code: # coding=utf-8
  # Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
  #
  # 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 XLM model.
  """
  
  
  import itertools
  import logging
  import math
  
  import numpy as np
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from torch.nn import functional as F
  
  from .activations import gelu
  from .configuration_xlm import XLMConfig
  from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
  from .modeling_utils import PreTrainedModel, SequenceSummary, SQuADHead, prune_linear_layer
  
  
  logger = logging.getLogger(__name__)
  
  XLM_PRETRAINED_MODEL_ARCHIVE_MAP = {
      "xlm-mlm-en-2048": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-pytorch_model.bin",
      "xlm-mlm-ende-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-pytorch_model.bin",
      "xlm-mlm-enfr-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-pytorch_model.bin",
      "xlm-mlm-enro-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-pytorch_model.bin",
      "xlm-mlm-tlm-xnli15-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-pytorch_model.bin",
      "xlm-mlm-xnli15-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-pytorch_model.bin",
      "xlm-clm-enfr-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-pytorch_model.bin",
      "xlm-clm-ende-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-pytorch_model.bin",
      "xlm-mlm-17-1280": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-pytorch_model.bin",
      "xlm-mlm-100-1280": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-pytorch_model.bin",
  }
  
  
  def create_sinusoidal_embeddings(n_pos, dim, out):
      position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
      out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
      out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
      out.detach_()
      out.requires_grad = False
  
  
  def get_masks(slen, lengths, causal, padding_mask=None):
      """
      Generate hidden states mask, and optionally an attention mask.
      """
      alen = torch.arange(slen, dtype=torch.long, device=lengths.device)
      if padding_mask is not None:
          mask = padding_mask
      else:
          assert lengths.max().item() <= slen
          mask = alen < lengths[:, None]
  
      # attention mask is the same as mask, or triangular inferior attention (causal)
      bs = lengths.size(0)
      if causal:
          attn_mask = alen[None, None, :].repeat(bs, slen, 1) <= alen[None, :, None]
      else:
          attn_mask = mask
  
      # sanity check
      assert mask.size() == (bs, slen)
      assert causal is False or attn_mask.size() == (bs, slen, slen)
  
      return mask, attn_mask
  
  
  class MultiHeadAttention(nn.Module):
  
      NEW_ID = itertools.count()
  
      def __init__(self, n_heads, dim, config):
          super().__init__()
          self.layer_id = next(MultiHeadAttention.NEW_ID)
          self.output_attentions = config.output_attentions
          self.dim = dim
          self.n_heads = n_heads
          self.dropout = config.attention_dropout
          assert self.dim % self.n_heads == 0
  
          self.q_lin = nn.Linear(dim, dim)
          self.k_lin = nn.Linear(dim, dim)
          self.v_lin = nn.Linear(dim, dim)
          self.out_lin = nn.Linear(dim, dim)
          self.pruned_heads = set()
  
      def prune_heads(self, heads):
          attention_head_size = self.dim // self.n_heads
          if len(heads) == 0:
              return
          mask = torch.ones(self.n_heads, attention_head_size)
          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()
          # Prune linear layers
          self.q_lin = prune_linear_layer(self.q_lin, index)
          self.k_lin = prune_linear_layer(self.k_lin, index)
          self.v_lin = prune_linear_layer(self.v_lin, index)
          self.out_lin = prune_linear_layer(self.out_lin, index, dim=1)
          # Update hyper params
          self.n_heads = self.n_heads - len(heads)
          self.dim = attention_head_size * self.n_heads
          self.pruned_heads = self.pruned_heads.union(heads)
  
      def forward(self, input, mask, kv=None, cache=None, head_mask=None):
          """
          Self-attention (if kv is None) or attention over source sentence (provided by kv).
          """
          # Input is (bs, qlen, dim)
          # Mask is (bs, klen) (non-causal) or (bs, klen, klen)
          bs, qlen, dim = input.size()
          if kv is None:
              klen = qlen if cache is None else cache["slen"] + qlen
          else:
              klen = kv.size(1)
          # assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
          n_heads = self.n_heads
          dim_per_head = self.dim // n_heads
          mask_reshape = (bs, 1, qlen, klen) if mask.dim() == 3 else (bs, 1, 1, klen)
  
          def shape(x):
              """  projection """
              return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)
  
          def unshape(x):
              """  compute context """
              return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head)
  
          q = shape(self.q_lin(input))  # (bs, n_heads, qlen, dim_per_head)
          if kv is None:
              k = shape(self.k_lin(input))  # (bs, n_heads, qlen, dim_per_head)
              v = shape(self.v_lin(input))  # (bs, n_heads, qlen, dim_per_head)
          elif cache is None or self.layer_id not in cache:
              k = v = kv
              k = shape(self.k_lin(k))  # (bs, n_heads, qlen, dim_per_head)
              v = shape(self.v_lin(v))  # (bs, n_heads, qlen, dim_per_head)
  
          if cache is not None:
              if self.layer_id in cache:
                  if kv is None:
                      k_, v_ = cache[self.layer_id]
                      k = torch.cat([k_, k], dim=2)  # (bs, n_heads, klen, dim_per_head)
                      v = torch.cat([v_, v], dim=2)  # (bs, n_heads, klen, dim_per_head)
                  else:
                      k, v = cache[self.layer_id]
              cache[self.layer_id] = (k, v)
  
          q = q / math.sqrt(dim_per_head)  # (bs, n_heads, qlen, dim_per_head)
          scores = torch.matmul(q, k.transpose(2, 3))  # (bs, n_heads, qlen, klen)
          mask = (mask == 0).view(mask_reshape).expand_as(scores)  # (bs, n_heads, qlen, klen)
          scores.masked_fill_(mask, -float("inf"))  # (bs, n_heads, qlen, klen)
  
          weights = F.softmax(scores.float(), dim=-1).type_as(scores)  # (bs, n_heads, qlen, klen)
          weights = F.dropout(weights, p=self.dropout, training=self.training)  # (bs, n_heads, qlen, klen)
  
          # Mask heads if we want to
          if head_mask is not None:
              weights = weights * head_mask
  
          context = torch.matmul(weights, v)  # (bs, n_heads, qlen, dim_per_head)
          context = unshape(context)  # (bs, qlen, dim)
  
          outputs = (self.out_lin(context),)
          if self.output_attentions:
              outputs = outputs + (weights,)
          return outputs
  
  
  class TransformerFFN(nn.Module):
      def __init__(self, in_dim, dim_hidden, out_dim, config):
          super().__init__()
          self.dropout = config.dropout
          self.lin1 = nn.Linear(in_dim, dim_hidden)
          self.lin2 = nn.Linear(dim_hidden, out_dim)
          self.act = gelu if config.gelu_activation else F.relu
  
      def forward(self, input):
          x = self.lin1(input)
          x = self.act(x)
          x = self.lin2(x)
          x = F.dropout(x, p=self.dropout, training=self.training)
          return x
  
  
  class XLMPreTrainedModel(PreTrainedModel):
      """ An abstract class to handle weights initialization and
          a simple interface for downloading and loading pretrained models.
      """
  
      config_class = XLMConfig
      pretrained_model_archive_map = XLM_PRETRAINED_MODEL_ARCHIVE_MAP
      load_tf_weights = None
      base_model_prefix = "transformer"
  
      def __init__(self, *inputs, **kwargs):
          super().__init__(*inputs, **kwargs)
  
      @property
      def dummy_inputs(self):
          inputs_list = torch.tensor([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
          attns_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
          if self.config.use_lang_emb and self.config.n_langs > 1:
              langs_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
          else:
              langs_list = None
          return {"input_ids": inputs_list, "attention_mask": attns_list, "langs": langs_list}
  
      def _init_weights(self, module):
          """ Initialize the weights. """
          if isinstance(module, nn.Embedding):
              if self.config is not None and self.config.embed_init_std is not None:
                  nn.init.normal_(module.weight, mean=0, std=self.config.embed_init_std)
          if isinstance(module, nn.Linear):
              if self.config is not None and self.config.init_std is not None:
                  nn.init.normal_(module.weight, mean=0, std=self.config.init_std)
                  if hasattr(module, "bias") and module.bias is not None:
                      nn.init.constant_(module.bias, 0.0)
          if isinstance(module, nn.LayerNorm):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0)
  
  
  XLM_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.XLMConfig`): 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.
  """
  
  XLM_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`transformers.BertTokenizer`.
              See :func:`transformers.PreTrainedTokenizer.encode` and
              :func:`transformers.PreTrainedTokenizer.encode_plus` for details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          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>`__
          langs (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              A parallel sequence of tokens to be used to indicate the language of each token in the input.
              Indices are languages ids which can be obtained from the language names by using two conversion mappings
              provided in the configuration of the model (only provided for multilingual models).
              More precisely, the `language name -> language id` mapping is in `model.config.lang2id` (dict str -> int) and
              the `language id -> language name` mapping is `model.config.id2lang` (dict int -> str).
  
              See usage examples detailed in the `multilingual documentation <https://huggingface.co/transformers/multilingual.html>`__.
          token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              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>`_
          lengths (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              Length of each sentence that can be used to avoid performing attention on padding token indices.
              You can also use `attention_mask` for the same result (see above), kept here for compatbility.
              Indices selected in ``[0, ..., input_ids.size(-1)]``:
          cache (:obj:`Dict[str, torch.FloatTensor]`, `optional`, defaults to :obj:`None`):
              dictionary with ``torch.FloatTensor`` that contains pre-computed
              hidden-states (key and values in the attention blocks) as computed by the model
              (see `cache` output below). Can be used to speed up sequential decoding.
              The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
          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**.
          input_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
              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.
  """
  
  
  @add_start_docstrings(
      "The bare XLM Model transformer outputting raw hidden-states without any specific head on top.",
      XLM_START_DOCSTRING,
  )
  class XLMModel(XLMPreTrainedModel):
      def __init__(self, config):  # , dico, is_encoder, with_output):
          super().__init__(config)
          self.output_attentions = config.output_attentions
          self.output_hidden_states = config.output_hidden_states
  
          # encoder / decoder, output layer
          self.is_encoder = config.is_encoder
          self.is_decoder = not config.is_encoder
          if self.is_decoder:
              raise NotImplementedError("Currently XLM can only be used as an encoder")
          # self.with_output = with_output
          self.causal = config.causal
  
          # dictionary / languages
          self.n_langs = config.n_langs
          self.use_lang_emb = config.use_lang_emb
          self.n_words = config.n_words
          self.eos_index = config.eos_index
          self.pad_index = config.pad_index
          # self.dico = dico
          # self.id2lang = config.id2lang
          # self.lang2id = config.lang2id
          # assert len(self.dico) == self.n_words
          # assert len(self.id2lang) == len(self.lang2id) == self.n_langs
  
          # model parameters
          self.dim = config.emb_dim  # 512 by default
          self.hidden_dim = self.dim * 4  # 2048 by default
          self.n_heads = config.n_heads  # 8 by default
          self.n_layers = config.n_layers
          self.dropout = config.dropout
          self.attention_dropout = config.attention_dropout
          assert self.dim % self.n_heads == 0, "transformer dim must be a multiple of n_heads"
  
          # embeddings
          self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.dim)
          if config.sinusoidal_embeddings:
              create_sinusoidal_embeddings(config.max_position_embeddings, self.dim, out=self.position_embeddings.weight)
          if config.n_langs > 1 and config.use_lang_emb:
              self.lang_embeddings = nn.Embedding(self.n_langs, self.dim)
          self.embeddings = nn.Embedding(self.n_words, self.dim, padding_idx=self.pad_index)
          self.layer_norm_emb = nn.LayerNorm(self.dim, eps=config.layer_norm_eps)
  
          # transformer layers
          self.attentions = nn.ModuleList()
          self.layer_norm1 = nn.ModuleList()
          self.ffns = nn.ModuleList()
          self.layer_norm2 = nn.ModuleList()
          # if self.is_decoder:
          #     self.layer_norm15 = nn.ModuleList()
          #     self.encoder_attn = nn.ModuleList()
  
          for _ in range(self.n_layers):
              self.attentions.append(MultiHeadAttention(self.n_heads, self.dim, config=config))
              self.layer_norm1.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
              # if self.is_decoder:
              #     self.layer_norm15.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
              #     self.encoder_attn.append(MultiHeadAttention(self.n_heads, self.dim, dropout=self.attention_dropout))
              self.ffns.append(TransformerFFN(self.dim, self.hidden_dim, self.dim, config=config))
              self.layer_norm2.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
  
          if hasattr(config, "pruned_heads"):
              pruned_heads = config.pruned_heads.copy().items()
              config.pruned_heads = {}
              for layer, heads in pruned_heads:
                  if self.attentions[int(layer)].n_heads == config.n_heads:
                      self.prune_heads({int(layer): list(map(int, heads))})
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embeddings
  
      def set_input_embeddings(self, new_embeddings):
          self.embeddings = 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}
              See base class PreTrainedModel
          """
          for layer, heads in heads_to_prune.items():
              self.attentions[layer].prune_heads(heads)
  
      @add_start_docstrings_to_callable(XLM_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          langs=None,
          token_type_ids=None,
          position_ids=None,
          lengths=None,
          cache=None,
          head_mask=None,
          inputs_embeds=None,
      ):
          r"""
      Return:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
          last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the model.
          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 XLMTokenizer, XLMModel
          import torch
  
          tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
          model = XLMModel.from_pretrained('xlm-mlm-en-2048')
          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:
              bs, slen = input_ids.size()
          else:
              bs, slen = inputs_embeds.size()[:-1]
  
          if lengths is None:
              if input_ids is not None:
                  lengths = (input_ids != self.pad_index).sum(dim=1).long()
              else:
                  lengths = torch.LongTensor([slen] * bs)
          # mask = input_ids != self.pad_index
  
          # check inputs
          assert lengths.size(0) == bs
          assert lengths.max().item() <= slen
          # input_ids = input_ids.transpose(0, 1)  # batch size as dimension 0
          # assert (src_enc is None) == (src_len is None)
          # if src_enc is not None:
          #     assert self.is_decoder
          #     assert src_enc.size(0) == bs
  
          # generate masks
          mask, attn_mask = get_masks(slen, lengths, self.causal, padding_mask=attention_mask)
          # if self.is_decoder and src_enc is not None:
          #     src_mask = torch.arange(src_len.max(), dtype=torch.long, device=lengths.device) < src_len[:, None]
  
          device = input_ids.device if input_ids is not None else inputs_embeds.device
  
          # position_ids
          if position_ids is None:
              position_ids = torch.arange(slen, dtype=torch.long, device=device)
              position_ids = position_ids.unsqueeze(0).expand((bs, slen))
          else:
              assert position_ids.size() == (bs, slen)  # (slen, bs)
              # position_ids = position_ids.transpose(0, 1)
  
          # langs
          if langs is not None:
              assert langs.size() == (bs, slen)  # (slen, bs)
              # langs = langs.transpose(0, 1)
  
          # 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 qlen x klen]
          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.n_layers, -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.n_layers
  
          # do not recompute cached elements
          if cache is not None and input_ids is not None:
              _slen = slen - cache["slen"]
              input_ids = input_ids[:, -_slen:]
              position_ids = position_ids[:, -_slen:]
              if langs is not None:
                  langs = langs[:, -_slen:]
              mask = mask[:, -_slen:]
              attn_mask = attn_mask[:, -_slen:]
  
          # embeddings
          if inputs_embeds is None:
              inputs_embeds = self.embeddings(input_ids)
  
          tensor = inputs_embeds + self.position_embeddings(position_ids).expand_as(inputs_embeds)
          if langs is not None and self.use_lang_emb and self.n_langs > 1:
              tensor = tensor + self.lang_embeddings(langs)
          if token_type_ids is not None:
              tensor = tensor + self.embeddings(token_type_ids)
          tensor = self.layer_norm_emb(tensor)
          tensor = F.dropout(tensor, p=self.dropout, training=self.training)
          tensor *= mask.unsqueeze(-1).to(tensor.dtype)
  
          # transformer layers
          hidden_states = ()
          attentions = ()
          for i in range(self.n_layers):
              if self.output_hidden_states:
                  hidden_states = hidden_states + (tensor,)
  
              # self attention
              attn_outputs = self.attentions[i](tensor, attn_mask, cache=cache, head_mask=head_mask[i])
              attn = attn_outputs[0]
              if self.output_attentions:
                  attentions = attentions + (attn_outputs[1],)
              attn = F.dropout(attn, p=self.dropout, training=self.training)
              tensor = tensor + attn
              tensor = self.layer_norm1[i](tensor)
  
              # encoder attention (for decoder only)
              # if self.is_decoder and src_enc is not None:
              #     attn = self.encoder_attn[i](tensor, src_mask, kv=src_enc, cache=cache)
              #     attn = F.dropout(attn, p=self.dropout, training=self.training)
              #     tensor = tensor + attn
              #     tensor = self.layer_norm15[i](tensor)
  
              # FFN
              tensor = tensor + self.ffns[i](tensor)
              tensor = self.layer_norm2[i](tensor)
              tensor *= mask.unsqueeze(-1).to(tensor.dtype)
  
          # Add last hidden state
          if self.output_hidden_states:
              hidden_states = hidden_states + (tensor,)
  
          # update cache length
          if cache is not None:
              cache["slen"] += tensor.size(1)
  
          # move back sequence length to dimension 0
          # tensor = tensor.transpose(0, 1)
  
          outputs = (tensor,)
          if self.output_hidden_states:
              outputs = outputs + (hidden_states,)
          if self.output_attentions:
              outputs = outputs + (attentions,)
          return outputs  # outputs, (hidden_states), (attentions)
  
  
  class XLMPredLayer(nn.Module):
      """
      Prediction layer (cross_entropy or adaptive_softmax).
      """
  
      def __init__(self, config):
          super().__init__()
          self.asm = config.asm
          self.n_words = config.n_words
          self.pad_index = config.pad_index
          dim = config.emb_dim
  
          if config.asm is False:
              self.proj = nn.Linear(dim, config.n_words, bias=True)
          else:
              self.proj = nn.AdaptiveLogSoftmaxWithLoss(
                  in_features=dim,
                  n_classes=config.n_words,
                  cutoffs=config.asm_cutoffs,
                  div_value=config.asm_div_value,
                  head_bias=True,  # default is False
              )
  
      def forward(self, x, y=None):
          """ Compute the loss, and optionally the scores.
          """
          outputs = ()
          if self.asm is False:
              scores = self.proj(x)
              outputs = (scores,) + outputs
              if y is not None:
                  loss = F.cross_entropy(scores.view(-1, self.n_words), y.view(-1), reduction="elementwise_mean")
                  outputs = (loss,) + outputs
          else:
              scores = self.proj.log_prob(x)
              outputs = (scores,) + outputs
              if y is not None:
                  _, loss = self.proj(x, y)
                  outputs = (loss,) + outputs
  
          return outputs
  
  
  @add_start_docstrings(
      """The XLM Model transformer with a language modeling head on top
      (linear layer with weights tied to the input embeddings). """,
      XLM_START_DOCSTRING,
  )
  class XLMWithLMHeadModel(XLMPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.transformer = XLMModel(config)
          self.pred_layer = XLMPredLayer(config)
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.pred_layer.proj
  
      def prepare_inputs_for_generation(self, input_ids, **kwargs):
          mask_token_id = self.config.mask_token_id
          lang_id = self.config.lang_id
  
          effective_batch_size = input_ids.shape[0]
          mask_token = torch.full((effective_batch_size, 1), mask_token_id, dtype=torch.long, device=input_ids.device)
          input_ids = torch.cat([input_ids, mask_token], dim=1)
          if lang_id is not None:
              langs = torch.full_like(input_ids, lang_id)
          else:
              langs = None
          return {"input_ids": input_ids, "langs": langs}
  
      @add_start_docstrings_to_callable(XLM_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          langs=None,
          token_type_ids=None,
          position_ids=None,
          lengths=None,
          cache=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
      ):
          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.XLMConfig`) 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).
          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 XLMTokenizer, XLMWithLMHeadModel
          import torch
  
          tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
          model = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048')
          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
  
          """
          transformer_outputs = self.transformer(
              input_ids,
              attention_mask=attention_mask,
              langs=langs,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              lengths=lengths,
              cache=cache,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
          )
  
          output = transformer_outputs[0]
          outputs = self.pred_layer(output, labels)
          outputs = outputs + transformer_outputs[1:]  # Keep new_mems and attention/hidden states if they are here
  
          return outputs
  
  
  @add_start_docstrings(
      """XLM Model with a sequence classification/regression head on top (a linear layer on top of
      the pooled output) e.g. for GLUE tasks. """,
      XLM_START_DOCSTRING,
  )
  class XLMForSequenceClassification(XLMPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.transformer = XLMModel(config)
          self.sequence_summary = SequenceSummary(config)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(XLM_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          langs=None,
          token_type_ids=None,
          position_ids=None,
          lengths=None,
          cache=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              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).
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
              Classification (or regression if config.num_labels==1) loss.
          logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
              Classification (or regression if config.num_labels==1) scores (before SoftMax).
          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 XLMTokenizer, XLMForSequenceClassification
          import torch
  
          tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
          model = XLMForSequenceClassification.from_pretrained('xlm-mlm-en-2048')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, labels=labels)
          loss, logits = outputs[:2]
  
          """
          transformer_outputs = self.transformer(
              input_ids,
              attention_mask=attention_mask,
              langs=langs,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              lengths=lengths,
              cache=cache,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
          )
  
          output = transformer_outputs[0]
          logits = self.sequence_summary(output)
  
          outputs = (logits,) + transformer_outputs[1:]  # Keep new_mems and attention/hidden states if they are here
  
          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))
              outputs = (loss,) + outputs
  
          return outputs
  
  
  @add_start_docstrings(
      """XLM 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`). """,
      XLM_START_DOCSTRING,
  )
  class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.transformer = XLMModel(config)
          self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(XLM_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          langs=None,
          token_type_ids=None,
          position_ids=None,
          lengths=None,
          cache=None,
          head_mask=None,
          inputs_embeds=None,
          start_positions=None,
          end_positions=None,
      ):
          r"""
          start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              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`, defaults to :obj:`None`):
              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.
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
          start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
              Span-start scores (before SoftMax).
          end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
              Span-end scores (before SoftMax).
          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 XLMTokenizer, XLMForQuestionAnsweringSimple
          import torch
  
          tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
          model = XLMForQuestionAnsweringSimple.from_pretrained('xlm-mlm-en-2048')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          start_positions = torch.tensor([1])
          end_positions = torch.tensor([3])
          outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
          loss = outputs[0]
  
          """
          transformer_outputs = self.transformer(
              input_ids,
              attention_mask=attention_mask,
              langs=langs,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              lengths=lengths,
              cache=cache,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
          )
  
          sequence_output = transformer_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)
  
          outputs = (
              start_logits,
              end_logits,
          )
          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
              outputs = (total_loss,) + outputs
  
          outputs = outputs + transformer_outputs[1:]  # Keep new_mems and attention/hidden states if they are here
  
          return outputs
  
  
  @add_start_docstrings(
      """XLM Model with a beam-search 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`). """,
      XLM_START_DOCSTRING,
  )
  class XLMForQuestionAnswering(XLMPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.transformer = XLMModel(config)
          self.qa_outputs = SQuADHead(config)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(XLM_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          langs=None,
          token_type_ids=None,
          position_ids=None,
          lengths=None,
          cache=None,
          head_mask=None,
          inputs_embeds=None,
          start_positions=None,
          end_positions=None,
          is_impossible=None,
          cls_index=None,
          p_mask=None,
      ):
          r"""
          start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              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`, defaults to :obj:`None`):
              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.
          is_impossible (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`, defaults to :obj:`None`):
              Labels whether a question has an answer or no answer (SQuAD 2.0)
          cls_index (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`, defaults to :obj:`None`):
              Labels for position (index) of the classification token to use as input for computing plausibility of the answer.
          p_mask (``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``, `optional`, defaults to :obj:`None`):
              Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...).
              1.0 means token should be masked. 0.0 mean token is not masked.
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned if both :obj:`start_positions` and :obj:`end_positions` are provided):
              Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
          start_top_log_probs (``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided):
              Log probabilities for the top config.start_n_top start token possibilities (beam-search).
          start_top_index (``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided):
              Indices for the top config.start_n_top start token possibilities (beam-search).
          end_top_log_probs (``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided):
              Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
          end_top_index (``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided):
              Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
          cls_logits (``torch.FloatTensor`` of shape ``(batch_size,)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided):
              Log probabilities for the ``is_impossible`` label of the answers.
          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 XLMTokenizer, XLMForQuestionAnswering
          import torch
  
          tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
          model = XLMForQuestionAnswering.from_pretrained('xlm-mlm-en-2048')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          start_positions = torch.tensor([1])
          end_positions = torch.tensor([3])
          outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
          loss = outputs[0]
  
          """
          transformer_outputs = self.transformer(
              input_ids,
              attention_mask=attention_mask,
              langs=langs,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              lengths=lengths,
              cache=cache,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
          )
  
          output = transformer_outputs[0]
  
          outputs = self.qa_outputs(
              output,
              start_positions=start_positions,
              end_positions=end_positions,
              cls_index=cls_index,
              is_impossible=is_impossible,
              p_mask=p_mask,
          )
  
          outputs = outputs + transformer_outputs[1:]  # Keep new_mems and attention/hidden states if they are here
  
          return outputs
  
  
  @add_start_docstrings(
      """XLM 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. """,
      XLM_START_DOCSTRING,
  )
  class XLMForTokenClassification(XLMPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.transformer = XLMModel(config)
          self.dropout = nn.Dropout(config.dropout)
          self.classifier = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(XLM_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          langs=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          labels=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Labels for computing the token classification loss.
              Indices should be in ``[0, ..., config.num_labels - 1]``.
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
              Classification loss.
          scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)
              Classification scores (before SoftMax).
          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 XLMTokenizer, XLMForTokenClassification
          import torch
  
          tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-100-1280')
          model = XLMForTokenClassification.from_pretrained('xlm-mlm-100-1280')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
          labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, labels=labels)
          loss, scores = outputs[:2]
  
          """
          outputs = self.transformer(
              input_ids,
              attention_mask=attention_mask,
              langs=langs,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
          )
  
          sequence_output = outputs[0]
  
          sequence_output = self.dropout(sequence_output)
          logits = self.classifier(sequence_output)
  
          outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
          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))
              outputs = (loss,) + outputs
  
          return outputs  # (loss), scores, (hidden_states), (attentions)
  