chromium/third_party/tflite_support/src/tensorflow_lite_support/python/task/processor/proto/embedding_options_pb2.py

# Copyright 2022 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.
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#
#     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,
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"""Embedding options protobuf."""

import dataclasses
from typing import Any, Optional

from tensorflow_lite_support.cc.task.processor.proto import embedding_options_pb2
from tensorflow_lite_support.python.task.core.optional_dependencies import doc_controls

_EmbeddingOptionsProto = embedding_options_pb2.EmbeddingOptions


@dataclasses.dataclass
class EmbeddingOptions:
  """Options for embedding processor.

  Attributes:
    l2_normalize: Whether to normalize the returned feature vector with L2 norm.
      Use this option only if the model does not already contain a native
      L2_NORMALIZATION TF Lite Op. In most cases, this is already the case and
      L2 norm is thus achieved through TF Lite inference.
    quantize: Whether the returned embedding should be quantized to bytes via
      scalar quantization. Embeddings are implicitly assumed to be unit-norm and
      therefore any dimension is guaranteed to have a value in [-1.0, 1.0]. Use
      the l2_normalize option if this is not the case.
  """

  l2_normalize: Optional[bool] = None
  quantize: Optional[bool] = None

  @doc_controls.do_not_generate_docs
  def to_pb2(self) -> _EmbeddingOptionsProto:
    """Generates a protobuf object to pass to the C++ layer."""
    return _EmbeddingOptionsProto(
        l2_normalize=self.l2_normalize, quantize=self.quantize)

  @classmethod
  @doc_controls.do_not_generate_docs
  def create_from_pb2(cls,
                      pb2_obj: _EmbeddingOptionsProto) -> "EmbeddingOptions":
    """Creates a `EmbeddingOptions` object from the given protobuf object."""
    return EmbeddingOptions(
        l2_normalize=pb2_obj.l2_normalize, quantize=pb2_obj.quantize)

  def __eq__(self, other: Any) -> bool:
    """Checks if this object is equal to the given object.

    Args:
      other: The object to be compared with.

    Returns:
      True if the objects are equal.
    """
    if not isinstance(other, EmbeddingOptions):
      return False

    return self.to_pb2().__eq__(other.to_pb2())