text: from dataclasses import dataclass
  from typing import Optional, Tuple
  
  import tensorflow as tf
  
  from transformers import ElectraConfig
  
  from .file_utils import (
      MULTIPLE_CHOICE_DUMMY_INPUTS,
      ModelOutput,
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_callable,
      replace_return_docstrings,
  )
  from .modeling_tf_bert import ACT2FN, TFBertEncoder, TFBertPreTrainedModel
  from .modeling_tf_outputs import (
      TFBaseModelOutput,
      TFMaskedLMOutput,
      TFMultipleChoiceModelOutput,
      TFQuestionAnsweringModelOutput,
      TFSequenceClassifierOutput,
      TFTokenClassifierOutput,
  )
  from .modeling_tf_utils import (
      TFMaskedLanguageModelingLoss,
      TFMultipleChoiceLoss,
      TFQuestionAnsweringLoss,
      TFSequenceClassificationLoss,
      TFSequenceSummary,
      TFTokenClassificationLoss,
      get_initializer,
      keras_serializable,
      shape_list,
  )
  from .tokenization_utils import BatchEncoding
  from .utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "ElectraConfig"
  _TOKENIZER_FOR_DOC = "ElectraTokenizer"
  
  TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "google/electra-small-generator",
      "google/electra-base-generator",
      "google/electra-large-generator",
      "google/electra-small-discriminator",
      "google/electra-base-discriminator",
      "google/electra-large-discriminator",
      # See all ELECTRA models at https://huggingface.co/models?filter=electra
  ]
  
  
  class TFElectraEmbeddings(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.embedding_size = config.embedding_size
          self.initializer_range = config.initializer_range
  
          self.position_embeddings = tf.keras.layers.Embedding(
              config.max_position_embeddings,
              config.embedding_size,
              embeddings_initializer=get_initializer(self.initializer_range),
              name="position_embeddings",
          )
          self.token_type_embeddings = tf.keras.layers.Embedding(
              config.type_vocab_size,
              config.embedding_size,
              embeddings_initializer=get_initializer(self.initializer_range),
              name="token_type_embeddings",
          )
  
          # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
          # any TensorFlow checkpoint file
          self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
          self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
  
      def build(self, input_shape):
          """Build shared word embedding layer """
          with tf.name_scope("word_embeddings"):
              # Create and initialize weights. The random normal initializer was chosen
              # arbitrarily, and works well.
              self.word_embeddings = self.add_weight(
                  "weight",
                  shape=[self.vocab_size, self.embedding_size],
                  initializer=get_initializer(self.initializer_range),
              )
          super().build(input_shape)
  
      def call(
          self,
          input_ids,
          position_ids=None,
          token_type_ids=None,
          inputs_embeds=None,
          mode="embedding",
          training=False,
      ):
          """Get token embeddings of inputs.
          Args:
              inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
              mode: string, a valid value is one of "embedding" and "linear".
          Returns:
              outputs: (1) If mode == "embedding", output embedding tensor, float32 with
                  shape [batch_size, length, embedding_size]; (2) mode == "linear", output
                  linear tensor, float32 with shape [batch_size, length, vocab_size].
          Raises:
              ValueError: if mode is not valid.
  
          Shared weights logic adapted from
              https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
          """
          if mode == "embedding":
              return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
          elif mode == "linear":
              return self._linear(input_ids)
          else:
              raise ValueError("mode {} is not valid.".format(mode))
  
      def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
          """Applies embedding based on inputs tensor."""
          assert not (input_ids is None and inputs_embeds is None)
  
          if input_ids is not None:
              input_shape = shape_list(input_ids)
          else:
              input_shape = shape_list(inputs_embeds)[:-1]
  
          seq_length = input_shape[1]
          if position_ids is None:
              position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
          if token_type_ids is None:
              token_type_ids = tf.fill(input_shape, 0)
  
          if inputs_embeds is None:
              inputs_embeds = tf.gather(self.word_embeddings, input_ids)
          position_embeddings = tf.cast(self.position_embeddings(position_ids), inputs_embeds.dtype)
          token_type_embeddings = tf.cast(self.token_type_embeddings(token_type_ids), inputs_embeds.dtype)
  
          embeddings = inputs_embeds + position_embeddings + token_type_embeddings
          embeddings = self.LayerNorm(embeddings)
          embeddings = self.dropout(embeddings, training=training)
          return embeddings
  
      def _linear(self, inputs):
          """Computes logits by running inputs through a linear layer.
          Args:
              inputs: A float32 tensor with shape [batch_size, length, hidden_size]
          Returns:
              float32 tensor with shape [batch_size, length, vocab_size].
          """
          batch_size = shape_list(inputs)[0]
          length = shape_list(inputs)[1]
  
          x = tf.reshape(inputs, [-1, self.embedding_size])
          logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
  
          return tf.reshape(logits, [batch_size, length, self.vocab_size])
  
  
  class TFElectraDiscriminatorPredictions(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.dense = tf.keras.layers.Dense(config.hidden_size, name="dense")
          self.dense_prediction = tf.keras.layers.Dense(1, name="dense_prediction")
          self.config = config
  
      def call(self, discriminator_hidden_states, training=False):
          hidden_states = self.dense(discriminator_hidden_states)
          hidden_states = ACT2FN[self.config.hidden_act](hidden_states)
          logits = tf.squeeze(self.dense_prediction(hidden_states))
  
          return logits
  
  
  class TFElectraGeneratorPredictions(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
          self.dense = tf.keras.layers.Dense(config.embedding_size, name="dense")
  
      def call(self, generator_hidden_states, training=False):
          hidden_states = self.dense(generator_hidden_states)
          hidden_states = ACT2FN["gelu"](hidden_states)
          hidden_states = self.LayerNorm(hidden_states)
  
          return hidden_states
  
  
  class TFElectraPreTrainedModel(TFBertPreTrainedModel):
  
      config_class = ElectraConfig
      base_model_prefix = "electra"
  
      def get_extended_attention_mask(self, attention_mask, input_shape, dtype):
          if attention_mask is None:
              attention_mask = tf.fill(input_shape, 1)
  
          # We create a 3D attention mask from a 2D tensor mask.
          # Sizes are [batch_size, 1, 1, to_seq_length]
          # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
          # this attention mask is more simple than the triangular masking of causal attention
          # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
          extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]
  
          # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
          # masked positions, this operation will create a tensor which is 0.0 for
          # positions we want to attend and -10000.0 for masked positions.
          # Since we are adding it to the raw scores before the softmax, this is
          # effectively the same as removing these entirely.
  
          extended_attention_mask = tf.cast(extended_attention_mask, dtype)
          extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
  
          return extended_attention_mask
  
      def get_head_mask(self, head_mask):
          if head_mask is not None:
              raise NotImplementedError
          else:
              head_mask = [None] * self.config.num_hidden_layers
  
          return head_mask
  
  
  @keras_serializable
  class TFElectraMainLayer(TFElectraPreTrainedModel):
  
      config_class = ElectraConfig
  
      def __init__(self, config, **kwargs):
          super().__init__(config, **kwargs)
          self.embeddings = TFElectraEmbeddings(config, name="embeddings")
  
          if config.embedding_size != config.hidden_size:
              self.embeddings_project = tf.keras.layers.Dense(config.hidden_size, name="embeddings_project")
          self.encoder = TFBertEncoder(config, name="encoder")
          self.config = config
  
      def get_input_embeddings(self):
          return self.embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.word_embeddings = value
          self.embeddings.vocab_size = value.shape[0]
  
      def _resize_token_embeddings(self, new_num_tokens):
          raise NotImplementedError
  
      def _prune_heads(self, heads_to_prune):
          """Prunes heads of the model.
          heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
          See base class PreTrainedModel
          """
          raise NotImplementedError
  
      def call(
          self,
          inputs,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
      ):
          if isinstance(inputs, (tuple, list)):
              input_ids = inputs[0]
              attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
              token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
              position_ids = inputs[3] if len(inputs) > 3 else position_ids
              head_mask = inputs[4] if len(inputs) > 4 else head_mask
              inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
              output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
              output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
              return_dict = inputs[8] if len(inputs) > 8 else return_dict
              assert len(inputs) <= 9, "Too many inputs."
          elif isinstance(inputs, (dict, BatchEncoding)):
              input_ids = inputs.get("input_ids")
              attention_mask = inputs.get("attention_mask", attention_mask)
              token_type_ids = inputs.get("token_type_ids", token_type_ids)
              position_ids = inputs.get("position_ids", position_ids)
              head_mask = inputs.get("head_mask", head_mask)
              inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
              output_attentions = inputs.get("output_attentions", output_attentions)
              output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
              return_dict = inputs.get("return_dict", return_dict)
              assert len(inputs) <= 9, "Too many inputs."
          else:
              input_ids = inputs
  
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
          elif input_ids is not None:
              input_shape = shape_list(input_ids)
          elif inputs_embeds is not None:
              input_shape = shape_list(inputs_embeds)[:-1]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          if attention_mask is None:
              attention_mask = tf.fill(input_shape, 1)
          if token_type_ids is None:
              token_type_ids = tf.fill(input_shape, 0)
  
          hidden_states = self.embeddings(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
  
          extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, hidden_states.dtype)
          head_mask = self.get_head_mask(head_mask)
  
          if hasattr(self, "embeddings_project"):
              hidden_states = self.embeddings_project(hidden_states, training=training)
  
          hidden_states = self.encoder(
              hidden_states,
              extended_attention_mask,
              head_mask,
              output_attentions,
              output_hidden_states,
              return_dict,
              training=training,
          )
  
          return hidden_states
  
  
  @dataclass
  class TFElectraForPreTrainingOutput(ModelOutput):
      """
      Output type of :class:`~transformers.TFElectraForPreTrainingModel`.
  
      Args:
          loss (`optional`, returned when ``labels`` is provided, ``tf.Tensor`` of shape :obj:`(1,)`):
              Total loss of the ELECTRA objective.
          logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
              Prediction scores of the head (scores for each token before SoftMax).
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      logits: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  ELECTRA_START_DOCSTRING = r"""
      This model is a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ sub-class.
      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 :obj:`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 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])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
          - a dictionary with one or several input Tensors associated to the input names given in the docstring:
            :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
  
      Parameters:
          config (:class:`~transformers.ElectraConfig`): 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.
  """
  
  ELECTRA_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`transformers.ElectraTokenizer`.
              See :func:`transformers.PreTrainedTokenizer.encode` and
              :func:`transformers.PreTrainedTokenizer.__call__` for details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `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 MASKED tokens.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          position_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`{0}`, `optional`):
              Indices of positions of each input sequence tokens in the position embeddings.
              Selected in the range ``[0, config.max_position_embeddings - 1]``.
  
              `What are position IDs? <../glossary.html#position-ids>`__
          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]``:
              :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
          inputs_embeds (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, embedding_dim)`, `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 `input_ids` indices into associated vectors
              than the model's internal embedding lookup matrix.
          training (:obj:`boolean`, `optional`, defaults to :obj:`False`):
              Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them
              (if set to :obj:`False`) for evaluation.
          output_attentions (:obj:`bool`, `optional`):
              If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
          output_hidden_states (:obj:`bool`, `optional`):
              If set to ``True``, the hidden states of all layers are returned. See ``hidden_states`` under returned tensors for more detail.
          return_dict (:obj:`bool`, `optional`):
              If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a
              plain tuple.
  """
  
  
  @add_start_docstrings(
      "The bare Electra Model transformer outputting raw hidden-states without any specific head on top. Identical to "
      "the BERT model except that it uses an additional linear layer between the embedding layer and the encoder if the "
      "hidden size and embedding size are different."
      ""
      "Both the generator and discriminator checkpoints may be loaded into this model.",
      ELECTRA_START_DOCSTRING,
  )
  class TFElectraModel(TFElectraPreTrainedModel):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.electra = TFElectraMainLayer(config, name="electra")
  
      @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/electra-small-discriminator",
          output_type=TFBaseModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(self, inputs, **kwargs):
          outputs = self.electra(inputs, **kwargs)
          return outputs
  
  
  @add_start_docstrings(
      """Electra model with a binary classification head on top as used during pre-training for identifying generated
      tokens.
  
      Even though both the discriminator and generator may be loaded into this model, the discriminator is
      the only model of the two to have the correct classification head to be used for this model.""",
      ELECTRA_START_DOCSTRING,
  )
  class TFElectraForPreTraining(TFElectraPreTrainedModel):
      def __init__(self, config, **kwargs):
          super().__init__(config, **kwargs)
  
          self.electra = TFElectraMainLayer(config, name="electra")
          self.discriminator_predictions = TFElectraDiscriminatorPredictions(config, name="discriminator_predictions")
  
      @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=TFElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
      def call(
          self,
          input_ids,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
      ):
          r"""
          Returns:
  
          Examples::
  
              import tensorflow as tf
              from transformers import ElectraTokenizer, TFElectraForPreTraining
  
              tokenizer = ElectraTokenizer.from_pretrained('google/electra-small-discriminator')
              model = TFElectraForPreTraining.from_pretrained('google/electra-small-discriminator')
              input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
              outputs = model(input_ids)
              scores = outputs[0]
          """
          return_dict = return_dict if return_dict is not None else self.electra.config.return_dict
  
          discriminator_hidden_states = self.electra(
              input_ids,
              attention_mask,
              token_type_ids,
              position_ids,
              head_mask,
              inputs_embeds,
              output_attentions,
              output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
          discriminator_sequence_output = discriminator_hidden_states[0]
          logits = self.discriminator_predictions(discriminator_sequence_output)
  
          if not return_dict:
              return (logits,) + discriminator_hidden_states[1:]
  
          return TFElectraForPreTrainingOutput(
              logits=logits,
              hidden_states=discriminator_hidden_states.hidden_states,
              attentions=discriminator_hidden_states.attentions,
          )
  
  
  class TFElectraMaskedLMHead(tf.keras.layers.Layer):
      def __init__(self, config, input_embeddings, **kwargs):
          super().__init__(**kwargs)
          self.vocab_size = config.vocab_size
          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 call(self, hidden_states, training=False):
          hidden_states = self.input_embeddings(hidden_states, mode="linear")
          hidden_states = hidden_states + self.bias
          return hidden_states
  
  
  @add_start_docstrings(
      """Electra model with a language modeling head on top.
  
      Even though both the discriminator and generator may be loaded into this model, the generator is
      the only model of the two to have been trained for the masked language modeling task.""",
      ELECTRA_START_DOCSTRING,
  )
  class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLoss):
      def __init__(self, config, **kwargs):
          super().__init__(config, **kwargs)
  
          self.vocab_size = config.vocab_size
          self.electra = TFElectraMainLayer(config, name="electra")
          self.generator_predictions = TFElectraGeneratorPredictions(config, name="generator_predictions")
          if isinstance(config.hidden_act, str):
              self.activation = ACT2FN[config.hidden_act]
          else:
              self.activation = config.hidden_act
          self.generator_lm_head = TFElectraMaskedLMHead(config, self.electra.embeddings, name="generator_lm_head")
  
      def get_output_embeddings(self):
          return self.generator_lm_head
  
      @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/electra-small-generator",
          output_type=TFMaskedLMOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
      ):
          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]``
          """
          return_dict = return_dict if return_dict is not None else self.electra.config.return_dict
          if isinstance(input_ids, (tuple, list)):
              labels = input_ids[9] if len(input_ids) > 9 else labels
              if len(input_ids) > 9:
                  input_ids = input_ids[:9]
          elif isinstance(input_ids, (dict, BatchEncoding)):
              labels = input_ids.pop("labels", labels)
  
          generator_hidden_states = self.electra(
              input_ids,
              attention_mask,
              token_type_ids,
              position_ids,
              head_mask,
              inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
          generator_sequence_output = generator_hidden_states[0]
          prediction_scores = self.generator_predictions(generator_sequence_output, training=training)
          prediction_scores = self.generator_lm_head(prediction_scores, training=training)
  
          loss = None if labels is None else self.compute_loss(labels, prediction_scores)
  
          if not return_dict:
              output = (prediction_scores,) + generator_hidden_states[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFMaskedLMOutput(
              loss=loss,
              logits=prediction_scores,
              hidden_states=generator_hidden_states.hidden_states,
              attentions=generator_hidden_states.attentions,
          )
  
  
  class TFElectraClassificationHead(tf.keras.layers.Layer):
      """Head for sentence-level classification tasks."""
  
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.dense = tf.keras.layers.Dense(
              config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
          )
          self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
          self.out_proj = tf.keras.layers.Dense(
              config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
          )
  
      def call(self, inputs, **kwargs):
          x = inputs[:, 0, :]  # take <s> token (equiv. to [CLS])
          x = self.dropout(x)
          x = self.dense(x)
          x = ACT2FN["gelu"](x)  # although BERT uses tanh here, it seems Electra authors used gelu here
          x = self.dropout(x)
          x = self.out_proj(x)
  
          return x
  
  
  @add_start_docstrings(
      """ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of
      the pooled output) e.g. for GLUE tasks. """,
      ELECTRA_START_DOCSTRING,
  )
  class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceClassificationLoss):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.num_labels = config.num_labels
          self.electra = TFElectraMainLayer(config, name="electra")
          self.classifier = TFElectraClassificationHead(config, name="classifier")
  
      @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/electra-small-discriminator",
          output_type=TFSequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
      ):
          r"""
          labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the sequence classification/regression loss.
              Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
              If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
              If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
          """
          return_dict = return_dict if return_dict is not None else self.electra.config.return_dict
          if isinstance(input_ids, (tuple, list)):
              labels = input_ids[9] if len(input_ids) > 9 else labels
              if len(input_ids) > 9:
                  input_ids = input_ids[:9]
          elif isinstance(input_ids, (dict, BatchEncoding)):
              labels = input_ids.pop("labels", labels)
  
          outputs = self.electra(
              input_ids,
              attention_mask,
              token_type_ids,
              position_ids,
              head_mask,
              inputs_embeds,
              output_attentions,
              output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
          logits = self.classifier(outputs[0])
  
          loss = None if labels is None else self.compute_loss(labels, logits)
  
          if not return_dict:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFSequenceClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """ELECTRA 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. """,
      ELECTRA_START_DOCSTRING,
  )
  class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.electra = TFElectraMainLayer(config, name="electra")
          self.sequence_summary = TFSequenceSummary(
              config, initializer_range=config.initializer_range, name="sequence_summary"
          )
          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_callable(ELECTRA_INPUTS_DOCSTRING.format("(batch_size, num_choices, sequence_length)"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/electra-small-discriminator",
          output_type=TFMultipleChoiceModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          inputs,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
      ):
          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 `num_choices` is the size of the second dimension
              of the input tensors. (see `input_ids` above)
          """
          if isinstance(inputs, (tuple, list)):
              input_ids = inputs[0]
              attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
              token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
              position_ids = inputs[3] if len(inputs) > 3 else position_ids
              head_mask = inputs[4] if len(inputs) > 4 else head_mask
              inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
              output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
              output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
              return_dict = inputs[8] if len(inputs) > 8 else return_dict
              labels = inputs[9] if len(inputs) > 9 else labels
              assert len(inputs) <= 10, "Too many inputs."
          elif isinstance(inputs, (dict, BatchEncoding)):
              input_ids = inputs.get("input_ids")
              attention_mask = inputs.get("attention_mask", attention_mask)
              token_type_ids = inputs.get("token_type_ids", token_type_ids)
              position_ids = inputs.get("position_ids", position_ids)
              head_mask = inputs.get("head_mask", head_mask)
              inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
              output_attentions = inputs.get("output_attentions", output_attentions)
              output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
              return_dict = inputs.get("return_dict", return_dict)
              labels = inputs.get("labels", labels)
              assert len(inputs) <= 10, "Too many inputs."
          else:
              input_ids = inputs
          return_dict = return_dict if return_dict is not None else self.electra.config.return_dict
  
          if input_ids is not None:
              num_choices = shape_list(input_ids)[1]
              seq_length = shape_list(input_ids)[2]
          else:
              num_choices = shape_list(inputs_embeds)[1]
              seq_length = shape_list(inputs_embeds)[2]
  
          flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
          flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
          flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
          flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
          flat_inputs_embeds = (
              tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
              if inputs_embeds is not None
              else None
          )
          outputs = self.electra(
              flat_input_ids,
              flat_attention_mask,
              flat_token_type_ids,
              flat_position_ids,
              head_mask,
              flat_inputs_embeds,
              output_attentions,
              output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
          logits = self.sequence_summary(outputs[0])
          logits = self.classifier(logits)
          reshaped_logits = tf.reshape(logits, (-1, num_choices))
  
          loss = None if labels is None else self.compute_loss(labels, reshaped_logits)
  
          if not return_dict:
              output = (reshaped_logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFMultipleChoiceModelOutput(
              loss=loss,
              logits=reshaped_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """Electra model with a token classification head on top.
  
      Both the discriminator and generator may be loaded into this model.""",
      ELECTRA_START_DOCSTRING,
  )
  class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassificationLoss):
      def __init__(self, config, **kwargs):
          super().__init__(config, **kwargs)
  
          self.electra = TFElectraMainLayer(config, name="electra")
          self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
          self.classifier = tf.keras.layers.Dense(
              config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
          )
  
      @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/electra-small-discriminator",
          output_type=TFTokenClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          inputs,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
      ):
          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]``.
          """
          return_dict = return_dict if return_dict is not None else self.electra.config.return_dict
          if isinstance(inputs, (tuple, list)):
              labels = inputs[9] if len(inputs) > 9 else labels
              if len(inputs) > 9:
                  inputs = inputs[:9]
          elif isinstance(inputs, (dict, BatchEncoding)):
              labels = inputs.pop("labels", labels)
  
          discriminator_hidden_states = self.electra(
              inputs,
              attention_mask,
              token_type_ids,
              position_ids,
              head_mask,
              inputs_embeds,
              output_attentions,
              output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
          discriminator_sequence_output = discriminator_hidden_states[0]
          discriminator_sequence_output = self.dropout(discriminator_sequence_output)
          logits = self.classifier(discriminator_sequence_output)
  
          loss = None if labels is None else self.compute_loss(labels, logits)
  
          if not return_dict:
              output = (logits,) + discriminator_hidden_states[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFTokenClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=discriminator_hidden_states.hidden_states,
              attentions=discriminator_hidden_states.attentions,
          )
  
  
  @add_start_docstrings(
      """Electra Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
      the hidden-states output to compute `span start logits` and `span end logits`). """,
      ELECTRA_START_DOCSTRING,
  )
  class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnsweringLoss):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.num_labels = config.num_labels
  
          self.electra = TFElectraMainLayer(config, name="electra")
          self.qa_outputs = tf.keras.layers.Dense(
              config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
          )
  
      @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/electra-small-discriminator",
          output_type=TFQuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          inputs,
          attention_mask=None,
          token_type_ids=None,
          position_ids=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,
      ):
          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 (`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 (`sequence_length`).
              Position outside of the sequence are not taken into account for computing the loss.
          """
          return_dict = return_dict if return_dict is not None else self.electra.config.return_dict
          if isinstance(inputs, (tuple, list)):
              start_positions = inputs[9] if len(inputs) > 9 else start_positions
              end_positions = inputs[10] if len(inputs) > 10 else end_positions
              if len(inputs) > 9:
                  inputs = inputs[:9]
          elif isinstance(inputs, (dict, BatchEncoding)):
              start_positions = inputs.pop("start_positions", start_positions)
              end_positions = inputs.pop("end_positions", start_positions)
  
          discriminator_hidden_states = self.electra(
              inputs,
              attention_mask,
              token_type_ids,
              position_ids,
              head_mask,
              inputs_embeds,
              output_attentions,
              output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
          discriminator_sequence_output = discriminator_hidden_states[0]
  
          logits = self.qa_outputs(discriminator_sequence_output)
          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 start_positions is not None and end_positions is not None:
              labels = {"start_position": start_positions}
              labels["end_position"] = end_positions
              loss = self.compute_loss(labels, (start_logits, end_logits))
  
          if not return_dict:
              output = (
                  start_logits,
                  end_logits,
              ) + discriminator_hidden_states[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=discriminator_hidden_states.hidden_states,
              attentions=discriminator_hidden_states.attentions,
          )
  