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 BERT model. """
  
  import math
  import warnings
  from dataclasses import dataclass
  from typing import Dict, Optional, Tuple, Union
  
  import numpy as np
  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,
      TFCausalLMOutput,
      TFMaskedLMOutput,
      TFMultipleChoiceModelOutput,
      TFNextSentencePredictorOutput,
      TFQuestionAnsweringModelOutput,
      TFSequenceClassifierOutput,
      TFTokenClassifierOutput,
  )
  from ....modeling_tf_utils import (
      TFCausalLanguageModelingLoss,
      TFMaskedLanguageModelingLoss,
      TFModelInputType,
      TFMultipleChoiceLoss,
      TFNextSentencePredictionLoss,
      TFPreTrainedModel,
      TFQuestionAnsweringLoss,
      TFSequenceClassificationLoss,
      TFTokenClassificationLoss,
      get_initializer,
      input_processing,
      keras_serializable,
      shape_list,
  )
  from ....utils import logging
  from .configuration_bert_performer import BertPerformerConfig
  from ..modeling_tf_performer_attention import TFPerformerAttention
  
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "BertPerformerConfig"
  _TOKENIZER_FOR_DOC = "BertTokenizer"
  
  TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "bert-base-uncased",
      "bert-large-uncased",
      "bert-base-cased",
      "bert-large-cased",
      "bert-base-multilingual-uncased",
      "bert-base-multilingual-cased",
      "bert-base-chinese",
      "bert-base-german-cased",
      "bert-large-uncased-whole-word-masking",
      "bert-large-cased-whole-word-masking",
      "bert-large-uncased-whole-word-masking-finetuned-squad",
      "bert-large-cased-whole-word-masking-finetuned-squad",
      "bert-base-cased-finetuned-mrpc",
      "cl-tohoku/bert-base-japanese",
      "cl-tohoku/bert-base-japanese-whole-word-masking",
      "cl-tohoku/bert-base-japanese-char",
      "cl-tohoku/bert-base-japanese-char-whole-word-masking",
      "TurkuNLP/bert-base-finnish-cased-v1",
      "TurkuNLP/bert-base-finnish-uncased-v1",
      "wietsedv/bert-base-dutch-cased",
      # See all BERT models at https://huggingface.co/models?filter=bert
  ]
  
  
  class TFBertPreTrainingLoss:
      """
      Loss function suitable for BERT-like pretraining, that is, the task of pretraining a language model by combining
      NSP + MLM. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss
      computation.
      """
  
      def compute_loss(self, labels: tf.Tensor, logits: tf.Tensor) -> tf.Tensor:
          loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
              from_logits=True, reduction=tf.keras.losses.Reduction.NONE
          )
          # make sure only labels that are not equal to -100
          # are taken into account as loss
          masked_lm_active_loss = tf.not_equal(tf.reshape(tensor=labels["labels"], shape=(-1,)), -100)
          masked_lm_reduced_logits = tf.boolean_mask(
              tensor=tf.reshape(tensor=logits[0], shape=(-1, shape_list(logits[0])[2])),
              mask=masked_lm_active_loss,
          )
          masked_lm_labels = tf.boolean_mask(
              tensor=tf.reshape(tensor=labels["labels"], shape=(-1,)), mask=masked_lm_active_loss
          )
          next_sentence_active_loss = tf.not_equal(tf.reshape(tensor=labels["next_sentence_label"], shape=(-1,)), -100)
          next_sentence_reduced_logits = tf.boolean_mask(
              tensor=tf.reshape(tensor=logits[1], shape=(-1, 2)), mask=next_sentence_active_loss
          )
          next_sentence_label = tf.boolean_mask(
              tensor=tf.reshape(tensor=labels["next_sentence_label"], shape=(-1,)), mask=next_sentence_active_loss
          )
          masked_lm_loss = loss_fn(y_true=masked_lm_labels, y_pred=masked_lm_reduced_logits)
          next_sentence_loss = loss_fn(y_true=next_sentence_label, y_pred=next_sentence_reduced_logits)
          masked_lm_loss = tf.reshape(tensor=masked_lm_loss, shape=(-1, shape_list(next_sentence_loss)[0]))
          masked_lm_loss = tf.reduce_mean(input_tensor=masked_lm_loss, axis=0)
  
          return masked_lm_loss + next_sentence_loss
  
  
  class TFBertEmbeddings(tf.keras.layers.Layer):
      """Construct the embeddings from word, position and token_type embeddings."""
  
      def __init__(self, config: BertPerformerConfig, **kwargs):
          super().__init__(**kwargs)
  
          self.vocab_size = config.vocab_size
          self.type_vocab_size = config.type_vocab_size
          self.hidden_size = config.hidden_size
          self.max_position_embeddings = config.max_position_embeddings
          self.initializer_range = config.initializer_range
          self.embeddings_sum = tf.keras.layers.Add()
          self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
          self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
  
      def build(self, input_shape: tf.TensorShape):
          with tf.name_scope("word_embeddings"):
              self.weight = self.add_weight(
                  name="weight",
                  shape=[self.vocab_size, self.hidden_size],
                  initializer=get_initializer(initializer_range=self.initializer_range),
              )
  
          with tf.name_scope("token_type_embeddings"):
              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),
              )
  
          with tf.name_scope("position_embeddings"):
              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 call(
          self,
          input_ids: tf.Tensor = None,
          position_ids: tf.Tensor = None,
          token_type_ids: tf.Tensor = None,
          inputs_embeds: tf.Tensor = None,
          training: bool = False,
      ) -> tf.Tensor:
          """
          Applies embedding based on inputs tensor.
  
          Returns:
              final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
          """
          assert not (input_ids is None and inputs_embeds is None)
  
          if input_ids is not None:
              inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
  
          input_shape = shape_list(inputs_embeds)[:-1]
  
          if token_type_ids is None:
              token_type_ids = tf.fill(dims=input_shape, value=0)
  
          if position_ids is None:
              position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
  
          position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
          position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
          token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=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 TFBertSelfAttention(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, **kwargs):
          super().__init__(**kwargs)
  
          if config.hidden_size % config.num_attention_heads != 0:
              raise ValueError(
                  f"The hidden size ({config.hidden_size}) is not a multiple of the number "
                  f"of attention heads ({config.num_attention_heads})"
              )
  
          self.num_attention_heads = config.num_attention_heads
          self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
          self.all_head_size = self.num_attention_heads * self.attention_head_size
          self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
  
          self.query = tf.keras.layers.Dense(
              units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
          )
          self.key = tf.keras.layers.Dense(
              units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
          )
          self.value = tf.keras.layers.Dense(
              units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
          )
          self.performer_attention = TFPerformerAttention(config.performer_attention_config)
          self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
  
      def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
          # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
          tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
  
          # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
          return tf.transpose(tensor, perm=[0, 2, 1, 3])
  
      def call(
          self,
          hidden_states: tf.Tensor,
          attention_mask: tf.Tensor,
          head_mask: tf.Tensor,
          output_attentions: bool,
          training: bool = False,
      ) -> Tuple[tf.Tensor]:
          batch_size = shape_list(hidden_states)[0]
          mixed_query_layer = self.query(inputs=hidden_states)
          mixed_key_layer = self.key(inputs=hidden_states)
          mixed_value_layer = self.value(inputs=hidden_states)
          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)
  
  
          # Note that dropout is not used in the original favor implementation so we also do not use it
          attention_output = self.performer_attention(query_layer, key_layer, value_layer, attention_mask, output_attentions)
  
          # Take the dot product between "query" and "key" to get the raw
          # attention scores.
          # (batch size, num_heads, seq_len_q, seq_len_k)
          #attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
          #dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
          #attention_scores = tf.divide(attention_scores, dk)
  
          #if attention_mask is not None:
              # Apply the attention mask is (precomputed for all layers in TFBertModel call() function)
          #    attention_scores = tf.add(attention_scores, attention_mask)
  
          # Normalize the attention scores to probabilities.
          #attention_probs = tf.nn.softmax(logits=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(inputs=attention_probs, training=training)
  
          # Mask heads if we want to
          if head_mask is not None:
              attention_output = tf.multiply(attention_output, head_mask)
  
          #attention_output = tf.matmul(attention_probs, value_layer)
          attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
  
          # (batch_size, seq_len_q, all_head_size)
          attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
          #outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
          outputs = (attention_output,)
  
          return outputs
  
  
  class TFBertSelfOutput(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, **kwargs):
          super().__init__(**kwargs)
  
          self.dense = tf.keras.layers.Dense(
              units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
          )
          self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
          self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
  
      def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
          hidden_states = self.dense(inputs=hidden_states)
          hidden_states = self.dropout(inputs=hidden_states, training=training)
          hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
  
          return hidden_states
  
  
  class TFBertAttention(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, **kwargs):
          super().__init__(**kwargs)
          self.self_attention = TFBertSelfAttention(config, name="self")
          self.dense_output = TFBertSelfOutput(config, name="output")
  
      def prune_heads(self, heads):
          raise NotImplementedError
  
      def call(
          self,
          input_tensor: tf.Tensor,
          attention_mask: tf.Tensor,
          head_mask: tf.Tensor,
          output_attentions: bool,
          training: bool = False,
      ) -> Tuple[tf.Tensor]:
          self_outputs = self.self_attention(
              hidden_states=input_tensor,
              attention_mask=attention_mask,
              head_mask=head_mask,
              output_attentions=output_attentions,
              training=training,
          )
          attention_output = self.dense_output(
              hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
          )
          outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
  
          return outputs
  
  
  class TFBertIntermediate(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, **kwargs):
          super().__init__(**kwargs)
  
          self.dense = tf.keras.layers.Dense(
              units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), 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: tf.Tensor) -> tf.Tensor:
          hidden_states = self.dense(inputs=hidden_states)
          hidden_states = self.intermediate_act_fn(hidden_states)
  
          return hidden_states
  
  
  class TFBertOutput(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, **kwargs):
          super().__init__(**kwargs)
  
          self.dense = tf.keras.layers.Dense(
              units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
          )
          self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
          self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
  
      def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
          hidden_states = self.dense(inputs=hidden_states)
          hidden_states = self.dropout(inputs=hidden_states, training=training)
          hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
  
          return hidden_states
  
  
  class TFBertLayer(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, **kwargs):
          super().__init__(**kwargs)
  
          self.attention = TFBertAttention(config, name="attention")
          self.intermediate = TFBertIntermediate(config, name="intermediate")
          self.bert_output = TFBertOutput(config, name="output")
  
      def call(
          self,
          hidden_states: tf.Tensor,
          attention_mask: tf.Tensor,
          head_mask: tf.Tensor,
          output_attentions: bool,
          training: bool = False,
      ) -> Tuple[tf.Tensor]:
          attention_outputs = self.attention(
              input_tensor=hidden_states,
              attention_mask=attention_mask,
              head_mask=head_mask,
              output_attentions=output_attentions,
              training=training,
          )
          attention_output = attention_outputs[0]
          intermediate_output = self.intermediate(hidden_states=attention_output)
          layer_output = self.bert_output(
              hidden_states=intermediate_output, input_tensor=attention_output, training=training
          )
          outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
  
          return outputs
  
  
  class TFBertEncoder(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, **kwargs):
          super().__init__(**kwargs)
  
          self.layer = [TFBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
  
      def call(
          self,
          hidden_states: tf.Tensor,
          attention_mask: tf.Tensor,
          head_mask: tf.Tensor,
          output_attentions: bool,
          output_hidden_states: bool,
          return_dict: bool,
          training: bool = False,
      ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
          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=hidden_states,
                  attention_mask=attention_mask,
                  head_mask=head_mask[i],
                  output_attentions=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 TFBertPooler(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, **kwargs):
          super().__init__(**kwargs)
  
          self.dense = tf.keras.layers.Dense(
              units=config.hidden_size,
              kernel_initializer=get_initializer(config.initializer_range),
              activation="tanh",
              name="dense",
          )
  
      def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
          # We "pool" the model by simply taking the hidden state corresponding
          # to the first token.
          first_token_tensor = hidden_states[:, 0]
          pooled_output = self.dense(inputs=first_token_tensor)
  
          return pooled_output
  
  
  class TFBertPredictionHeadTransform(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, **kwargs):
          super().__init__(**kwargs)
  
          self.dense = tf.keras.layers.Dense(
              units=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 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
  
      def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
          hidden_states = self.dense(inputs=hidden_states)
          hidden_states = self.transform_act_fn(hidden_states)
          hidden_states = self.LayerNorm(inputs=hidden_states)
  
          return hidden_states
  
  
  class TFBertLMPredictionHead(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
          super().__init__(**kwargs)
  
          self.vocab_size = config.vocab_size
          self.hidden_size = config.hidden_size
  
          self.transform = TFBertPredictionHeadTransform(config, name="transform")
  
          # The output weights are the same as the input embeddings, but there is
          # an output-only bias for each token.
          self.input_embeddings = input_embeddings
  
      def build(self, input_shape: tf.TensorShape):
          self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
  
          super().build(input_shape)
  
      def get_output_embeddings(self) -> tf.keras.layers.Layer:
          return self.input_embeddings
  
      def set_output_embeddings(self, value: tf.Variable):
          self.input_embeddings.weight = value
          self.input_embeddings.vocab_size = shape_list(value)[0]
  
      def get_bias(self) -> Dict[str, tf.Variable]:
          return {"bias": self.bias}
  
      def set_bias(self, value: tf.Variable):
          self.bias = value["bias"]
          self.vocab_size = shape_list(value["bias"])[0]
  
      def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
          hidden_states = self.transform(hidden_states=hidden_states)
          seq_length = shape_list(hidden_states)[1]
          hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
          hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
          hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
          hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
  
          return hidden_states
  
  
  class TFBertMLMHead(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
          super().__init__(**kwargs)
  
          self.predictions = TFBertLMPredictionHead(config, input_embeddings, name="predictions")
  
      def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
          prediction_scores = self.predictions(hidden_states=sequence_output)
  
          return prediction_scores
  
  
  class TFBertNSPHead(tf.keras.layers.Layer):
      def __init__(self, config: BertPerformerConfig, **kwargs):
          super().__init__(**kwargs)
  
          self.seq_relationship = tf.keras.layers.Dense(
              units=2,
              kernel_initializer=get_initializer(config.initializer_range),
              name="seq_relationship",
          )
  
      def call(self, pooled_output: tf.Tensor) -> tf.Tensor:
          seq_relationship_score = self.seq_relationship(inputs=pooled_output)
  
          return seq_relationship_score
  
  
  @keras_serializable
  class TFBertPerformerMainLayer(tf.keras.layers.Layer):
      config_class = BertPerformerConfig
  
      def __init__(self, config: BertPerformerConfig, add_pooling_layer: bool = True, **kwargs):
          super().__init__(**kwargs)
  
          self.config = config
  
          self.embeddings = TFBertEmbeddings(config, name="embeddings")
          self.encoder = TFBertEncoder(config, name="encoder")
          self.pooler = TFBertPooler(config, name="pooler") if add_pooling_layer else None
  
      def get_input_embeddings(self) -> tf.keras.layers.Layer:
          return self.embeddings
  
      def set_input_embeddings(self, value: tf.Variable):
          self.embeddings.weight = value
          self.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: Optional[TFModelInputType] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          training: bool = False,
          **kwargs,
      ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
          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(dims=input_shape, value=1)
  
          if inputs["token_type_ids"] is None:
              inputs["token_type_ids"] = tf.fill(dims=input_shape, value=0)
  
          embedding_output = self.embeddings(
              input_ids=inputs["input_ids"],
              position_ids=inputs["position_ids"],
              token_type_ids=inputs["token_type_ids"],
              inputs_embeds=inputs["inputs_embeds"],
              training=inputs["training"],
          )
          # 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.
  
          # We change the dimension here for Performer Attention.
          extended_attention_mask = tf.reshape(inputs["attention_mask"], (input_shape[0], 1, input_shape[1], 1))
  
          # 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=embedding_output.dtype)
  
          #one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
          #ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
          #extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
  
          # 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.config.num_hidden_layers
  
          encoder_outputs = self.encoder(
              hidden_states=embedding_output,
              attention_mask=extended_attention_mask,
              head_mask=inputs["head_mask"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
  
          sequence_output = encoder_outputs[0]
          pooled_output = self.pooler(hidden_states=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 TFBertPerformerPreTrainedModel(TFPreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = BertPerformerConfig
      base_model_prefix = "bert"
  
  
  @dataclass
  class TFBertForPreTrainingOutput(ModelOutput):
      """
      Output type of :class:`~transformers.TFBertForPreTraining`.
  
      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[Union[Tuple[tf.Tensor], tf.Tensor]] = None
      attentions: Optional[Union[Tuple[tf.Tensor], tf.Tensor]] = None
  
  
  BERT_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})`
  
      Args:
          config (:class:`~transformers.BertPerformerConfig`): 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.TFPreTrainedModel.from_pretrained` method to load the
              model weights.
  """
  
  BERT_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`np.ndarray`, :obj:`tf.Tensor`, :obj:`List[tf.Tensor]` :obj:`Dict[str, tf.Tensor]` or :obj:`Dict[str, np.ndarray]` and each example must have the shape :obj:`({0})`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`~transformers.BertTokenizer`. See
              :func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`np.ndarray` 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:`np.ndarray` 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:`np.ndarray` 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:`np.ndarray` 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:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`({0}, hidden_size)`, `optional`):
              Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
              vectors than the model's internal embedding lookup matrix.
          output_attentions (:obj:`bool`, `optional`):
              Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
              tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
              config will be used instead.
          output_hidden_states (:obj:`bool`, `optional`):
              Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
              more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
              used instead.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
              argument can be used in eager mode, in graph mode the value will always be set to True.
          training (:obj:`bool`, `optional`, defaults to :obj:`False`):
              Whether or not to use the model in training mode (some modules like dropout modules have different
              behaviors between training and evaluation).
  """
  
  
  @add_start_docstrings(
      "The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
      BERT_START_DOCSTRING,
  )
  class TFBertPerformerModel(TFBertPerformerPreTrainedModel):
      def __init__(self, config: BertPerformerConfig, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.bert = TFBertPerformerMainLayer(config, name="bert")
  
      @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="bert-base-cased",
          output_type=TFBaseModelOutputWithPooling,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids: Optional[TFModelInputType] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          training: Optional[bool] = False,
          **kwargs,
      ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
          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.bert(
              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
  
      def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
          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(
      """
  Bert Model with two heads on top as done during the pretraining:
      a `masked language modeling` head and a `next sentence prediction (classification)` head.
      """,
      BERT_START_DOCSTRING,
  )
  class TFBertPerformerForPreTraining(TFBertPerformerPreTrainedModel, TFBertPreTrainingLoss):
      # 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"cls.predictions.decoder.weight"]
  
      def __init__(self, config: BertPerformerConfig, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.bert = TFBertPerformerMainLayer(config, name="bert")
          self.nsp = TFBertNSPHead(config, name="nsp___cls")
          self.mlm = TFBertMLMHead(config, input_embeddings=self.bert.embeddings, name="mlm___cls")
  
      def get_lm_head(self) -> tf.keras.layers.Layer:
          return self.mlm.predictions
  
      def get_prefix_bias_name(self) -> str:
          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(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=TFBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
      def call(
          self,
          input_ids: Optional[TFModelInputType] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
          next_sentence_label: Optional[Union[np.ndarray, tf.Tensor]] = None,
          training: Optional[bool] = False,
          **kwargs,
      ) -> Union[TFBertForPreTrainingOutput, Tuple[tf.Tensor]]:
          r"""
          Return:
  
          Examples::
  
              >>> import tensorflow as tf
              >>> from transformers import BertTokenizer, TFBertForPreTraining
  
              >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              >>> model = TFBertForPreTraining.from_pretrained('bert-base-uncased')
              >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[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,
              labels=labels,
              next_sentence_label=next_sentence_label,
              training=training,
              kwargs_call=kwargs,
          )
          outputs = self.bert(
              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"],
          )
          sequence_output, pooled_output = outputs[:2]
          prediction_scores = self.mlm(sequence_output=sequence_output, training=inputs["training"])
          seq_relationship_score = self.nsp(pooled_output=pooled_output)
          total_loss = None
  
          if inputs["labels"] is not None and inputs["next_sentence_label"] is not None:
              d_labels = {"labels": inputs["labels"]}
              d_labels["next_sentence_label"] = inputs["next_sentence_label"]
              total_loss = self.compute_loss(labels=d_labels, logits=(prediction_scores, seq_relationship_score))
  
          if not inputs["return_dict"]:
              return (prediction_scores, seq_relationship_score) + outputs[2:]
  
          return TFBertForPreTrainingOutput(
              loss=total_loss,
              prediction_logits=prediction_scores,
              seq_relationship_logits=seq_relationship_score,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
      def serving_output(self, output: TFBertForPreTrainingOutput) -> TFBertForPreTrainingOutput:
          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 TFBertForPreTrainingOutput(
              prediction_logits=output.prediction_logits,
              seq_relationship_logits=output.seq_relationship_logits,
              hidden_states=hs,
              attentions=attns,
          )
  
  
  @add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
  class TFBertPerformerForMaskedLM(TFBertPerformerPreTrainedModel, 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"cls.seq_relationship",
          r"cls.predictions.decoder.weight",
          r"nsp___cls",
      ]
  
      def __init__(self, config: BertPerformerConfig, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          if config.is_decoder:
              logger.warning(
                  "If you want to use `TFBertForMaskedLM` make sure `config.is_decoder=False` for "
                  "bi-directional self-attention."
              )
  
          self.bert = TFBertPerformerMainLayer(config, add_pooling_layer=False, name="bert")
          self.mlm = TFBertMLMHead(config, input_embeddings=self.bert.embeddings, name="mlm___cls")
  
      def get_lm_head(self) -> tf.keras.layers.Layer:
          return self.mlm.predictions
  
      def get_prefix_bias_name(self) -> str:
          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(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="bert-base-cased",
          output_type=TFMaskedLMOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids: Optional[TFModelInputType] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
          training: Optional[bool] = False,
          **kwargs,
      ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
          r"""
          labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
              config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
              (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              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.bert(
              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"],
          )
          sequence_output = outputs[0]
          prediction_scores = self.mlm(sequence_output=sequence_output, training=inputs["training"])
          loss = (
              None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=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,
          )
  
      def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  class TFBertPerformerLMHeadModel(TFBertPerformerPreTrainedModel, TFCausalLanguageModelingLoss):
      # 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"cls.seq_relationship",
          r"cls.predictions.decoder.weight",
          r"nsp___cls",
      ]
  
      def __init__(self, config: BertPerformerConfig, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          if not config.is_decoder:
              logger.warning("If you want to use `TFBertLMHeadModel` as a standalone, add `is_decoder=True.`")
  
          self.bert = TFBertPerformerMainLayer(config, add_pooling_layer=False, name="bert")
          self.mlm = TFBertMLMHead(config, input_embeddings=self.bert.embeddings, name="mlm___cls")
  
      def get_lm_head(self) -> tf.keras.layers.Layer:
          return self.mlm.predictions
  
      def get_prefix_bias_name(self) -> str:
          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_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="bert-base-cased",
          output_type=TFCausalLMOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids: Optional[TFModelInputType] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
          training: Optional[bool] = False,
          **kwargs,
      ) -> Union[TFCausalLMOutput, Tuple[tf.Tensor]]:
          r"""
          labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
              config.vocab_size - 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.bert(
              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"],
          )
          sequence_output = outputs[0]
          logits = self.mlm(sequence_output=sequence_output, training=inputs["training"])
          loss = None
  
          if inputs["labels"] is not None:
              # shift labels to the left and cut last logit token
              logits = logits[:, :-1]
              labels = inputs["labels"][:, 1:]
              loss = self.compute_loss(labels=labels, logits=logits)
  
          if not inputs["return_dict"]:
              output = (logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return TFCausalLMOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
      def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
          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 TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  @add_start_docstrings(
      """Bert Model with a `next sentence prediction (classification)` head on top. """,
      BERT_START_DOCSTRING,
  )
  class TFBertPerformerForNextSentencePrediction(TFBertPerformerPreTrainedModel, 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"mlm___cls", r"cls.predictions"]
  
      def __init__(self, config: BertPerformerConfig, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.bert = TFBertPerformerMainLayer(config, name="bert")
          self.nsp = TFBertNSPHead(config, name="nsp___cls")
  
      @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=TFNextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
      def call(
          self,
          input_ids: Optional[TFModelInputType] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          next_sentence_label: Optional[Union[np.ndarray, tf.Tensor]] = None,
          training: Optional[bool] = False,
          **kwargs,
      ) -> Union[TFNextSentencePredictorOutput, Tuple[tf.Tensor]]:
          r"""
          Return:
  
          Examples::
  
              >>> import tensorflow as tf
              >>> from transformers import BertTokenizer, TFBertForNextSentencePrediction
  
              >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              >>> model = TFBertForNextSentencePrediction.from_pretrained('bert-base-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]
              >>> assert logits[0][0] < logits[0][1] # the next sentence was random
          """
          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.bert(
              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"],
          )
          pooled_output = outputs[1]
          seq_relationship_scores = self.nsp(pooled_output=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,
          )
  
      def serving_output(self, output: TFNextSentencePredictorOutput) -> TFNextSentencePredictorOutput:
          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(
      """
      Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
      output) e.g. for GLUE tasks.
      """,
      BERT_START_DOCSTRING,
  )
  class TFBertPerformerForSequenceClassification(TFBertPerformerPreTrainedModel, 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"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship"]
      _keys_to_ignore_on_load_missing = [r"dropout"]
  
      def __init__(self, config: BertPerformerConfig, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.num_labels = config.num_labels
  
          self.bert = TFBertPerformerMainLayer(config, name="bert")
          self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
          self.classifier = tf.keras.layers.Dense(
              units=config.num_labels,
              kernel_initializer=get_initializer(config.initializer_range),
              name="classifier",
          )
  
      @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="bert-base-cased",
          output_type=TFSequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids: Optional[TFModelInputType] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
          training: Optional[bool] = False,
          **kwargs,
      ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
          r"""
          labels (:obj:`tf.Tensor` or :obj:`np.ndarray` 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.bert(
              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"],
          )
          pooled_output = outputs[1]
          pooled_output = self.dropout(inputs=pooled_output, training=inputs["training"])
          logits = self.classifier(inputs=pooled_output)
          loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=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,
          )
  
      def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  @add_start_docstrings(
      """
      Bert 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.
      """,
      BERT_START_DOCSTRING,
  )
  class TFBertPerformerForMultipleChoice(TFBertPerformerPreTrainedModel, 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"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship"]
      _keys_to_ignore_on_load_missing = [r"dropout"]
  
      def __init__(self, config: BertPerformerConfig, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.bert = TFBertPerformerMainLayer(config, name="bert")
          self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
          self.classifier = tf.keras.layers.Dense(
              units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
          )
  
      @property
      def dummy_inputs(self) -> Dict[str, tf.Tensor]:
          """
          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(BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="bert-base-cased",
          output_type=TFMultipleChoiceModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids: Optional[TFModelInputType] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
          training: Optional[bool] = False,
          **kwargs,
      ) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
          r"""
          labels (:obj:`tf.Tensor` or :obj:`np.ndarray` 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(tensor=inputs["input_ids"], shape=(-1, seq_length)) if inputs["input_ids"] is not None else None
          )
          flat_attention_mask = (
              tf.reshape(tensor=inputs["attention_mask"], shape=(-1, seq_length))
              if inputs["attention_mask"] is not None
              else None
          )
          flat_token_type_ids = (
              tf.reshape(tensor=inputs["token_type_ids"], shape=(-1, seq_length))
              if inputs["token_type_ids"] is not None
              else None
          )
          flat_position_ids = (
              tf.reshape(tensor=inputs["position_ids"], shape=(-1, seq_length))
              if inputs["position_ids"] is not None
              else None
          )
          flat_inputs_embeds = (
              tf.reshape(tensor=inputs["inputs_embeds"], shape=(-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
              if inputs["inputs_embeds"] is not None
              else None
          )
          outputs = self.bert(
              input_ids=flat_input_ids,
              attention_mask=flat_attention_mask,
              token_type_ids=flat_token_type_ids,
              position_ids=flat_position_ids,
              head_mask=inputs["head_mask"],
              inputs_embeds=flat_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(inputs=pooled_output, training=inputs["training"])
          logits = self.classifier(inputs=pooled_output)
          reshaped_logits = tf.reshape(tensor=logits, shape=(-1, num_choices))
          loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=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: Dict[str, tf.Tensor]):
          output = self.call(input_ids=inputs)
  
          return self.serving_output(output)
  
      def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  @add_start_docstrings(
      """
      Bert 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.
      """,
      BERT_START_DOCSTRING,
  )
  class TFBertPerformerForTokenClassification(TFBertPerformerPreTrainedModel, 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"mlm___cls",
          r"nsp___cls",
          r"cls.predictions",
          r"cls.seq_relationship",
      ]
      _keys_to_ignore_on_load_missing = [r"dropout"]
  
      def __init__(self, config: BertPerformerConfig, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.num_labels = config.num_labels
  
          self.bert = TFBertPerformerMainLayer(config, add_pooling_layer=False, name="bert")
          self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
          self.classifier = tf.keras.layers.Dense(
              units=config.num_labels,
              kernel_initializer=get_initializer(config.initializer_range),
              name="classifier",
          )
  
      @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="bert-base-cased",
          output_type=TFTokenClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids: Optional[TFModelInputType] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
          training: Optional[bool] = False,
          **kwargs,
      ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
          r"""
          labels (:obj:`tf.Tensor` or :obj:`np.ndarray` 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.bert(
              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"],
          )
          sequence_output = outputs[0]
          sequence_output = self.dropout(inputs=sequence_output, training=inputs["training"])
          logits = self.classifier(inputs=sequence_output)
          loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=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,
          )
  
      def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  @add_start_docstrings(
      """
      Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
      layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
      """,
      BERT_START_DOCSTRING,
  )
  class TFBertPerformerForQuestionAnswering(TFBertPerformerPreTrainedModel, 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"mlm___cls",
          r"nsp___cls",
          r"cls.predictions",
          r"cls.seq_relationship",
      ]
  
      def __init__(self, config: BertPerformerConfig, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.num_labels = config.num_labels
  
          self.bert = TFBertPerformerMainLayer(config, add_pooling_layer=False, name="bert")
          self.qa_outputs = tf.keras.layers.Dense(
              units=config.num_labels,
              kernel_initializer=get_initializer(config.initializer_range),
              name="qa_outputs",
          )
  
      @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="bert-base-cased",
          output_type=TFQuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids: Optional[TFModelInputType] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
          end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
          training: Optional[bool] = False,
          **kwargs,
      ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
          r"""
          start_positions (:obj:`tf.Tensor` or :obj:`np.ndarray` 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` or :obj:`np.ndarray` 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.bert(
              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"],
          )
          sequence_output = outputs[0]
          logits = self.qa_outputs(inputs=sequence_output)
          start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
          start_logits = tf.squeeze(input=start_logits, axis=-1)
          end_logits = tf.squeeze(input=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=labels, logits=(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,
          )
  
      def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFQuestionAnsweringModelOutput(
              start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
          )
  