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.
  """ PyTorch {{cookiecutter.modelname}} model. """
  
  {% if cookiecutter.is_encoder_decoder_model == "False" %}
  
  
  import math
  import os
  
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from ...file_utils import (
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from ...modeling_outputs import (
      BaseModelOutputWithPastAndCrossAttentions,
      CausalLMOutputWithCrossAttentions,
      MaskedLMOutput,
      MultipleChoiceModelOutput,
      QuestionAnsweringModelOutput,
      SequenceClassifierOutput,
      TokenClassifierOutput,
  )
  from ...modeling_utils import (
      PreTrainedModel,
      SequenceSummary,
      apply_chunking_to_forward,
      find_pruneable_heads_and_indices,
      prune_linear_layer,
  )
  from ...utils import logging
  from ...activations import ACT2FN
  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"
  
  {{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "{{cookiecutter.checkpoint_identifier}}",
      # See all {{cookiecutter.modelname}} models at https://huggingface.co/models?filter={{cookiecutter.lowercase_modelname}}
  ]
  
  
  def load_tf_weights_in_{{cookiecutter.lowercase_modelname}}(model, config, tf_checkpoint_path):
      """Load tf checkpoints in a pytorch model."""
      try:
          import re
  
          import numpy as np
          import tensorflow as tf
      except ImportError:
          logger.error(
              "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
              "https://www.tensorflow.org/install/ for installation instructions."
          )
          raise
      tf_path = os.path.abspath(tf_checkpoint_path)
      logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
      # Load weights from TF model
      init_vars = tf.train.list_variables(tf_path)
      names = []
      arrays = []
      for name, shape in init_vars:
          logger.info("Loading TF weight {} with shape {}".format(name, shape))
          array = tf.train.load_variable(tf_path, name)
          names.append(name)
          arrays.append(array)
  
      for name, array in zip(names, arrays):
          name = name.split("/")
          # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
          # which are not required for using pretrained model
          if any(
              n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
              for n in name
          ):
              logger.info("Skipping {}".format("/".join(name)))
              continue
          pointer = model
          for m_name in name:
              if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
                  scope_names = re.split(r"_(\d+)", m_name)
              else:
                  scope_names = [m_name]
              if scope_names[0] == "kernel" or scope_names[0] == "gamma":
                  pointer = getattr(pointer, "weight")
              elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
                  pointer = getattr(pointer, "bias")
              elif scope_names[0] == "output_weights":
                  pointer = getattr(pointer, "weight")
              elif scope_names[0] == "squad":
                  pointer = getattr(pointer, "classifier")
              else:
                  try:
                      pointer = getattr(pointer, scope_names[0])
                  except AttributeError:
                      logger.info("Skipping {}".format("/".join(name)))
                      continue
              if len(scope_names) >= 2:
                  num = int(scope_names[1])
                  pointer = pointer[num]
          if m_name[-11:] == "_embeddings":
              pointer = getattr(pointer, "weight")
          elif m_name == "kernel":
              array = np.transpose(array)
          try:
              assert (
                  pointer.shape == array.shape
              ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
          except AssertionError as e:
              e.args += (pointer.shape, array.shape)
              raise
          logger.info("Initialize PyTorch weight {}".format(name))
          pointer.data = torch.from_numpy(array)
      return model
  
  
  def mish(x):
      return x * torch.tanh(nn.functional.softplus(x))
  
  
  # Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}Embeddings(nn.Module):
      """Construct the embeddings from word, position and token_type embeddings."""
  
      def __init__(self, config):
          super().__init__()
          self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
          self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
          self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
  
          # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
          # any TensorFlow checkpoint file
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
          # position_ids (1, len position emb) is contiguous in memory and exported when serialized
          self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
          self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
  
      def forward(
          self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
      ):
          if input_ids is not None:
              input_shape = input_ids.size()
          else:
              input_shape = inputs_embeds.size()[:-1]
  
          seq_length = input_shape[1]
  
          if position_ids is None:
              position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
  
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
  
          if inputs_embeds is None:
              inputs_embeds = self.word_embeddings(input_ids)
          token_type_embeddings = self.token_type_embeddings(token_type_ids)
  
          embeddings = inputs_embeds + token_type_embeddings
          if self.position_embedding_type == "absolute":
              position_embeddings = self.position_embeddings(position_ids)
              embeddings += position_embeddings
          embeddings = self.LayerNorm(embeddings)
          embeddings = self.dropout(embeddings)
          return embeddings
  
  
  # Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}SelfAttention(nn.Module):
      def __init__(self, config):
          super().__init__()
          if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
              raise ValueError(
                  "The hidden size (%d) is not a multiple of the number of attention "
                  "heads (%d)" % (config.hidden_size, config.num_attention_heads)
              )
  
          self.num_attention_heads = config.num_attention_heads
          self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
          self.all_head_size = self.num_attention_heads * self.attention_head_size
  
          self.query = nn.Linear(config.hidden_size, self.all_head_size)
          self.key = nn.Linear(config.hidden_size, self.all_head_size)
          self.value = nn.Linear(config.hidden_size, self.all_head_size)
  
          self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
          self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
          if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
              self.max_position_embeddings = config.max_position_embeddings
              self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
  
          self.is_decoder = config.is_decoder
  
      def transpose_for_scores(self, x):
          new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
          x = x.view(*new_x_shape)
          return x.permute(0, 2, 1, 3)
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_value=None,
          output_attentions=False,
      ):
          mixed_query_layer = self.query(hidden_states)
  
          # If this is instantiated as a cross-attention module, the keys
          # and values come from an encoder; the attention mask needs to be
          # such that the encoder's padding tokens are not attended to.
          is_cross_attention = encoder_hidden_states is not None
  
          if is_cross_attention and past_key_value is not None:
              # reuse k,v, cross_attentions
              key_layer = past_key_value[0]
              value_layer = past_key_value[1]
              attention_mask = encoder_attention_mask
          elif is_cross_attention:
              key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
              value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
              attention_mask = encoder_attention_mask
          elif past_key_value is not None:
              key_layer = self.transpose_for_scores(self.key(hidden_states))
              value_layer = self.transpose_for_scores(self.value(hidden_states))
              key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
              value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
          else:
              key_layer = self.transpose_for_scores(self.key(hidden_states))
              value_layer = self.transpose_for_scores(self.value(hidden_states))
  
          query_layer = self.transpose_for_scores(mixed_query_layer)
  
          if self.is_decoder:
              # if cross_attention save Tuple(torch.Tensor, torch.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(torch.Tensor, torch.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_layer, value_layer)
  
          # Take the dot product between "query" and "key" to get the raw attention scores.
          attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
  
          if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
              seq_length = hidden_states.size()[1]
              position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
              position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
              distance = position_ids_l - position_ids_r
              positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
              positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
  
              if self.position_embedding_type == "relative_key":
                  relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                  attention_scores = attention_scores + relative_position_scores
              elif self.position_embedding_type == "relative_key_query":
                  relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                  relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                  attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
  
          attention_scores = attention_scores / math.sqrt(self.attention_head_size)
          if attention_mask is not None:
              # Apply the attention mask is (precomputed for all layers in {{cookiecutter.camelcase_modelname}}Model forward() function)
              attention_scores = attention_scores + attention_mask
  
          # Normalize the attention scores to probabilities.
          attention_probs = nn.Softmax(dim=-1)(attention_scores)
  
          # 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)
  
          # Mask heads if we want to
          if head_mask is not None:
              attention_probs = attention_probs * head_mask
  
          context_layer = torch.matmul(attention_probs, value_layer)
  
          context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
          new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
          context_layer = context_layer.view(*new_context_layer_shape)
  
          outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
  
          if self.is_decoder:
              outputs = outputs + (past_key_value,)
          return outputs
  
  
  # Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}SelfOutput(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, hidden_states, input_tensor):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.LayerNorm(hidden_states + input_tensor)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}Attention(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.self = {{cookiecutter.camelcase_modelname}}SelfAttention(config)
          self.output = {{cookiecutter.camelcase_modelname}}SelfOutput(config)
          self.pruned_heads = set()
  
      def prune_heads(self, heads):
          if len(heads) == 0:
              return
          heads, index = find_pruneable_heads_and_indices(
              heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
          )
  
          # Prune linear layers
          self.self.query = prune_linear_layer(self.self.query, index)
          self.self.key = prune_linear_layer(self.self.key, index)
          self.self.value = prune_linear_layer(self.self.value, index)
          self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
  
          # Update hyper params and store pruned heads
          self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
          self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
          self.pruned_heads = self.pruned_heads.union(heads)
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_value=None,
          output_attentions=False,
      ):
          self_outputs = self.self(
              hidden_states,
              attention_mask,
              head_mask,
              encoder_hidden_states,
              encoder_attention_mask,
              past_key_value,
              output_attentions,
          )
          attention_output = self.output(self_outputs[0], hidden_states)
          outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
          return outputs
  
  
  # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}Intermediate(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
          if isinstance(config.hidden_act, str):
              self.intermediate_act_fn = ACT2FN[config.hidden_act]
          else:
              self.intermediate_act_fn = config.hidden_act
  
      def forward(self, hidden_states):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.intermediate_act_fn(hidden_states)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}Output(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, hidden_states, input_tensor):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.LayerNorm(hidden_states + input_tensor)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}Layer(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.chunk_size_feed_forward = config.chunk_size_feed_forward
          self.seq_len_dim = 1
          self.attention = {{cookiecutter.camelcase_modelname}}Attention(config)
          self.is_decoder = config.is_decoder
          self.add_cross_attention = config.add_cross_attention
          if self.add_cross_attention:
              assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
              self.crossattention = {{cookiecutter.camelcase_modelname}}Attention(config)
          self.intermediate = {{cookiecutter.camelcase_modelname}}Intermediate(config)
          self.output = {{cookiecutter.camelcase_modelname}}Output(config)
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_value=None,
          output_attentions=False,
      ):
          # 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
          self_attention_outputs = self.attention(
              hidden_states,
              attention_mask,
              head_mask,
              output_attentions=output_attentions,
              past_key_value=self_attn_past_key_value,
          )
          attention_output = self_attention_outputs[0]
  
          # if decoder, the last output is tuple of self-attn cache
          if self.is_decoder:
              outputs = self_attention_outputs[1:-1]
              present_key_value = self_attention_outputs[-1]
          else:
              outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
  
          cross_attn_present_key_value = None
          if self.is_decoder and encoder_hidden_states is not None:
              assert hasattr(
                  self, "crossattention"
              ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
  
              # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
              cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
              cross_attention_outputs = self.crossattention(
                  attention_output,
                  attention_mask,
                  head_mask,
                  encoder_hidden_states,
                  encoder_attention_mask,
                  cross_attn_past_key_value,
                  output_attentions,
              )
              attention_output = cross_attention_outputs[0]
              outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights
  
              # add cross-attn cache to positions 3,4 of present_key_value tuple
              cross_attn_present_key_value = cross_attention_outputs[-1]
              present_key_value = present_key_value + cross_attn_present_key_value
  
          layer_output = apply_chunking_to_forward(
              self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
          )
          outputs = (layer_output,) + outputs
  
          # if decoder, return the attn key/values as the last output
          if self.is_decoder:
              outputs = outputs + (present_key_value,)
  
          return outputs
  
      def feed_forward_chunk(self, attention_output):
          intermediate_output = self.intermediate(attention_output)
          layer_output = self.output(intermediate_output, attention_output)
          return layer_output
  
  
  # Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}Encoder(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.config = config
          self.layer = nn.ModuleList([{{cookiecutter.camelcase_modelname}}Layer(config) for _ in range(config.num_hidden_layers)])
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_values=None,
          use_cache=None,
          output_attentions=False,
          output_hidden_states=False,
          return_dict=True,
      ):
          all_hidden_states = () if output_hidden_states else None
          all_self_attentions = () if output_attentions else None
          all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
  
          next_decoder_cache = () if use_cache else None
          for i, layer_module in enumerate(self.layer):
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
  
              layer_head_mask = head_mask[i] if head_mask is not None else None
              past_key_value = past_key_values[i] if past_key_values is not None else None
              if getattr(self.config, "gradient_checkpointing", False):
  
                  def create_custom_forward(module):
                      def custom_forward(*inputs):
                          return module(*inputs, past_key_value, output_attentions)
  
                      return custom_forward
  
                  layer_outputs = torch.utils.checkpoint.checkpoint(
                      create_custom_forward(layer_module),
                      hidden_states,
                      attention_mask,
                      layer_head_mask,
                      encoder_hidden_states,
                      encoder_attention_mask,
                  )
              else:
                  layer_outputs = layer_module(
                      hidden_states,
                      attention_mask,
                      layer_head_mask,
                      encoder_hidden_states,
                      encoder_attention_mask,
                      past_key_value,
                      output_attentions,
                  )
  
              hidden_states = layer_outputs[0]
              if use_cache:
                  next_decoder_cache += (layer_outputs[-1],)
              if output_attentions:
                  all_self_attentions = all_self_attentions + (layer_outputs[1],)
                  if self.config.add_cross_attention:
                      all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
  
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if not return_dict:
              return tuple(
                  v
                  for v in [
                      hidden_states,
                      next_decoder_cache,
                      all_hidden_states,
                      all_self_attentions,
                      all_cross_attentions,
                  ]
                  if v is not None
              )
          return BaseModelOutputWithPastAndCrossAttentions(
              last_hidden_state=hidden_states,
              past_key_values=next_decoder_cache,
              hidden_states=all_hidden_states,
              attentions=all_self_attentions,
              cross_attentions=all_cross_attentions,
          )
  
  
  # Copied from transformers.models.bert.modeling_bert.BertPredictionHead with Bert->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}PredictionHeadTransform(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          if isinstance(config.hidden_act, str):
              self.transform_act_fn = ACT2FN[config.hidden_act]
          else:
              self.transform_act_fn = config.hidden_act
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
  
      def forward(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_bert.BertLMPredictionHead with Bert->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}LMPredictionHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.transform = {{cookiecutter.camelcase_modelname}}PredictionHeadTransform(config)
  
          # The output weights are the same as the input embeddings, but there is
          # an output-only bias for each token.
          self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
  
          self.bias = nn.Parameter(torch.zeros(config.vocab_size))
  
          # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
          self.decoder.bias = self.bias
  
      def forward(self, hidden_states):
          hidden_states = self.transform(hidden_states)
          hidden_states = self.decoder(hidden_states)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}OnlyMLMHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.predictions = {{cookiecutter.camelcase_modelname}}LMPredictionHead(config)
  
      def forward(self, sequence_output):
          prediction_scores = self.predictions(sequence_output)
          return prediction_scores
  
  
  class {{cookiecutter.camelcase_modelname}}PreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and
      a simple interface for downloading and loading pretrained models.
      """
  
      config_class = {{cookiecutter.camelcase_modelname}}Config
      load_tf_weights = load_tf_weights_in_{{cookiecutter.lowercase_modelname}}
      base_model_prefix = "{{cookiecutter.lowercase_modelname}}"
      _keys_to_ignore_on_load_missing = [r"position_ids"]
  
      def _init_weights(self, module):
          """ Initialize the weights """
          if isinstance(module, (nn.Linear, nn.Embedding)):
              # Slightly different from the TF version which uses truncated_normal for initialization
              # cf https://github.com/pytorch/pytorch/pull/5617
              module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
          elif isinstance(module, nn.LayerNorm):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0)
          if isinstance(module, nn.Linear) and module.bias is not None:
              module.bias.data.zero_()
  
  
  {{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r"""
      This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
      Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
      usage and behavior.
  
      Parameters:
          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:`torch.LongTensor` 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.encode` and
              :func:`transformers.PreTrainedTokenizer.__call__` for details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`):
              Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
              
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
              
              `What are attention masks? <../glossary.html#attention-mask>`__
          token_type_ids (:obj:`torch.LongTensor` 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:`torch.LongTensor` 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:`torch.FloatTensor` 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:`torch.FloatTensor` 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 `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.
  """
  
  
  @add_start_docstrings(
      "The bare {{cookiecutter.modelname}} Model transformer outputting raw hidden-states without any specific head on top.",
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  class {{cookiecutter.camelcase_modelname}}Model({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      """
  
      The model can behave as an encoder (with only self-attention) as well
      as a decoder, in which case a layer of cross-attention is added between
      the self-attention layers, following the architecture described in `Attention is
      all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani,
      Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
  
      To behave as an decoder the model needs to be initialized with the
      :obj:`is_decoder` argument of the configuration set to :obj:`True`.
      To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder`
      argument and :obj:`add_cross_attention` set to :obj:`True`; an
      :obj:`encoder_hidden_states` is then expected as an input to the forward pass.
      """
  
      def __init__(self, config):
          super().__init__(config)
          self.config = config
  
          self.embeddings = {{cookiecutter.camelcase_modelname}}Embeddings(config)
          self.encoder = {{cookiecutter.camelcase_modelname}}Encoder(config)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embeddings.word_embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.word_embeddings = value
  
      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
          """
          for layer, heads in heads_to_prune.items():
              self.encoder.layer[layer].attention.prune_heads(heads)
  
      @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=BaseModelOutputWithPastAndCrossAttentions,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=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,
      ):
          r"""
          encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
              if the model is configured as a decoder.
          encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Mask to avoid performing attention on the padding token indices of the encoder input. This mask
              is used in the cross-attention if the model is configured as a decoder.
              Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
          past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 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)`.
          use_cache (:obj:`bool`, `optional`):
              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`).
          """
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          if self.config.is_decoder:
              use_cache = use_cache if use_cache is not None else self.config.use_cache
          else:
              use_cache = False
  
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
          elif input_ids is not None:
              input_shape = input_ids.size()
              batch_size, seq_length = input_shape
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-1]
              batch_size, seq_length = input_shape
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          device = input_ids.device if input_ids is not None else inputs_embeds.device
  
          # past_key_values_length
          past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
  
  
          if attention_mask is None:
              attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
  
          # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
          # ourselves in which case we just need to make it broadcastable to all heads.
          extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
  
          # If a 2D or 3D attention mask is provided for the cross-attention
          # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
          if self.config.is_decoder and encoder_hidden_states is not None:
              encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
              encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
              if encoder_attention_mask is None:
                  encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
              encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
          else:
              encoder_extended_attention_mask = None
  
          # 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]
          head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
  
          embedding_output = self.embeddings(
              input_ids=input_ids,
              position_ids=position_ids,
              token_type_ids=token_type_ids,
              inputs_embeds=inputs_embeds,
              past_key_values_length=past_key_values_length,
          )
          encoder_outputs = self.encoder(
              embedding_output,
              attention_mask=extended_attention_mask,
              head_mask=head_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_extended_attention_mask,
              past_key_values=past_key_values,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          sequence_output = encoder_outputs[0]
  
          if not return_dict:
              return (sequence_output,) + encoder_outputs[1:]
  
          return BaseModelOutputWithPastAndCrossAttentions(
              last_hidden_state=sequence_output,
              past_key_values=encoder_outputs.past_key_values,
              hidden_states=encoder_outputs.hidden_states,
              attentions=encoder_outputs.attentions,
              cross_attentions=encoder_outputs.cross_attentions,
          )
  
  
  @add_start_docstrings("""{{cookiecutter.modelname}} Model with a `language modeling` head on top. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING)
  class {{cookiecutter.camelcase_modelname}}ForMaskedLM({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          if config.is_decoder:
              logger.warning(
                  "If you want to use `{{cookiecutter.camelcase_modelname}}ForMaskedLM` make sure `config.is_decoder=False` for "
                  "bi-directional self-attention."
              )
  
          self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
          self.cls = {{cookiecutter.camelcase_modelname}}OnlyMLMHead(config)
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.cls.predictions.decoder
  
      def set_output_embeddings(self, new_embeddings):
          self.cls.predictions.decoder = new_embeddings
  
      @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=MaskedLMOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          labels=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the masked language modeling loss.
              Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
              Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
              in ``[0, ..., config.vocab_size]``.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.{{cookiecutter.lowercase_modelname}}(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
          prediction_scores = self.cls(sequence_output)
  
          masked_lm_loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()  # -100 index = padding token
              masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
  
          if not return_dict:
              output = (prediction_scores,) + outputs[1:]
              return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
  
          return MaskedLMOutput(
              loss=masked_lm_loss,
              logits=prediction_scores,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
      def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
          input_shape = input_ids.shape
          effective_batch_size = input_shape[0]
  
          #  add a dummy token
          assert self.config.pad_token_id is not None, "The PAD token should be defined for generation"
          attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
          dummy_token = torch.full(
              (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
          )
          input_ids = torch.cat([input_ids, dummy_token], dim=1)
  
          return {"input_ids": input_ids, "attention_mask": attention_mask}
  
  
  @add_start_docstrings(
      """{{cookiecutter.modelname}} Model with a `language modeling` head on top for CLM fine-tuning. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING
  )
  class {{cookiecutter.camelcase_modelname}}ForCausalLM({{cookiecutter.camelcase_modelname}}PreTrainedModel):
  
      _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
  
      def __init__(self, config):
          super().__init__(config)
  
          if not config.is_decoder:
              logger.warning("If you want to use `{{cookiecutter.camelcase_modelname}}ForCausalLM` as a standalone, add `is_decoder=True.`")
  
          self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
          self.cls = {{cookiecutter.camelcase_modelname}}OnlyMLMHead(config)
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.cls.predictions.decoder
  
      def set_output_embeddings(self, new_embeddings):
          self.cls.predictions.decoder = new_embeddings
  
      @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
      def forward(
              self,
              input_ids=None,
              attention_mask=None,
              token_type_ids=None,
              position_ids=None,
              head_mask=None,
              inputs_embeds=None,
              encoder_hidden_states=None,
              encoder_attention_mask=None,
              past_key_values=None,
              labels=None,
              use_cache=None,
              output_attentions=None,
              output_hidden_states=None,
              return_dict=None,
      ):
          r"""
          encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
              the model is configured as a decoder.
          encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
              the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
          past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 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)`.
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the left-to-right language modeling loss (next word prediction). 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 n ``[0, ..., config.vocab_size]``.
          use_cache (:obj:`bool`, `optional`):
              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`).
  
          Returns:
  
          Example::
  
              >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}ForCausalLM, {{cookiecutter.camelcase_modelname}}Config
              >>> import torch
  
              >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
              >>> config = {{cookiecutter.camelcase_modelname}}Config.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
              >>> config.is_decoder = True
              >>> model = {{cookiecutter.camelcase_modelname}}ForCausalLM.from_pretrained('{{cookiecutter.checkpoint_identifier}}', config=config)
  
              >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
              >>> outputs = model(**inputs)
  
              >>> prediction_logits = outputs.logits
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.{{cookiecutter.lowercase_modelname}}(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              past_key_values=past_key_values,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
          prediction_scores = self.cls(sequence_output)
  
          lm_loss = None
          if labels is not None:
              # we are doing next-token prediction; shift prediction scores and input ids by one
              shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
              labels = labels[:, 1:].contiguous()
              loss_fct = CrossEntropyLoss()
              lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
  
          if not return_dict:
              output = (prediction_scores,) + outputs[1:]
              return ((lm_loss,) + output) if lm_loss is not None else output
  
          return CausalLMOutputWithCrossAttentions(
              loss=lm_loss,
              logits=prediction_scores,
              past_key_values=outputs.past_key_values,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
              cross_attentions=outputs.cross_attentions,
          )
  
      def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
          input_shape = input_ids.shape
  
          # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
          if attention_mask is None:
              attention_mask = input_ids.new_ones(input_shape)
  
          # cut decoder_input_ids if past is used
          if past is not None:
              input_ids = input_ids[:, -1:]
  
          return {"input_ids": input_ids, "attention_mask": attention_mask}
  
      def _reorder_cache(self, past, beam_idx):
          reordered_past = ()
          for layer_past in past:
              reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],)
          return reordered_past
  
  class {{cookiecutter.camelcase_modelname}}ClassificationHead(nn.Module):
      """Head for sentence-level classification tasks."""
  
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
  
          self.config = config
  
      def forward(self, features, **kwargs):
          x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
          x = self.dropout(x)
          x = self.dense(x)
          x = ACT2FN[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 (a linear layer on top of
      the pooled output) e.g. for GLUE tasks. """,
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  class {{cookiecutter.camelcase_modelname}}ForSequenceClassification({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
          self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
          self.classifier = {{cookiecutter.camelcase_modelname}}ClassificationHead(config)
  
          self.init_weights()
  
      @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=SequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
              self,
              input_ids=None,
              attention_mask=None,
              token_type_ids=None,
              position_ids=None,
              head_mask=None,
              inputs_embeds=None,
              labels=None,
              output_attentions=None,
              output_hidden_states=None,
              return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the sequence classification/regression loss.
              Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
              If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
              If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.{{cookiecutter.lowercase_modelname}}(
              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,
          )
  
          sequence_output = outputs[0]
          logits = self.classifier(sequence_output)
  
          loss = None
          if labels is not None:
              if self.num_labels == 1:
                  #  We are doing regression
                  loss_fct = MSELoss()
                  loss = loss_fct(logits.view(-1), labels.view(-1))
              else:
                  loss_fct = CrossEntropyLoss()
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
  
          if not return_dict:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return SequenceClassifierOutput(
              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 {{cookiecutter.camelcase_modelname}}ForMultipleChoice({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
          self.sequence_summary = SequenceSummary(config)
          self.classifier = nn.Linear(config.hidden_size, 1)
  
          self.init_weights()
  
      @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=MultipleChoiceModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
              self,
              input_ids=None,
              attention_mask=None,
              token_type_ids=None,
              position_ids=None,
              head_mask=None,
              inputs_embeds=None,
              labels=None,
              output_attentions=None,
              output_hidden_states=None,
              return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the multiple choice classification loss.
              Indices should be in ``[0, ..., num_choices-1]`` where :obj:`num_choices` is the size of the second dimension
              of the input tensors. (See :obj:`input_ids` above)
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
          num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
  
          input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
          attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
          token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
          position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
          inputs_embeds = (
              inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
              if inputs_embeds is not None
              else None
          )
  
          outputs = self.{{cookiecutter.lowercase_modelname}}(
              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,
          )
  
          sequence_output = outputs[0]
  
          pooled_output = self.sequence_summary(sequence_output)
          logits = self.classifier(pooled_output)
          reshaped_logits = logits.view(-1, num_choices)
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(reshaped_logits, labels)
  
          if not return_dict:
              output = (reshaped_logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return MultipleChoiceModelOutput(
              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 {{cookiecutter.camelcase_modelname}}ForTokenClassification({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @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=TokenClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the token classification loss.
              Indices should be in ``[0, ..., config.num_labels - 1]``.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.{{cookiecutter.lowercase_modelname}}(
              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,
          )
  
          sequence_output = outputs[0]
  
          sequence_output = self.dropout(sequence_output)
          logits = self.classifier(sequence_output)
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              # Only keep active parts of the loss
              if attention_mask is not None:
                  active_loss = attention_mask.view(-1) == 1
                  active_logits = logits.view(-1, self.num_labels)
                  active_labels = torch.where(
                      active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
                  )
                  loss = loss_fct(active_logits, active_labels)
              else:
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
  
          if not return_dict:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TokenClassifierOutput(
              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
      layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """,
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  class {{cookiecutter.camelcase_modelname}}ForQuestionAnswering({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          config.num_labels = 2
          self.num_labels = config.num_labels
  
          self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
          self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @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=QuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          start_positions=None,
          end_positions=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          start_positions (:obj:`torch.LongTensor` 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:`torch.LongTensor` 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.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.{{cookiecutter.lowercase_modelname}}(
              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,
          )
  
          sequence_output = outputs[0]
  
          logits = self.qa_outputs(sequence_output)
          start_logits, end_logits = logits.split(1, dim=-1)
          start_logits = start_logits.squeeze(-1)
          end_logits = end_logits.squeeze(-1)
  
          total_loss = None
          if start_positions is not None and end_positions is not None:
              # If we are on multi-GPU, split add a dimension
              if len(start_positions.size()) > 1:
                  start_positions = start_positions.squeeze(-1)
              if len(end_positions.size()) > 1:
                  end_positions = end_positions.squeeze(-1)
              # sometimes the start/end positions are outside our model inputs, we ignore these terms
              ignored_index = start_logits.size(1)
              start_positions.clamp_(0, ignored_index)
              end_positions.clamp_(0, ignored_index)
  
              loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
              start_loss = loss_fct(start_logits, start_positions)
              end_loss = loss_fct(end_logits, end_positions)
              total_loss = (start_loss + end_loss) / 2
  
          if not return_dict:
              output = (start_logits, end_logits) + outputs[1:]
              return ((total_loss,) + output) if total_loss is not None else output
  
          return QuestionAnsweringModelOutput(
              loss=total_loss,
              start_logits=start_logits,
              end_logits=end_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  {% else %}
  import random
  from typing import Optional, Tuple
  
  import torch
  import torch.nn.functional as F
  from torch import nn
  from torch.nn import CrossEntropyLoss
  
  from ...activations import ACT2FN
  from ...file_utils import (
      add_code_sample_docstrings,
      add_end_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from ...modeling_outputs import (
      BaseModelOutput,
      BaseModelOutputWithPastAndCrossAttentions,
      Seq2SeqLMOutput,
      Seq2SeqModelOutput,
      Seq2SeqQuestionAnsweringModelOutput,
      Seq2SeqSequenceClassifierOutput,
  )
  from ...modeling_utils import PreTrainedModel
  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"
  
  
  {{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "{{cookiecutter.checkpoint_identifier}}",
      # See all {{cookiecutter.modelname}} models at https://huggingface.co/models?filter={{cookiecutter.lowercase_modelname}}
  ]
  
  
  def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
      """
      Shift input ids one token to the right.
      """
      shifted_input_ids = input_ids.new_zeros(input_ids.shape)
      shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
      shifted_input_ids[:, 0] = decoder_start_token_id
  
      assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
      # replace possible -100 values in labels by `pad_token_id`
      shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
  
      return shifted_input_ids
  
  
  def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
      """
      Make causal mask used for bi-directional self-attention.
      """
      bsz, tgt_len = input_ids_shape
      mask = torch.full((tgt_len, tgt_len), float("-inf"))
      mask_cond = torch.arange(mask.size(-1))
      mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
      mask = mask.to(dtype)
  
      if past_key_values_length > 0:
          mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
      return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
  
  
  def _expand_mask(
      mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None
  ):
      """
      Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
      """
      bsz, src_len = mask.size()
      tgt_len = tgt_len if tgt_len is not None else src_len
  
      expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
  
      inverted_mask = 1.0 - expanded_mask
  
      return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)
  
  
  def {{cookiecutter.camelcase_modelname}}LayerNorm(normalized_shape: torch.Size, eps: float = 1e-5, elementwise_affine: bool = True):
      if torch.cuda.is_available():
          try:
              from apex.normalization import FusedLayerNorm
  
              return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
          except ImportError:
              pass
      return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
  
  
  class {{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(nn.Embedding):
      """
      This module learns positional embeddings up to a fixed maximum size. 
      """
  
      def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
          assert padding_idx is not None, "`padding_idx` should not be None, but of type int"
          num_embeddings
          super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx)
  
      def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
          """`input_ids_shape` is expected to be [bsz x seqlen]."""
          bsz, seq_len = input_ids_shape[:2]
          positions = torch.arange(
              past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
          )
          return super().forward(positions)
  
  
  class {{cookiecutter.camelcase_modelname}}Attention(nn.Module):
      """Multi-headed attention from 'Attention Is All You Need' paper"""
  
      def __init__(
          self,
          embed_dim: int,
          num_heads: int,
          dropout: float = 0.0,
          is_decoder: bool = False,
          bias: bool = True,
      ):
          super().__init__()
          self.embed_dim = embed_dim
          self.num_heads = num_heads
          self.dropout = dropout
          self.head_dim = embed_dim // num_heads
          assert (
              self.head_dim * num_heads == self.embed_dim
          ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads})."
          self.scaling = self.head_dim ** -0.5
          self.is_decoder = is_decoder
  
          self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
  
      def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
          return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          key_value_states: Optional[torch.Tensor] = None,
          past_key_value: Optional[Tuple[torch.Tensor]] = None,
          attention_mask: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
      ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.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 = hidden_states.size()
  
          # 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 = torch.cat([past_key_value[0], key_states], dim=2)
              value_states = torch.cat([past_key_value[1], value_states], dim=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(torch.Tensor, torch.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(torch.Tensor, torch.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 = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
          key_states = key_states.view(*proj_shape)
          value_states = value_states.view(*proj_shape)
  
          src_len = key_states.size(1)
          attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
  
          assert attn_weights.size() == (
              bsz * self.num_heads,
              tgt_len,
              src_len,
          ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
  
          if attention_mask is not None:
              assert attention_mask.size() == (
                  bsz,
                  1,
                  tgt_len,
                  src_len,
              ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
              attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
              attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
  
          attn_weights = F.softmax(attn_weights, dim=-1)
  
          if output_attentions:
              # this operation is a bit akward, but it's required to
              # make sure that attn_weights keeps its gradient.
              # In order to do so, attn_weights have to reshaped
              # twice and have to be reused in the following
              attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
              attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
          else:
              attn_weights_reshaped = None
  
          attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training)
  
          attn_output = torch.bmm(attn_probs, value_states)
  
          assert attn_output.size() == (
              bsz * self.num_heads,
              tgt_len,
              self.head_dim,
          ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
  
          attn_output = (
              attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
              .transpose(1, 2)
              .reshape(bsz, tgt_len, embed_dim)
          )
  
          attn_output = self.out_proj(attn_output)
  
          return attn_output, attn_weights_reshaped, past_key_value
  
  
  class {{cookiecutter.camelcase_modelname}}EncoderLayer(nn.Module):
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config):
          super().__init__()
          self.embed_dim = config.d_model
          self.self_attn = {{cookiecutter.camelcase_modelname}}Attention(
              embed_dim=self.embed_dim,
              num_heads=config.encoder_attention_heads,
              dropout=config.attention_dropout,
          )
          self.self_attn_layer_norm = {{cookiecutter.camelcase_modelname}}LayerNorm(self.embed_dim)
          self.dropout = config.dropout
          self.activation_fn = ACT2FN[config.activation_function]
          self.activation_dropout = config.activation_dropout
          self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
          self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
          self.final_layer_norm = {{cookiecutter.camelcase_modelname}}LayerNorm(self.embed_dim)
  
      def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, output_attentions: bool = False):
          """
          Args:
              hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
              attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                  `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              output_attentions (:obj:`bool`): Whether the base model outputs attentions.
                  This requires the attentions tensor to be reshaped in this function.
          """
          residual = hidden_states
          hidden_states, attn_weights, _ = self.self_attn(
              hidden_states=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
          )
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.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 = F.dropout(hidden_states, p=self.activation_dropout, training=self.training)
          hidden_states = self.fc2(hidden_states)
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
          hidden_states = self.final_layer_norm(hidden_states)
  
          if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
              clamp_value = torch.finfo(hidden_states.dtype).max - 1000
              hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
          return hidden_states, attn_weights
  
  
  class {{cookiecutter.camelcase_modelname}}DecoderLayer(nn.Module):
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config):
          super().__init__()
          self.embed_dim = config.d_model
  
          self.self_attn = {{cookiecutter.camelcase_modelname}}Attention(
              embed_dim=self.embed_dim,
              num_heads=config.decoder_attention_heads,
              dropout=config.attention_dropout,
              is_decoder=True,
          )
          self.dropout = config.dropout
          self.activation_fn = ACT2FN[config.activation_function]
          self.activation_dropout = config.activation_dropout
  
          self.self_attn_layer_norm = {{cookiecutter.camelcase_modelname}}LayerNorm(self.embed_dim)
          self.encoder_attn = {{cookiecutter.camelcase_modelname}}Attention(
              self.embed_dim,
              config.decoder_attention_heads,
              dropout=config.attention_dropout,
              is_decoder=True,
          )
          self.encoder_attn_layer_norm = {{cookiecutter.camelcase_modelname}}LayerNorm(self.embed_dim)
          self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
          self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
          self.final_layer_norm = {{cookiecutter.camelcase_modelname}}LayerNorm(self.embed_dim)
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          attention_mask: Optional[torch.Tensor] = None,
          encoder_hidden_states: Optional[torch.Tensor] = None,
          encoder_attention_mask: Optional[torch.Tensor] = None,
          past_key_value: Optional[Tuple[torch.Tensor]] = None,
          output_attentions: Optional[torch.Tensor] = False,
      ):
          """
          Args:
              hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
              attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                  `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              encoder_hidden_states (:obj:`torch.FloatTensor`): cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
              encoder_attention_mask (:obj:`torch.FloatTensor`): 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(torch.FloatTensor)`): cached past key and value projection states
              output_attentions (:obj:`bool`): Whether the base model outputs attentions.
                  This requires the attentions tensor to be reshaped in this function.
          """
          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,
              output_attentions=output_attentions,
          )
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
          hidden_states = self.self_attn_layer_norm(hidden_states)
  
          # Cross-Attention Block
          cross_attn_present_key_value = None
          cross_attn_weights = 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_weights, 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,
                  output_attentions=output_attentions,
              )
              hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.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 = F.dropout(hidden_states, p=self.activation_dropout, training=self.training)
          hidden_states = self.fc2(hidden_states)
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
          hidden_states = self.final_layer_norm(hidden_states)
  
          return (
              hidden_states,
              self_attn_weights,
              present_key_value,
              cross_attn_weights,
          )
  
  
  # Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->{{cookiecutter.camelcase_modelname}}
  class {{cookiecutter.camelcase_modelname}}ClassificationHead(nn.Module):
      """Head for sentence-level classification tasks."""
  
      def __init__(
          self,
          input_dim: int,
          inner_dim: int,
          num_classes: int,
          pooler_dropout: float,
      ):
          super().__init__()
          self.dense = nn.Linear(input_dim, inner_dim)
          self.dropout = nn.Dropout(p=pooler_dropout)
          self.out_proj = nn.Linear(inner_dim, num_classes)
  
      def forward(self, hidden_states: torch.Tensor):
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.dense(hidden_states)
          hidden_states = torch.tanh(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.out_proj(hidden_states)
          return hidden_states
  
  
  class {{cookiecutter.camelcase_modelname}}PreTrainedModel(PreTrainedModel):
      config_class = {{cookiecutter.camelcase_modelname}}Config
      base_model_prefix = "model"
  
      def _init_weights(self, module):
          std = self.config.init_std
          if isinstance(module, nn.Linear):
              module.weight.data.normal_(mean=0.0, std=std)
              if module.bias is not None:
                  module.bias.data.zero_()
          elif isinstance(module, nn.Embedding):
              module.weight.data.normal_(mean=0.0, std=std)
              if module.padding_idx is not None:
                  module.weight.data[module.padding_idx].zero_()
  
      @property
      def dummy_inputs(self):
          pad_token = self.config.pad_token_id
          input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
          dummy_inputs = {
              "attention_mask": input_ids.ne(pad_token),
              "input_ids": input_ids,
          }
          return dummy_inputs
  
  
  {{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r"""
      This model inherits from :class:`~transformers.PreTrainedModel`. 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 PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
      subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
      general usage and behavior.
  
      Parameters:
          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}}_GENERATION_EXAMPLE = r"""
      Summarization example::
  
          >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}ForConditionalGeneration, {{cookiecutter.camelcase_modelname}}Config
  
          >>> model = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
          >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
  
          >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
          >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
  
          >>> # Generate Summary
          >>> summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
          >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
  """
  
  {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`torch.LongTensor` 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:`torch.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>`__
          decoder_input_ids (:obj:`torch.LongTensor` 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 :obj:`input_ids` to the right, following the paper.
          decoder_attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`):
              Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
              also be used by default.
  
              If you want to change padding behavior, you should read :func:`modeling_{{cookiecutter.lowercase_modelname}}._prepare_decoder_inputs` and
              modify to your needs. See diagram 1 in `the paper <https://arxiv.org/abs/1910.13461>`__ for more
              information on the default strategy.
          encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`):
              Tuple consists of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`:
              :obj:`attentions`) :obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`,
              `optional`) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
              cross-attention of the decoder.
          past_key_values (:obj:`Tuple[Tuple[torch.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:`torch.FloatTensor` 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.
          decoder_inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`):
              Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded
              representation. If :obj:`past_key_values` is used, optionally only the last :obj:`decoder_inputs_embeds`
              have to be input (see :obj:`past_key_values`). This is useful if you want more control over how to convert
              :obj:`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
  
              If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset, :obj:`decoder_inputs_embeds`
              takes the value of :obj:`inputs_embeds`.
          use_cache (:obj:`bool`, `optional`):
              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`).
          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.
  """
  
  
  class {{cookiecutter.camelcase_modelname}}Encoder({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      """
      Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
      :class:`{{cookiecutter.camelcase_modelname}}EncoderLayer`.
  
      Args:
          config: {{cookiecutter.camelcase_modelname}}Config
          embed_tokens (torch.nn.Embedding): output embedding
      """
  
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[nn.Embedding] = None):
          super().__init__(config)
  
          self.dropout = config.dropout
          self.layerdrop = config.encoder_layerdrop
  
          embed_dim = config.d_model
          self.padding_idx = config.pad_token_id
          self.max_source_positions = config.max_position_embeddings
  
          if embed_tokens is not None:
              self.embed_tokens = embed_tokens
          else:
              self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
  
          self.embed_positions = {{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
              config.max_position_embeddings,
              embed_dim,
              self.padding_idx,
          )
          self.layers = nn.ModuleList([{{cookiecutter.camelcase_modelname}}EncoderLayer(config) for _ in range(config.encoder_layers)])
          self.layernorm_embedding = {{cookiecutter.camelcase_modelname}}LayerNorm(embed_dim)
  
          self.init_weights()
  
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          Args:
              input_ids (:obj:`torch.LongTensor` 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:`torch.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:`torch.FloatTensor` 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.
          """
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          # retrieve input_ids and inputs_embeds
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
          elif input_ids is not None:
              input_shape = input_ids.size()
              input_ids = input_ids.view(-1, input_shape[-1])
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-1]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          if inputs_embeds is None:
              inputs_embeds = self.embed_tokens(input_ids)
  
          embed_pos = self.embed_positions(input_shape)
  
          hidden_states = inputs_embeds + embed_pos
          hidden_states = self.layernorm_embedding(hidden_states)
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
  
          # expand attention_mask
          if attention_mask is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
  
          encoder_states = () if output_hidden_states else None
          all_attentions = () if output_attentions else None
          for encoder_layer in self.layers:
              if 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 self.training and (dropout_probability < self.layerdrop):  # skip the layer
                  attn = None
              else:
                  hidden_states, attn = encoder_layer(hidden_states, attention_mask, output_attentions=output_attentions)
  
              if output_attentions:
                  all_attentions = all_attentions + (attn,)
  
          if output_hidden_states:
              encoder_states = encoder_states + (hidden_states,)
  
          if not return_dict:
              return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
          return BaseModelOutput(
              last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
          )
  
  
  class {{cookiecutter.camelcase_modelname}}Decoder({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      """
      Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`{{cookiecutter.camelcase_modelname}}DecoderLayer`
  
      Args:
          config: {{cookiecutter.camelcase_modelname}}Config
          embed_tokens (torch.nn.Embedding): output embedding
      """
  
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[nn.Embedding] = None):
          super().__init__(config)
          self.dropout = config.dropout
          self.layerdrop = config.decoder_layerdrop
          self.padding_idx = config.pad_token_id
          self.max_target_positions = config.max_position_embeddings
  
          if embed_tokens is not None:
              self.embed_tokens = embed_tokens
          else:
              self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
  
          self.embed_positions = {{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
              config.max_position_embeddings,
              config.d_model,
              self.padding_idx,
          )
          self.layers = nn.ModuleList([{{cookiecutter.camelcase_modelname}}DecoderLayer(config) for _ in range(config.decoder_layers)])
          self.layernorm_embedding = {{cookiecutter.camelcase_modelname}}LayerNorm(config.d_model)
  
          self.init_weights()
  
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_values=None,
          inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          Args:
              input_ids (:obj:`torch.LongTensor` 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:`torch.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:`torch.FloatTensor` 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:`torch.LongTensor` 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[torch.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:`torch.FloatTensor` 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.
          """
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          use_cache = use_cache if use_cache is not None else self.config.use_cache
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          # retrieve input_ids and inputs_embeds
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
          elif input_ids is not None:
              input_shape = input_ids.size()
              input_ids = input_ids.view(-1, input_shape[-1])
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-1]
          else:
              raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
  
          # past_key_values_length
          past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
  
          if inputs_embeds is None:
              inputs_embeds = self.embed_tokens(input_ids)
  
          # create causal mask
          # [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, inputs_embeds.dtype, past_key_values_length=past_key_values_length
              ).to(self.device)
  
          if attention_mask is not None and combined_attention_mask is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              combined_attention_mask = combined_attention_mask + _expand_mask(
                  attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
              )
  
          # expand encoder attention mask
          if encoder_hidden_states is not None and encoder_attention_mask is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
  
          # embed positions
          positions = self.embed_positions(input_shape, past_key_values_length)
  
          hidden_states = inputs_embeds + positions
          hidden_states = self.layernorm_embedding(hidden_states)
  
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
  
          # decoder layers
          all_hidden_states = () if output_hidden_states else None
          all_self_attns = () if output_attentions else None
          all_cross_attentions = () if output_attentions else None
          next_decoder_cache = () if use_cache else None
          for idx, decoder_layer in enumerate(self.layers):
              # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
              if output_hidden_states:
                  all_hidden_states += (hidden_states,)
              dropout_probability = random.uniform(0, 1)
              if self.training and (dropout_probability < self.layerdrop):
                  continue
  
              past_key_value = past_key_values[idx] if past_key_values is not None else None
  
              hidden_states, layer_self_attn, present_key_value, layer_cross_attn = decoder_layer(
                  hidden_states,
                  attention_mask=combined_attention_mask,
                  encoder_hidden_states=encoder_hidden_states,
                  encoder_attention_mask=encoder_attention_mask,
                  past_key_value=past_key_value,
                  output_attentions=output_attentions,
              )
  
              if use_cache:
                  next_decoder_cache += (present_key_value,)
  
              if output_attentions:
                  all_self_attns += (layer_self_attn,)
                  all_cross_attentions += (layer_cross_attn,)
  
          # add hidden states from the last decoder layer
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
  
          next_cache = next_decoder_cache if use_cache else None
          if not return_dict:
              return tuple(
                  v
                  for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
                  if v is not None
              )
          return BaseModelOutputWithPastAndCrossAttentions(
              last_hidden_state=hidden_states,
              past_key_values=next_cache,
              hidden_states=all_hidden_states,
              attentions=all_self_attns,
              cross_attentions=all_cross_attentions,
          )
  
  
  @add_start_docstrings(
      "The bare {{cookiecutter.modelname}} Model outputting raw hidden-states without any specific head on top.",
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  class {{cookiecutter.camelcase_modelname}}Model({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config):
          super().__init__(config)
  
          padding_idx, vocab_size = config.pad_token_id, config.vocab_size
          self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
  
          self.encoder = {{cookiecutter.camelcase_modelname}}Encoder(config, self.shared)
          self.decoder = {{cookiecutter.camelcase_modelname}}Decoder(config, self.shared)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.shared
  
      def set_input_embeddings(self, value):
          self.shared = value
          self.encoder.embed_tokens = self.shared
          self.decoder.embed_tokens = self.shared
  
      def get_encoder(self):
          return self.encoder
  
      def get_decoder(self):
          return self.decoder
  
      @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="{{cookiecutter.checkpoint_identifier}}",
          output_type=Seq2SeqModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=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,
      ):
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          use_cache = use_cache if use_cache is not None else self.config.use_cache
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          if encoder_outputs is None:
              encoder_outputs = self.encoder(
                  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,
              )
          # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=False
          elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
              encoder_outputs = BaseModelOutput(
                  last_hidden_state=encoder_outputs[0],
                  hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                  attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
              )
  
          # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
          decoder_outputs = self.decoder(
              input_ids=decoder_input_ids,
              attention_mask=decoder_attention_mask,
              encoder_hidden_states=encoder_outputs[0],
              encoder_attention_mask=attention_mask,
              past_key_values=past_key_values,
              inputs_embeds=decoder_inputs_embeds,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          if not return_dict:
              return decoder_outputs + encoder_outputs
  
          return Seq2SeqModelOutput(
              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,
              cross_attentions=decoder_outputs.cross_attentions,
              encoder_last_hidden_state=encoder_outputs.last_hidden_state,
              encoder_hidden_states=encoder_outputs.hidden_states,
              encoder_attentions=encoder_outputs.attentions,
          )
  
  
  @add_start_docstrings(
      "The {{cookiecutter.modelname}} Model with a language modeling head. Can be used for summarization.", {{cookiecutter.uppercase_modelname}}_START_DOCSTRING
  )
  class {{cookiecutter.camelcase_modelname}}ForConditionalGeneration({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      base_model_prefix = "model"
      _keys_to_ignore_on_load_missing = [
          r"final_logits_bias",
          r"encoder\.version",
          r"decoder\.version",
          r"lm_head\.weight",
      ]
  
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config):
          super().__init__(config)
          self.model = {{cookiecutter.camelcase_modelname}}Model(config)
          self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
          self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
  
          self.init_weights()
  
      def get_encoder(self):
          return self.model.get_encoder()
  
      def get_decoder(self):
          return self.model.get_decoder()
  
      def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
          new_embeddings = super().resize_token_embeddings(new_num_tokens)
          self._resize_final_logits_bias(new_num_tokens)
          return new_embeddings
  
      def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
          old_num_tokens = self.final_logits_bias.shape[-1]
          if new_num_tokens <= old_num_tokens:
              new_bias = self.final_logits_bias[:, :new_num_tokens]
          else:
              extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
              new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
          self.register_buffer("final_logits_bias", new_bias)
  
      def get_output_embeddings(self):
          return self.lm_head
  
      def set_output_embeddings(self, new_embeddings):
          self.lm_head = new_embeddings
  
      @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
      @add_end_docstrings({{cookiecutter.uppercase_modelname}}_GENERATION_EXAMPLE)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          past_key_values=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          labels=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the masked language modeling loss. Indices should either be in ``[0, ...,
              config.vocab_size]`` or -100 (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]``.
  
          Returns:
  
          Conditional generation example::
  
              >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}ForConditionalGeneration
              >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
              >>> TXT = "My friends are <mask> but they eat too many carbs."
  
              >>> model = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
              >>> input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
              >>> logits = model(input_ids).logits
  
              >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
              >>> probs = logits[0, masked_index].softmax(dim=0)
              >>> values, predictions = probs.topk(5)
  
              >>> tokenizer.decode(predictions).split()
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          if labels is not None:
              if decoder_input_ids is None:
                  decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
  
          outputs = self.model(
              input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              encoder_outputs=encoder_outputs,
              decoder_attention_mask=decoder_attention_mask,
              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,
          )
          lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
  
          masked_lm_loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
  
          if not return_dict:
              output = (lm_logits,) + outputs[1:]
              return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
  
          return Seq2SeqLMOutput(
              loss=masked_lm_loss,
              logits=lm_logits,
              past_key_values=outputs.past_key_values,
              decoder_hidden_states=outputs.decoder_hidden_states,
              decoder_attentions=outputs.decoder_attentions,
              cross_attentions=outputs.cross_attentions,
              encoder_last_hidden_state=outputs.encoder_last_hidden_state,
              encoder_hidden_states=outputs.encoder_hidden_states,
              encoder_attentions=outputs.encoder_attentions,
          )
  
      def prepare_inputs_for_generation(
          self, decoder_input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
      ):
          # cut decoder_input_ids if past is used
          if past is not None:
              decoder_input_ids = decoder_input_ids[:, -1:]
  
          return {
              "input_ids": None,  # encoder_outputs is defined. input_ids not needed
              "encoder_outputs": encoder_outputs,
              "past_key_values": past,
              "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):
          reordered_past = ()
          for layer_past in past:
              reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
          return reordered_past
  
  
  @add_start_docstrings(
      """
      {{cookiecutter.camelcase_modelname}} model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
      tasks.
      """,
      {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
  )
  class {{cookiecutter.camelcase_modelname}}ForSequenceClassification({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
          super().__init__(config, **kwargs)
          self.model = {{cookiecutter.camelcase_modelname}}Model(config)
          self.classification_head = {{cookiecutter.camelcase_modelname}}ClassificationHead(
              config.d_model,
              config.d_model,
              config.num_labels,
              config.classifier_dropout,
          )
          self.model._init_weights(self.classification_head.dense)
          self.model._init_weights(self.classification_head.out_proj)
  
      @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="{{cookiecutter.checkpoint_identifier}}",
          output_type=Seq2SeqSequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          labels=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` 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 classification loss is computed (Cross-Entropy).
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
          if labels is not None:
              use_cache = False
  
          if input_ids is None and inputs_embeds is not None:
              raise NotImplementedError(
                  f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
              )
  
          outputs = self.model(
              input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              encoder_outputs=encoder_outputs,
              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,
          )
          hidden_states = outputs[0]  # last hidden state
  
          eos_mask = input_ids.eq(self.config.eos_token_id)
  
          if len(torch.unique(eos_mask.sum(1))) > 1:
              raise ValueError("All examples must have the same number of <eos> tokens.")
          sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
              :, -1, :
          ]
          logits = self.classification_head(sentence_representation)
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
  
          if not return_dict:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return Seq2SeqSequenceClassifierOutput(
              loss=loss,
              logits=logits,
              past_key_values=outputs.past_key_values,
              decoder_hidden_states=outputs.decoder_hidden_states,
              decoder_attentions=outputs.decoder_attentions,
              cross_attentions=outputs.cross_attentions,
              encoder_last_hidden_state=outputs.encoder_last_hidden_state,
              encoder_hidden_states=outputs.encoder_hidden_states,
              encoder_attentions=outputs.encoder_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 {{cookiecutter.camelcase_modelname}}ForQuestionAnswering({{cookiecutter.camelcase_modelname}}PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          config.num_labels = 2
          self.num_labels = config.num_labels
  
          self.model = {{cookiecutter.camelcase_modelname}}Model(config)
          self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
  
          self.model._init_weights(self.qa_outputs)
  
      @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="{{cookiecutter.checkpoint_identifier}}",
          output_type=Seq2SeqQuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          start_positions=None,
          end_positions=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for position (index) of the start of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
              are not taken into account for computing the loss.
          end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for position (index) of the end of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
              are not taken into account for computing the loss.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
          if start_positions is not None and end_positions is not None:
              use_cache = False
  
          outputs = self.model(
              input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              encoder_outputs=encoder_outputs,
              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,
          )
  
          sequence_output = outputs[0]
  
          logits = self.qa_outputs(sequence_output)
          start_logits, end_logits = logits.split(1, dim=-1)
          start_logits = start_logits.squeeze(-1)
          end_logits = end_logits.squeeze(-1)
  
          total_loss = None
          if start_positions is not None and end_positions is not None:
              # If we are on multi-GPU, split add a dimension
              if len(start_positions.size()) > 1:
                  start_positions = start_positions.squeeze(-1)
              if len(end_positions.size()) > 1:
                  end_positions = end_positions.squeeze(-1)
              # sometimes the start/end positions are outside our model inputs, we ignore these terms
              ignored_index = start_logits.size(1)
              start_positions.clamp_(0, ignored_index)
              end_positions.clamp_(0, ignored_index)
  
              loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
              start_loss = loss_fct(start_logits, start_positions)
              end_loss = loss_fct(end_logits, end_positions)
              total_loss = (start_loss + end_loss) / 2
  
          if not return_dict:
              output = (
                  start_logits,
                  end_logits,
              ) + outputs[1:]
              return ((total_loss,) + output) if total_loss is not None else output
  
          return Seq2SeqQuestionAnsweringModelOutput(
              loss=total_loss,
              start_logits=start_logits,
              end_logits=end_logits,
              past_key_values=outputs.past_key_values,
              decoder_hidden_states=outputs.decoder_hidden_states,
              decoder_attentions=outputs.decoder_attentions,
              cross_attentions=outputs.cross_attentions,
              encoder_last_hidden_state=outputs.encoder_last_hidden_state,
              encoder_hidden_states=outputs.encoder_hidden_states,
              encoder_attentions=outputs.encoder_attentions,
          )
  {% endif -%}
  