text: # coding=utf-8
  # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """PyTorch OpenAI GPT-2 model."""
  
  
  import logging
  import os
  
  import torch
  import torch.nn as nn
  from torch.nn import CrossEntropyLoss
  
  from .activations import ACT2FN
  from .configuration_gpt2 import GPT2Config
  from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
  from .modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, prune_conv1d_layer
  
  
  logger = logging.getLogger(__name__)
  
  GPT2_PRETRAINED_MODEL_ARCHIVE_MAP = {
      "gpt2": "https://cdn.huggingface.co/gpt2-pytorch_model.bin",
      "gpt2-medium": "https://cdn.huggingface.co/gpt2-medium-pytorch_model.bin",
      "gpt2-large": "https://cdn.huggingface.co/gpt2-large-pytorch_model.bin",
      "gpt2-xl": "https://cdn.huggingface.co/gpt2-xl-pytorch_model.bin",
      "distilgpt2": "https://cdn.huggingface.co/distilgpt2-pytorch_model.bin",
  }
  
  
  def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
      """ Load tf checkpoints in a pytorch model
      """
      try:
          import re
          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(gpt2_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.squeeze())
  
      for name, array in zip(names, arrays):
          name = name[6:]  # skip "model/"
          name = name.split("/")
          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] == "w" or scope_names[0] == "g":
                  pointer = getattr(pointer, "weight")
              elif scope_names[0] == "b":
                  pointer = getattr(pointer, "bias")
              elif scope_names[0] == "wpe" or scope_names[0] == "wte":
                  pointer = getattr(pointer, scope_names[0])
                  pointer = getattr(pointer, "weight")
              else:
                  pointer = getattr(pointer, scope_names[0])
              if len(scope_names) >= 2:
                  num = int(scope_names[1])
                  pointer = pointer[num]
          try:
              assert pointer.shape == array.shape
          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
  
  
  class Attention(nn.Module):
      def __init__(self, nx, n_ctx, config, scale=False):
          super().__init__()
          self.output_attentions = config.output_attentions
  
          n_state = nx  # in Attention: n_state=768 (nx=n_embd)
          # [switch nx => n_state from Block to Attention to keep identical to TF implem]
          assert n_state % config.n_head == 0
          self.register_buffer(
              "bias", torch.tril(torch.ones((n_ctx, n_ctx), dtype=torch.uint8)).view(1, 1, n_ctx, n_ctx)
          )
          self.register_buffer("masked_bias", torch.tensor(-1e4))
          self.n_head = config.n_head
          self.split_size = n_state
          self.scale = scale
  
          self.c_attn = Conv1D(n_state * 3, nx)
          self.c_proj = Conv1D(n_state, nx)
          self.attn_dropout = nn.Dropout(config.attn_pdrop)
          self.resid_dropout = nn.Dropout(config.resid_pdrop)
          self.pruned_heads = set()
  
      def prune_heads(self, heads):
          if len(heads) == 0:
              return
          mask = torch.ones(self.n_head, self.split_size // self.n_head)
          heads = set(heads) - self.pruned_heads  # Convert to set and emove already pruned heads
          for head in heads:
              # Compute how many pruned heads are before the head and move the index accordingly
              head = head - sum(1 if h < head else 0 for h in self.pruned_heads)
              mask[head] = 0
          mask = mask.view(-1).contiguous().eq(1)
          index = torch.arange(len(mask))[mask].long()
          index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
  
          # Prune conv1d layers
          self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
          self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
  
          # Update hyper params
          self.split_size = (self.split_size // self.n_head) * (self.n_head - len(heads))
          self.n_head = self.n_head - len(heads)
          self.pruned_heads = self.pruned_heads.union(heads)
  
      def _attn(self, q, k, v, attention_mask=None, head_mask=None):
          w = torch.matmul(q, k)
          if self.scale:
              w = w / (float(v.size(-1)) ** 0.5)
          nd, ns = w.size(-2), w.size(-1)
          mask = self.bias[:, :, ns - nd : ns, :ns]
          w = torch.where(mask.bool(), w, self.masked_bias.to(w.dtype))
  
          if attention_mask is not None:
              # Apply the attention mask
              w = w + attention_mask
  
          w = nn.Softmax(dim=-1)(w)
          w = self.attn_dropout(w)
  
          # Mask heads if we want to
          if head_mask is not None:
              w = w * head_mask
  
          outputs = [torch.matmul(w, v)]
          if self.output_attentions:
              outputs.append(w)
          return outputs
  
      def merge_heads(self, x):
          x = x.permute(0, 2, 1, 3).contiguous()
          new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
          return x.view(*new_x_shape)  # in Tensorflow implem: fct merge_states
  
      def split_heads(self, x, k=False):
          new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
          x = x.view(*new_x_shape)  # in Tensorflow implem: fct split_states
          if k:
              return x.permute(0, 2, 3, 1)  # (batch, head, head_features, seq_length)
          else:
              return x.permute(0, 2, 1, 3)  # (batch, head, seq_length, head_features)
  
      def forward(self, x, layer_past=None, attention_mask=None, head_mask=None, use_cache=False):
          x = self.c_attn(x)
          query, key, value = x.split(self.split_size, dim=2)
          query = self.split_heads(query)
          key = self.split_heads(key, k=True)
          value = self.split_heads(value)
          if layer_past is not None:
              past_key, past_value = layer_past[0].transpose(-2, -1), layer_past[1]  # transpose back cf below
              key = torch.cat((past_key, key), dim=-1)
              value = torch.cat((past_value, value), dim=-2)
  
          if use_cache is True:
              present = torch.stack((key.transpose(-2, -1), value))  # transpose to have same shapes for stacking
          else:
              present = (None,)
  
          attn_outputs = self._attn(query, key, value, attention_mask, head_mask)
          a = attn_outputs[0]
  
          a = self.merge_heads(a)
          a = self.c_proj(a)
          a = self.resid_dropout(a)
  
          outputs = [a, present] + attn_outputs[1:]
          return outputs  # a, present, (attentions)
  
  
  class MLP(nn.Module):
      def __init__(self, n_state, config):  # in MLP: n_state=3072 (4 * n_embd)
          super().__init__()
          nx = config.n_embd
          self.c_fc = Conv1D(n_state, nx)
          self.c_proj = Conv1D(nx, n_state)
          self.act = ACT2FN[config.activation_function]
          self.dropout = nn.Dropout(config.resid_pdrop)
  
      def forward(self, x):
          h = self.act(self.c_fc(x))
          h2 = self.c_proj(h)
          return self.dropout(h2)
  
  
  class Block(nn.Module):
      def __init__(self, n_ctx, config, scale=False):
          super().__init__()
          nx = config.n_embd
          self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
          self.attn = Attention(nx, n_ctx, config, scale)
          self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
          self.mlp = MLP(4 * nx, config)
  
      def forward(self, x, layer_past=None, attention_mask=None, head_mask=None, use_cache=False):
          output_attn = self.attn(
              self.ln_1(x),
              layer_past=layer_past,
              attention_mask=attention_mask,
              head_mask=head_mask,
              use_cache=use_cache,
          )
          a = output_attn[0]  # output_attn: a, present, (attentions)
  
          x = x + a
          m = self.mlp(self.ln_2(x))
          x = x + m
  
          outputs = [x] + output_attn[1:]
          return outputs  # x, present, (attentions)
  
  
  class GPT2PreTrainedModel(PreTrainedModel):
      """ An abstract class to handle weights initialization and
          a simple interface for downloading and loading pretrained models.
      """
  
      config_class = GPT2Config
      pretrained_model_archive_map = GPT2_PRETRAINED_MODEL_ARCHIVE_MAP
      load_tf_weights = load_tf_weights_in_gpt2
      base_model_prefix = "transformer"
  
      def __init__(self, *inputs, **kwargs):
          super().__init__(*inputs, **kwargs)
  
      def _init_weights(self, module):
          """ Initialize the weights.
          """
          if isinstance(module, (nn.Linear, nn.Embedding, Conv1D)):
              # 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)
              if isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
                  module.bias.data.zero_()
          elif isinstance(module, nn.LayerNorm):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0)
  
  
  GPT2_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.GPT2Config`): 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.
  """
  
  GPT2_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`):
              :obj:`input_ids_length` = ``sequence_length`` if ``past`` is ``None`` else ``past[0].shape[-2]`` (``sequence_length`` of input past key value states).
              Indices of input sequence tokens in the vocabulary.
  
              If `past` is used, only `input_ids` that do not have their past calculated should be passed as `input_ids`.
  
              Indices can be obtained using :class:`transformers.GPT2Tokenizer`.
              See :func:`transformers.PreTrainedTokenizer.encode` and
              :func:`transformers.PreTrainedTokenizer.encode_plus` for details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
  
          past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
              Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
              (see `past` output below). Can be used to speed up sequential decoding.
              The `input_ids` which have their past given to this model should not be passed as `input_ids` as they have already been computed.
          attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Mask to avoid performing attention on padding token indices.
              Mask values selected in ``[0, 1]``:
              ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`, `optional`, defaults to :obj:`None`):
              `input_ids_length` = `sequence_length if `past` is None else 1
              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:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              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`, defaults to :obj:`None`):
              Mask to nullify selected heads of the self-attention modules.
              Mask values selected in ``[0, 1]``:
              :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
          inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
              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.
              If `past` is used, optionally only the last `inputs_embeds` have to be input (see `past`).
          use_cache (:obj:`bool`):
              If `use_cache` is True, `past` key value states are returned and can be used to speed up decoding (see `past`). Defaults to `True`.
  """
  
  
  @add_start_docstrings(
      "The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.",
      GPT2_START_DOCSTRING,
  )
  class GPT2Model(GPT2PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.output_hidden_states = config.output_hidden_states
          self.output_attentions = config.output_attentions
  
          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
          self.wpe = nn.Embedding(config.n_positions, config.n_embd)
          self.drop = nn.Dropout(config.embd_pdrop)
          self.h = nn.ModuleList([Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
          self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.wte
  
      def set_input_embeddings(self, new_embeddings):
          self.wte = new_embeddings
  
      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}
          """
          for layer, heads in heads_to_prune.items():
              self.h[layer].attn.prune_heads(heads)
  
      @add_start_docstrings_to_callable(GPT2_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          past=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          use_cache=True,
      ):
          r"""
      Return:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
          last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the last layer of the model.
              If `past` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
          past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
              Contains pre-computed hidden-states (key and values in the attention blocks).
              Can be used (see `past` input) to speed up sequential decoding.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
      Examples::
  
          from transformers import GPT2Tokenizer, GPT2Model
          import torch
  
          tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
          model = GPT2Model.from_pretrained('gpt2')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids)
          last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
  
          """
  
          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])
              batch_size = input_ids.shape[0]
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-1]
              batch_size = inputs_embeds.shape[0]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          if token_type_ids is not None:
              token_type_ids = token_type_ids.view(-1, input_shape[-1])
          if position_ids is not None:
              position_ids = position_ids.view(-1, input_shape[-1])
  
          if past is None:
              past_length = 0
              past = [None] * len(self.h)
          else:
              past_length = past[0][0].size(-2)
          if position_ids is None:
              device = input_ids.device if input_ids is not None else inputs_embeds.device
              position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
              position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
  
          # Attention mask.
          if attention_mask is not None:
              assert batch_size > 0, "batch_size has to be defined and > 0"
              attention_mask = attention_mask.view(batch_size, -1)
              # We create a 3D attention mask from a 2D tensor mask.
              # Sizes are [batch_size, 1, 1, to_seq_length]
              # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
              # this attention mask is more simple than the triangular masking of causal attention
              # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
              attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
  
              # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
              # masked positions, this operation will create a tensor which is 0.0 for
              # positions we want to attend and -10000.0 for masked positions.
              # Since we are adding it to the raw scores before the softmax, this is
              # effectively the same as removing these entirely.
              attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
              attention_mask = (1.0 - attention_mask) * -10000.0
  
          # Prepare head mask if needed
          # 1.0 in head_mask indicate we keep the head
          # attention_probs has shape bsz x n_heads x N x N
          # head_mask has shape n_layer x batch x n_heads x N x N
          head_mask = self.get_head_mask(head_mask, self.config.n_layer)
  
          if inputs_embeds is None:
              inputs_embeds = self.wte(input_ids)
          position_embeds = self.wpe(position_ids)
          if token_type_ids is not None:
              token_type_embeds = self.wte(token_type_ids)
          else:
              token_type_embeds = 0
          hidden_states = inputs_embeds + position_embeds + token_type_embeds
          hidden_states = self.drop(hidden_states)
  
          output_shape = input_shape + (hidden_states.size(-1),)
  
          presents = ()
          all_attentions = []
          all_hidden_states = ()
          for i, (block, layer_past) in enumerate(zip(self.h, past)):
              if self.output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
  
              outputs = block(
                  hidden_states,
                  layer_past=layer_past,
                  attention_mask=attention_mask,
                  head_mask=head_mask[i],
                  use_cache=use_cache,
              )
  
              hidden_states, present = outputs[:2]
              if use_cache is True:
                  presents = presents + (present,)
  
              if self.output_attentions:
                  all_attentions.append(outputs[2])
  
          hidden_states = self.ln_f(hidden_states)
  
          hidden_states = hidden_states.view(*output_shape)
          # Add last hidden state
          if self.output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          outputs = (hidden_states,)
          if use_cache is True:
              outputs = outputs + (presents,)
          if self.output_hidden_states:
              outputs = outputs + (all_hidden_states,)
          if self.output_attentions:
              # let the number of heads free (-1) so we can extract attention even after head pruning
              attention_output_shape = input_shape[:-1] + (-1,) + all_attentions[0].shape[-2:]
              all_attentions = tuple(t.view(*attention_output_shape) for t in all_attentions)
              outputs = outputs + (all_attentions,)
          return outputs  # last hidden state, (presents), (all hidden_states), (attentions)
  
  
  @add_start_docstrings(
      """The GPT2 Model transformer with a language modeling head on top
      (linear layer with weights tied to the input embeddings). """,
      GPT2_START_DOCSTRING,
  )
  class GPT2LMHeadModel(GPT2PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.transformer = GPT2Model(config)
          self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.lm_head
  
      def prepare_inputs_for_generation(self, input_ids, past, **kwargs):
          # only last token for inputs_ids if past is defined in kwargs
          if past:
              input_ids = input_ids[:, -1].unsqueeze(-1)
  
          return {"input_ids": input_ids, "past": past, "use_cache": kwargs["use_cache"]}
  
      @add_start_docstrings_to_callable(GPT2_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          past=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          use_cache=True,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Labels for language modeling.
              Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
              Indices are selected in ``[-100, 0, ..., config.vocab_size]``
              All labels set to ``-100`` are ignored (masked), the loss is only
              computed for labels in ``[0, ..., config.vocab_size]``
  
      Return:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
          loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided)
              Language modeling loss.
          prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
              Contains pre-computed hidden-states (key and values in the attention blocks).
              Can be used (see `past` input) to speed up sequential decoding.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
      Examples::
  
          import torch
          from transformers import GPT2Tokenizer, GPT2LMHeadModel
  
          tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
          model = GPT2LMHeadModel.from_pretrained('gpt2')
  
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, labels=input_ids)
          loss, logits = outputs[:2]
  
          """
          transformer_outputs = self.transformer(
              input_ids,
              past=past,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              use_cache=use_cache,
          )
          hidden_states = transformer_outputs[0]
  
          lm_logits = self.lm_head(hidden_states)
  
          outputs = (lm_logits,) + transformer_outputs[1:]
          if labels is not None:
              # Shift so that tokens < n predict n
              shift_logits = lm_logits[..., :-1, :].contiguous()
              shift_labels = labels[..., 1:].contiguous()
              # Flatten the tokens
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
              outputs = (loss,) + outputs
  
          return outputs  # (loss), lm_logits, presents, (all hidden_states), (attentions)
  
  
  @add_start_docstrings(
      """The GPT2 Model transformer with a language modeling and a multiple-choice classification
      head on top e.g. for RocStories/SWAG tasks. The two heads are two linear layers.
      The language modeling head has its weights tied to the input embeddings,
      the classification head takes as input the input of a specified classification token index in the input sequence).
  """,
      GPT2_START_DOCSTRING,
  )
  class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          config.num_labels = 1
          self.transformer = GPT2Model(config)
          self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
          self.multiple_choice_head = SequenceSummary(config)
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.lm_head
  
      @add_start_docstrings_to_callable(GPT2_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          past=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          mc_token_ids=None,
          lm_labels=None,
          mc_labels=None,
          use_cache=True,
      ):
          r"""
          mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input)
              Index of the classification token in each input sequence.
              Selected in the range ``[0, input_ids.size(-1) - 1[``.
          lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`)
              Labels for language modeling.
              Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
              Indices are selected in ``[-1, 0, ..., config.vocab_size]``
              All labels set to ``-100`` are ignored (masked), the loss is only
              computed for labels in ``[0, ..., config.vocab_size]``
          mc_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size)`, `optional`, defaults to :obj:`None`)
              Labels for computing the multiple choice classification loss.
              Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
              of the input tensors. (see `input_ids` above)
  
      Return:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
          lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``lm_labels`` is provided):
              Language modeling loss.
          mc_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`multiple_choice_labels` is provided):
              Multiple choice classification loss.
          lm_prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          mc_prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
              Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
          past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
              Contains pre-computed hidden-states (key and values in the attention blocks).
              Can be used (see `past` input) to speed up sequential decoding.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
      Examples::
  
          import torch
          from transformers import GPT2Tokenizer, GPT2DoubleHeadsModel
  
          tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
          model = GPT2DoubleHeadsModel.from_pretrained('gpt2')
  
          # Add a [CLS] to the vocabulary (we should train it also!)
          tokenizer.add_special_tokens({'cls_token': '[CLS]'})
          model.resize_token_embeddings(len(tokenizer))  # Update the model embeddings with the new vocabulary size
          print(tokenizer.cls_token_id, len(tokenizer))  # The newly token the last token of the vocabulary
  
          choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
          encoded_choices = [tokenizer.encode(s) for s in choices]
          cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices]
  
          input_ids = torch.tensor(encoded_choices).unsqueeze(0)  # Batch size: 1, number of choices: 2
          mc_token_ids = torch.tensor([cls_token_location])  # Batch size: 1
  
          outputs = model(input_ids, mc_token_ids=mc_token_ids)
          lm_prediction_scores, mc_prediction_scores = outputs[:2]
  
          """
          transformer_outputs = self.transformer(
              input_ids,
              past=past,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              use_cache=use_cache,
          )
  
          hidden_states = transformer_outputs[0]
  
          lm_logits = self.lm_head(hidden_states)
          mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)
  
          outputs = (lm_logits, mc_logits) + transformer_outputs[1:]
          if mc_labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))
              outputs = (loss,) + outputs
          if lm_labels is not None:
              shift_logits = lm_logits[..., :-1, :].contiguous()
              shift_labels = lm_labels[..., 1:].contiguous()
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
              outputs = (loss,) + outputs
  
          return outputs  # (lm loss), (mc loss), lm logits, mc logits, presents, (all hidden_states), (attentions)
  