code: # coding=utf-8
  # Copyright {{cookiecutter.authors}} and The HuggingFace Inc. team. 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 {{cookiecutter.modelname}} model. """
  
  {% if cookiecutter.is_encoder_decoder_model == "False" %}
  
  
  import tensorflow as tf
  
  from transformers.modeling_tf_outputs import TFCausalLMOutput
  from ...activations_tf import get_tf_activation
  from ...file_utils import (
      MULTIPLE_CHOICE_DUMMY_INPUTS,
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
  )
  from ...modeling_tf_outputs import (
      TFBaseModelOutput,
      TFBaseModelOutputWithPooling,
      TFMaskedLMOutput,
      TFMultipleChoiceModelOutput,
      TFQuestionAnsweringModelOutput,
      TFSequenceClassifierOutput,
      TFTokenClassifierOutput,
  )
  from ...modeling_tf_utils import (
      TFMaskedLanguageModelingLoss,
      TFMultipleChoiceLoss,
      TFPreTrainedModel,
      TFQuestionAnsweringLoss,
      TFSequenceClassificationLoss,
      TFTokenClassificationLoss,
      TFCausalLanguageModelingLoss,
      TFSequenceSummary,
      get_initializer,
      input_processing,
      keras_serializable,
      shape_list,
  )
  from ...utils import logging
  from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config"
  _TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer"
  
  TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "{{cookiecutter.checkpoint_identifier}}",
      # See all {{cookiecutter.modelname}} models at https://huggingface.co/models?filter={{cookiecutter.lowercase_modelname}}
  ]
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings with Bert->{{cookiecutter.camelcase_modelname}}
  class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
      """Construct the embeddings from word, position and token_type embeddings."""
  
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.vocab_size = config.vocab_size
          self.hidden_size = config.hidden_size
          self.initializer_range = config.initializer_range
          self.position_embeddings = tf.keras.layers.Embedding(
              config.max_position_embeddings,
              config.hidden_size,
              embeddings_initializer=get_initializer(self.initializer_range),
              name="position_embeddings",
          )
          self.token_type_embeddings = tf.keras.layers.Embedding(
              config.type_vocab_size,
              config.hidden_size,
              embeddings_initializer=get_initializer(self.initializer_range),
              name="token_type_embeddings",
          )
  
          # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
          # any TensorFlow checkpoint file
          self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
          self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
  
      def build(self, input_shape):
          """Build shared word embedding layer """
          with tf.name_scope("word_embeddings"):
              # Create and initialize weights. The random normal initializer was chosen
              # arbitrarily, and works well.
              self.word_embeddings = self.add_weight(
                  "weight",
                  shape=[self.vocab_size, self.hidden_size],
                  initializer=get_initializer(self.initializer_range),
              )
  
          super().build(input_shape)
  
      def call(
          self,
          input_ids=None,
          position_ids=None,
          token_type_ids=None,
          inputs_embeds=None,
          mode="embedding",
          training=False,
      ):
          """
          Get token embeddings of inputs.
  
          Args:
              inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
              mode: string, a valid value is one of "embedding" and "linear".
  
          Returns:
              outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
              embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
              vocab_size].
  
          Raises:
              ValueError: if mode is not valid.
  
          Shared weights logic adapted from
          https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
          """
          if mode == "embedding":
              return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
          elif mode == "linear":
              return self._linear(input_ids)
          else:
              raise ValueError("mode {} is not valid.".format(mode))
  
      def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
          """Applies embedding based on inputs tensor."""
          assert not (input_ids is None and inputs_embeds is None)
  
          if input_ids is not None:
              input_shape = shape_list(input_ids)
          else:
              input_shape = shape_list(inputs_embeds)[:-1]
  
          seq_length = input_shape[1]
  
          if position_ids is None:
              position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
  
          if token_type_ids is None:
              token_type_ids = tf.fill(input_shape, 0)
  
          if inputs_embeds is None:
              inputs_embeds = tf.gather(self.word_embeddings, input_ids)
  
          position_embeddings = tf.cast(self.position_embeddings(position_ids), inputs_embeds.dtype)
          token_type_embeddings = tf.cast(self.token_type_embeddings(token_type_ids), inputs_embeds.dtype)
          embeddings = inputs_embeds + position_embeddings + token_type_embeddings
          embeddings = self.LayerNorm(embeddings)
          embeddings = self.dropout(embeddings, training=training)
  
          return embeddings
  
      def _linear(self, inputs):
          """
          Computes logits by running inputs through a linear layer.
  
          Args:
              inputs: A float32 tensor with shape [batch_size, length, hidden_size].
  
          Returns:
              float32 tensor with shape [batch_size, length, vocab_size].
          """
          batch_size = shape_list(inputs)[0]
          length = shape_list(inputs)[1]
          x = tf.reshape(inputs, [-1, self.hidden_size])
          logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
  
          return tf.reshape(logits, [batch_size, length, self.vocab_size])
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}}
  class TF{{cookiecutter.camelcase_modelname}}SelfAttention(tf.keras.layers.Layer):
      def __init__(self, config, **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.query = tf.keras.layers.experimental.EinsumDense(
              equation="abc,cde->abde",
              output_shape=(None, config.num_attention_heads, self.attention_head_size),
              bias_axes="de",
              kernel_initializer=get_initializer(initializer_range=config.initializer_range),
              name="query",
          )
          self.key = tf.keras.layers.experimental.EinsumDense(
              equation="abc,cde->abde",
              output_shape=(None, config.num_attention_heads, self.attention_head_size),
              bias_axes="de",
              kernel_initializer=get_initializer(initializer_range=config.initializer_range),
              name="key",
          )
          self.value = tf.keras.layers.experimental.EinsumDense(
              equation="abc,cde->abde",
              output_shape=(None, config.num_attention_heads, self.attention_head_size),
              bias_axes="de",
              kernel_initializer=get_initializer(initializer_range=config.initializer_range),
              name="value",
          )
          self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
  
      def call(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, training=False):
          query_layer = self.query(inputs=hidden_states)
          key_layer = self.key(inputs=hidden_states)
          value_layer = self.value(inputs=hidden_states)
  
          # Take the dot product between "query" and "key" to get the raw
          # attention scores.
          dk = tf.cast(x=self.attention_head_size, dtype=query_layer.dtype)
          query_layer = tf.multiply(x=query_layer, y=tf.math.rsqrt(x=dk))
          attention_scores = tf.einsum("aecd,abcd->acbe", key_layer, query_layer)
  
          if attention_mask is not None:
              # Apply the attention mask is (precomputed for all layers in TF{{cookiecutter.camelcase_modelname}}Model call() function)
              attention_scores = 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(attention_probs, training=training)
  
          # Mask heads if we want to
          if head_mask is not None:
              attention_scores = attention_scores * head_mask
  
          attention_output = tf.einsum("acbe,aecd->abcd", attention_probs, value_layer)
          outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
  
          return outputs
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->{{cookiecutter.camelcase_modelname}}
  class TF{{cookiecutter.camelcase_modelname}}SelfOutput(tf.keras.layers.Layer):
      def __init__(self, config, **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.dense = tf.keras.layers.experimental.EinsumDense(
              equation="abcd,cde->abe",
              output_shape=(None, self.all_head_size),
              bias_axes="e",
              kernel_initializer=get_initializer(initializer_range=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, input_tensor, training=False):
          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
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->{{cookiecutter.camelcase_modelname}}
  class TF{{cookiecutter.camelcase_modelname}}Attention(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.self_attention = TF{{cookiecutter.camelcase_modelname}}SelfAttention(config, name="self")
          self.dense_output = TF{{cookiecutter.camelcase_modelname}}SelfOutput(config, name="output")
  
      def prune_heads(self, heads):
          raise NotImplementedError
  
      def call(self, input_tensor, attention_mask, head_mask, output_attentions, training=False):
          self_outputs = self.self_attention(
              input_tensor, attention_mask, head_mask, output_attentions, training=training
          )
          attention_output = self.dense_output(self_outputs[0], input_tensor, training=training)
          outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
  
          return outputs
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->{{cookiecutter.camelcase_modelname}}
  class TF{{cookiecutter.camelcase_modelname}}Intermediate(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.dense = tf.keras.layers.experimental.EinsumDense(
              equation="abc,cd->abd",
              output_shape=(None, config.intermediate_size),
              bias_axes="d",
              kernel_initializer=get_initializer(initializer_range=config.initializer_range),
              name="dense",
          )
  
          if isinstance(config.hidden_act, str):
              self.intermediate_act_fn = get_tf_activation(activation_string=config.hidden_act)
          else:
              self.intermediate_act_fn = config.hidden_act
  
      def call(self, hidden_states):
          hidden_states = self.dense(inputs=hidden_states)
          hidden_states = self.intermediate_act_fn(inputs=hidden_states)
  
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->{{cookiecutter.camelcase_modelname}}
  class TF{{cookiecutter.camelcase_modelname}}Output(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.dense = tf.keras.layers.experimental.EinsumDense(
              equation="abc,cd->abd",
              bias_axes="d",
              output_shape=(None, 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, input_tensor, training=False):
          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 TF{{cookiecutter.camelcase_modelname}}Layer(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.attention = TF{{cookiecutter.camelcase_modelname}}Attention(config, name="attention")
          self.intermediate = TF{{cookiecutter.camelcase_modelname}}Intermediate(config, name="intermediate")
          self.{{cookiecutter.lowercase_modelname}}_output = TF{{cookiecutter.camelcase_modelname}}Output(config, name="output")
  
      # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer.call with bert->{{cookiecutter.lowercase_modelname}}
      def call(self, hidden_states, attention_mask, head_mask, output_attentions, training=False):
          attention_outputs = self.attention(
              hidden_states, attention_mask, head_mask, output_attentions, training=training
          )
          attention_output = attention_outputs[0]
          intermediate_output = self.intermediate(attention_output)
          layer_output = self.{{cookiecutter.lowercase_modelname}}_output(intermediate_output, attention_output, training=training)
          outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
  
          return outputs
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->{{cookiecutter.camelcase_modelname}}
  class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.layer = [TF{{cookiecutter.camelcase_modelname}}Layer(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
          )
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHead with Bert->{{cookiecutter.camelcase_modelname}}
  class TF{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(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 = tf.keras.layers.LayerNormalization(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
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->{{cookiecutter.camelcase_modelname}}
  class TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(tf.keras.layers.Layer):
      def __init__(self, config, input_embeddings, **kwargs):
          super().__init__(**kwargs)
  
          self.vocab_size = config.vocab_size
          self.transform = TF{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(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):
          self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
  
          super().build(input_shape)
  
      def call(self, hidden_states):
          hidden_states = self.transform(hidden_states)
          hidden_states = self.input_embeddings(hidden_states, mode="linear")
          hidden_states = hidden_states + self.bias
  
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->{{cookiecutter.camelcase_modelname}}
  class TF{{cookiecutter.camelcase_modelname}}MLMHead(tf.keras.layers.Layer):
      def __init__(self, config, input_embeddings, **kwargs):
          super().__init__(**kwargs)
  
          self.predictions = TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(config, input_embeddings, name="predictions")
  
      def call(self, sequence_output):
          prediction_scores = self.predictions(sequence_output)
  
          return prediction_scores
  
  
  @keras_serializable
  class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
      config_class = {{cookiecutter.camelcase_modelname}}Config
  
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.config = config
          self.num_hidden_layers = config.num_hidden_layers
          self.initializer_range = config.initializer_range
          self.output_attentions = config.output_attentions
          self.output_hidden_states = config.output_hidden_states
          self.return_dict = config.use_return_dict
          self.embeddings = TF{{cookiecutter.camelcase_modelname}}Embeddings(config, name="embeddings")
          self.encoder = TF{{cookiecutter.camelcase_modelname}}Encoder(config, name="encoder")
          self.config = config
  
      def get_input_embeddings(self):
          return self.embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.word_embeddings = value
          self.embeddings.vocab_size = value.shape[0]
  
      def _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)
  
          embedding_output = self.embeddings(
              inputs["input_ids"],
              inputs["position_ids"],
              inputs["token_type_ids"],
              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.
          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, embedding_output.dtype)
          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
  
          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]
  
          if not return_dict:
              return (
                  sequence_output,
              ) + encoder_outputs[1:]
  
          return TFBaseModelOutput(
              last_hidden_state=sequence_output,
              hidden_states=encoder_outputs.hidden_states,
              attentions=encoder_outputs.attentions,
          )
  
  
  class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
      """An abstract class to handle weights initialization and
      a simple interface for downloading and loading pretrained models.
      """
  
      config_class = {{cookiecutter.camelcase_modelname}}Config
      base_model_prefix = "{{cookiecutter.lowercase_modelname}}"
  
  
  
  {{cookiecutter.uppercase_modelname}}_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.{{cookiecutter.camelcase_modelname}}Config`): 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.
  """
  
  {{cookiecutter.uppercase_modelname}}_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.{{cookiecutter.camelcase_modelname}}Tokenizer`.
              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 **maked**.
  
              `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 {{cookiecutter.modelname}} Model transformer outputing raw hidden-states without any specific head on top.",
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
  
      @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="{{cookiecutter.checkpoint_identifier}}",
          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.{{cookiecutter.lowercase_modelname}}(
              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
  
  
  @add_start_docstrings("""{{cookiecutter.modelname}} Model with a `language modeling` head on top. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING)
  class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFMaskedLanguageModelingLoss):
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          if config.is_decoder:
              logger.warning(
                  "If you want to use `TF{{cookiecutter.camelcase_modelname}}ForMaskedLM` make sure `config.is_decoder=False` for "
                  "bi-directional self-attention."
              )
  
          self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
          self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
      
      def get_output_embeddings(self):
          return self.{{cookiecutter.lowercase_modelname}}.embeddings
  
      def get_output_layer_with_bias(self):
          return self.mlm.predictions
  
      def get_prefix_bias_name(self):
          return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
  
      @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="{{cookiecutter.checkpoint_identifier}}",
          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
              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.{{cookiecutter.lowercase_modelname}}(
              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[1:]
              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,
          )
  
  @add_start_docstrings(
      """{{cookiecutter.modelname}} Model with a `language modeling` head on top for CLM fine-tuning. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING
  )
  class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFCausalLanguageModelingLoss):
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          if not config.is_decoder:
              logger.warning("If you want to use `TF{{cookiecutter.camelcase_modelname}}ForCausalLM` as a standalone, add `is_decoder=True.`")
  
          self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
          self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
  
      def get_output_embeddings(self):
          return self.{{cookiecutter.lowercase_modelname}}.embeddings
  
      def get_output_layer_with_bias(self):
          return self.mlm.predictions
  
      def get_prefix_bias_name(self):
          return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
  
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="{{cookiecutter.checkpoint_identifier}}",
          output_type=TFCausalLMOutput,
          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 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.{{cookiecutter.lowercase_modelname}}(
              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, 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, logits)
  
          if not inputs["return_dict"]:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFCausalLMOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  class TF{{cookiecutter.camelcase_modelname}}ClassificationHead(tf.keras.layers.Layer):
      """Head for sentence-level classification tasks."""
  
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.dense = tf.keras.layers.Dense(
              config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
          )
          self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
          self.out_proj = tf.keras.layers.Dense(
              config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
          )
  
          self.config = config
  
      def call(self, inputs, **kwargs):
          x = inputs[:, 0, :]  # take <s> token (equiv. to [CLS])
          x = self.dropout(x)
          x = self.dense(x)
          x = get_tf_activation(self.config.hidden_act)(x)
          x = self.dropout(x)
          x = self.out_proj(x)
  
          return x
  
  
  @add_start_docstrings(
      """{{cookiecutter.modelname}} Model transformer with a sequence classification/regression head on top 
      e.g., for GLUE tasks. """,
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFSequenceClassificationLoss):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.num_labels = config.num_labels
          self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
          self.classifier = TF{{cookiecutter.camelcase_modelname}}ClassificationHead(config, name="classifier")
  
      @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="{{cookiecutter.checkpoint_identifier}}",
          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.{{cookiecutter.lowercase_modelname}}(
              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"],
          )
          logits = self.classifier(outputs[0], training=inputs["training"])
          loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
  
          if not inputs["return_dict"]:
              output = (logits,) + outputs[1:]
  
              return ((loss,) + output) if loss is not None else output
  
          return TFSequenceClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """{{cookiecutter.modelname}} 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. """,
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFMultipleChoiceLoss):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
          self.sequence_summary = TFSequenceSummary(
              config, initializer_range=config.initializer_range, name="sequence_summary"
          )
          self.classifier = tf.keras.layers.Dense(
              1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
          )
  
      @property
      def dummy_inputs(self):
          """
          Dummy inputs to build the network.
  
          Returns:
              tf.Tensor with dummy inputs
          """
          return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
  
      @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="{{cookiecutter.checkpoint_identifier}}",
          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.{{cookiecutter.lowercase_modelname}}(
              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"],
          )
          logits = self.sequence_summary(outputs[0], training=inputs["training"])
          logits = self.classifier(logits)
          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[1:]
  
              return ((loss,) + output) if loss is not None else output
  
          return TFMultipleChoiceModelOutput(
              loss=loss,
              logits=reshaped_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  @add_start_docstrings(
      """{{cookiecutter.modelname}} 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. """,
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFTokenClassificationLoss):
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.num_labels = config.num_labels
          self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
          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({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="{{cookiecutter.checkpoint_identifier}}",
          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.{{cookiecutter.lowercase_modelname}}(
              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(sequence_output, training=inputs["training"])
          logits = self.classifier(sequence_output)
          loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
  
          if not inputs["return_dict"]:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFTokenClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """{{cookiecutter.modelname}} 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`). """,
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFQuestionAnsweringLoss):
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.num_labels = config.num_labels
          self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
          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({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="{{cookiecutter.checkpoint_identifier}}",
          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.{{cookiecutter.lowercase_modelname}}(
              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[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TFQuestionAnsweringModelOutput(
              loss=loss,
              start_logits=start_logits,
              end_logits=end_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  {% else %}
  import random
  from typing import Dict, Optional, Tuple, Union
  
  import tensorflow as tf
  
  from ...activations_tf import ACT2FN
  from ...file_utils import (
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from ...modeling_tf_outputs import (
      TFBaseModelOutput,
      TFBaseModelOutputWithPast,
      TFSeq2SeqLMOutput,
      TFSeq2SeqModelOutput,
  )
  
  # Public API
  from ...modeling_tf_utils import (
      DUMMY_INPUTS,
      TFPreTrainedModel,
      TFSharedEmbeddings,
      TFWrappedEmbeddings,
      input_processing,
      keras_serializable,
      shape_list,
  )
  from ...utils import logging
  from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config"
  _TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer"
  
  
  LARGE_NEGATIVE = -1e8
  
  
  def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
      shifted_input_ids = tf.cast(input_ids, tf.int32)
      shifted_input_ids = tf.roll(shifted_input_ids, 1, axis=-1)
      start_tokens = tf.fill((shape_list(shifted_input_ids)[0], 1), decoder_start_token_id)
      shifted_input_ids = tf.concat([start_tokens, shifted_input_ids[:, 1:]], -1)
      # replace possible -100 values in labels by `pad_token_id`
      shifted_input_ids = tf.where(
          shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids
      )
  
      # "Verify that `labels` has only positive values and -100"
      assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.cast(0, tf.int32))
  
      # Make sure the assertion op is called by wrapping the result in an identity no-op
      with tf.control_dependencies([assert_gte0]):
          shifted_input_ids = tf.identity(shifted_input_ids)
  
      return shifted_input_ids
  
  
  def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
      """
      Make causal mask used for bi-directional self-attention.
      """
      bsz, tgt_len = input_ids_shape
      mask = tf.ones((tgt_len, tgt_len), dtype=tf.float32) * LARGE_NEGATIVE
      mask_cond = tf.range(shape_list(mask)[-1])
  
      mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
      mask = tf.cast(mask, tf.float32)
  
      if past_key_values_length > 0:
          mask = tf.concat([tf.zeros((tgt_len, past_key_values_length), dtype=tf.float32), mask], axis=-1)
      return tf.broadcast_to(mask[None, None, :, :], (bsz, 1, tgt_len, tgt_len + past_key_values_length))
  
  
  def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None, past_key_values_length: int = 0):
      """
      Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
      """
      bsz, src_len = shape_list(mask)
      tgt_len = tgt_len if tgt_len is not None else src_len
  
      expanded_mask = tf.cast(tf.broadcast_to(mask[:, None, None, :], (bsz, 1, tgt_len, src_len)), tf.float32)
  
      return (1.0 - expanded_mask) * LARGE_NEGATIVE
  
  
  class TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(TFSharedEmbeddings):
      """
      This module learns positional embeddings up to a fixed maximum size.
      """
  
      def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, **kwargs):
          assert padding_idx is not None, "padding_idx cannot be None"
          super().__init__(num_embeddings, embedding_dim, **kwargs)
  
      def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0):
          """Input is expected to be of size [bsz x seqlen]."""
          bsz, seq_len = input_shape[:2]
  
          positions = tf.range(
              past_key_values_length, seq_len + past_key_values_length, delta=1, dtype=tf.int32, name="range"
          )
          return super().call(positions)
  
  
  class TF{{cookiecutter.camelcase_modelname}}Attention(tf.keras.layers.Layer):
      """Multi-headed attention from "Attention Is All You Need"""
  
      def __init__(
          self,
          embed_dim: int,
          num_heads: int,
          dropout: float = 0.0,
          is_decoder: bool = False,
          bias: bool = True,
          **kwargs,
      ):
          super().__init__(**kwargs)
          self.embed_dim = embed_dim
  
          self.num_heads = num_heads
          self.dropout = tf.keras.layers.Dropout(dropout)
          self.head_dim = embed_dim // num_heads
          assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
          self.scaling = self.head_dim ** -0.5
          self.is_decoder = is_decoder
  
          self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
          self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
          self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
          self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
  
      def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
          return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
  
      def call(
          self,
          hidden_states: tf.Tensor,
          key_value_states: Optional[tf.Tensor] = None,
          past_key_value: Optional[Tuple[Tuple[tf.Tensor]]] = None,
          attention_mask: Optional[tf.Tensor] = None,
          training=False,
      ) -> Tuple[tf.Tensor, Optional[tf.Tensor]]:
          """Input shape: Batch x Time x Channel"""
  
          # if key_value_states are provided this layer is used as a cross-attention layer
          # for the decoder
          is_cross_attention = key_value_states is not None
          bsz, tgt_len, embed_dim = shape_list(hidden_states)
  
          # get query proj
          query_states = self.q_proj(hidden_states) * self.scaling
          # get key, value proj
          if is_cross_attention and past_key_value is not None:
              # reuse k,v, cross_attentions
              key_states = past_key_value[0]
              value_states = past_key_value[1]
          elif is_cross_attention:
              # cross_attentions
              key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
              value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
          elif past_key_value is not None:
              # reuse k, v, self_attention
              key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
              value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
              key_states = tf.concat([past_key_value[0], key_states], axis=2)
              value_states = tf.concat([past_key_value[1], value_states], axis=2)
          else:
              # self_attention
              key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
              value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
  
          if self.is_decoder:
              # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
              # Further calls to cross_attention layer can then reuse all cross-attention
              # key/value_states (first "if" case)
              # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
              # all previous decoder key/value_states. Further calls to uni-directional self-attention
              # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
              # if encoder bi-directional self-attention `past_key_value` is always `None`
              past_key_value = (key_states, value_states)
  
          proj_shape = (bsz * self.num_heads, -1, self.head_dim)
          query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
          key_states = tf.reshape(key_states, proj_shape)
          value_states = tf.reshape(value_states, proj_shape)
  
          src_len = shape_list(key_states)[1]
          attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
  
          tf.debugging.assert_equal(
              shape_list(attn_weights),
              [bsz * self.num_heads, tgt_len, src_len],
              message=f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {shape_list(attn_weights)}",
          )
  
          if attention_mask is not None:
              tf.debugging.assert_equal(
                  shape_list(attention_mask),
                  [bsz, 1, tgt_len, src_len],
                  message=f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {shape_list(attention_mask)}",
              )
              attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
              attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
  
          attn_weights = tf.nn.softmax(attn_weights, axis=-1)
  
          attn_probs = self.dropout(attn_weights, training=training)
  
          attn_output = tf.matmul(attn_probs, value_states)
  
          tf.debugging.assert_equal(
              shape_list(attn_output),
              [bsz * self.num_heads, tgt_len, self.head_dim],
              message=f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {shape_list(attn_output)}",
          )
  
          attn_output = tf.transpose(
              tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
          )
          attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
  
          attn_output = self.out_proj(attn_output)
          attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
  
          return attn_output, attn_weights, past_key_value
  
  
  class TF{{cookiecutter.camelcase_modelname}}EncoderLayer(tf.keras.layers.Layer):
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
          super().__init__(**kwargs)
          self.embed_dim = config.d_model
          self.self_attn = TF{{cookiecutter.camelcase_modelname}}Attention(
              self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn"
          )
          self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
          self.dropout = tf.keras.layers.Dropout(config.dropout)
          self.activation_fn = ACT2FN[config.activation_function]
          self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
          self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
          self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
          self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
  
      def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, training=False):
          """
          Args:
              hidden_states (:obj:`tf.Tensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
              attention_mask (:obj:`tf.Tensor`): attention mask of size
                  `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
          """
          residual = hidden_states
          hidden_states, self_attn_weights, _ = self.self_attn(
              hidden_states=hidden_states, attention_mask=attention_mask
          )
          tf.debugging.assert_equal(
              shape_list(hidden_states),
              shape_list(residual),
              message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
          )
          hidden_states = self.dropout(hidden_states, training=training)
          hidden_states = residual + hidden_states
          hidden_states = self.self_attn_layer_norm(hidden_states)
  
          residual = hidden_states
          hidden_states = self.activation_fn(self.fc1(hidden_states))
          hidden_states = self.activation_dropout(hidden_states, training=training)
          hidden_states = self.fc2(hidden_states)
          hidden_states = self.dropout(hidden_states, training=training)
          hidden_states = residual + hidden_states
          hidden_states = self.final_layer_norm(hidden_states)
  
          return hidden_states, self_attn_weights
  
  
  class TF{{cookiecutter.camelcase_modelname}}DecoderLayer(tf.keras.layers.Layer):
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
          super().__init__(**kwargs)
          self.embed_dim = config.d_model
          self.self_attn = TF{{cookiecutter.camelcase_modelname}}Attention(
              embed_dim=self.embed_dim,
              num_heads=config.decoder_attention_heads,
              dropout=config.attention_dropout,
              name="self_attn",
              is_decoder=True,
          )
          self.dropout = tf.keras.layers.Dropout(config.dropout)
          self.activation_fn = ACT2FN[config.activation_function]
          self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
  
          self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
          self.encoder_attn = TF{{cookiecutter.camelcase_modelname}}Attention(
              self.embed_dim,
              config.decoder_attention_heads,
              dropout=config.attention_dropout,
              name="encoder_attn",
              is_decoder=True,
          )
          self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
          self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
          self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
          self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
  
      def call(
          self,
          hidden_states,
          attention_mask: Optional[tf.Tensor] = None,
          encoder_hidden_states: Optional[tf.Tensor] = None,
          encoder_attention_mask: Optional[tf.Tensor] = None,
          past_key_value: Optional[Tuple[tf.Tensor]] = None,
          training=False,
      ) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]:
          """
          Args:
              hidden_states (:obj:`tf.Tensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
              attention_mask (:obj:`tf.Tensor`): attention mask of size
                  `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              encoder_hidden_states (:obj:`tf.Tensor`): cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
              encoder_attention_mask (:obj:`tf.Tensor`): encoder attention mask of size
                  `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              past_key_value (:obj:`Tuple(tf.Tensor)`): cached past key and value projection states
          """
          residual = hidden_states
  
          # Self Attention
          # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
          self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
          # add present self-attn cache to positions 1,2 of present_key_value tuple
          hidden_states, self_attn_weights, present_key_value = self.self_attn(
              hidden_states=hidden_states,
              past_key_value=self_attn_past_key_value,
              attention_mask=attention_mask,
          )
          hidden_states = self.dropout(hidden_states, training=training)
          hidden_states = residual + hidden_states
          hidden_states = self.self_attn_layer_norm(hidden_states)
  
          # Cross-Attention Block
          cross_attn_present_key_value = None
          if encoder_hidden_states is not None:
              residual = hidden_states
  
              # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
              cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
              hidden_states, _, cross_attn_present_key_value = self.encoder_attn(
                  hidden_states=hidden_states,
                  key_value_states=encoder_hidden_states,
                  attention_mask=encoder_attention_mask,
                  past_key_value=cross_attn_past_key_value,
              )
              hidden_states = self.dropout(hidden_states, training=training)
              hidden_states = residual + hidden_states
              hidden_states = self.encoder_attn_layer_norm(hidden_states)
  
              # add cross-attn to positions 3,4 of present_key_value tuple
              present_key_value = present_key_value + cross_attn_present_key_value
  
          # Fully Connected
          residual = hidden_states
          hidden_states = self.activation_fn(self.fc1(hidden_states))
          hidden_states = self.activation_dropout(hidden_states, training=training)
          hidden_states = self.fc2(hidden_states)
          hidden_states = self.dropout(hidden_states, training=training)
          hidden_states = residual + hidden_states
          hidden_states = self.final_layer_norm(hidden_states)
  
          return (
              hidden_states,
              self_attn_weights,
              present_key_value,
          )
  
  
  class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
      config_class = {{cookiecutter.camelcase_modelname}}Config
      base_model_prefix = "model"
  
      @property
      def dummy_inputs(self):
          pad_token = 1
          input_ids = tf.cast(tf.constant(DUMMY_INPUTS), tf.int32)
          decoder_input_ids = tf.cast(tf.constant(DUMMY_INPUTS), tf.int32)
          dummy_inputs = {
              "decoder_input_ids": decoder_input_ids,
              "attention_mask": tf.math.not_equal(input_ids, pad_token),
              "input_ids": input_ids,
          }
          return dummy_inputs
  
  
  {{cookiecutter.uppercase_modelname}}_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(input_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.{{cookiecutter.camelcase_modelname}}Config`): 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.
  """
  
  {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`tf.Tensor` of shape :obj:`({0})`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`~transformers.BertTokenizer`. See
              :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (: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>`__
          decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
              Provide for translation and summarization training. By default, the model will create this tensor by
              shifting the input_ids right, following the paper.
          decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`):
              will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
          encoder_outputs (:obj:`tf.FloatTensor`, `optional`):
              hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
              of shape :obj:`(batch_size, sequence_length, hidden_size)` is a sequence of
          past_key_values (:obj:`Tuple[Tuple[tf.Tensor]]` of length :obj:`config.n_layers`)
              contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
              If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
              (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
              instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
          use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
              If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
              decoding (see :obj:`past_key_values`). Set to :obj:`False` during training, :obj:`True` during generation
          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.TFModelOutput` 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).
  """
  
  
  @keras_serializable
  class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
      config_class = {{cookiecutter.camelcase_modelname}}Config
      """
      Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
      :class:`TF{{cookiecutter.camelcase_modelname}}EncoderLayer`.
  
      Args:
          config: {{cookiecutter.camelcase_modelname}}Config
      """
  
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[TFSharedEmbeddings] = None, **kwargs):
          super().__init__(**kwargs)
          self.config = config
          self.dropout = tf.keras.layers.Dropout(config.dropout)
          self.layerdrop = config.encoder_layerdrop
          self.padding_idx = config.pad_token_id
          self.max_source_positions = config.max_position_embeddings
  
          self.embed_tokens = embed_tokens
          self.embed_positions = TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
              config.max_position_embeddings,
              config.d_model,
              self.padding_idx,
              name="embed_positions",
          )
          self.layers = [TF{{cookiecutter.camelcase_modelname}}EncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
          self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
  
      def call(
          self,
          input_ids=None,
          inputs_embeds=None,
          attention_mask=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
          **kwargs,
      ):
          """
          Args:
              input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
                  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                  provide it.
  
                  Indices can be obtained using :class:`~transformers.{{cookiecutter.camelcase_modelname}}Tokenizer`. See
                  :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
                  for details.
  
                  `What are input IDs? <../glossary.html#input-ids>`__
              attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                  Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
  
                  - 1 for tokens that are **not masked**,
                  - 0 for tokens that are **masked**.
  
                  `What are attention masks? <../glossary.html#attention-mask>`__
              inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 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.
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_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["inputs_embeds"] is None:
              inputs_embeds = self.embed_tokens(inputs["input_ids"])
          else:
              inputs_embeds = inputs["inputs_embeds"]
  
          inputs_embeds = inputs_embeds
  
          embed_pos = self.embed_positions(input_shape)
          hidden_states = inputs_embeds + embed_pos
          hidden_states = self.layernorm_embedding(hidden_states)
          hidden_states = self.dropout(hidden_states, training=inputs["training"])
  
          # check attention mask and invert
          if inputs["attention_mask"] is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              attention_mask = _expand_mask(inputs["attention_mask"])
          else:
              attention_mask = None
  
          encoder_states = () if inputs["output_hidden_states"] else None
          all_attentions = () if inputs["output_attentions"] else None
  
          # encoder layers
          for encoder_layer in self.layers:
  
              if inputs["output_hidden_states"]:
                  encoder_states = encoder_states + (hidden_states,)
              # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
              dropout_probability = random.uniform(0, 1)
              if inputs["training"] and (dropout_probability < self.layerdrop):  # skip the layer
                  continue
  
              hidden_states, attn = encoder_layer(hidden_states, attention_mask)
  
              if inputs["output_attentions"]:
                  all_attentions += (attn,)
  
          if inputs["output_hidden_states"]:
              encoder_states = encoder_states + (hidden_states,)
  
          if not inputs["return_dict"]:
              return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
          return TFBaseModelOutput(
              last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
          )
  
  
  @keras_serializable
  class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
      config_class = {{cookiecutter.camelcase_modelname}}Config
      """
      Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`TF{{cookiecutter.camelcase_modelname}}DecoderLayer`
  
      Args:
          config: {{cookiecutter.camelcase_modelname}}Config
          embed_tokens: output embedding
      """
  
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[TFSharedEmbeddings] = None, **kwargs):
          super().__init__(**kwargs)
          self.config = config
          self.padding_idx = config.pad_token_id
          self.embed_tokens = embed_tokens
          self.layerdrop = config.decoder_layerdrop
          self.embed_positions = TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
              config.max_position_embeddings,
              config.d_model,
              self.padding_idx,
              name="embed_positions",
          )
          self.layers = [TF{{cookiecutter.camelcase_modelname}}DecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
          self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
  
          self.dropout = tf.keras.layers.Dropout(config.dropout)
  
      def call(
          self,
          input_ids=None,
          inputs_embeds=None,
          attention_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_values=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
          **kwargs,
      ):
          r"""
          Args:
              input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
                  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                  provide it.
  
                  Indices can be obtained using :class:`~transformers.{{cookiecutter.camelcase_modelname}}Tokenizer`. See
                  :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
                  for details.
  
                  `What are input IDs? <../glossary.html#input-ids>`__
              attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                  Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
  
                  - 1 for tokens that are **not masked**,
                  - 0 for tokens that are **masked**.
  
                  `What are attention masks? <../glossary.html#attention-mask>`__
              encoder_hidden_states (:obj:`tf.Tensor` of shape :obj:`(batch_size, encoder_sequence_length, hidden_size)`, `optional`):
                  Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                  of the decoder.
              encoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, encoder_sequence_length)`, `optional`):
                  Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. 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>`__
              past_key_values (:obj:`Tuple[Tuple[tf.Tensor]]` of length :obj:`config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
                  Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
                  decoding.
  
                  If :obj:`past_key_values` are used, the user can optionally input only the last
                  :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of
                  shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids`` of shape :obj:`(batch_size,
                  sequence_length)`.
              inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 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.
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              inputs_embeds=inputs_embeds,
              past_key_values=past_key_values,
              use_cache=use_cache,
              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 decoder_input_ids and decoder_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 decoder_input_ids or decoder_inputs_embeds")
  
          past_key_values_length = (
              inputs["past_key_values"][0][0].shape[2] if inputs["past_key_values"] is not None else 0
          )
  
          # embed positions
          positions = self.embed_positions(input_shape, past_key_values_length)
  
          if inputs["inputs_embeds"] is None:
              inputs["inputs_embeds"] = self.embed_tokens(inputs["input_ids"])
  
          hidden_states = inputs["inputs_embeds"]
  
          # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
          combined_attention_mask = None
          if input_shape[-1] > 1:
              combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
          else:
              combined_attention_mask = _expand_mask(
                  tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
              )
  
          if inputs["attention_mask"] is None and inputs["input_ids"] is not None and input_shape[-1] > 1:
              inputs["attention_mask"] = tf.cast(
                  tf.math.not_equal(inputs["input_ids"], self.config.pad_token_id), inputs["input_ids"].dtype
              )
              inputs["attention_mask"] = tf.concat(
                  [
                      tf.ones((input_shape[0], past_key_values_length), dtype=inputs["attention_mask"].dtype),
                      inputs["attention_mask"],
                  ],
                  axis=-1,
              )
          else:
              inputs["attention_mask"] = tf.ones(
                  (input_shape[0], input_shape[1] + past_key_values_length), dtype=tf.int32
              )
  
          if inputs["encoder_hidden_states"] is not None and inputs["encoder_attention_mask"] is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              inputs["encoder_attention_mask"] = _expand_mask(inputs["encoder_attention_mask"], tgt_len=input_shape[-1])
  
          hidden_states = self.layernorm_embedding(hidden_states + positions)
          hidden_states = self.dropout(hidden_states, training=inputs["training"])
  
          # decoder layers
          all_hidden_states = ()
          all_self_attns = ()
          present_key_values = ()
          for idx, decoder_layer in enumerate(self.layers):
              # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
              if inputs["output_hidden_states"]:
                  all_hidden_states += (hidden_states,)
              dropout_probability = random.uniform(0, 1)
  
              if inputs["training"] and (dropout_probability < self.layerdrop):
                  continue
  
              past_key_value = inputs["past_key_values"][idx] if inputs["past_key_values"] is not None else None
  
              hidden_states, layer_self_attn, present_key_value = decoder_layer(
                  hidden_states,
                  attention_mask=combined_attention_mask,
                  encoder_hidden_states=inputs["encoder_hidden_states"],
                  encoder_attention_mask=inputs["encoder_attention_mask"],
                  past_key_value=past_key_value,
              )
  
              if inputs["use_cache"]:
                  present_key_values += (present_key_value,)
  
              if inputs["output_attentions"]:
                  all_self_attns += (layer_self_attn,)
  
          if inputs["output_hidden_states"]:
              all_hidden_states += (hidden_states,)
          else:
              all_hidden_states = None
  
          all_self_attns = list(all_self_attns) if inputs["output_attentions"] else None
  
          present_key_values = (encoder_hidden_states, present_key_values) if inputs["use_cache"] else None
  
          if not inputs["return_dict"]:
              return hidden_states, present_key_values, all_hidden_states, all_self_attns
          else:
              return TFBaseModelOutputWithPast(
                  last_hidden_state=hidden_states,
                  past_key_values=present_key_values,
                  hidden_states=all_hidden_states,
                  attentions=all_self_attns,
              )
  
  
  @add_start_docstrings(
      "The bare {{cookiecutter.uppercase_modelname}} Model outputting raw hidden-states without any specific head on top.",
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  @keras_serializable
  class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
      base_model_prefix = "model"
  
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="model.shared")
  
          with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
              pass
  
          # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
          embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
          embed_tokens.vocab_size = self.shared.vocab_size
          embed_tokens.hidden_size = self.shared.hidden_size
  
          self.encoder = TF{{cookiecutter.camelcase_modelname}}Encoder(config, embed_tokens, name="encoder")
          self.decoder = TF{{cookiecutter.camelcase_modelname}}Decoder(config, embed_tokens, name="decoder")
  
      def get_decoder(self):
          return self.decoder
  
      @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="{{cookiecutter.checkpoint_identifier}}",
          output_type=TFSeq2SeqModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
          past_key_values=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          use_cache=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,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              encoder_outputs=encoder_outputs,
              past_key_values=past_key_values,
              inputs_embeds=inputs_embeds,
              decoder_inputs_embeds=decoder_inputs_embeds,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
              kwargs_call=kwargs,
          )
  
          if inputs["decoder_input_ids"] is None and inputs["decoder_inputs_embeds"] is None:
              inputs["use_cache"] = False
  
          inputs["output_hidden_states"] = (
              inputs["output_hidden_states"]
              if inputs["output_hidden_states"] is not None
              else self.config.output_hidden_states
          )
  
          if inputs["encoder_outputs"] is None:
              inputs["encoder_outputs"] = self.encoder(
                  input_ids=inputs["input_ids"],
                  attention_mask=inputs["attention_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"],
              )
          # If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
          elif inputs["return_dict"] and not isinstance(inputs["encoder_outputs"], TFBaseModelOutput):
              inputs["encoder_outputs"] = TFBaseModelOutput(
                  last_hidden_state=inputs["encoder_outputs"][0],
                  hidden_states=inputs["encoder_outputs"][1] if len(inputs["encoder_outputs"]) > 1 else None,
                  attentions=inputs["encoder_outputs"][2] if len(inputs["encoder_outputs"]) > 2 else None,
              )
          # If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
          elif not inputs["return_dict"] and not isinstance(inputs["encoder_outputs"], tuple):
              inputs["encoder_outputs"] = inputs["encoder_outputs"].to_tuple()
  
          decoder_outputs = self.decoder(
              inputs["decoder_input_ids"],
              attention_mask=inputs["decoder_attention_mask"],
              encoder_hidden_states=inputs["encoder_outputs"][0],
              encoder_attention_mask=inputs["attention_mask"],
              past_key_values=inputs["past_key_values"],
              inputs_embeds=inputs["decoder_inputs_embeds"],
              use_cache=inputs["use_cache"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
  
          if not inputs["return_dict"]:
              return decoder_outputs + inputs["encoder_outputs"]
  
          return TFSeq2SeqModelOutput(
              last_hidden_state=decoder_outputs.last_hidden_state,
              past_key_values=decoder_outputs.past_key_values,
              decoder_hidden_states=decoder_outputs.hidden_states,
              decoder_attentions=decoder_outputs.attentions,
              encoder_last_hidden_state=inputs["encoder_outputs"].last_hidden_state,
              encoder_hidden_states=inputs["encoder_outputs"].hidden_states,
              encoder_attentions=inputs["encoder_outputs"].attentions,
          )
  
      def get_input_embeddings(self):
          return self.shared
  
      def set_input_embeddings(self, value):
          self.shared = value
  
      def get_output_embeddings(self):
          return self.shared
  
  
  @add_start_docstrings(
      "The {{cookiecutter.uppercase_modelname}} Model with a language modeling head. Can be used for summarization.",
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
      _keys_to_ignore_on_load_unexpected = [
          r"model.encoder.embed_tokens.weight",
          r"model.decoder.embed_tokens.weight",
      ]
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.model = TF{{cookiecutter.camelcase_modelname}}Model(config, name="model")
          self.use_cache = config.use_cache
          # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
          self.final_logits_bias = self.add_weight(
              name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
          )
  
      def get_decoder(self):
          return self.model.decoder
  
      def resize_token_embeddings(self, new_num_tokens):
          super().resize_token_embeddings(new_num_tokens=new_num_tokens)
  
          # {{cookiecutter.uppercase_modelname}} is a special case where the bias has two dimensions
          # and not named just `bias`
          if new_num_tokens is not None:
              num_tokens_to_copy = min(self.final_logits_bias.shape[0], new_num_tokens)
              init_bias = tf.zeros((new_num_tokens,))
              init_bias[:num_tokens_to_copy] = self.final_logits_bias.value()[:num_tokens_to_copy]
              self.final_logits_bias = self.add_weight(
                  shape=(1, new_num_tokens),
                  initializer="zeros",
                  trainable=False,
                  name="final_logits_bias",
              )
              self.final_logits_bias.assign(init_bias)
  
      @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs: Optional[TFBaseModelOutput] = None,
          past_key_values=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
          **kwargs,
      ):
          """
          Returns:
  
          Examples::
  
              >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
              >>> import tensorflow as tf
              >>> mname = '{{cookiecutter.checkpoint_identifier}}'
              >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained(mname)
              >>> TXT = "My friends are <mask> but they eat too many carbs."
              >>> model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained(mname)
              >>> batch = tokenizer([TXT], return_tensors='tf')
              >>> logits = model(inputs=batch.input_ids).logits
              >>> probs = tf.nn.softmax(logits[0])
              >>> # probs[5] is associated with the mask token
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              encoder_outputs=encoder_outputs,
              past_key_values=past_key_values,
              inputs_embeds=inputs_embeds,
              decoder_inputs_embeds=decoder_inputs_embeds,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              labels=labels,
              training=training,
              kwargs_call=kwargs,
          )
  
          if inputs["labels"] is not None:
              inputs["use_cache"] = False
              if inputs["decoder_input_ids"] is None:
                  inputs["decoder_input_ids"] = shift_tokens_right(
                      inputs["labels"], self.config.pad_token_id, self.config.decoder_start_token_id
                  )
  
          outputs = self.model(
              inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              decoder_input_ids=inputs["decoder_input_ids"],
              encoder_outputs=inputs["encoder_outputs"],
              decoder_attention_mask=inputs["decoder_attention_mask"],
              past_key_values=inputs["past_key_values"],
              inputs_embeds=inputs["inputs_embeds"],
              decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
              use_cache=inputs["use_cache"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"]
          )
          lm_logits = self.model.shared(outputs[0], mode="linear")
          lm_logits = lm_logits + self.final_logits_bias
          masked_lm_loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], lm_logits)
  
          if not inputs["return_dict"]:
              output = (lm_logits,) + outputs[1:]
              return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
          return TFSeq2SeqLMOutput(
              loss=masked_lm_loss,
              logits=lm_logits,
              past_key_values=outputs.past_key_values,  # index 1 of d outputs
              decoder_hidden_states=outputs.decoder_hidden_states,  # index 2 of d outputs
              decoder_attentions=outputs.decoder_attentions,  # index 3 of d outputs
              encoder_last_hidden_state=outputs.last_hidden_state,  # index 0 of encoder outputs
              encoder_hidden_states=outputs.encoder_hidden_states,  # 1 of e out
              encoder_attentions=outputs.encoder_attentions,  # 2 of e out
          )
  
      def prepare_inputs_for_generation(self, decoder_input_ids, past, attention_mask, use_cache, **kwargs) -> Dict:
          assert past is not None and len(past) in {1, 2}, f"past has to be an iterable of length 1,2 got {past}"
          if len(past) == 1:
              assert isinstance(past[0], tf.Tensor), f"`past[0]` has to be of type `tf.Tensor`, but is {type(past[0])}"
              encoder_outputs = TFBaseModelOutput(last_hidden_state=past[0])
              past_key_values = None
          else:
              assert (
                  len(past) == 2
              ), "`past` has to be of length 2 with the encoder_outputs at the first position and past_key_values at the second position."
              encoder_outputs, past_key_values = past
              if isinstance(encoder_outputs, tuple):
                  assert isinstance(
                      encoder_outputs[0], tf.Tensor
                  ), f"`encoder_outputs[0]` has to be of type `tf.Tensor`, but is {type(encoder_outputs[0])}"
                  encoder_outputs = TFBaseModelOutput(last_hidden_state=encoder_outputs[0])
              elif isinstance(encoder_outputs, tf.Tensor):
                  encoder_outputs = TFBaseModelOutput(last_hidden_state=encoder_outputs)
              assert (
                  past_key_values
              ), f"decoder cached states must be truthy. got {past_key_values} from the 2nd element of past"
              decoder_input_ids = decoder_input_ids[:, -1:]
  
          assert isinstance(
              encoder_outputs, TFBaseModelOutput
          ), f"encoder_outputs should be a TFBaseModelOutput, Instead got {type(encoder_outputs)}."
          return {
              "input_ids": None,  # encoder_outputs is defined. input_ids not needed
              "encoder_outputs": encoder_outputs,
              "past_key_values": past_key_values,
              "decoder_input_ids": decoder_input_ids,
              "attention_mask": attention_mask,
              "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
          }
  
      @staticmethod
      def _reorder_cache(past, beam_idx):
          if len(past) == 1:
              return past
  
          past_key_values = past[1]
  
          reordered_past = ()
          for layer_past_key_values in past_key_values:
              reordered_past += (
                  tuple(tf.gather(layer_past_key_value, beam_idx) for layer_past_key_value in layer_past_key_values),
              )
          return (past[0], reordered_past)
  
      def get_output_embeddings(self):
          return self.model.shared
  
      def get_encoder(self):
          return self.model.encoder
  
      def compute_loss(self, labels, logits):
          """CrossEntropyLoss that ignores pad tokens"""
          loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
              from_logits=True,
              reduction=tf.keras.losses.Reduction.NONE,
          )
          melted_labels = tf.reshape(labels, (-1,))
          active_loss = tf.not_equal(melted_labels, self.config.pad_token_id)
          reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
          labels = tf.boolean_mask(melted_labels, active_loss)
          return loss_fn(labels, reduced_logits)
  {% endif -%}
  