text: # Copyright (c) 2021 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.
  
  import paddle
  import paddle.nn as nn
  import paddle.nn.functional as F
  
  from .. import PretrainedModel, register_base_model
  
  __all__ = [
      'PPMiniLMModel',
      'PPMiniLMPretrainedModel',
      'PPMiniLMForSequenceClassification',
  ]
  
  
  class PPMiniLMEmbeddings(nn.Layer):
      r"""
      Include embeddings from word, position and token_type embeddings.
      """
  
      def __init__(
              self,
              vocab_size,
              hidden_size=768,
              hidden_dropout_prob=0.1,
              max_position_embeddings=512,
              type_vocab_size=2,
              pad_token_id=0,
              weight_attr=None, ):
          super(PPMiniLMEmbeddings, self).__init__()
  
          self.word_embeddings = nn.Embedding(
              vocab_size,
              hidden_size,
              padding_idx=pad_token_id,
              weight_attr=weight_attr)
          self.position_embeddings = nn.Embedding(
              max_position_embeddings, hidden_size, weight_attr=weight_attr)
          self.token_type_embeddings = nn.Embedding(
              type_vocab_size, hidden_size, weight_attr=weight_attr)
          self.layer_norm = nn.LayerNorm(hidden_size)
          self.dropout = nn.Dropout(hidden_dropout_prob)
  
      def forward(self, input_ids, token_type_ids=None, position_ids=None):
          if position_ids is None:
              # maybe need use shape op to unify static graph and dynamic graph
              #seq_length = input_ids.shape[1]
              ones = paddle.ones_like(input_ids, dtype="int64")
              seq_length = paddle.cumsum(ones, axis=1)
              position_ids = seq_length - ones
              position_ids.stop_gradient = True
          if token_type_ids is None:
              token_type_ids = paddle.zeros_like(input_ids, dtype="int64")
          input_embedings = self.word_embeddings(input_ids)
          position_embeddings = self.position_embeddings(position_ids)
          token_type_embeddings = self.token_type_embeddings(token_type_ids)
  
          embeddings = input_embedings + position_embeddings + token_type_embeddings
          embeddings = self.layer_norm(embeddings)
          embeddings = self.dropout(embeddings)
          return embeddings
  
  
  class PPMiniLMPooler(nn.Layer):
      def __init__(self, hidden_size, weight_attr=None):
          super(PPMiniLMPooler, self).__init__()
          self.dense = nn.Linear(
              hidden_size, hidden_size, weight_attr=weight_attr)
          self.activation = nn.Tanh()
  
      def forward(self, hidden_states):
          # We "pool" the model by simply taking the hidden state corresponding
          # to the first token.
          first_token_tensor = hidden_states[:, 0]
          pooled_output = self.dense(first_token_tensor)
          pooled_output = self.activation(pooled_output)
          return pooled_output
  
  
  class PPMiniLMPretrainedModel(PretrainedModel):
      r"""
      An abstract class for pretrained PPMiniLM models. It provides PPMiniLM related
      `model_config_file`, `pretrained_init_configuration`, `resource_files_names`,
      `pretrained_resource_files_map`, `base_model_prefix` for downloading and
      loading pretrained models. 
      Refer to :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
  
      """
  
      model_config_file = "model_config.json"
      pretrained_init_configuration = {
          "ppminilm-6l-768h": {
              "attention_probs_dropout_prob": 0.1,
              "intermediate_size": 3072,
              "hidden_act": "relu",
              "hidden_dropout_prob": 0.1,
              "hidden_size": 768,
              "initializer_range": 0.02,
              "max_position_embeddings": 512,
              "num_attention_heads": 12,
              "num_hidden_layers": 6,
              "type_vocab_size": 4,
              "vocab_size": 21128,
              "pad_token_id": 0,
          },
      }
      resource_files_names = {"model_state": "model_state.pdparams"}
      pretrained_resource_files_map = {
          "model_state": {
              "ppminilm-6l-768h":
              "https://bj.bcebos.com/paddlenlp/models/transformers/ppminilm-6l-768h/ppminilm-6l-768h.pdparams",
          }
      }
      base_model_prefix = "ppminilm"
  
      def init_weights(self, layer):
          """ Initialization hook """
          if isinstance(layer, (nn.Linear, nn.Embedding)):
              # only support dygraph, use truncated_normal and make it inplace
              # and configurable later
              if isinstance(layer.weight, paddle.Tensor):
                  layer.weight.set_value(
                      paddle.tensor.normal(
                          mean=0.0,
                          std=self.initializer_range
                          if hasattr(self, "initializer_range") else
                          self.ppminilm.config["initializer_range"],
                          shape=layer.weight.shape))
          elif isinstance(layer, nn.LayerNorm):
              layer._epsilon = 1e-12
  
  
  @register_base_model
  class PPMiniLMModel(PPMiniLMPretrainedModel):
      r"""
      The bare PPMiniLM Model transformer outputting raw hidden-states.
  
      This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
      Refer to the superclass documentation for the generic methods.
  
      This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
      /docs/en/api/paddle/fluid/dygraph/layers/Layer_en.html>`__ subclass. Use it as a regular Paddle Layer
      and refer to the Paddle documentation for all matter related to general usage and behavior.
  
      Args:
          vocab_size (int):
              Vocabulary size of `inputs_ids` in `PPMiniLMModel`. Also is the vocab size of token embedding matrix.
              Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `PPMiniLMModel`.
          hidden_size (int, optional):
              Dimensionality of the embedding layer, encoder layers and pooler layer. Defaults to `768`.
          num_hidden_layers (int, optional):
              Number of hidden layers in the Transformer encoder. Defaults to `12`.
          num_attention_heads (int, optional):
              Number of attention heads for each attention layer in the Transformer encoder.
              Defaults to `12`.
          intermediate_size (int, optional):
              Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors
              to ff layers are firstly projected from `hidden_size` to `intermediate_size`,
              and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`.
              Defaults to `3072`.
          hidden_act (str, optional):
              The non-linear activation function in the feed-forward layer.
              ``"gelu"``, ``"relu"`` and any other paddle supported activation functions
              are supported. Defaults to `"gelu"`.
          hidden_dropout_prob (float, optional):
              The dropout probability for all fully connected layers in the embeddings and encoder.
              Defaults to `0.1`.
          attention_probs_dropout_prob (float, optional):
              The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target.
              Defaults to `0.1`.
          max_position_embeddings (int, optional):
              The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input
              sequence. Defaults to `512`.
          type_vocab_size (int, optional):
              The vocabulary size of the `token_type_ids`.
              Defaults to `2`.
          initializer_range (float, optional):
              The standard deviation of the normal initializer for initializing all weight matrices.
              Defaults to `0.02`.
              
              .. note::
                  A normal_initializer initializes weight matrices as normal distributions.
                  See :meth:`PPMiniLMPretrainedModel._init_weights()` for how weights are initialized in `PPMiniLMModel`.
  
          pad_token_id(int, optional):
              The index of padding token in the token vocabulary.
              Defaults to `0`.
  
      """
  
      def __init__(self,
                   vocab_size,
                   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=2,
                   initializer_range=0.02,
                   pad_token_id=0):
          super(PPMiniLMModel, self).__init__()
          self.pad_token_id = pad_token_id
          self.initializer_range = initializer_range
          weight_attr = paddle.ParamAttr(
              initializer=nn.initializer.TruncatedNormal(
                  mean=0.0, std=self.initializer_range))
          self.embeddings = PPMiniLMEmbeddings(
              vocab_size, hidden_size, hidden_dropout_prob,
              max_position_embeddings, type_vocab_size, pad_token_id,
              weight_attr)
          encoder_layer = nn.TransformerEncoderLayer(
              hidden_size,
              num_attention_heads,
              intermediate_size,
              dropout=hidden_dropout_prob,
              activation=hidden_act,
              attn_dropout=attention_probs_dropout_prob,
              act_dropout=0,
              weight_attr=weight_attr,
              normalize_before=False)
          self.encoder = nn.TransformerEncoder(encoder_layer, num_hidden_layers)
          self.pooler = PPMiniLMPooler(hidden_size, weight_attr)
          self.apply(self.init_weights)
  
      def forward(self,
                  input_ids,
                  token_type_ids=None,
                  position_ids=None,
                  attention_mask=None):
          r"""
          Args:
              input_ids (Tensor):
                  Indices of input sequence tokens in the vocabulary. They are
                  numerical representations of tokens that build the input sequence.
                  It's data type should be `int64` and has a shape of [batch_size, sequence_length].
              token_type_ids (Tensor, optional):
                  Segment token indices to indicate different portions of the inputs.
                  Selected in the range ``[0, type_vocab_size - 1]``.
                  If `type_vocab_size` is 2, which means the inputs have two portions.
                  Indices can either be 0 or 1:
  
                  - 0 corresponds to a *sentence A* token,
                  - 1 corresponds to a *sentence B* token.
  
                  Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
                  Defaults to `None`, which means we don't add segment embeddings.
              position_ids (Tensor, optional):
                  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
                  max_position_embeddings - 1]``.
                  Shape as `[batch_size, num_tokens]` and dtype as int64. Defaults to `None`.
              attention_mask (Tensor, optional):
                  Mask used in multi-head attention to avoid performing attention on to some unwanted positions,
                  usually the paddings or the subsequent positions.
                  Its data type can be int, float and bool.
                  When the data type is bool, the `masked` tokens have `False` values and the others have `True` values.
                  When the data type is int, the `masked` tokens have `0` values and the others have `1` values.
                  When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values.
                  It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
                  For example, its shape can be  [batch_size, sequence_length], [batch_size, sequence_length, sequence_length],
                  [batch_size, num_attention_heads, sequence_length, sequence_length].
                  We use whole-word-mask in PPMiniLM, so the whole word will have the same value. For example, "使用" as a word,
                  "使" and "用" will have the same value.
                  Defaults to `None`, which means nothing needed to be prevented attention to.
  
          Returns:
              tuple: Returns tuple (``sequence_output``, ``pooled_output``).
  
              With the fields:
  
              - `sequence_output` (Tensor):
                  Sequence of hidden-states at the last layer of the model.
                  It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
  
              - `pooled_output` (Tensor):
                  The output of first token (`[CLS]`) in sequence.
                  We "pool" the model by simply taking the hidden state corresponding to the first token.
                  Its data type should be float32 and its shape is [batch_size, hidden_size].
  
          Example:
              .. code-block::
  
                  import paddle
                  from paddlenlp.transformers import PPMiniLMModel, PPMiniLMTokenizer
  
                  tokenizer = PPMiniLMTokenizer.from_pretrained('ppminilm-6l-768h')
                  model = PPMiniLMModel.from_pretrained('ppminilm-6l-768h')
  
                  inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                  inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                  sequence_output, pooled_output = model(**inputs)
  
          """
          if attention_mask is None:
              attention_mask = paddle.unsqueeze(
                  (input_ids == self.pad_token_id
                   ).astype(self.pooler.dense.weight.dtype) * -1e9,
                  axis=[1, 2])
          embedding_output = self.embeddings(
              input_ids=input_ids,
              position_ids=position_ids,
              token_type_ids=token_type_ids)
  
          encoder_outputs = self.encoder(embedding_output, attention_mask)
          sequence_output = encoder_outputs
          pooled_output = self.pooler(sequence_output)
          return sequence_output, pooled_output
  
  
  class PPMiniLMForSequenceClassification(PPMiniLMPretrainedModel):
      r"""
      PPMiniLM Model with a linear layer on top of the output layer,
      designed for sequence classification/regression tasks like GLUE tasks.
  
      Args:
          ppminilm (PPMiniLMModel): 
              An instance of `paddlenlp.transformers.PPMiniLMModel`.
          num_classes (int, optional): 
              The number of classes. Default to `2`.
          dropout (float, optional): 
              The dropout probability for output of PPMiniLM. 
              If None, use the same value as `hidden_dropout_prob` 
              of `paddlenlp.transformers.PPMiniLMModel` instance. Defaults to `None`.
      """
  
      def __init__(self, ppminilm, num_classes=2, dropout=None):
          super(PPMiniLMForSequenceClassification, self).__init__()
          self.num_classes = num_classes
          self.ppminilm = ppminilm  # allow ppminilm to be config
          self.dropout = nn.Dropout(dropout if dropout is not None else
                                    self.ppminilm.config["hidden_dropout_prob"])
          self.classifier = nn.Linear(self.ppminilm.config["hidden_size"],
                                      num_classes)
          self.apply(self.init_weights)
  
      def forward(self,
                  input_ids,
                  token_type_ids=None,
                  position_ids=None,
                  attention_mask=None):
          r"""
          Args:
              input_ids (Tensor):
                  See :class:`PPMiniLMModel`.
              token_type_ids (Tensor, optional):
                  See :class:`PPMiniLMModel`.
              position_ids (Tensor, optional):
                  See :class:`PPMiniLMModel`.
              attention_mask (Tensor, optional):
                  See :class:`MiniLMModel`.
  
          Returns:
              Tensor: Returns tensor `logits`, a tensor of the input text classification logits.
              Shape as `[batch_size, num_classes]` and dtype as float32.
  
          Example:
              .. code-block::
  
                  import paddle
                  from paddlenlp.transformers import PPMiniLMForSequenceClassification, PPMiniLMTokenizer
  
                  tokenizer = PPMiniLMTokenizer.from_pretrained('ppminilm-6l-768h')
                  model = PPMiniLMForSequenceClassification.from_pretrained('ppminilm-6l-768h0')
  
                  inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                  inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                  logits = model(**inputs)
  
          """
          _, pooled_output = self.ppminilm(
              input_ids,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              attention_mask=attention_mask)
  
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
          return logits
  