved "https://registry.yarnpkg.com/magic-string/-/magic-string-0.26.7.tgz#caf7daf61b34e9982f8228c4527474dac8981d6f"
+  integrity sha512-hX9XH3ziStPoPhJxLq1syWuZMxbDvGNbVchfrdCtanC7D13888bMFow61x8axrx+GfHLtVeAx2kxL7tTGRl+Ow==
+  dependencies:
+    sourcemap-codec "^1.4.8"
+
 markdown-it-anchor@^8.4.1:
   version "8.6.5"
   resolved "https://registry.yarnpkg.com/markdown-it-anchor/-/markdown-it-anchor-8.6.5.tgz#30c4bc5bbff327f15ce3c429010ec7ba75e7b5f8"
@@ -400,9 +520,9 @@ markdown-it@^12.3.2:
     uc.micro "^1.0.5"
 
 marked@^4.0.10:
-  version "4.2.1"
+  version "4.2.2"
-  resolved "https://registry.yarnpkg.com/marked/-/marked-4.2.1.tgz#eaa32594e45b4e58c02e4d118531fd04345de3b4"
+  resolved "https://registry.yarnpkg.com/marked/-/marked-4.2.2.tgz#1d2075ad6cdfe42e651ac221c32d949a26c0672a"
-  integrity sha512-VK1/jNtwqDLvPktNpL0Fdg3qoeUZhmRsuiIjPEy/lHwXW4ouLoZfO4XoWd4ClDt+hupV1VLpkZhEovjU0W/kqA==
+  integrity sha512-JjBTFTAvuTgANXx82a5vzK9JLSMoV6V3LBVn4Uhdso6t7vXrGx7g1Cd2r6NYSsxrYbQGFCMqBDhFHyK5q2UvcQ==
 
 mdurl@^1.0.1:
   version "1.0.1"
@@ -457,6 +577,16 @@ path-is-absolute@^1.0.0:
   resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f"
   integrity sha512-AVbw3UJ2e9bq64vSaS9Am0fje1Pa8pbGqTTsmXfaIiMpnr5DlDhfJOuLj9Sf95ZPVDAUerDfEk88MPmPe7UCQg==
 
+path-parse@^1.0.7:
+  version "1.0.7"
+  resolved "https://registry.yarnpkg.com/path-parse/-/path-parse-1.0.7.tgz#fbc114b60ca42b30d9daf5858e4bd68bbedb6735"
+  integrity sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==
+
+picomatch@^2.3.1:
+  version "2.3.1"
+  resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-2.3.1.tgz#3ba3833733646d9d3e4995946c1365a67fb07a42"
+  integrity sha512-JU3teHTNjmE2VCGFzuY8EXzCDVwEqB2a8fsIvwaStHhAWJEeVd1o1QD80CU6+ZdEXXSLbSsuLwJjkCBWqRQUVA==
+
 prelude-ls@~1.1.2:
   version "1.1.2"
   resolved "https://registry.yarnpkg.com/prelude-ls/-/prelude-ls-1.1.2.tgz#21932a549f5e52ffd9a827f570e04be62a97da54"
@@ -503,6 +633,15 @@ requizzle@^0.2.3:
   dependencies:
     lodash "^4.17.14"
 
+resolve@^1.22.1:
+  version "1.22.1"
+  resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.22.1.tgz#27cb2ebb53f91abb49470a928bba7558066ac177"
+  integrity sha512-nBpuuYuY5jFsli/JIs1oldw6fOQCBioohqWZg/2hiaOybXOft4lonv85uDOKXdf8rhyK159cxU5cDcK/NKk8zw==
+  dependencies:
+    is-core-module "^2.9.0"
+    path-parse "^1.0.7"
+    supports-preserve-symlinks-flag "^1.0.0"
+
 rimraf@^3.0.0:
   version "3.0.2"
   resolved "https://registry.yarnpkg.com/rimraf/-/rimraf-3.0.2.tgz#f1a5402ba6220ad52cc1282bac1ae3aa49fd061a"
@@ -510,6 +649,13 @@ rimraf@^3.0.0:
   dependencies:
     glob "^7.1.3"
 
+rollup@^2.3.0:
+  version "2.79.1"
+  resolved "https://registry.yarnpkg.com/rollup/-/rollup-2.79.1.tgz#bedee8faef7c9f93a2647ac0108748f497f081c7"
+  integrity sha512-uKxbd0IhMZOhjAiD5oAFp7BqvkA4Dv47qpOCtaNvng4HBwdbWtdOh8f5nZNuk2rp51PMGk3bzfWu5oayNEuYnw==
+  optionalDependencies:
+    fsevents "~2.3.2"
+
 semver@5.6.0:
   version "5.6.0"
   resolved "https://registry.yarnpkg.com/semver/-/semver-5.6.0.tgz#7e74256fbaa49c75aa7c7a205cc22799cac80004"
@@ -535,6 +681,11 @@ source-map@^0.6.0, source-map@~0.6.1:
   resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263"
   integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==
 
+sourcemap-codec@^1.4.8:
+  version "1.4.8"
+  resolved "https://registry.yarnpkg.com/sourcemap-codec/-/sourcemap-codec-1.4.8.tgz#ea804bd94857402e6992d05a38ef1ae35a9ab4c4"
+  integrity sha512-9NykojV5Uih4lgo5So5dtw+f0JgJX30KCNI8gwhz2J9A15wD0Ml6tjHKwf6fTSa6fAdVBdZeNOs9eJ71qCk8vA==
+
 strip-json-comments@^3.1.0:
   version "3.1.1"
   resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-3.1.1.tgz#31f1281b3832630434831c310c01cccda8cbe006"
@@ -547,6 +698,11 @@ supports-color@^7.1.0:
   dependencies:
     has-flag "^4.0.0"
 
+supports-preserve-symlinks-flag@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/supports-preserve-symlinks-flag/-/supports-preserve-symlinks-flag-1.0.0.tgz#6eda4bd344a3c94aea376d4cc31bc77311039e09"
+  integrity sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==
+
 taffydb@2.6.2:
   version "2.6.2"
   resolved "https://registry.yarnpkg.com/taffydb/-/taffydb-2.6.2.tgz#7cbcb64b5a141b6a2efc2c5d2c67b4e150b2a268"