text: """
  Copyright 2020 The OneFlow 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 oneflow as flow
  import bert as bert_util
  import oneflow.core.operator.op_conf_pb2 as op_conf_util
  
  
  def PreTrain(
      input_ids_blob,
      input_mask_blob,
      token_type_ids_blob,
      masked_lm_positions_blob,
      masked_lm_ids_blob,
      masked_lm_weights_blob,
      next_sentence_label_blob,
      vocab_size,
      seq_length=512,
      hidden_size=768,
      num_hidden_layers=12,
      num_attention_heads=12,
      intermediate_size=3072,
      hidden_act="gelu",
      hidden_dropout_prob=0.1,
      attention_probs_dropout_prob=0.1,
      max_position_embeddings=512,
      type_vocab_size=16,
      max_predictions_per_seq=20,
      initializer_range=0.02,
  ):
      backbone = bert_util.BertBackbone(
          input_ids_blob=input_ids_blob,
          input_mask_blob=input_mask_blob,
          token_type_ids_blob=token_type_ids_blob,
          vocab_size=vocab_size,
          seq_length=seq_length,
          hidden_size=hidden_size,
          num_hidden_layers=num_hidden_layers,
          num_attention_heads=num_attention_heads,
          intermediate_size=intermediate_size,
          hidden_act=hidden_act,
          hidden_dropout_prob=hidden_dropout_prob,
          attention_probs_dropout_prob=attention_probs_dropout_prob,
          max_position_embeddings=max_position_embeddings,
          type_vocab_size=type_vocab_size,
          initializer_range=initializer_range,
      )
  
      (lm_loss, _, _) = _AddMaskedLanguageModelLoss(
          input_blob=backbone.sequence_output(),
          output_weights_blob=backbone.embedding_table(),
          positions_blob=masked_lm_positions_blob,
          label_id_blob=masked_lm_ids_blob,
          label_weight_blob=masked_lm_weights_blob,
          seq_length=seq_length,
          hidden_size=hidden_size,
          vocab_size=vocab_size,
          max_predictions_per_seq=max_predictions_per_seq,
          hidden_act=bert_util.GetActivation(hidden_act),
          initializer_range=initializer_range,
      )
      pooled_output = PooledOutput(
          backbone.sequence_output(), hidden_size, initializer_range
      )
      (ns_loss, _, _) = _AddNextSentenceOutput(
          input_blob=pooled_output,
          label_blob=next_sentence_label_blob,
          hidden_size=hidden_size,
          initializer_range=initializer_range,
      )
      with flow.scope.namespace("cls-loss"):
          total_loss = lm_loss + ns_loss
      return total_loss, lm_loss, ns_loss
  
  
  def PooledOutput(sequence_output, hidden_size, initializer_range):
      with flow.scope.namespace("bert-pooler"):
          first_token_tensor = flow.slice(sequence_output, [None, 0, 0], [None, 1, -1])
          first_token_tensor = flow.reshape(first_token_tensor, [-1, hidden_size])
          pooled_output = bert_util._FullyConnected(
              first_token_tensor,
              input_size=hidden_size,
              units=hidden_size,
              weight_initializer=bert_util.CreateInitializer(initializer_range),
              name="dense",
          )
          pooled_output = flow.math.tanh(pooled_output)
      return pooled_output
  
  
  def _AddMaskedLanguageModelLoss(
      input_blob,
      output_weights_blob,
      positions_blob,
      label_id_blob,
      label_weight_blob,
      seq_length,
      hidden_size,
      vocab_size,
      max_predictions_per_seq,
      hidden_act,
      initializer_range,
  ):
      with flow.scope.namespace("other"):
          sum_label_weight_blob = flow.math.reduce_sum(label_weight_blob, axis=[-1])
          ones = sum_label_weight_blob * 0.0 + 1.0
          sum_label_weight_blob = flow.math.reduce_sum(sum_label_weight_blob)
          batch_size = flow.math.reduce_sum(ones)
          sum_label_weight_blob = sum_label_weight_blob / batch_size
      with flow.scope.namespace("cls-predictions"):
          input_blob = _GatherIndexes(input_blob, positions_blob, seq_length, hidden_size)
          with flow.scope.namespace("transform"):
              if callable(hidden_act):
                  act_fn = op_conf_util.kNone
              else:
                  act_fn = hidden_act
              input_blob = bert_util._FullyConnected(
                  input_blob,
                  input_size=hidden_size,
                  units=hidden_size,
                  activation=act_fn,
                  weight_initializer=bert_util.CreateInitializer(initializer_range),
                  name="dense",
              )
              if callable(hidden_act):
                  input_blob = hidden_act(input_blob)
                  input_blob = bert_util._LayerNorm(input_blob, hidden_size)
          output_bias = flow.get_variable(
              name="output_bias",
              shape=[vocab_size],
              dtype=input_blob.dtype,
              initializer=flow.constant_initializer(1.0),
          )
          logit_blob = flow.matmul(input_blob, output_weights_blob, transpose_b=True)
          logit_blob = flow.nn.bias_add(logit_blob, output_bias)
          label_id_blob = flow.reshape(label_id_blob, [-1])
          pre_example_loss = flow.nn.sparse_softmax_cross_entropy_with_logits(
              logits=logit_blob, labels=label_id_blob
          )
          pre_example_loss = flow.reshape(pre_example_loss, [-1, max_predictions_per_seq])
          numerator = pre_example_loss * label_weight_blob
          with flow.scope.namespace("loss"):
              numerator = flow.math.reduce_sum(numerator, axis=[-1])
              denominator = sum_label_weight_blob + 1e-5
              loss = numerator / denominator
          return loss, pre_example_loss, logit_blob
  
  
  def _GatherIndexes(sequence_blob, positions_blob, seq_length, hidden_size):
      output = flow.gather(
          params=sequence_blob, indices=positions_blob, axis=2, batch_dims=2
      )
      output = flow.reshape(output, [-1, hidden_size])
      return output
  
  
  def _AddNextSentenceOutput(input_blob, label_blob, hidden_size, initializer_range):
      with flow.scope.namespace("cls-seq_relationship"):
          output_weight_blob = flow.get_variable(
              name="output_weights",
              shape=[2, hidden_size],
              dtype=input_blob.dtype,
              initializer=bert_util.CreateInitializer(initializer_range),
          )
          output_bias_blob = flow.get_variable(
              name="output_bias",
              shape=[2],
              dtype=input_blob.dtype,
              initializer=flow.constant_initializer(0.0),
          )
          logit_blob = flow.matmul(input_blob, output_weight_blob, transpose_b=True)
          logit_blob = flow.nn.bias_add(logit_blob, output_bias_blob)
          pre_example_loss = flow.nn.sparse_softmax_cross_entropy_with_logits(
              logits=logit_blob, labels=label_blob
          )
          loss = pre_example_loss
          return loss, pre_example_loss, logit_blob
  