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 model."""
  
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import collections
  import json
  import logging
  import math
  import os
  import sys
  from io import open
  
  import torch
  import torch.nn as nn
  from torch.nn import CrossEntropyLoss
  from torch.nn.parameter import Parameter
  
  from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
  from .configuration_openai import OpenAIGPTConfig
  from .file_utils import add_start_docstrings
  
  logger = logging.getLogger(__name__)
  
  OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP = {"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-pytorch_model.bin"}
  
  
  def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
      """ Load tf pre-trained weights in a pytorch model (from NumPy arrays here)
      """
      import re
      import numpy as np
  
      if '.ckpt' in openai_checkpoint_folder_path:
          openai_checkpoint_folder_path = os.path.dirname(openai_checkpoint_folder_path)
  
      logger.info("Loading weights from {}".format(openai_checkpoint_folder_path))
  
      names = json.load(open(openai_checkpoint_folder_path + '/parameters_names.json', "r", encoding='utf-8'))
      shapes = json.load(open(openai_checkpoint_folder_path + '/params_shapes.json', "r", encoding='utf-8'))
      offsets = np.cumsum([np.prod(shape) for shape in shapes])
      init_params = [np.load(openai_checkpoint_folder_path + '/params_{}.npy'.format(n)) for n in range(10)]
      init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
      init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]
  
      # This was used when we had a single embedding matrix for positions and tokens
      # init_params[0] = np.concatenate([init_params[1], init_params[0]], 0)
      # del init_params[1]
      init_params = [arr.squeeze() for arr in init_params]
  
      try:
          assert model.tokens_embed.weight.shape == init_params[1].shape
          assert model.positions_embed.weight.shape == init_params[0].shape
      except AssertionError as e:
          e.args += (model.tokens_embed.weight.shape, init_params[1].shape)
          e.args += (model.positions_embed.weight.shape, init_params[0].shape)
          raise
  
      model.tokens_embed.weight.data = torch.from_numpy(init_params[1])
      model.positions_embed.weight.data = torch.from_numpy(init_params[0])
      names.pop(0)
      # Pop position and token embedding arrays
      init_params.pop(0)
      init_params.pop(0)
  
      for name, array in zip(names, init_params): # names[1:n_transfer], init_params[1:n_transfer]):
          name = name[6:]  # skip "model/"
          assert name[-2:] == ":0"
          name = name[:-2]
          name = name.split('/')
          pointer = model
          for m_name in name:
              if re.fullmatch(r'[A-Za-z]+\d+', m_name):
                  l = re.split(r'(\d+)', m_name)
              else:
                  l = [m_name]
              if l[0] == 'g':
                  pointer = getattr(pointer, 'weight')
              elif l[0] == 'b':
                  pointer = getattr(pointer, 'bias')
              elif l[0] == 'w':
                  pointer = getattr(pointer, 'weight')
              else:
                  pointer = getattr(pointer, l[0])
              if len(l) >= 2:
                  num = int(l[1])
                  pointer = pointer[num]
          try:
              assert pointer.shape == array.shape
          except AssertionError as e:
              e.args += (pointer.shape, array.shape)
              raise
          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
  
  
  def gelu(x):
      return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
  
  
  def swish(x):
      return x * torch.sigmoid(x)
  
  
  ACT_FNS = {"relu": nn.ReLU, "swish": swish, "gelu": gelu}
  
  
  class Attention(nn.Module):
      def __init__(self, nx, n_ctx, config, scale=False):
          super(Attention, self).__init__()
          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)).view(1, 1, n_ctx, n_ctx))
          self.n_head = config.n_head
          self.split_size = n_state
          self.scale = scale
  
          self.output_attentions = config.output_attentions
  
          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
          for head in heads:
              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 / math.sqrt(v.size(-1))
          # w = w * self.bias + -1e9 * (1 - self.bias)  # TF implem method: mask_attn_weights
          # XD: self.b may be larger than w, so we need to crop it
          b = self.bias[:, :, : w.size(-2), : w.size(-1)]
          w = w * b + -1e9 * (1 - b)
  
          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)
          else:
              return x.permute(0, 2, 1, 3)
  
      def forward(self, x, attention_mask=None, head_mask=None):
          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)
  
          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] + attn_outputs[1:]
          return outputs  # a, (attentions)
  
  
  class MLP(nn.Module):
      def __init__(self, n_state, config):  # in MLP: n_state=3072 (4 * n_embd)
          super(MLP, self).__init__()
          nx = config.n_embd
          self.c_fc = Conv1D(n_state, nx)
          self.c_proj = Conv1D(nx, n_state)
          self.act = ACT_FNS[config.afn]
          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(Block, self).__init__()
          nx = config.n_embd
          self.attn = Attention(nx, n_ctx, config, scale)
          self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
          self.mlp = MLP(4 * nx, config)
          self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
  
      def forward(self, x, attention_mask=None, head_mask=None):
          attn_outputs = self.attn(x, attention_mask=attention_mask, head_mask=head_mask)
          a = attn_outputs[0]
  
          n = self.ln_1(x + a)
          m = self.mlp(n)
          h = self.ln_2(n + m)
  
          outputs = [h] + attn_outputs[1:]
          return outputs
  
  
  class OpenAIGPTPreTrainedModel(PreTrainedModel):
      """ An abstract class to handle weights initialization and
          a simple interface for dowloading and loading pretrained models.
      """
      config_class = OpenAIGPTConfig
      pretrained_model_archive_map = OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP
      load_tf_weights = load_tf_weights_in_openai_gpt
      base_model_prefix = "transformer"
  
      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)
  
  
  OPENAI_GPT_START_DOCSTRING = r"""    OpenAI GPT model was proposed in
      `Improving Language Understanding by Generative Pre-Training`_
      by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
      It's a causal (unidirectional) transformer pre-trained using language modeling on a large
      corpus will long range dependencies, the Toronto Book Corpus.
  
      This model is a PyTorch `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.
  
      .. _`Improving Language Understanding by Generative Pre-Training`:
          https://openai.com/blog/language-unsupervised/
  
      .. _`torch.nn.Module`:
          https://pytorch.org/docs/stable/nn.html#module
  
      Parameters:
          config (:class:`~pytorch_transformers.OpenAIGPTConfig`): 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:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
  """
  
  OPENAI_GPT_INPUTS_DOCSTRING = r"""    Inputs:
          **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
              Indices of input sequence tokens in the vocabulary.
              GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
              the right rather than the left.
              Indices can be obtained using :class:`pytorch_transformers.BPT2Tokenizer`.
              See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
              :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
          **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
              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.
          **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
              A parallel sequence of tokens (can be used to indicate various portions of the inputs).
              The embeddings from these tokens will be summed with the respective token embeddings.
              Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices)
          **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
              Indices of positions of each input sequence tokens in the position embeddings.
              Selected in the range ``[0, config.max_position_embeddings - 1]``.
          **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
              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**.
  """
  
  @add_start_docstrings("The bare OpenAI GPT transformer model outputing raw hidden-states without any specific head on top.",
                        OPENAI_GPT_START_DOCSTRING, OPENAI_GPT_INPUTS_DOCSTRING)
  class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
      r"""
      Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
          **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
              Sequence of hidden-states at the last layer of the model.
          **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
              list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
              of shape ``(batch_size, sequence_length, hidden_size)``:
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          **attentions**: (`optional`, returned when ``config.output_attentions=True``)
              list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
  
          tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
          model = OpenAIGPTModel.from_pretrained('openai-gpt')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).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
  
      """
      def __init__(self, config):
          super(OpenAIGPTModel, self).__init__(config)
          self.output_attentions = config.output_attentions
          self.output_hidden_states = config.output_hidden_states
  
          self.tokens_embed = nn.Embedding(config.vocab_size, config.n_embd)
          self.positions_embed = 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.init_weights()
  
      def _resize_token_embeddings(self, new_num_tokens):
          self.tokens_embed = self._get_resized_embeddings(self.tokens_embed, new_num_tokens)
          return self.tokens_embed
  
      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)
  
      def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
          if position_ids is None:
              # This was used when we had a single embedding matrice from position and token embeddings
              # start = self.config.vocab_size + self.config.n_special
              # end = start + input_ids.size(-1)
              # position_ids = torch.arange(start, end, dtype=torch.long, device=input_ids.device)
              position_ids = torch.arange(input_ids.size(-1), dtype=torch.long, device=input_ids.device)
              position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
  
          # Attention mask.
          if attention_mask is not None:
              # 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
          if head_mask is not None:
              if head_mask.dim() == 1:
                  head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
                  head_mask = head_mask.expand(self.config.n_layer, -1, -1, -1, -1)
              elif head_mask.dim() == 2:
                  head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)  # We can specify head_mask for each layer
              head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
          else:
              head_mask = [None] * self.config.n_layer
  
          input_shape = input_ids.size()
          input_ids = input_ids.view(-1, input_ids.size(-1))
          position_ids = position_ids.view(-1, position_ids.size(-1))
  
          inputs_embeds = self.tokens_embed(input_ids)
          position_embeds = self.positions_embed(position_ids)
          if token_type_ids is not None:
              token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
              token_type_embeds = self.tokens_embed(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),)
  
          all_attentions = ()
          all_hidden_states = ()
          for i, block in enumerate(self.h):
              if self.output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
  
              outputs = block(hidden_states, attention_mask, head_mask[i])
              hidden_states = outputs[0]
              if self.output_attentions:
                  all_attentions = all_attentions + (outputs[1],)
  
          # Add last layer
          if self.output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
  
          outputs = (hidden_states.view(*output_shape),)
          if self.output_hidden_states:
              outputs = outputs + (all_hidden_states,)
          if self.output_attentions:
              outputs = outputs + (all_attentions,)
          return outputs  # last hidden state, (all hidden states), (all attentions)
  
  
  @add_start_docstrings("""OpenAI GPT Model transformer with a language modeling head on top
  (linear layer with weights tied to the input embeddings). """, OPENAI_GPT_START_DOCSTRING, OPENAI_GPT_INPUTS_DOCSTRING)
  class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
      r"""
          **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
              Labels for language modeling.
              Note that the labels **are shifted** inside the model, i.e. you can set ``labels = input_ids``
              Indices are selected in ``[-1, 0, ..., config.vocab_size]``
              All labels set to ``-1`` are ignored (masked), the loss is only
              computed for labels in ``[0, ..., config.vocab_size]``
  
      Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
          **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
              Language modeling loss.
          **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
              list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
              of shape ``(batch_size, sequence_length, hidden_size)``:
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          **attentions**: (`optional`, returned when ``config.output_attentions=True``)
              list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
  
          tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
          model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, labels=input_ids)
          loss, logits = outputs[:2]
  
      """
      def __init__(self, config):
          super(OpenAIGPTLMHeadModel, self).__init__(config)
          self.transformer = OpenAIGPTModel(config)
          self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
  
          self.init_weights()
          self.tie_weights()
  
      def tie_weights(self):
          """ Make sure we are sharing the input and output embeddings.
              Export to TorchScript can't handle parameter sharing so we are cloning them instead.
          """
          self._tie_or_clone_weights(self.lm_head,
                                     self.transformer.tokens_embed)
  
      def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
                  labels=None):
          transformer_outputs = self.transformer(input_ids,
                                                 attention_mask=attention_mask,
                                                 token_type_ids=token_type_ids,
                                                 position_ids=position_ids,
                                                 head_mask=head_mask)
          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(ignore_index=-1)
              loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
                              shift_labels.view(-1))
              outputs = (loss,) + outputs
  
          return outputs  # (loss), lm_logits, (all hidden states), (all attentions)
  
  
  @add_start_docstrings("""OpenAI GPT 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).
  """, OPENAI_GPT_START_DOCSTRING, OPENAI_GPT_INPUTS_DOCSTRING)
  class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
      r"""
          **mc_token_ids**: (`optional`, default to index of the last token of the input) ``torch.LongTensor`` of shape ``(batch_size, num_choices)``:
              Index of the classification token in each input sequence.
              Selected in the range ``[0, input_ids.size(-1) - 1[``.
          **lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
              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 ``-1`` are ignored (masked), the loss is only
              computed for labels in ``[0, ..., config.vocab_size]``
          **mc_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size)``:
              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)
  
              `multiple_choice_labels`: optional multiple choice labels: ``torch.LongTensor`` of shape [batch_size]
                  with indices selected in [0, ..., num_choices].
  
      Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
          **lm_loss**: (`optional`, returned when ``lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
              Language modeling loss.
          **mc_loss**: (`optional`, returned when ``multiple_choice_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
              Multiple choice classification loss.
          **lm_prediction_scores**: ``torch.FloatTensor`` of shape ``(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**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)``
              Prediction scores of the multiplechoice classification head (scores for each choice before SoftMax).
          **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
              list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
              of shape ``(batch_size, sequence_length, hidden_size)``:
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          **attentions**: (`optional`, returned when ``config.output_attentions=True``)
              list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
  
          tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
          model = OpenAIGPTDoubleHeadsModel.from_pretrained('openai-gpt')
          tokenizer.add_special_tokens({'cls_token': '[CLS]'})  # Add a [CLS] to the vocabulary (we should train it also!)
          choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
          input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
          mc_token_ids = torch.tensor([input_ids.size(-1), input_ids.size(-1)]).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, mc_token_ids=mc_token_ids)
          lm_prediction_scores, mc_prediction_scores = outputs[:2]
  
      """
      def __init__(self, config):
          super(OpenAIGPTDoubleHeadsModel, self).__init__(config)
  
          self.transformer = OpenAIGPTModel(config)
          self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
          self.multiple_choice_head = SequenceSummary(config)
  
          self.init_weights()
          self.tie_weights()
  
      def tie_weights(self):
          """ Make sure we are sharing the input and output embeddings.
              Export to TorchScript can't handle parameter sharing so we are cloning them instead.
          """
          self._tie_or_clone_weights(self.lm_head,
                                     self.transformer.tokens_embed)
  
      def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
                  mc_token_ids=None, lm_labels=None, mc_labels=None):
          transformer_outputs = self.transformer(input_ids,
                                                 attention_mask=attention_mask,
                                                 token_type_ids=token_type_ids,
                                                 position_ids=position_ids,
                                                 head_mask=head_mask)
          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(ignore_index=-1)
              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, (all hidden_states), (attentions)
  