Avoid deeply nested control flow statements.

Open

          if span_contains_answer:
            answer_text = " ".join(tokens[start_position:(end_position + 1)])
            logging.info("start_position: %d", (start_position))
            logging.info("end_position: %d", (end_position))
            logging.info("answer: %s", tokenization.printable_text(answer_text))

Avoid deeply nested control flow statements.

Open

          if score_diff > null_score_diff_threshold:
            all_predictions[example.qas_id] = ""
          else:
            all_predictions[example.qas_id] = best_non_null_entry.text
      else:

Function _generate_detections has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def _generate_detections(boxes,

Avoid deeply nested control flow statements.

Open

          if not is_impossible:
            answer = qa["answers"][0]
            orig_answer_text = answer["text"]
            start_position = answer["answer_start"]
          else:

Function generate_sentence_retrevial_tf_record has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def generate_sentence_retrevial_tf_record(processor,

Function truncate_seq_pair has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng):
  """Truncates a pair of sequences to a maximum sequence length."""
  while True:
    total_length = len(tokens_a) + len(tokens_b)
    if total_length <= max_num_tokens:

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Avoid deeply nested control flow statements.

Open

          if use_eod:
            token_ids = [eod_symbol]
            sentence_id = not sentence_id
          else:
            continue

Function _create_fake_bert_dataset has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def _create_fake_bert_dataset(

Function _online_sample_mask has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

  def _online_sample_mask(self, inputs: tf.Tensor,
                          boundary: tf.Tensor) -> tf.Tensor:
    """Samples target positions for predictions.

    Descriptions of each strategy:

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Avoid deeply nested control flow statements.

Open

          if prev_is_whitespace:
            doc_tokens.append(c)
          else:
            doc_tokens[-1] += c
          prev_is_whitespace = False

Avoid deeply nested control flow statements.

Open

          for _ in range(10):
            random_document_index = rng.randint(0, len(all_documents) - 1)
            if random_document_index != document_index:
              break

Avoid deeply nested control flow statements.

Open

          if (len(qa["answers"]) != 1) and (not is_impossible):
            raise ValueError(
                "For training, each question should have exactly 1 answer.")
          if not is_impossible:

Function _decode has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

  def _decode(self, record: tf.Tensor):
    """Decodes a serialized tf.Example."""
    name_to_features = {
        'input_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
        'input_mask': tf.io.FixedLenFeature([self._seq_length], tf.int64),

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Function __call__ has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def __call__(self, box_outputs, class_outputs, anchor_boxes, image_shape,

Function _check_is_max_context has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

def _check_is_max_context(doc_spans, cur_span_index, position):
  """Check if this is the 'max context' doc span for the token."""

  # Because of the sliding window approach taken to scoring documents, a single
  # token can appear in multiple documents. E.g.

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Function __init__ has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def __init__(self,

Avoid deeply nested control flow statements.

Open

          if not is_impossible:
            answer = qa["answers"][0]
            orig_answer_text = answer["text"]
            answer_offset = answer["answer_start"]
            answer_length = len(orig_answer_text)

Avoid deeply nested control flow statements.

Open

            if is_box_lrtb:  # Box in left-right-top-bottom format.
              this_level_boxes = box_utils.decode_boxes_lrtb(
                  this_level_boxes, this_level_anchors)
            else:  # Box in standard x-y-h-w format.
              this_level_boxes = box_utils.decode_boxes(

Function _check_is_max_context has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

def _check_is_max_context(doc_spans, cur_span_index, position):
  """Check if this is the 'max context' doc span for the token."""

  # Because of the sliding window approach taken to scoring documents, a single
  # token can appear in multiple documents. E.g.

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Function _maybe_truncate has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

  def _maybe_truncate(self, inputs):
    truncated_inputs = {}
    for k, v in inputs.items():
      if k == 'inputs' or k == 'targets':
        truncated_inputs[k] = tf.pad(

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