text: # coding=utf-8
  # Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
  #
  # 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.
  """
   TF 2.0 DistilBERT model
  """
  
  import warnings
  
  import tensorflow as tf
  
  from ...activations_tf import get_tf_activation
  from ...file_utils import (
      MULTIPLE_CHOICE_DUMMY_INPUTS,
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
  )
  from ...modeling_tf_outputs import (
      TFBaseModelOutput,
      TFMaskedLMOutput,
      TFMultipleChoiceModelOutput,
      TFQuestionAnsweringModelOutput,
      TFSequenceClassifierOutput,
      TFTokenClassifierOutput,
  )
  from ...modeling_tf_utils import (
      TFMaskedLanguageModelingLoss,
      TFMultipleChoiceLoss,
      TFPreTrainedModel,
      TFQuestionAnsweringLoss,
      TFSequenceClassificationLoss,
      TFTokenClassificationLoss,
      get_initializer,
      input_processing,
      keras_serializable,
      shape_list,
  )
  from ...utils import logging
  from .configuration_distilbert import DistilBertConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CHECKPOINT_FOR_DOC = "distilbert-base-uncased"
  _CONFIG_FOR_DOC = "DistilBertConfig"
  _TOKENIZER_FOR_DOC = "DistilBertTokenizer"
  
  TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "distilbert-base-uncased",
      "distilbert-base-uncased-distilled-squad",
      "distilbert-base-cased",
      "distilbert-base-cased-distilled-squad",
      "distilbert-base-multilingual-cased",
      "distilbert-base-uncased-finetuned-sst-2-english",
      # See all DistilBERT models at https://huggingface.co/models?filter=distilbert
  ]
  
  
  class TFEmbeddings(tf.keras.layers.Layer):
      """Construct the embeddings from word, position and token_type embeddings."""
  
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.vocab_size = config.vocab_size
          self.dim = config.dim
          self.initializer_range = config.initializer_range
          self.max_position_embeddings = config.max_position_embeddings
  
          self.embeddings_sum = tf.keras.layers.Add()
          self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="LayerNorm")
          self.dropout = tf.keras.layers.Dropout(rate=config.dropout)
  
      def build(self, input_shape: tf.TensorShape):
          with tf.name_scope("word_embeddings"):
              self.weight = self.add_weight(
                  name="weight",
                  shape=[self.vocab_size, self.dim],
                  initializer=get_initializer(initializer_range=self.initializer_range),
              )
  
          with tf.name_scope("position_embeddings"):
              self.position_embeddings = self.add_weight(
                  name="embeddings",
                  shape=[self.max_position_embeddings, self.dim],
                  initializer=get_initializer(initializer_range=self.initializer_range),
              )
  
          super().build(input_shape)
  
      def call(self, input_ids=None, position_ids=None, inputs_embeds=None, training=False):
          """
          Applies embedding based on inputs tensor.
  
          Returns:
              final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
          """
          assert not (input_ids is None and inputs_embeds is None)
  
          if input_ids is not None:
              inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
  
          input_shape = shape_list(inputs_embeds)[:-1]
  
          if position_ids is None:
              position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
  
          position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
          position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
          final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds])
          final_embeddings = self.LayerNorm(inputs=final_embeddings)
          final_embeddings = self.dropout(inputs=final_embeddings, training=training)
  
          return final_embeddings
  
  
  class TFMultiHeadSelfAttention(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.n_heads = config.n_heads
          self.dim = config.dim
          self.dropout = tf.keras.layers.Dropout(config.attention_dropout)
          self.output_attentions = config.output_attentions
  
          assert self.dim % self.n_heads == 0, f"Hidden size {self.dim} not dividable by number of heads {self.n_heads}"
  
          self.q_lin = tf.keras.layers.Dense(
              config.dim, kernel_initializer=get_initializer(config.initializer_range), name="q_lin"
          )
          self.k_lin = tf.keras.layers.Dense(
              config.dim, kernel_initializer=get_initializer(config.initializer_range), name="k_lin"
          )
          self.v_lin = tf.keras.layers.Dense(
              config.dim, kernel_initializer=get_initializer(config.initializer_range), name="v_lin"
          )
          self.out_lin = tf.keras.layers.Dense(
              config.dim, kernel_initializer=get_initializer(config.initializer_range), name="out_lin"
          )
  
          self.pruned_heads = set()
  
      def prune_heads(self, heads):
          raise NotImplementedError
  
      def call(self, query, key, value, mask, head_mask, output_attentions, training=False):
          """
          Parameters:
              query: tf.Tensor(bs, seq_length, dim)
              key: tf.Tensor(bs, seq_length, dim)
              value: tf.Tensor(bs, seq_length, dim)
              mask: tf.Tensor(bs, seq_length)
  
          Returns:
              weights: tf.Tensor(bs, n_heads, seq_length, seq_length) Attention weights context: tf.Tensor(bs,
              seq_length, dim) Contextualized layer. Optional: only if `output_attentions=True`
          """
          bs, q_length, dim = shape_list(query)
          k_length = shape_list(key)[1]
          # assert dim == self.dim, f'Dimensions do not match: {dim} input vs {self.dim} configured'
          # assert key.size() == value.size()
          dim_per_head = tf.math.divide(self.dim, self.n_heads)
          dim_per_head = tf.cast(dim_per_head, dtype=tf.int32)
          mask_reshape = [bs, 1, 1, k_length]
  
          def shape(x):
              """separate heads"""
              return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))
  
          def unshape(x):
              """group heads"""
              return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))
  
          q = shape(self.q_lin(query))  # (bs, n_heads, q_length, dim_per_head)
          k = shape(self.k_lin(key))  # (bs, n_heads, k_length, dim_per_head)
          v = shape(self.v_lin(value))  # (bs, n_heads, k_length, dim_per_head)
          q = tf.cast(q, dtype=tf.float32)
          q = tf.multiply(q, tf.math.rsqrt(tf.cast(dim_per_head, dtype=tf.float32)))
          k = tf.cast(k, dtype=q.dtype)
          scores = tf.matmul(q, k, transpose_b=True)  # (bs, n_heads, q_length, k_length)
          mask = tf.reshape(mask, mask_reshape)  # (bs, n_heads, qlen, klen)
          # scores.masked_fill_(mask, -float('inf'))            # (bs, n_heads, q_length, k_length)
  
          mask = tf.cast(mask, dtype=scores.dtype)
          scores = scores - 1e30 * (1.0 - mask)
          weights = tf.nn.softmax(scores, axis=-1)  # (bs, n_heads, qlen, klen)
          weights = self.dropout(weights, training=training)  # (bs, n_heads, qlen, klen)
  
          # Mask heads if we want to
          if head_mask is not None:
              weights = weights * head_mask
  
          context = tf.matmul(weights, v)  # (bs, n_heads, qlen, dim_per_head)
          context = unshape(context)  # (bs, q_length, dim)
          context = self.out_lin(context)  # (bs, q_length, dim)
  
          if output_attentions:
              return (context, weights)
          else:
              return (context,)
  
  
  class TFFFN(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.dropout = tf.keras.layers.Dropout(config.dropout)
          self.lin1 = tf.keras.layers.Dense(
              config.hidden_dim, kernel_initializer=get_initializer(config.initializer_range), name="lin1"
          )
          self.lin2 = tf.keras.layers.Dense(
              config.dim, kernel_initializer=get_initializer(config.initializer_range), name="lin2"
          )
          assert config.activation in ["relu", "gelu"], f"activation ({config.activation}) must be in ['relu', 'gelu']"
          self.activation = get_tf_activation(config.activation)
  
      def call(self, input, training=False):
          x = self.lin1(input)
          x = self.activation(x)
          x = self.lin2(x)
          x = self.dropout(x, training=training)
          return x
  
  
  class TFTransformerBlock(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.n_heads = config.n_heads
          self.dim = config.dim
          self.hidden_dim = config.hidden_dim
          self.dropout = tf.keras.layers.Dropout(config.dropout)
          self.activation = config.activation
          self.output_attentions = config.output_attentions
  
          assert (
              config.dim % config.n_heads == 0
          ), f"Hidden size {config.dim} not dividable by number of heads {config.n_heads}"
  
          self.attention = TFMultiHeadSelfAttention(config, name="attention")
          self.sa_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="sa_layer_norm")
  
          self.ffn = TFFFN(config, name="ffn")
          self.output_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="output_layer_norm")
  
      def call(self, x, attn_mask, head_mask, output_attentions, training=False):  # removed: src_enc=None, src_len=None
          """
          Parameters:
              x: tf.Tensor(bs, seq_length, dim)
              attn_mask: tf.Tensor(bs, seq_length)
  
          Outputs: sa_weights: tf.Tensor(bs, n_heads, seq_length, seq_length) The attention weights ffn_output:
          tf.Tensor(bs, seq_length, dim) The output of the transformer block contextualization.
          """
          # Self-Attention
          sa_output = self.attention(x, x, x, attn_mask, head_mask, output_attentions, training=training)
          if output_attentions:
              sa_output, sa_weights = sa_output  # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length)
          else:  # To handle these `output_attentions` or `output_hidden_states` cases returning tuples
              # assert type(sa_output) == tuple
              sa_output = sa_output[0]
          sa_output = self.sa_layer_norm(sa_output + x)  # (bs, seq_length, dim)
  
          # Feed Forward Network
          ffn_output = self.ffn(sa_output, training=training)  # (bs, seq_length, dim)
          ffn_output = self.output_layer_norm(ffn_output + sa_output)  # (bs, seq_length, dim)
  
          output = (ffn_output,)
          if output_attentions:
              output = (sa_weights,) + output
          return output
  
  
  class TFTransformer(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.n_layers = config.n_layers
          self.output_hidden_states = config.output_hidden_states
          self.output_attentions = config.output_attentions
  
          self.layer = [TFTransformerBlock(config, name=f"layer_._{i}") for i in range(config.n_layers)]
  
      def call(self, x, attn_mask, head_mask, output_attentions, output_hidden_states, return_dict, training=False):
          # docstyle-ignore
          """
          Parameters:
              x: tf.Tensor(bs, seq_length, dim) Input sequence embedded.
              attn_mask: tf.Tensor(bs, seq_length) Attention mask on the sequence.
  
          Returns:
              hidden_state: tf.Tensor(bs, seq_length, dim)
                  Sequence of hidden states in the last (top) layer
              all_hidden_states: Tuple[tf.Tensor(bs, seq_length, dim)]
                  Tuple of length n_layers with the hidden states from each layer.
                  Optional: only if output_hidden_states=True
              all_attentions: Tuple[tf.Tensor(bs, n_heads, seq_length, seq_length)]
                  Tuple of length n_layers with the attention weights from each layer
                  Optional: only if output_attentions=True
          """
          all_hidden_states = () if output_hidden_states else None
          all_attentions = () if output_attentions else None
  
          hidden_state = x
          for i, layer_module in enumerate(self.layer):
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_state,)
  
              layer_outputs = layer_module(hidden_state, attn_mask, head_mask[i], output_attentions, training=training)
              hidden_state = layer_outputs[-1]
  
              if output_attentions:
                  assert len(layer_outputs) == 2
                  attentions = layer_outputs[0]
                  all_attentions = all_attentions + (attentions,)
              else:
                  assert len(layer_outputs) == 1, f"Incorrect number of outputs {len(layer_outputs)} instead of 1"
  
          # Add last layer
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_state,)
  
          if not return_dict:
              return tuple(v for v in [hidden_state, all_hidden_states, all_attentions] if v is not None)
          return TFBaseModelOutput(
              last_hidden_state=hidden_state, hidden_states=all_hidden_states, attentions=all_attentions
          )
  
  
  @keras_serializable
  class TFDistilBertMainLayer(tf.keras.layers.Layer):
      config_class = DistilBertConfig
  
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.config = config
          self.num_hidden_layers = config.num_hidden_layers
          self.output_attentions = config.output_attentions
          self.output_hidden_states = config.output_hidden_states
          self.return_dict = config.use_return_dict
  
          self.embeddings = TFEmbeddings(config, name="embeddings")  # Embeddings
          self.transformer = TFTransformer(config, name="transformer")  # Encoder
  
      def get_input_embeddings(self):
          return self.embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.weight = value
          self.embeddings.vocab_size = value.shape[0]
  
      def _prune_heads(self, heads_to_prune):
          raise NotImplementedError
  
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
          **kwargs,
      ):
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
              kwargs_call=kwargs,
          )
  
          if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
              raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
          elif inputs["input_ids"] is not None:
              input_shape = shape_list(inputs["input_ids"])
          elif inputs["inputs_embeds"] is not None:
              input_shape = shape_list(inputs["inputs_embeds"])[:-1]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          if inputs["attention_mask"] is None:
              inputs["attention_mask"] = tf.ones(input_shape)  # (bs, seq_length)
  
          inputs["attention_mask"] = tf.cast(inputs["attention_mask"], dtype=tf.float32)
  
          # Prepare head mask if needed
          # 1.0 in head_mask indicate we keep the head
          # attention_probs has shape bsz x n_heads x N x N
          # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
          # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
          if inputs["head_mask"] is not None:
              raise NotImplementedError
          else:
              inputs["head_mask"] = [None] * self.num_hidden_layers
  
          embedding_output = self.embeddings(
              inputs["input_ids"], inputs_embeds=inputs["inputs_embeds"]
          )  # (bs, seq_length, dim)
          tfmr_output = self.transformer(
              embedding_output,
              inputs["attention_mask"],
              inputs["head_mask"],
              inputs["output_attentions"],
              inputs["output_hidden_states"],
              inputs["return_dict"],
              training=inputs["training"],
          )
  
          return tfmr_output  # last-layer hidden-state, (all hidden_states), (all attentions)
  
  
  # INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
  class TFDistilBertPreTrainedModel(TFPreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = DistilBertConfig
      base_model_prefix = "distilbert"
  
      @tf.function(
          input_signature=[
              {
                  "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
                  "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
              }
          ]
      )
      def serving(self, inputs):
          output = self.call(inputs)
  
          return self.serving_output(output)
  
  
  DISTILBERT_START_DOCSTRING = r"""
  
      This model inherits from :class:`~transformers.TFPreTrainedModel`. Check the superclass documentation for the
      generic methods the library implements for all its model (such as downloading or saving, resizing the input
      embeddings, pruning heads etc.)
  
      This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. Use
      it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
      and behavior.
  
      .. note::
  
          TF 2.0 models accepts two formats as inputs:
  
          - having all inputs as keyword arguments (like PyTorch models), or
          - having all inputs as a list, tuple or dict in the first positional arguments.
  
          This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
          the tensors in the first argument of the model call function: :obj:`model(inputs)`.
  
          If you choose this second option, there are three possibilities you can use to gather all the input Tensors in
          the first positional argument :
  
          - a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
          - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
            :obj:`model([input_ids, attention_mask])`
          - a dictionary with one or several input Tensors associated to the input names given in the docstring:
            :obj:`model({"input_ids": input_ids})`
  
      Parameters:
          config (:class:`~transformers.DistilBertConfig`): Model configuration class with all the parameters of the model.
              Initializing with a config file does not load the weights associated with the model, only the
              configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
              weights.
  """
  
  DISTILBERT_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`~transformers.DistilBertTokenizer`. See
              :func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
              Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          inputs_embeds (:obj:`tf.Tensor` of shape :obj:`({0}, hidden_size)`, `optional`):
              Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
              vectors than the model's internal embedding lookup matrix.
          output_attentions (:obj:`bool`, `optional`):
              Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
              tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
              config will be used instead.
          output_hidden_states (:obj:`bool`, `optional`):
              Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
              more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
              used instead.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
              argument can be used in eager mode, in graph mode the value will always be set to True.
          training (:obj:`bool`, `optional`, defaults to :obj:`False`):
              Whether or not to use the model in training mode (some modules like dropout modules have different
              behaviors between training and evaluation).
  """
  
  
  @add_start_docstrings(
      "The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",
      DISTILBERT_START_DOCSTRING,
  )
  class TFDistilBertModel(TFDistilBertPreTrainedModel):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.distilbert = TFDistilBertMainLayer(config, name="distilbert")  # Embeddings
  
      @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TFBaseModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
          **kwargs,
      ):
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
              kwargs_call=kwargs,
          )
          outputs = self.distilbert(
              input_ids=inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              head_mask=inputs["head_mask"],
              inputs_embeds=inputs["inputs_embeds"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
          return outputs
  
      def serving_output(self, output):
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)
  
  
  class TFDistilBertLMHead(tf.keras.layers.Layer):
      def __init__(self, config, input_embeddings, **kwargs):
          super().__init__(**kwargs)
  
          self.vocab_size = config.vocab_size
          self.dim = config.dim
  
          # The output weights are the same as the input embeddings, but there is
          # an output-only bias for each token.
          self.input_embeddings = input_embeddings
  
      def build(self, input_shape):
          self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
  
          super().build(input_shape)
  
      def get_output_embeddings(self):
          return self.input_embeddings
  
      def set_output_embeddings(self, value):
          self.input_embeddings.weight = value
          self.input_embeddings.vocab_size = shape_list(value)[0]
  
      def get_bias(self):
          return {"bias": self.bias}
  
      def set_bias(self, value):
          self.bias = value["bias"]
          self.vocab_size = shape_list(value["bias"])[0]
  
      def call(self, hidden_states):
          seq_length = shape_list(tensor=hidden_states)[1]
          hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.dim])
          hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
          hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
          hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
  
          return hidden_states
  
  
  @add_start_docstrings(
      """DistilBert Model with a `masked language modeling` head on top. """,
      DISTILBERT_START_DOCSTRING,
  )
  class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel, TFMaskedLanguageModelingLoss):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.vocab_size = config.vocab_size
  
          self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
          self.vocab_transform = tf.keras.layers.Dense(
              config.dim, kernel_initializer=get_initializer(config.initializer_range), name="vocab_transform"
          )
          self.act = get_tf_activation("gelu")
          self.vocab_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="vocab_layer_norm")
          self.vocab_projector = TFDistilBertLMHead(config, self.distilbert.embeddings, name="vocab_projector")
  
      def get_lm_head(self):
          return self.vocab_projector
  
      def get_prefix_bias_name(self):
          warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
          return self.name + "/" + self.vocab_projector.name
  
      @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TFMaskedLMOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
          **kwargs,
      ):
          r"""
          labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
              config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
              (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              labels=labels,
              training=training,
              kwargs_call=kwargs,
          )
          distilbert_output = self.distilbert(
              input_ids=inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              head_mask=inputs["head_mask"],
              inputs_embeds=inputs["inputs_embeds"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
          hidden_states = distilbert_output[0]  # (bs, seq_length, dim)
          prediction_logits = self.vocab_transform(hidden_states)  # (bs, seq_length, dim)
          prediction_logits = self.act(prediction_logits)  # (bs, seq_length, dim)
          prediction_logits = self.vocab_layer_norm(prediction_logits)  # (bs, seq_length, dim)
          prediction_logits = self.vocab_projector(prediction_logits)
  
          loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], prediction_logits)
  
          if not inputs["return_dict"]:
              output = (prediction_logits,) + distilbert_output[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFMaskedLMOutput(
              loss=loss,
              logits=prediction_logits,
              hidden_states=distilbert_output.hidden_states,
              attentions=distilbert_output.attentions,
          )
  
      # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output
      def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  @add_start_docstrings(
      """
      DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
      pooled output) e.g. for GLUE tasks.
      """,
      DISTILBERT_START_DOCSTRING,
  )
  class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel, TFSequenceClassificationLoss):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.num_labels = config.num_labels
  
          self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
          self.pre_classifier = tf.keras.layers.Dense(
              config.dim,
              kernel_initializer=get_initializer(config.initializer_range),
              activation="relu",
              name="pre_classifier",
          )
          self.classifier = tf.keras.layers.Dense(
              config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
          )
          self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)
  
      @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TFSequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
          **kwargs,
      ):
          r"""
          labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the sequence classification/regression loss. Indices should be in ``[0, ...,
              config.num_labels - 1]``. If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
              If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              labels=labels,
              training=training,
              kwargs_call=kwargs,
          )
          distilbert_output = self.distilbert(
              input_ids=inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              head_mask=inputs["head_mask"],
              inputs_embeds=inputs["inputs_embeds"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
          hidden_state = distilbert_output[0]  # (bs, seq_len, dim)
          pooled_output = hidden_state[:, 0]  # (bs, dim)
          pooled_output = self.pre_classifier(pooled_output)  # (bs, dim)
          pooled_output = self.dropout(pooled_output, training=inputs["training"])  # (bs, dim)
          logits = self.classifier(pooled_output)  # (bs, dim)
  
          loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
  
          if not inputs["return_dict"]:
              output = (logits,) + distilbert_output[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFSequenceClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=distilbert_output.hidden_states,
              attentions=distilbert_output.attentions,
          )
  
      # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output
      def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  @add_start_docstrings(
      """
      DistilBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
      for Named-Entity-Recognition (NER) tasks.
      """,
      DISTILBERT_START_DOCSTRING,
  )
  class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel, TFTokenClassificationLoss):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.num_labels = config.num_labels
  
          self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
          self.dropout = tf.keras.layers.Dropout(config.dropout)
          self.classifier = tf.keras.layers.Dense(
              config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
          )
  
      @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TFTokenClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
          **kwargs,
      ):
          r"""
          labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
              1]``.
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              labels=labels,
              training=training,
              kwargs_call=kwargs,
          )
          outputs = self.distilbert(
              input_ids=inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              head_mask=inputs["head_mask"],
              inputs_embeds=inputs["inputs_embeds"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
          sequence_output = outputs[0]
          sequence_output = self.dropout(sequence_output, training=inputs["training"])
          logits = self.classifier(sequence_output)
          loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
  
          if not inputs["return_dict"]:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFTokenClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
      # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output
      def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  @add_start_docstrings(
      """
      DistilBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
      a softmax) e.g. for RocStories/SWAG tasks.
      """,
      DISTILBERT_START_DOCSTRING,
  )
  class TFDistilBertForMultipleChoice(TFDistilBertPreTrainedModel, TFMultipleChoiceLoss):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
          self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)
          self.pre_classifier = tf.keras.layers.Dense(
              config.dim,
              kernel_initializer=get_initializer(config.initializer_range),
              activation="relu",
              name="pre_classifier",
          )
          self.classifier = tf.keras.layers.Dense(
              1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
          )
  
      @property
      def dummy_inputs(self):
          """
          Dummy inputs to build the network.
  
          Returns:
              tf.Tensor with dummy inputs
          """
          return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
  
      @add_start_docstrings_to_model_forward(
          DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
      )
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TFMultipleChoiceModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
          **kwargs,
      ):
          r"""
          labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
              num_choices]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
              :obj:`input_ids` above)
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              labels=labels,
              training=training,
              kwargs_call=kwargs,
          )
  
          if inputs["input_ids"] is not None:
              num_choices = shape_list(inputs["input_ids"])[1]
              seq_length = shape_list(inputs["input_ids"])[2]
          else:
              num_choices = shape_list(inputs["inputs_embeds"])[1]
              seq_length = shape_list(inputs["inputs_embeds"])[2]
  
          flat_input_ids = tf.reshape(inputs["input_ids"], (-1, seq_length)) if inputs["input_ids"] is not None else None
          flat_attention_mask = (
              tf.reshape(inputs["attention_mask"], (-1, seq_length)) if inputs["attention_mask"] is not None else None
          )
          flat_inputs_embeds = (
              tf.reshape(inputs["inputs_embeds"], (-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
              if inputs["inputs_embeds"] is not None
              else None
          )
          distilbert_output = self.distilbert(
              flat_input_ids,
              flat_attention_mask,
              inputs["head_mask"],
              flat_inputs_embeds,
              inputs["output_attentions"],
              inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
          hidden_state = distilbert_output[0]  # (bs, seq_len, dim)
          pooled_output = hidden_state[:, 0]  # (bs, dim)
          pooled_output = self.pre_classifier(pooled_output)  # (bs, dim)
          pooled_output = self.dropout(pooled_output, training=inputs["training"])  # (bs, dim)
          logits = self.classifier(pooled_output)
          reshaped_logits = tf.reshape(logits, (-1, num_choices))
  
          loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], reshaped_logits)
  
          if not inputs["return_dict"]:
              output = (reshaped_logits,) + distilbert_output[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFMultipleChoiceModelOutput(
              loss=loss,
              logits=reshaped_logits,
              hidden_states=distilbert_output.hidden_states,
              attentions=distilbert_output.attentions,
          )
  
      @tf.function(
          input_signature=[
              {
                  "input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
                  "attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
              }
          ]
      )
      def serving(self, inputs):
          output = self.call(inputs)
  
          return self.serving_output(output)
  
      # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output
      def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  @add_start_docstrings(
      """
      DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
      linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
      """,
      DISTILBERT_START_DOCSTRING,
  )
  class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel, TFQuestionAnsweringLoss):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
          self.qa_outputs = tf.keras.layers.Dense(
              config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
          )
          assert config.num_labels == 2, f"Incorrect number of labels {config.num_labels} instead of 2"
          self.dropout = tf.keras.layers.Dropout(config.qa_dropout)
  
      @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TFQuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          start_positions=None,
          end_positions=None,
          training=False,
          **kwargs,
      ):
          r"""
          start_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for position (index) of the start of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
              sequence are not taken into account for computing the loss.
          end_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for position (index) of the end of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
              sequence are not taken into account for computing the loss.
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              start_positions=start_positions,
              end_positions=end_positions,
              training=training,
              kwargs_call=kwargs,
          )
          distilbert_output = self.distilbert(
              input_ids=inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              head_mask=inputs["head_mask"],
              inputs_embeds=inputs["inputs_embeds"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
          hidden_states = distilbert_output[0]  # (bs, max_query_len, dim)
          hidden_states = self.dropout(hidden_states, training=inputs["training"])  # (bs, max_query_len, dim)
          logits = self.qa_outputs(hidden_states)  # (bs, max_query_len, 2)
          start_logits, end_logits = tf.split(logits, 2, axis=-1)
          start_logits = tf.squeeze(start_logits, axis=-1)
          end_logits = tf.squeeze(end_logits, axis=-1)
  
          loss = None
          if inputs["start_positions"] is not None and inputs["end_positions"] is not None:
              labels = {"start_position": inputs["start_positions"]}
              labels["end_position"] = inputs["end_positions"]
              loss = self.compute_loss(labels, (start_logits, end_logits))
  
          if not inputs["return_dict"]:
              output = (start_logits, end_logits) + distilbert_output[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFQuestionAnsweringModelOutput(
              loss=loss,
              start_logits=start_logits,
              end_logits=end_logits,
              hidden_states=distilbert_output.hidden_states,
              attentions=distilbert_output.attentions,
          )
  
      # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output
      def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFQuestionAnsweringModelOutput(
              start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
          )
  