text: #   Copyright (c) 2019 PaddlePaddle 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.
  """Ernie model."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import json
  
  import six
  import paddle.fluid as fluid
  
  from .transformer_encoder import encoder, pre_process_layer
  
  
  class ErnieConfig(object):
      def __init__(self, config_path):
          self._config_dict = self._parse(config_path)
  
      def _parse(self, config_path):
          try:
              with open(config_path) as json_file:
                  config_dict = json.load(json_file)
          except Exception:
              raise IOError("Error in parsing Ernie model config file '%s'" %
                            config_path)
          else:
              return config_dict
  
      def __getitem__(self, key):
          return self._config_dict[key]
  
      def print_config(self):
          for arg, value in sorted(six.iteritems(self._config_dict)):
              print('%s: %s' % (arg, value))
          print('------------------------------------------------')
  
  
  class ErnieModel(object):
      def __init__(self,
                   src_ids,
                   position_ids,
                   sentence_ids,
                   input_mask,
                   config,
                   weight_sharing=True,
                   use_fp16=False):
  
          self._emb_size = config['hidden_size']
          self._n_layer = config['num_hidden_layers']
          self._n_head = config['num_attention_heads']
          self._voc_size = config['vocab_size']
          self._max_position_seq_len = config['max_position_embeddings']
          self._sent_types = config['type_vocab_size']
          self._hidden_act = config['hidden_act']
          self._prepostprocess_dropout = config['hidden_dropout_prob']
          self._attention_dropout = config['attention_probs_dropout_prob']
          self._weight_sharing = weight_sharing
  
          self._word_emb_name = "word_embedding"
          self._pos_emb_name = "pos_embedding"
          self._sent_emb_name = "sent_embedding"
          self._dtype = "float16" if use_fp16 else "float32"
  
          # Initialize all weigths by truncated normal initializer, and all biases
          # will be initialized by constant zero by default.
          self._param_initializer = fluid.initializer.TruncatedNormal(
              scale=config['initializer_range'])
  
          self._build_model(src_ids, position_ids, sentence_ids, input_mask)
  
      def _build_model(self, src_ids, position_ids, sentence_ids, input_mask):
          # padding id in vocabulary must be set to 0
          emb_out = fluid.layers.embedding(
              input=src_ids,
              size=[self._voc_size, self._emb_size],
              dtype=self._dtype,
              param_attr=fluid.ParamAttr(
                  name=self._word_emb_name, initializer=self._param_initializer),
              is_sparse=False)
          position_emb_out = fluid.layers.embedding(
              input=position_ids,
              size=[self._max_position_seq_len, self._emb_size],
              dtype=self._dtype,
              param_attr=fluid.ParamAttr(
                  name=self._pos_emb_name, initializer=self._param_initializer))
  
          sent_emb_out = fluid.layers.embedding(
              sentence_ids,
              size=[self._sent_types, self._emb_size],
              dtype=self._dtype,
              param_attr=fluid.ParamAttr(
                  name=self._sent_emb_name, initializer=self._param_initializer))
  
          emb_out = emb_out + position_emb_out
          emb_out = emb_out + sent_emb_out
  
          emb_out = pre_process_layer(
              emb_out, 'nd', self._prepostprocess_dropout, name='pre_encoder')
  
          if self._dtype == "float16":
              input_mask = fluid.layers.cast(x=input_mask, dtype=self._dtype)
          self_attn_mask = fluid.layers.matmul(
              x=input_mask, y=input_mask, transpose_y=True)
  
          self_attn_mask = fluid.layers.scale(
              x=self_attn_mask, scale=10000.0, bias=-1.0, bias_after_scale=False)
          n_head_self_attn_mask = fluid.layers.stack(
              x=[self_attn_mask] * self._n_head, axis=1)
          n_head_self_attn_mask.stop_gradient = True
  
          self._enc_out = encoder(
              enc_input=emb_out,
              attn_bias=n_head_self_attn_mask,
              n_layer=self._n_layer,
              n_head=self._n_head,
              d_key=self._emb_size // self._n_head,
              d_value=self._emb_size // self._n_head,
              d_model=self._emb_size,
              d_inner_hid=self._emb_size * 4,
              prepostprocess_dropout=self._prepostprocess_dropout,
              attention_dropout=self._attention_dropout,
              relu_dropout=0,
              hidden_act=self._hidden_act,
              preprocess_cmd="",
              postprocess_cmd="dan",
              param_initializer=self._param_initializer,
              name='encoder')
  
      def get_sequence_output(self):
          return self._enc_out
  
      def get_pooled_output(self):
          """Get the first feature of each sequence for classification"""
          next_sent_feat = fluid.layers.slice(
              input=self._enc_out, axes=[1], starts=[0], ends=[1])
          next_sent_feat = fluid.layers.fc(
              input=next_sent_feat,
              size=self._emb_size,
              act="tanh",
              param_attr=fluid.ParamAttr(
                  name="pooled_fc.w_0", initializer=self._param_initializer),
              bias_attr="pooled_fc.b_0")
          return next_sent_feat
  
      def get_pretraining_output(self, mask_label, mask_pos, labels):
          """Get the loss & accuracy for pretraining"""
  
          mask_pos = fluid.layers.cast(x=mask_pos, dtype='int32')
  
          # extract the first token feature in each sentence
          next_sent_feat = self.get_pooled_output()
          reshaped_emb_out = fluid.layers.reshape(
              x=self._enc_out, shape=[-1, self._emb_size])
          # extract masked tokens' feature
          mask_feat = fluid.layers.gather(input=reshaped_emb_out, index=mask_pos)
  
          # transform: fc
          mask_trans_feat = fluid.layers.fc(
              input=mask_feat,
              size=self._emb_size,
              act=self._hidden_act,
              param_attr=fluid.ParamAttr(
                  name='mask_lm_trans_fc.w_0',
                  initializer=self._param_initializer),
              bias_attr=fluid.ParamAttr(name='mask_lm_trans_fc.b_0'))
          # transform: layer norm
          mask_trans_feat = pre_process_layer(
              mask_trans_feat, 'n', name='mask_lm_trans')
  
          mask_lm_out_bias_attr = fluid.ParamAttr(
              name="mask_lm_out_fc.b_0",
              initializer=fluid.initializer.Constant(value=0.0))
          if self._weight_sharing:
              fc_out = fluid.layers.matmul(
                  x=mask_trans_feat,
                  y=fluid.default_main_program().global_block().var(
                      self._word_emb_name),
                  transpose_y=True)
              fc_out += fluid.layers.create_parameter(
                  shape=[self._voc_size],
                  dtype=self._dtype,
                  attr=mask_lm_out_bias_attr,
                  is_bias=True)
  
          else:
              fc_out = fluid.layers.fc(input=mask_trans_feat,
                                       size=self._voc_size,
                                       param_attr=fluid.ParamAttr(
                                           name="mask_lm_out_fc.w_0",
                                           initializer=self._param_initializer),
                                       bias_attr=mask_lm_out_bias_attr)
  
          mask_lm_loss = fluid.layers.softmax_with_cross_entropy(
              logits=fc_out, label=mask_label)
          mean_mask_lm_loss = fluid.layers.mean(mask_lm_loss)
  
          next_sent_fc_out = fluid.layers.fc(
              input=next_sent_feat,
              size=2,
              param_attr=fluid.ParamAttr(
                  name="next_sent_fc.w_0", initializer=self._param_initializer),
              bias_attr="next_sent_fc.b_0")
  
          next_sent_loss, next_sent_softmax = fluid.layers.softmax_with_cross_entropy(
              logits=next_sent_fc_out, label=labels, return_softmax=True)
  
          next_sent_acc = fluid.layers.accuracy(
              input=next_sent_softmax, label=labels)
  
          mean_next_sent_loss = fluid.layers.mean(next_sent_loss)
  
          loss = mean_next_sent_loss + mean_mask_lm_loss
          return next_sent_acc, mean_mask_lm_loss, loss
  