text: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ TF 2.0 MobileBERT model. """
  
  import warnings
  from dataclasses import dataclass
  from typing import Optional, Tuple
  
  import tensorflow as tf
  
  from ...activations_tf import get_tf_activation
  from ...file_utils import (
      MULTIPLE_CHOICE_DUMMY_INPUTS,
      ModelOutput,
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from ...modeling_tf_outputs import (
      TFBaseModelOutput,
      TFBaseModelOutputWithPooling,
      TFMaskedLMOutput,
      TFMultipleChoiceModelOutput,
      TFNextSentencePredictorOutput,
      TFQuestionAnsweringModelOutput,
      TFSequenceClassifierOutput,
      TFTokenClassifierOutput,
  )
  from ...modeling_tf_utils import (
      TFMaskedLanguageModelingLoss,
      TFMultipleChoiceLoss,
      TFNextSentencePredictionLoss,
      TFPreTrainedModel,
      TFQuestionAnsweringLoss,
      TFSequenceClassificationLoss,
      TFTokenClassificationLoss,
      get_initializer,
      input_processing,
      keras_serializable,
      shape_list,
  )
  from ...utils import logging
  from .configuration_mobilebert import MobileBertConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "MobileBertConfig"
  _TOKENIZER_FOR_DOC = "MobileBertTokenizer"
  
  TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "google/mobilebert-uncased",
      # See all MobileBERT models at https://huggingface.co/models?filter=mobilebert
  ]
  
  
  class TFMobileBertIntermediate(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.dense = tf.keras.layers.Dense(config.intermediate_size, name="dense")
  
          if isinstance(config.hidden_act, str):
              self.intermediate_act_fn = get_tf_activation(config.hidden_act)
          else:
              self.intermediate_act_fn = config.hidden_act
  
      def call(self, hidden_states):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.intermediate_act_fn(hidden_states)
  
          return hidden_states
  
  
  class TFLayerNorm(tf.keras.layers.LayerNormalization):
      def __init__(self, feat_size, *args, **kwargs):
          super().__init__(*args, **kwargs)
  
  
  class TFNoNorm(tf.keras.layers.Layer):
      def __init__(self, feat_size, epsilon=None, **kwargs):
          super().__init__(**kwargs)
          self.feat_size = feat_size
  
      def build(self, input_shape):
          self.bias = self.add_weight("bias", shape=[self.feat_size], initializer="zeros")
          self.weight = self.add_weight("weight", shape=[self.feat_size], initializer="ones")
  
      def call(self, inputs: tf.Tensor):
          return inputs * self.weight + self.bias
  
  
  NORM2FN = {"layer_norm": TFLayerNorm, "no_norm": TFNoNorm}
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
  class TFMobileBertWordEmbeddings(tf.keras.layers.Layer):
      def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
          super().__init__(**kwargs)
  
          self.vocab_size = vocab_size
          self.hidden_size = hidden_size
          self.initializer_range = initializer_range
  
      def build(self, input_shape):
          self.weight = self.add_weight(
              name="weight",
              shape=[self.vocab_size, self.hidden_size],
              initializer=get_initializer(initializer_range=self.initializer_range),
          )
  
          super().build(input_shape=input_shape)
  
      def get_config(self):
          config = {
              "vocab_size": self.vocab_size,
              "hidden_size": self.hidden_size,
              "initializer_range": self.initializer_range,
          }
          base_config = super().get_config()
  
          return dict(list(base_config.items()) + list(config.items()))
  
      def call(self, input_ids):
          flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
          embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
          embeddings = tf.reshape(
              tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
          )
  
          embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
  
          return embeddings
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertTokenTypeEmbeddings
  class TFMobileBertTokenTypeEmbeddings(tf.keras.layers.Layer):
      def __init__(self, type_vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
          super().__init__(**kwargs)
  
          self.type_vocab_size = type_vocab_size
          self.hidden_size = hidden_size
          self.initializer_range = initializer_range
  
      def build(self, input_shape):
          self.token_type_embeddings = self.add_weight(
              name="embeddings",
              shape=[self.type_vocab_size, self.hidden_size],
              initializer=get_initializer(initializer_range=self.initializer_range),
          )
  
          super().build(input_shape=input_shape)
  
      def get_config(self):
          config = {
              "type_vocab_size": self.type_vocab_size,
              "hidden_size": self.hidden_size,
              "initializer_range": self.initializer_range,
          }
          base_config = super().get_config()
  
          return dict(list(base_config.items()) + list(config.items()))
  
      def call(self, token_type_ids):
          flat_token_type_ids = tf.reshape(tensor=token_type_ids, shape=[-1])
          one_hot_data = tf.one_hot(indices=flat_token_type_ids, depth=self.type_vocab_size, dtype=self._compute_dtype)
          embeddings = tf.matmul(a=one_hot_data, b=self.token_type_embeddings)
          embeddings = tf.reshape(
              tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=token_type_ids), [self.hidden_size]], axis=0)
          )
  
          embeddings.set_shape(shape=token_type_ids.shape.as_list() + [self.hidden_size])
  
          return embeddings
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertPositionEmbeddings
  class TFMobileBertPositionEmbeddings(tf.keras.layers.Layer):
      def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
          super().__init__(**kwargs)
  
          self.max_position_embeddings = max_position_embeddings
          self.hidden_size = hidden_size
          self.initializer_range = initializer_range
  
      def build(self, input_shape):
          self.position_embeddings = self.add_weight(
              name="embeddings",
              shape=[self.max_position_embeddings, self.hidden_size],
              initializer=get_initializer(initializer_range=self.initializer_range),
          )
  
          super().build(input_shape)
  
      def get_config(self):
          config = {
              "max_position_embeddings": self.max_position_embeddings,
              "hidden_size": self.hidden_size,
              "initializer_range": self.initializer_range,
          }
          base_config = super().get_config()
  
          return dict(list(base_config.items()) + list(config.items()))
  
      def call(self, position_ids):
          input_shape = shape_list(tensor=position_ids)
          position_embeddings = self.position_embeddings[: input_shape[1], :]
  
          return tf.broadcast_to(input=position_embeddings, shape=input_shape)
  
  
  class TFMobileBertEmbeddings(tf.keras.layers.Layer):
      """Construct the embeddings from word, position and token_type embeddings."""
  
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.trigram_input = config.trigram_input
          self.embedding_size = config.embedding_size
          self.hidden_size = config.hidden_size
          self.word_embeddings = TFMobileBertWordEmbeddings(
              vocab_size=config.vocab_size,
              hidden_size=config.embedding_size,
              initializer_range=config.initializer_range,
              name="word_embeddings",
          )
          self.position_embeddings = TFMobileBertPositionEmbeddings(
              max_position_embeddings=config.max_position_embeddings,
              hidden_size=config.hidden_size,
              initializer_range=config.initializer_range,
              name="position_embeddings",
          )
          self.token_type_embeddings = TFMobileBertTokenTypeEmbeddings(
              type_vocab_size=config.type_vocab_size,
              hidden_size=config.hidden_size,
              initializer_range=config.initializer_range,
              name="token_type_embeddings",
          )
          self.embeddings_sum = tf.keras.layers.Add()
          self.embedding_transformation = tf.keras.layers.Dense(config.hidden_size, name="embedding_transformation")
  
          # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
          # any TensorFlow checkpoint file
          self.LayerNorm = NORM2FN[config.normalization_type](
              config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
          )
          self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
  
      def call(self, input_ids=None, position_ids=None, token_type_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 = self.word_embeddings(input_ids=input_ids)
  
          if token_type_ids is None:
              input_shape = shape_list(tensor=inputs_embeds)[:-1]
              token_type_ids = tf.fill(dims=input_shape, value=0)
  
          if self.trigram_input:
              # From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited
              # Devices (https://arxiv.org/abs/2004.02984)
              #
              # The embedding table in BERT models accounts for a substantial proportion of model size. To compress
              # the embedding layer, we reduce the embedding dimension to 128 in MobileBERT.
              # Then, we apply a 1D convolution with kernel size 3 on the raw token embedding to produce a 512
              # dimensional output.
              inputs_embeds = tf.concat(
                  [
                      tf.pad(inputs_embeds[:, 1:], ((0, 0), (0, 1), (0, 0))),
                      inputs_embeds,
                      tf.pad(inputs_embeds[:, :-1], ((0, 0), (1, 0), (0, 0))),
                  ],
                  axis=2,
              )
  
          if self.trigram_input or self.embedding_size != self.hidden_size:
              inputs_embeds = self.embedding_transformation(inputs_embeds)
  
          if position_ids is None:
              position_embeds = self.position_embeddings(position_ids=inputs_embeds)
          else:
              position_embeds = self.position_embeddings(position_ids=position_ids)
  
          token_type_embeds = self.token_type_embeddings(token_type_ids=token_type_ids)
          final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
          final_embeddings = self.LayerNorm(inputs=final_embeddings)
          final_embeddings = self.dropout(inputs=final_embeddings, training=training)
  
          return final_embeddings
  
  
  class TFMobileBertSelfAttention(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          if config.hidden_size % config.num_attention_heads != 0:
              raise ValueError(
                  "The hidden size (%d) is not a multiple of the number of attention "
                  "heads (%d)" % (config.hidden_size, config.num_attention_heads)
              )
  
          self.num_attention_heads = config.num_attention_heads
          self.output_attentions = config.output_attentions
          assert config.hidden_size % config.num_attention_heads == 0
          self.attention_head_size = int(config.true_hidden_size / config.num_attention_heads)
          self.all_head_size = self.num_attention_heads * self.attention_head_size
  
          self.query = tf.keras.layers.Dense(
              self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
          )
          self.key = tf.keras.layers.Dense(
              self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
          )
          self.value = tf.keras.layers.Dense(
              self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
          )
  
          self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
  
      def transpose_for_scores(self, x, batch_size):
          x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
          return tf.transpose(x, perm=[0, 2, 1, 3])
  
      def call(
          self, query_tensor, key_tensor, value_tensor, attention_mask, head_mask, output_attentions, training=False
      ):
          batch_size = shape_list(attention_mask)[0]
          mixed_query_layer = self.query(query_tensor)
          mixed_key_layer = self.key(key_tensor)
          mixed_value_layer = self.value(value_tensor)
          query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
          key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
          value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
  
          # Take the dot product between "query" and "key" to get the raw attention scores.
          attention_scores = tf.matmul(
              query_layer, key_layer, transpose_b=True
          )  # (batch size, num_heads, seq_len_q, seq_len_k)
          dk = tf.cast(shape_list(key_layer)[-1], tf.float32)  # scale attention_scores
          attention_scores = attention_scores / tf.math.sqrt(dk)
  
          if attention_mask is not None:
              # Apply the attention mask is (precomputed for all layers in TFBertModel call() function)
              attention_scores = attention_scores + attention_mask
  
          # Normalize the attention scores to probabilities.
          attention_probs = tf.nn.softmax(attention_scores, axis=-1)
  
          # This is actually dropping out entire tokens to attend to, which might
          # seem a bit unusual, but is taken from the original Transformer paper.
          attention_probs = self.dropout(attention_probs, training=training)
  
          # Mask heads if we want to
          if head_mask is not None:
              attention_probs = attention_probs * head_mask
  
          context_layer = tf.matmul(attention_probs, value_layer)
  
          context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
          context_layer = tf.reshape(
              context_layer, (batch_size, -1, self.all_head_size)
          )  # (batch_size, seq_len_q, all_head_size)
  
          outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
  
          return outputs
  
  
  class TFMobileBertSelfOutput(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.use_bottleneck = config.use_bottleneck
          self.dense = tf.keras.layers.Dense(
              config.true_hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
          )
          self.LayerNorm = NORM2FN[config.normalization_type](
              config.true_hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
          )
          if not self.use_bottleneck:
              self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
  
      def call(self, hidden_states, residual_tensor, training=False):
          hidden_states = self.dense(hidden_states)
          if not self.use_bottleneck:
              hidden_states = self.dropout(hidden_states, training=training)
          hidden_states = self.LayerNorm(hidden_states + residual_tensor)
          return hidden_states
  
  
  class TFMobileBertAttention(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.self = TFMobileBertSelfAttention(config, name="self")
          self.mobilebert_output = TFMobileBertSelfOutput(config, name="output")
  
      def prune_heads(self, heads):
          raise NotImplementedError
  
      def call(
          self,
          query_tensor,
          key_tensor,
          value_tensor,
          layer_input,
          attention_mask,
          head_mask,
          output_attentions,
          training=False,
      ):
          self_outputs = self.self(
              query_tensor, key_tensor, value_tensor, attention_mask, head_mask, output_attentions, training=training
          )
  
          attention_output = self.mobilebert_output(self_outputs[0], layer_input, training=training)
          outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
          return outputs
  
  
  class TFOutputBottleneck(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.dense = tf.keras.layers.Dense(config.hidden_size, name="dense")
          self.LayerNorm = NORM2FN[config.normalization_type](
              config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
          )
          self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
  
      def call(self, hidden_states, residual_tensor, training=False):
          layer_outputs = self.dense(hidden_states)
          layer_outputs = self.dropout(layer_outputs, training=training)
          layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
          return layer_outputs
  
  
  class TFMobileBertOutput(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.use_bottleneck = config.use_bottleneck
          self.dense = tf.keras.layers.Dense(
              config.true_hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
          )
          self.LayerNorm = NORM2FN[config.normalization_type](
              config.true_hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
          )
          if not self.use_bottleneck:
              self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
          else:
              self.bottleneck = TFOutputBottleneck(config, name="bottleneck")
  
      def call(self, hidden_states, residual_tensor_1, residual_tensor_2, training=False):
          hidden_states = self.dense(hidden_states)
          if not self.use_bottleneck:
              hidden_states = self.dropout(hidden_states, training=training)
              hidden_states = self.LayerNorm(hidden_states + residual_tensor_1)
          else:
              hidden_states = self.LayerNorm(hidden_states + residual_tensor_1)
              hidden_states = self.bottleneck(hidden_states, residual_tensor_2)
          return hidden_states
  
  
  class TFBottleneckLayer(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.dense = tf.keras.layers.Dense(config.intra_bottleneck_size, name="dense")
          self.LayerNorm = NORM2FN[config.normalization_type](
              config.intra_bottleneck_size, epsilon=config.layer_norm_eps, name="LayerNorm"
          )
  
      def call(self, inputs):
          hidden_states = self.dense(inputs)
          hidden_states = self.LayerNorm(hidden_states)
          return hidden_states
  
  
  class TFBottleneck(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.key_query_shared_bottleneck = config.key_query_shared_bottleneck
          self.use_bottleneck_attention = config.use_bottleneck_attention
          self.bottleneck_input = TFBottleneckLayer(config, name="input")
          if self.key_query_shared_bottleneck:
              self.attention = TFBottleneckLayer(config, name="attention")
  
      def call(self, hidden_states):
          # This method can return three different tuples of values. These different values make use of bottlenecks,
          # which are linear layers used to project the hidden states to a lower-dimensional vector, reducing memory
          # usage. These linear layer have weights that are learned during training.
          #
          # If `config.use_bottleneck_attention`, it will return the result of the bottleneck layer four times for the
          # key, query, value, and "layer input" to be used by the attention layer.
          # This bottleneck is used to project the hidden. This last layer input will be used as a residual tensor
          # in the attention self output, after the attention scores have been computed.
          #
          # If not `config.use_bottleneck_attention` and `config.key_query_shared_bottleneck`, this will return
          # four values, three of which have been passed through a bottleneck: the query and key, passed through the same
          # bottleneck, and the residual layer to be applied in the attention self output, through another bottleneck.
          #
          # Finally, in the last case, the values for the query, key and values are the hidden states without bottleneck,
          # and the residual layer will be this value passed through a bottleneck.
  
          bottlenecked_hidden_states = self.bottleneck_input(hidden_states)
          if self.use_bottleneck_attention:
              return (bottlenecked_hidden_states,) * 4
          elif self.key_query_shared_bottleneck:
              shared_attention_input = self.attention(hidden_states)
              return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states)
          else:
              return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states)
  
  
  class TFFFNOutput(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.dense = tf.keras.layers.Dense(config.true_hidden_size, name="dense")
          self.LayerNorm = NORM2FN[config.normalization_type](
              config.true_hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
          )
  
      def call(self, hidden_states, residual_tensor):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.LayerNorm(hidden_states + residual_tensor)
          return hidden_states
  
  
  class TFFFNLayer(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.intermediate = TFMobileBertIntermediate(config, name="intermediate")
          self.mobilebert_output = TFFFNOutput(config, name="output")
  
      def call(self, hidden_states):
          intermediate_output = self.intermediate(hidden_states)
          layer_outputs = self.mobilebert_output(intermediate_output, hidden_states)
          return layer_outputs
  
  
  class TFMobileBertLayer(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.use_bottleneck = config.use_bottleneck
          self.num_feedforward_networks = config.num_feedforward_networks
          self.attention = TFMobileBertAttention(config, name="attention")
          self.intermediate = TFMobileBertIntermediate(config, name="intermediate")
          self.mobilebert_output = TFMobileBertOutput(config, name="output")
  
          if self.use_bottleneck:
              self.bottleneck = TFBottleneck(config, name="bottleneck")
          if config.num_feedforward_networks > 1:
              self.ffn = [
                  TFFFNLayer(config, name="ffn.{}".format(i)) for i in range(config.num_feedforward_networks - 1)
              ]
  
      def call(self, hidden_states, attention_mask, head_mask, output_attentions, training=False):
          if self.use_bottleneck:
              query_tensor, key_tensor, value_tensor, layer_input = self.bottleneck(hidden_states)
          else:
              query_tensor, key_tensor, value_tensor, layer_input = [hidden_states] * 4
  
          attention_outputs = self.attention(
              query_tensor,
              key_tensor,
              value_tensor,
              layer_input,
              attention_mask,
              head_mask,
              output_attentions,
              training=training,
          )
  
          attention_output = attention_outputs[0]
          s = (attention_output,)
  
          if self.num_feedforward_networks != 1:
              for i, ffn_module in enumerate(self.ffn):
                  attention_output = ffn_module(attention_output)
                  s += (attention_output,)
  
          intermediate_output = self.intermediate(attention_output)
          layer_output = self.mobilebert_output(intermediate_output, attention_output, hidden_states, training=training)
  
          outputs = (
              (layer_output,)
              + attention_outputs[1:]
              + (
                  tf.constant(0),
                  query_tensor,
                  key_tensor,
                  value_tensor,
                  layer_input,
                  attention_output,
                  intermediate_output,
              )
              + s
          )  # add attentions if we output them
  
          return outputs
  
  
  class TFMobileBertEncoder(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.output_attentions = config.output_attentions
          self.output_hidden_states = config.output_hidden_states
          self.layer = [TFMobileBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
  
      def call(
          self,
          hidden_states,
          attention_mask,
          head_mask,
          output_attentions,
          output_hidden_states,
          return_dict,
          training=False,
      ):
          all_hidden_states = () if output_hidden_states else None
          all_attentions = () if output_attentions else None
          for i, layer_module in enumerate(self.layer):
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
  
              layer_outputs = layer_module(
                  hidden_states, attention_mask, head_mask[i], output_attentions, training=training
              )
  
              hidden_states = layer_outputs[0]
  
              if output_attentions:
                  all_attentions = all_attentions + (layer_outputs[1],)
  
          # Add last layer
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if not return_dict:
              return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
          return TFBaseModelOutput(
              last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
          )
  
  
  class TFMobileBertPooler(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.do_activate = config.classifier_activation
          if self.do_activate:
              self.dense = tf.keras.layers.Dense(
                  config.hidden_size,
                  kernel_initializer=get_initializer(config.initializer_range),
                  activation="tanh",
                  name="dense",
              )
  
      def call(self, hidden_states):
          # We "pool" the model by simply taking the hidden state corresponding
          # to the first token.
          first_token_tensor = hidden_states[:, 0]
          if not self.do_activate:
              return first_token_tensor
          else:
              pooled_output = self.dense(first_token_tensor)
              return pooled_output
  
  
  class TFMobileBertPredictionHeadTransform(tf.keras.layers.Layer):
      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"
          )
          if isinstance(config.hidden_act, str):
              self.transform_act_fn = get_tf_activation(config.hidden_act)
          else:
              self.transform_act_fn = config.hidden_act
          self.LayerNorm = NORM2FN["layer_norm"](config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm")
  
      def call(self, hidden_states):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.transform_act_fn(hidden_states)
          hidden_states = self.LayerNorm(hidden_states)
          return hidden_states
  
  
  class TFMobileBertLMPredictionHead(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.transform = TFMobileBertPredictionHeadTransform(config, name="transform")
          self.vocab_size = config.vocab_size
          self.config = config
  
      def build(self, input_shape):
          self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
          self.dense = self.add_weight(
              shape=(self.config.hidden_size - self.config.embedding_size, self.vocab_size),
              initializer="zeros",
              trainable=True,
              name="dense/weight",
          )
          self.decoder = self.add_weight(
              shape=(self.config.vocab_size, self.config.embedding_size),
              initializer="zeros",
              trainable=True,
              name="decoder/weight",
          )
          super().build(input_shape)
  
      def get_output_embeddings(self):
          return self
  
      def set_output_embeddings(self, value):
          self.decoder = value
          self.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):
          hidden_states = self.transform(hidden_states)
          hidden_states = tf.matmul(hidden_states, tf.concat([tf.transpose(self.decoder), self.dense], axis=0))
          hidden_states = hidden_states + self.bias
          return hidden_states
  
  
  class TFMobileBertMLMHead(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.predictions = TFMobileBertLMPredictionHead(config, name="predictions")
  
      def call(self, sequence_output):
          prediction_scores = self.predictions(sequence_output)
          return prediction_scores
  
  
  @keras_serializable
  class TFMobileBertMainLayer(tf.keras.layers.Layer):
      config_class = MobileBertConfig
  
      def __init__(self, config, add_pooling_layer=True, **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 = TFMobileBertEmbeddings(config, name="embeddings")
          self.encoder = TFMobileBertEncoder(config, name="encoder")
          self.pooler = TFMobileBertPooler(config, name="pooler") if add_pooling_layer else None
  
      def get_input_embeddings(self):
          return self.embeddings.word_embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.word_embeddings.weight = value
          self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
  
      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,
          input_ids=None,
          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,
          **kwargs,
      ):
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              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.fill(input_shape, 1)
  
          if inputs["token_type_ids"] is None:
              inputs["token_type_ids"] = tf.fill(input_shape, 0)
  
          # 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 = inputs["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, tf.float32)
          extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
  
          # 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["position_ids"],
              inputs["token_type_ids"],
              inputs["inputs_embeds"],
              training=inputs["training"],
          )
          encoder_outputs = self.encoder(
              embedding_output,
              extended_attention_mask,
              inputs["head_mask"],
              inputs["output_attentions"],
              inputs["output_hidden_states"],
              inputs["return_dict"],
              training=inputs["training"],
          )
  
          sequence_output = encoder_outputs[0]
          pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
  
          if not inputs["return_dict"]:
              return (
                  sequence_output,
                  pooled_output,
              ) + encoder_outputs[1:]
  
          return TFBaseModelOutputWithPooling(
              last_hidden_state=sequence_output,
              pooler_output=pooled_output,
              hidden_states=encoder_outputs.hidden_states,
              attentions=encoder_outputs.attentions,
          )
  
  
  class TFMobileBertPreTrainedModel(TFPreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = MobileBertConfig
      base_model_prefix = "mobilebert"
  
  
  @dataclass
  class TFMobileBertForPreTrainingOutput(ModelOutput):
      """
      Output type of :class:`~transformers.TFMobileBertForPreTraining`.
  
      Args:
          prediction_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          seq_relationship_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, 2)`):
              Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
              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.
      """
  
      loss: Optional[tf.Tensor] = None
      prediction_logits: tf.Tensor = None
      seq_relationship_logits: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  MOBILEBERT_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])` 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.MobileBertConfig`): 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.
  """
  
  MOBILEBERT_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.MobileBertTokenizer`. 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>`__
          token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
              Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
              1]``:
  
              - 0 corresponds to a `sentence A` token,
              - 1 corresponds to a `sentence B` token.
  
              `What are token type IDs? <../glossary.html#token-type-ids>`__
          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]``:
  
              - 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.
          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.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
          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 MobileBert Model transformer outputting raw hidden-states without any specific head on top.",
      MOBILEBERT_START_DOCSTRING,
  )
  class TFMobileBertModel(TFMobileBertPreTrainedModel):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
  
      @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/mobilebert-uncased",
          output_type=TFBaseModelOutputWithPooling,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          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,
          **kwargs,
      ):
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              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.mobilebert(
              input_ids=inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              token_type_ids=inputs["token_type_ids"],
              position_ids=inputs["position_ids"],
              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
  
      # Copied from transformers.models.bert.modeling_tf_bert.TFBertModel.serving_output
      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 TFBaseModelOutputWithPooling(
              last_hidden_state=output.last_hidden_state,
              pooler_output=output.pooler_output,
              hidden_states=hs,
              attentions=attns,
          )
  
  
  @add_start_docstrings(
      """
      MobileBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
      `next sentence prediction (classification)` head.
      """,
      MOBILEBERT_START_DOCSTRING,
  )
  class TFMobileBertForPreTraining(TFMobileBertPreTrainedModel):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
          self.predictions = TFMobileBertMLMHead(config, name="predictions___cls")
          self.seq_relationship = TFMobileBertOnlyNSPHead(2, name="seq_relationship___cls")
  
      def get_lm_head(self):
          return self.predictions.predictions
  
      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.predictions.name + "/" + self.predictions.predictions.name
  
      @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=TFMobileBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
      def call(
          self,
          input_ids=None,
          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,
          **kwargs,
      ):
          r"""
          Return:
  
          Examples::
  
              >>> import tensorflow as tf
              >>> from transformers import MobileBertTokenizer, TFMobileBertForPreTraining
  
              >>> tokenizer = MobileBertTokenizer.from_pretrained('google/mobilebert-uncased')
              >>> model = TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased')
              >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
              >>> outputs = model(input_ids)
              >>> prediction_scores, seq_relationship_scores = outputs[:2]
  
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              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.mobilebert(
              inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              token_type_ids=inputs["token_type_ids"],
              position_ids=inputs["position_ids"],
              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, pooled_output = outputs[:2]
          prediction_scores = self.predictions(sequence_output)
          seq_relationship_score = self.seq_relationship(pooled_output)
  
          if not inputs["return_dict"]:
              return (prediction_scores, seq_relationship_score) + outputs[2:]
  
          return TFMobileBertForPreTrainingOutput(
              prediction_logits=prediction_scores,
              seq_relationship_logits=seq_relationship_score,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
      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 TFMobileBertForPreTrainingOutput(
              prediction_logits=output.prediction_logits,
              seq_relationship_logits=output.seq_relationship_logits,
              hidden_states=hs,
              attentions=attns,
          )
  
  
  @add_start_docstrings("""MobileBert Model with a `language modeling` head on top. """, MOBILEBERT_START_DOCSTRING)
  class TFMobileBertForMaskedLM(TFMobileBertPreTrainedModel, TFMaskedLanguageModelingLoss):
      # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
      _keys_to_ignore_on_load_unexpected = [
          r"pooler",
          r"seq_relationship___cls",
          r"predictions___cls",
          r"cls.seq_relationship",
      ]
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.mobilebert = TFMobileBertMainLayer(config, add_pooling_layer=False, name="mobilebert")
          self.mlm = TFMobileBertMLMHead(config, name="mlm___cls")
  
      def get_lm_head(self):
          return self.mlm.predictions
  
      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.mlm.name + "/" + self.mlm.predictions.name
  
      @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/mobilebert-uncased",
          output_type=TFMaskedLMOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          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,
          **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
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              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.mobilebert(
              inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              token_type_ids=inputs["token_type_ids"],
              position_ids=inputs["position_ids"],
              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]
          prediction_scores = self.mlm(sequence_output, training=inputs["training"])
  
          loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], prediction_scores)
  
          if not inputs["return_dict"]:
              output = (prediction_scores,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return TFMaskedLMOutput(
              loss=loss,
              logits=prediction_scores,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
      # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output
      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 TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  class TFMobileBertOnlyNSPHead(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.seq_relationship = tf.keras.layers.Dense(2, name="seq_relationship")
  
      def call(self, pooled_output):
          seq_relationship_score = self.seq_relationship(pooled_output)
          return seq_relationship_score
  
  
  @add_start_docstrings(
      """MobileBert Model with a `next sentence prediction (classification)` head on top. """,
      MOBILEBERT_START_DOCSTRING,
  )
  class TFMobileBertForNextSentencePrediction(TFMobileBertPreTrainedModel, TFNextSentencePredictionLoss):
      # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
      _keys_to_ignore_on_load_unexpected = [r"predictions___cls", r"cls.predictions"]
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
          self.cls = TFMobileBertOnlyNSPHead(config, name="seq_relationship___cls")
  
      @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=TFNextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
      def call(
          self,
          input_ids=None,
          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,
          next_sentence_label=None,
          training=False,
          **kwargs,
      ):
          r"""
          Return:
  
          Examples::
  
              >>> import tensorflow as tf
              >>> from transformers import MobileBertTokenizer, TFMobileBertForNextSentencePrediction
  
              >>> tokenizer = MobileBertTokenizer.from_pretrained('google/mobilebert-uncased')
              >>> model = TFMobileBertForNextSentencePrediction.from_pretrained('google/mobilebert-uncased')
  
              >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
              >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
              >>> encoding = tokenizer(prompt, next_sentence, return_tensors='tf')
  
              >>> logits = model(encoding['input_ids'], token_type_ids=encoding['token_type_ids'])[0]
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              next_sentence_label=next_sentence_label,
              training=training,
              kwargs_call=kwargs,
          )
          outputs = self.mobilebert(
              inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              token_type_ids=inputs["token_type_ids"],
              position_ids=inputs["position_ids"],
              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"],
          )
          pooled_output = outputs[1]
          seq_relationship_scores = self.cls(pooled_output)
  
          next_sentence_loss = (
              None
              if inputs["next_sentence_label"] is None
              else self.compute_loss(labels=inputs["next_sentence_label"], logits=seq_relationship_scores)
          )
  
          if not inputs["return_dict"]:
              output = (seq_relationship_scores,) + outputs[2:]
              return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
  
          return TFNextSentencePredictorOutput(
              loss=next_sentence_loss,
              logits=seq_relationship_scores,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
      # Copied from transformers.models.bert.modeling_tf_bert.TFBertForNextSentencePrediction.serving_output
      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 TFNextSentencePredictorOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  @add_start_docstrings(
      """
      MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
      pooled output) e.g. for GLUE tasks.
      """,
      MOBILEBERT_START_DOCSTRING,
  )
  class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSequenceClassificationLoss):
      # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
      _keys_to_ignore_on_load_unexpected = [
          r"predictions___cls",
          r"seq_relationship___cls",
          r"cls.predictions",
          r"cls.seq_relationship",
      ]
      _keys_to_ignore_on_load_missing = [r"dropout"]
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.num_labels = config.num_labels
  
          self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
          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_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/mobilebert-uncased",
          output_type=TFSequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          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,
          **kwargs,
      ):
          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).
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              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.mobilebert(
              inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              token_type_ids=inputs["token_type_ids"],
              position_ids=inputs["position_ids"],
              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"],
          )
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output, training=inputs["training"])
          logits = self.classifier(pooled_output)
  
          loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
  
          if not inputs["return_dict"]:
              output = (logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return TFSequenceClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
      # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output
      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 TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  @add_start_docstrings(
      """
      MobileBert 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`).
      """,
      MOBILEBERT_START_DOCSTRING,
  )
  class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAnsweringLoss):
      # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
      _keys_to_ignore_on_load_unexpected = [
          r"pooler",
          r"predictions___cls",
          r"seq_relationship___cls",
          r"cls.predictions",
          r"cls.seq_relationship",
      ]
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.num_labels = config.num_labels
  
          self.mobilebert = TFMobileBertMainLayer(config, add_pooling_layer=False, name="mobilebert")
          self.qa_outputs = tf.keras.layers.Dense(
              config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
          )
  
      @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/mobilebert-uncased",
          output_type=TFQuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          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,
          **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,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              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,
          )
          outputs = self.mobilebert(
              inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              token_type_ids=inputs["token_type_ids"],
              position_ids=inputs["position_ids"],
              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]
  
          logits = self.qa_outputs(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 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) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return TFQuestionAnsweringModelOutput(
              loss=loss,
              start_logits=start_logits,
              end_logits=end_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
      # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output
      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 TFQuestionAnsweringModelOutput(
              start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
          )
  
  
  @add_start_docstrings(
      """
      MobileBert 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.
      """,
      MOBILEBERT_START_DOCSTRING,
  )
  class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoiceLoss):
      # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
      _keys_to_ignore_on_load_unexpected = [
          r"predictions___cls",
          r"seq_relationship___cls",
          r"cls.predictions",
          r"cls.seq_relationship",
      ]
      _keys_to_ignore_on_load_missing = [r"dropout"]
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
          self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
          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(
          MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
      )
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/mobilebert-uncased",
          output_type=TFMultipleChoiceModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          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,
          **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,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              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_token_type_ids = (
              tf.reshape(inputs["token_type_ids"], (-1, seq_length)) if inputs["token_type_ids"] is not None else None
          )
          flat_position_ids = (
              tf.reshape(inputs["position_ids"], (-1, seq_length)) if inputs["position_ids"] 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
          )
          outputs = self.mobilebert(
              flat_input_ids,
              flat_attention_mask,
              flat_token_type_ids,
              flat_position_ids,
              inputs["head_mask"],
              flat_inputs_embeds,
              inputs["output_attentions"],
              inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
          pooled_output = outputs[1]
          pooled_output = self.dropout(pooled_output, training=inputs["training"])
          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,) + outputs[2:]
              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,
          )
  
      @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"),
                  "token_type_ids": tf.TensorSpec((None, None, None), tf.int32, name="token_type_ids"),
              }
          ]
      )
      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):
          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(
      """
      MobileBert 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.
      """,
      MOBILEBERT_START_DOCSTRING,
  )
  class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenClassificationLoss):
      # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
      _keys_to_ignore_on_load_unexpected = [
          r"pooler",
          r"predictions___cls",
          r"seq_relationship___cls",
          r"cls.predictions",
          r"cls.seq_relationship",
      ]
      _keys_to_ignore_on_load_missing = [r"dropout"]
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.num_labels = config.num_labels
  
          self.mobilebert = TFMobileBertMainLayer(config, add_pooling_layer=False, name="mobilebert")
          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_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="google/mobilebert-uncased",
          output_type=TFTokenClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          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,
          **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,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              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.mobilebert(
              inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              token_type_ids=inputs["token_type_ids"],
              position_ids=inputs["position_ids"],
              head_mask=inputs["head_mask"],
              inputs_embeds=inputs["inputs_embeds"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=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[2:]
              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):
          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)
  