text: # coding=utf-8
  # Copyright 2020 The Google Research Authors.
  #
  # 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.
  
  """Helper functions for dual encoder SMITH model."""
  
  import collections
  from typing import Any, Text
  
  import tensorflow.compat.v1 as tf
  
  from google.protobuf import text_format
  from smith import constants
  
  
  def transfer_2d_array_to_str(array):
    """Transfer a 2D float32 array to a string."""
    str_list = []
    for r in array:
      str_list.append(",".join([str(e) for e in r]))
    return " ".join(str_list)
  
  
  def get_actual_max_seq_len(model_name, max_doc_length_by_sentence,
                             max_sent_length_by_word, max_predictions_per_seq):
    """Get the actual maximum sequence length.
  
    Args:
      model_name: The name of the model.
      max_doc_length_by_sentence: The maximum document length by the number of
        sentences.
      max_sent_length_by_word: The maximum sentence length by the number of words.
      max_predictions_per_seq: The maxinum number of predicted masked tokens in
        sequence, which can be useful for the masked LM prediction task.
  
    Returns:
      The actual maximum sequence length and maximum number of masked LM
      predictions per sequence. For SMITH model, we need to consider the
      maximum number of sentence blocks in a document to compute these
      statistics.
  
    Raises:
      ValueError: if the arguments are not usable.
  
    """
    if model_name == constants.MODEL_NAME_SMITH_DUAL_ENCODER:
      max_seq_length_actual = \
          max_doc_length_by_sentence * max_sent_length_by_word
      max_predictions_per_seq_actual = \
          max_doc_length_by_sentence * max_predictions_per_seq
    else:
      raise ValueError("Only the SMITH model is supported: %s" % model_name)
    return (max_seq_length_actual, max_predictions_per_seq_actual)
  
  
  def get_export_outputs_prediction_dict_smith_de(
      seq_embed_1, seq_embed_2, predicted_score, predicted_class,
      documents_match_labels, input_sent_embed_1, input_sent_embed_2,
      output_sent_embed_1, output_sent_embed_2):
    """Generates export and prediction dict for dual encoder SMITH model."""
    export_outputs = {
        tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
            tf.estimator.export.PredictOutput(predicted_score),
        "seq_embed_1":
            tf.estimator.export.PredictOutput(seq_embed_1),
        "seq_embed_2":
            tf.estimator.export.PredictOutput(seq_embed_2),
        "input_sent_embed_1":
            tf.estimator.export.PredictOutput(input_sent_embed_1),
        "input_sent_embed_2":
            tf.estimator.export.PredictOutput(input_sent_embed_2),
        "output_sent_embed_1":
            tf.estimator.export.PredictOutput(output_sent_embed_1),
        "output_sent_embed_2":
            tf.estimator.export.PredictOutput(output_sent_embed_2),
        "predicted_class":
            tf.estimator.export.PredictOutput(predicted_class),
        "documents_match_labels":
            tf.estimator.export.PredictOutput(documents_match_labels)
    }
  
    prediction_dict = {
        "predicted_score": predicted_score,
        "predicted_class": predicted_class,
        "documents_match_labels": documents_match_labels,
        "seq_embed_1": seq_embed_1,
        "seq_embed_2": seq_embed_2,
        "input_sent_embed_1": input_sent_embed_1,
        "input_sent_embed_2": input_sent_embed_2,
        "output_sent_embed_1": output_sent_embed_1,
        "output_sent_embed_2": output_sent_embed_2
    }
    return (export_outputs, prediction_dict)
  
  
  def get_pred_res_list_item_smith_de(result):
    """Update the prediction results list for the dual encoder SMITH model."""
    pred_item_dict = {}
    pred_item_dict["predicted_score"] = str(result["predicted_score"])
    pred_item_dict["predicted_class"] = str(result["predicted_class"])
    pred_item_dict["documents_match_labels"] = str(
        result["documents_match_labels"][0])
    pred_item_dict["seq_embed_1"] = ",".join(
        [str(e) for e in result["seq_embed_1"]])
    pred_item_dict["seq_embed_2"] = ",".join(
        [str(e) for e in result["seq_embed_2"]])
    pred_item_dict["input_sent_embed_1"] = transfer_2d_array_to_str(
        result["input_sent_embed_1"])
    pred_item_dict["input_sent_embed_2"] = transfer_2d_array_to_str(
        result["input_sent_embed_2"])
    pred_item_dict["output_sent_embed_1"] = transfer_2d_array_to_str(
        result["output_sent_embed_1"])
    pred_item_dict["output_sent_embed_2"] = transfer_2d_array_to_str(
        result["output_sent_embed_2"])
    return pred_item_dict
  
  
  def load_config_from_file(config_file, protobuf):
    """Return the config proto loaded from config_file.
  
    Args:
      config_file: a string to the path of a pbtxt file.
      protobuf: an instance of a proto.
  
    Returns:
      An parsed of proto with the same type of protobuf.
  
    Raises:
      IOError: if config_file does not exist.
      ParseError: if a wrong protobuf is given.
    """
    if not tf.io.gfile.exists(config_file):
      raise IOError("{} does not exist!".format(config_file))
    with tf.gfile.Open(config_file, "r") as reader:
      proto = text_format.Parse(reader.read(), protobuf)
    return proto
  
  
  MaskedLmInstance = collections.namedtuple("MaskedLmInstance",
                                            ["index", "label"])
  
  
  def create_masked_lm_predictions(tokens, masked_lm_prob,
                                   max_predictions_per_seq, vocab_words, rng):
    """Creates the predictions for the masked LM objective in preprocessing."""
  
    cand_indexes = []
    for (i, token) in enumerate(tokens):
      if token == "[CLS]" or token == "[SEP]":
        continue
      cand_indexes.append(i)
  
    rng.shuffle(cand_indexes)
  
    output_tokens = list(tokens)
  
    num_to_predict = min(max_predictions_per_seq,
                         max(1, int(round(len(tokens) * masked_lm_prob))))
  
    masked_lms = []
    covered_indexes = set()
    for index in cand_indexes:
      if len(masked_lms) >= num_to_predict:
        break
      if index in covered_indexes:
        continue
      covered_indexes.add(index)
  
      masked_token = None
      # 80% of the time, replace with [MASK]
      if rng.random() < 0.8:
        masked_token = "[MASK]"
      else:
        # 10% of the time, keep original
        if rng.random() < 0.5:
          masked_token = tokens[index]
        # 10% of the time, replace with random word
        else:
          masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]
  
      output_tokens[index] = masked_token
  
      masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))
  
    masked_lms = sorted(masked_lms, key=lambda x: x.index)
  
    masked_lm_positions = []
    masked_lm_labels = []
    for p in masked_lms:
      masked_lm_positions.append(p.index)
      masked_lm_labels.append(p.label)
  
    return (output_tokens, masked_lm_positions, masked_lm_labels)
  
  
  def get_majority_vote(rating_scores):
    """Compute majority vote rating given a list.
  
    Args:
      rating_scores: a list of rating scores.
  
    Returns:
      The majority voting rating.
  
    """
    return collections.Counter(rating_scores).most_common()[0][0]
  
  
  def get_mean_score(rating_scores):
    """Compute the mean rating score given a list.
  
    Args:
      rating_scores: a list of rating scores.
  
    Returns:
      The mean rating.
  
    """
    return sum(rating_scores) / len(rating_scores)
  
  
  def create_int_feature(values):
    feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
    return feature
  
  
  def create_float_feature(values):
    feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
    return feature
  
  
  def create_bytes_feature(values):
    feature = tf.train.Feature(bytes_list=tf.train.BytesList(value=list(values)))
    return feature
  