text: # coding=utf-8
  # Copyright 2020 The SqueezeBert authors 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 SqueezeBert model. """
  
  
  import math
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from ...activations import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_outputs import (
      BaseModelOutput,
      BaseModelOutputWithPooling,
      MaskedLMOutput,
      MultipleChoiceModelOutput,
      QuestionAnsweringModelOutput,
      SequenceClassifierOutput,
      TokenClassifierOutput,
  )
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_squeezebert import SqueezeBertConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "SqueezeBertConfig"
  _TOKENIZER_FOR_DOC = "SqueezeBertTokenizer"
  
  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "squeezebert/squeezebert-uncased",
      "squeezebert/squeezebert-mnli",
      "squeezebert/squeezebert-mnli-headless",
  ]
  
  
  class SqueezeBertEmbeddings(nn.Module):
      """Construct the embeddings from word, position and token_type embeddings."""
  
      def __init__(self, config):
          super().__init__()
          self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
          self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
          self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
  
          # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
          # any TensorFlow checkpoint file
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
          # position_ids (1, len position emb) is contiguous in memory and exported when serialized
          self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
  
      def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
          if input_ids is not None:
              input_shape = input_ids.size()
          else:
              input_shape = inputs_embeds.size()[:-1]
  
          seq_length = input_shape[1]
  
          if position_ids is None:
              position_ids = self.position_ids[:, :seq_length]
  
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
  
          if inputs_embeds is None:
              inputs_embeds = self.word_embeddings(input_ids)
          position_embeddings = self.position_embeddings(position_ids)
          token_type_embeddings = self.token_type_embeddings(token_type_ids)
  
          embeddings = inputs_embeds + position_embeddings + token_type_embeddings
          embeddings = self.LayerNorm(embeddings)
          embeddings = self.dropout(embeddings)
          return embeddings
  
  
  class MatMulWrapper(torch.nn.Module):
      """
      Wrapper for torch.matmul(). This makes flop-counting easier to implement. Note that if you directly call
      torch.matmul() in your code, the flop counter will typically ignore the flops of the matmul.
      """
  
      def __init__(self):
          super().__init__()
  
      def forward(self, mat1, mat2):
          """
  
          :param inputs: two torch tensors :return: matmul of these tensors
  
          Here are the typical dimensions found in BERT (the B is optional) mat1.shape: [B, <optional extra dims>, M, K]
          mat2.shape: [B, <optional extra dims>, K, N] output shape: [B, <optional extra dims>, M, N]
          """
          return torch.matmul(mat1, mat2)
  
  
  class SqueezeBertLayerNorm(nn.LayerNorm):
      """
      This is a nn.LayerNorm subclass that accepts NCW data layout and performs normalization in the C dimension.
  
      N = batch C = channels W = sequence length
      """
  
      def __init__(self, hidden_size, eps=1e-12):
          nn.LayerNorm.__init__(self, normalized_shape=hidden_size, eps=eps)  # instantiates self.{weight, bias, eps}
  
      def forward(self, x):
          x = x.permute(0, 2, 1)
          x = nn.LayerNorm.forward(self, x)
          return x.permute(0, 2, 1)
  
  
  class ConvDropoutLayerNorm(nn.Module):
      """
      ConvDropoutLayerNorm: Conv, Dropout, LayerNorm
      """
  
      def __init__(self, cin, cout, groups, dropout_prob):
          super().__init__()
  
          self.conv1d = nn.Conv1d(in_channels=cin, out_channels=cout, kernel_size=1, groups=groups)
          self.layernorm = SqueezeBertLayerNorm(cout)
          self.dropout = nn.Dropout(dropout_prob)
  
      def forward(self, hidden_states, input_tensor):
          x = self.conv1d(hidden_states)
          x = self.dropout(x)
          x = x + input_tensor
          x = self.layernorm(x)
          return x
  
  
  class ConvActivation(nn.Module):
      """
      ConvActivation: Conv, Activation
      """
  
      def __init__(self, cin, cout, groups, act):
          super().__init__()
          self.conv1d = nn.Conv1d(in_channels=cin, out_channels=cout, kernel_size=1, groups=groups)
          self.act = ACT2FN[act]
  
      def forward(self, x):
          output = self.conv1d(x)
          return self.act(output)
  
  
  class SqueezeBertSelfAttention(nn.Module):
      def __init__(self, config, cin, q_groups=1, k_groups=1, v_groups=1):
          """
          config = used for some things; ignored for others (work in progress...) cin = input channels = output channels
          groups = number of groups to use in conv1d layers
          """
          super().__init__()
          if cin % config.num_attention_heads != 0:
              raise ValueError(
                  "cin (%d) is not a multiple of the number of attention "
                  "heads (%d)" % (cin, config.num_attention_heads)
              )
          self.num_attention_heads = config.num_attention_heads
          self.attention_head_size = int(cin / config.num_attention_heads)
          self.all_head_size = self.num_attention_heads * self.attention_head_size
  
          self.query = nn.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=q_groups)
          self.key = nn.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=k_groups)
          self.value = nn.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=v_groups)
  
          self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
          self.softmax = nn.Softmax(dim=-1)
  
          self.matmul_qk = MatMulWrapper()
          self.matmul_qkv = MatMulWrapper()
  
      def transpose_for_scores(self, x):
          """
          - input: [N, C, W]
          - output: [N, C1, W, C2] where C1 is the head index, and C2 is one head's contents
          """
          new_x_shape = (x.size()[0], self.num_attention_heads, self.attention_head_size, x.size()[-1])  # [N, C1, C2, W]
          x = x.view(*new_x_shape)
          return x.permute(0, 1, 3, 2)  # [N, C1, C2, W] --> [N, C1, W, C2]
  
      def transpose_key_for_scores(self, x):
          """
          - input: [N, C, W]
          - output: [N, C1, C2, W] where C1 is the head index, and C2 is one head's contents
          """
          new_x_shape = (x.size()[0], self.num_attention_heads, self.attention_head_size, x.size()[-1])  # [N, C1, C2, W]
          x = x.view(*new_x_shape)
          # no `permute` needed
          return x
  
      def transpose_output(self, x):
          """
          - input: [N, C1, W, C2]
          - output: [N, C, W]
          """
          x = x.permute(0, 1, 3, 2).contiguous()  # [N, C1, C2, W]
          new_x_shape = (x.size()[0], self.all_head_size, x.size()[3])  # [N, C, W]
          x = x.view(*new_x_shape)
          return x
  
      def forward(self, hidden_states, attention_mask, output_attentions):
          """
          expects hidden_states in [N, C, W] data layout.
  
          The attention_mask data layout is [N, W], and it does not need to be transposed.
          """
          mixed_query_layer = self.query(hidden_states)
          mixed_key_layer = self.key(hidden_states)
          mixed_value_layer = self.value(hidden_states)
  
          query_layer = self.transpose_for_scores(mixed_query_layer)
          key_layer = self.transpose_key_for_scores(mixed_key_layer)
          value_layer = self.transpose_for_scores(mixed_value_layer)
  
          # Take the dot product between "query" and "key" to get the raw attention scores.
          attention_score = self.matmul_qk(query_layer, key_layer)
          attention_score = attention_score / math.sqrt(self.attention_head_size)
          # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
          attention_score = attention_score + attention_mask
  
          # Normalize the attention scores to probabilities.
          attention_probs = self.softmax(attention_score)
  
          # This is actually dropping out entire tokens to attend to, which might
          # seem a bit unusual, but is taken from the original Transformer paper.
          attention_probs = self.dropout(attention_probs)
  
          context_layer = self.matmul_qkv(attention_probs, value_layer)
          context_layer = self.transpose_output(context_layer)
  
          result = {"context_layer": context_layer}
          if output_attentions:
              result["attention_score"] = attention_score
          return result
  
  
  class SqueezeBertModule(nn.Module):
      def __init__(self, config):
          """
          - hidden_size = input chans = output chans for Q, K, V (they are all the same ... for now) = output chans for
            the module
          - intermediate_size = output chans for intermediate layer
          - groups = number of groups for all layers in the BertModule. (eventually we could change the interface to
            allow different groups for different layers)
          """
          super().__init__()
  
          c0 = config.hidden_size
          c1 = config.hidden_size
          c2 = config.intermediate_size
          c3 = config.hidden_size
  
          self.attention = SqueezeBertSelfAttention(
              config=config, cin=c0, q_groups=config.q_groups, k_groups=config.k_groups, v_groups=config.v_groups
          )
          self.post_attention = ConvDropoutLayerNorm(
              cin=c0, cout=c1, groups=config.post_attention_groups, dropout_prob=config.hidden_dropout_prob
          )
          self.intermediate = ConvActivation(cin=c1, cout=c2, groups=config.intermediate_groups, act=config.hidden_act)
          self.output = ConvDropoutLayerNorm(
              cin=c2, cout=c3, groups=config.output_groups, dropout_prob=config.hidden_dropout_prob
          )
  
      def forward(self, hidden_states, attention_mask, output_attentions):
          att = self.attention(hidden_states, attention_mask, output_attentions)
          attention_output = att["context_layer"]
  
          post_attention_output = self.post_attention(attention_output, hidden_states)
          intermediate_output = self.intermediate(post_attention_output)
          layer_output = self.output(intermediate_output, post_attention_output)
  
          output_dict = {"feature_map": layer_output}
          if output_attentions:
              output_dict["attention_score"] = att["attention_score"]
  
          return output_dict
  
  
  class SqueezeBertEncoder(nn.Module):
      def __init__(self, config):
          super().__init__()
  
          assert config.embedding_size == config.hidden_size, (
              "If you want embedding_size != intermediate hidden_size,"
              "please insert a Conv1d layer to adjust the number of channels "
              "before the first SqueezeBertModule."
          )
  
          self.layers = nn.ModuleList(SqueezeBertModule(config) for _ in range(config.num_hidden_layers))
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          output_attentions=False,
          output_hidden_states=False,
          return_dict=True,
      ):
  
          if head_mask is None:
              head_mask_is_all_none = True
          elif head_mask.count(None) == len(head_mask):
              head_mask_is_all_none = True
          else:
              head_mask_is_all_none = False
          assert head_mask_is_all_none is True, "head_mask is not yet supported in the SqueezeBert implementation."
  
          # [batch_size, sequence_length, hidden_size] --> [batch_size, hidden_size, sequence_length]
          hidden_states = hidden_states.permute(0, 2, 1)
  
          all_hidden_states = (hidden_states,) if output_hidden_states else None
          all_attentions = () if output_attentions else None
  
          for layer in self.layers:
              layer_output = layer.forward(hidden_states, attention_mask, output_attentions)
  
              if output_attentions:
                  all_attentions += (layer_output["attention_score"],)
              if output_hidden_states:
                  all_hidden_states += (layer_output["feature_map"],)
              hidden_states = layer_output["feature_map"]
  
          # Transpose hidden states to be compatible with the standard format in Transformers.
          if all_hidden_states:
              old_all_hidden_states = all_hidden_states
              all_hidden_states = ()
              for hs in old_all_hidden_states:
                  # [batch_size, hidden_size, sequence_length] --> [batch_size, sequence_length, hidden_size]
                  all_hidden_states += (hs.permute(0, 2, 1),)
  
          # [batch_size, hidden_size, sequence_length] --> [batch_size, sequence_length, hidden_size]
          hidden_states = hidden_states.permute(0, 2, 1)
  
          if not return_dict:
              return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
          return BaseModelOutput(
              last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
          )
  
  
  class SqueezeBertPooler(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.activation = nn.Tanh()
  
      def forward(self, hidden_states):
          # We "pool" the model by simply taking the hidden state corresponding
          # to the first token.
          first_token_tensor = hidden_states[:, 0]
          pooled_output = self.dense(first_token_tensor)
          pooled_output = self.activation(pooled_output)
          return pooled_output
  
  
  class SqueezeBertPredictionHeadTransform(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          if isinstance(config.hidden_act, str):
              self.transform_act_fn = ACT2FN[config.hidden_act]
          else:
              self.transform_act_fn = config.hidden_act
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
  
      def forward(self, hidden_states):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.transform_act_fn(hidden_states)
          hidden_states = self.LayerNorm(hidden_states)
          return hidden_states
  
  
  class SqueezeBertLMPredictionHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.transform = SqueezeBertPredictionHeadTransform(config)
  
          # The output weights are the same as the input embeddings, but there is
          # an output-only bias for each token.
          self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
  
          self.bias = nn.Parameter(torch.zeros(config.vocab_size))
  
          # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
          self.decoder.bias = self.bias
  
      def forward(self, hidden_states):
          hidden_states = self.transform(hidden_states)
          hidden_states = self.decoder(hidden_states)
          return hidden_states
  
  
  class SqueezeBertOnlyMLMHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.predictions = SqueezeBertLMPredictionHead(config)
  
      def forward(self, sequence_output):
          prediction_scores = self.predictions(sequence_output)
          return prediction_scores
  
  
  class SqueezeBertPreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = SqueezeBertConfig
      base_model_prefix = "transformer"
      _keys_to_ignore_on_load_missing = [r"position_ids"]
  
      def _init_weights(self, module):
          """ Initialize the weights """
          if isinstance(module, (nn.Linear, nn.Conv1d, nn.Embedding)):
              # Slightly different from the TF version which uses truncated_normal for initialization
              # cf https://github.com/pytorch/pytorch/pull/5617
              module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
          elif isinstance(module, SqueezeBertLayerNorm):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0)
          if isinstance(module, (nn.Linear, nn.Conv1d)) and module.bias is not None:
              module.bias.data.zero_()
  
  
  SQUEEZEBERT_START_DOCSTRING = r"""
  
      The SqueezeBERT model was proposed in `SqueezeBERT: What can computer vision teach NLP about efficient neural
      networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W.
      Keutzer
  
      This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
      methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
      pruning heads etc.)
  
      This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
      subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
      general usage and behavior.
  
      For best results finetuning SqueezeBERT on text classification tasks, it is recommended to use the
      `squeezebert/squeezebert-mnli-headless` checkpoint as a starting point.
  
      Parameters:
          config (:class:`~transformers.SqueezeBertConfig`): 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.
  
      Hierarchy::
  
          Internal class hierarchy:
              SqueezeBertModel
                  SqueezeBertEncoder
                      SqueezeBertModule
                      SqueezeBertSelfAttention
                          ConvActivation
                          ConvDropoutLayerNorm
  
      Data layouts::
  
          Input data is in [batch, sequence_length, hidden_size] format.
  
          Data inside the encoder is in [batch, hidden_size, sequence_length] format. But, if :obj:`output_hidden_states
          == True`, the data from inside the encoder is returned in [batch, sequence_length, hidden_size] format.
  
          The final output of the encoder is in [batch, sequence_length, hidden_size] format.
  """
  
  SQUEEZEBERT_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`~transformers.SqueezeBertTokenizer`. See
              :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
              Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
              Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
              1]``:
  
              - 0 corresponds to a `sentence A` token,
              - 1 corresponds to a `sentence B` token.
  
              `What are token type IDs? <../glossary.html#token-type-ids>`_
          position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
              Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
              config.max_position_embeddings - 1]``.
  
              `What are position IDs? <../glossary.html#position-ids>`_
          head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
              Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
              vectors than the model's internal embedding lookup matrix.
          output_attentions (:obj:`bool`, `optional`):
              Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
              tensors for more detail.
          output_hidden_states (:obj:`bool`, `optional`):
              Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
              more detail.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
  """
  
  
  @add_start_docstrings(
      "The bare SqueezeBERT Model transformer outputting raw hidden-states without any specific head on top.",
      SQUEEZEBERT_START_DOCSTRING,
  )
  class SqueezeBertModel(SqueezeBertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.embeddings = SqueezeBertEmbeddings(config)
          self.encoder = SqueezeBertEncoder(config)
          self.pooler = SqueezeBertPooler(config)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embeddings.word_embeddings
  
      def set_input_embeddings(self, new_embeddings):
          self.embeddings.word_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.encoder.layer[layer].attention.prune_heads(heads)
  
      @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="squeezebert/squeezebert-mnli-headless",
          output_type=BaseModelOutputWithPooling,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          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()
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-1]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          device = input_ids.device if input_ids is not None else inputs_embeds.device
  
          if attention_mask is None:
              attention_mask = torch.ones(input_shape, device=device)
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
  
          extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device)
          # Prepare head mask if needed
          # 1.0 in head_mask indicate we keep the head
          # attention_probs has shape bsz x n_heads x N x N
          # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
          # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
          head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
  
          embedding_output = self.embeddings(
              input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
          )
          encoder_outputs = self.encoder(
              hidden_states=embedding_output,
              attention_mask=extended_attention_mask,
              head_mask=head_mask,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          sequence_output = encoder_outputs[0]
          pooled_output = self.pooler(sequence_output)
  
          if not return_dict:
              return (sequence_output, pooled_output) + encoder_outputs[1:]
  
          return BaseModelOutputWithPooling(
              last_hidden_state=sequence_output,
              pooler_output=pooled_output,
              hidden_states=encoder_outputs.hidden_states,
              attentions=encoder_outputs.attentions,
          )
  
  
  @add_start_docstrings("""SqueezeBERT Model with a `language modeling` head on top. """, SQUEEZEBERT_START_DOCSTRING)
  class SqueezeBertForMaskedLM(SqueezeBertPreTrainedModel):
  
      _keys_to_ignore_on_load_missing = [r"predictions.decoder.bias"]
  
      def __init__(self, config):
          super().__init__(config)
  
          self.transformer = SqueezeBertModel(config)
          self.cls = SqueezeBertOnlyMLMHead(config)
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.cls.predictions.decoder
  
      @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="squeezebert/squeezebert-uncased",
          output_type=MaskedLMOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
              config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
              (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.transformer(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
          prediction_scores = self.cls(sequence_output)
  
          masked_lm_loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()  # -100 index = padding token
              masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
  
          if not return_dict:
              output = (prediction_scores,) + outputs[2:]
              return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
  
          return MaskedLMOutput(
              loss=masked_lm_loss,
              logits=prediction_scores,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
      SqueezeBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the
      pooled output) e.g. for GLUE tasks.
      """,
      SQUEEZEBERT_START_DOCSTRING,
  )
  class SqueezeBertForSequenceClassification(SqueezeBertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.transformer = SqueezeBertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="squeezebert/squeezebert-mnli-headless",
          output_type=SequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
              config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
              If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.transformer(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
  
          loss = None
          if labels is not None:
              if self.num_labels == 1:
                  #  We are doing regression
                  loss_fct = MSELoss()
                  loss = loss_fct(logits.view(-1), labels.view(-1))
              else:
                  loss_fct = CrossEntropyLoss()
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
  
          if not return_dict:
              output = (logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return SequenceClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
      SqueezeBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
      a softmax) e.g. for RocStories/SWAG tasks.
      """,
      SQUEEZEBERT_START_DOCSTRING,
  )
  class SqueezeBertForMultipleChoice(SqueezeBertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.transformer = SqueezeBertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, 1)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(
          SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, num_choices, sequence_length)")
      )
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="squeezebert/squeezebert-mnli-headless",
          output_type=MultipleChoiceModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
              num_choices-1]`` where `num_choices` is the size of the second dimension of the input tensors. (see
              `input_ids` above)
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
          num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
  
          input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
          attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
          token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
          position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
          inputs_embeds = (
              inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
              if inputs_embeds is not None
              else None
          )
  
          outputs = self.transformer(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
          reshaped_logits = logits.view(-1, num_choices)
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(reshaped_logits, labels)
  
          if not return_dict:
              output = (reshaped_logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return MultipleChoiceModelOutput(
              loss=loss,
              logits=reshaped_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
      SqueezeBERT 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.
      """,
      SQUEEZEBERT_START_DOCSTRING,
  )
  class SqueezeBertForTokenClassification(SqueezeBertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.transformer = SqueezeBertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="squeezebert/squeezebert-mnli-headless",
          output_type=TokenClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
              1]``.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.transformer(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
  
          sequence_output = self.dropout(sequence_output)
          logits = self.classifier(sequence_output)
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              # Only keep active parts of the loss
              if attention_mask is not None:
                  active_loss = attention_mask.view(-1) == 1
                  active_logits = logits.view(-1, self.num_labels)
                  active_labels = torch.where(
                      active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
                  )
                  loss = loss_fct(active_logits, active_labels)
              else:
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
  
          if not return_dict:
              output = (logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return TokenClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
       SqueezeBERT 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`).
       """,
      SQUEEZEBERT_START_DOCSTRING,
  )
  class SqueezeBertForQuestionAnswering(SqueezeBertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.transformer = SqueezeBertModel(config)
          self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="squeezebert/squeezebert-mnli-headless",
          output_type=QuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          start_positions=None,
          end_positions=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for position (index) of the start of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
              are not taken into account for computing the loss.
          end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for position (index) of the end of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
              are not taken into account for computing the loss.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.transformer(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
  
          logits = self.qa_outputs(sequence_output)
          start_logits, end_logits = logits.split(1, dim=-1)
          start_logits = start_logits.squeeze(-1)
          end_logits = end_logits.squeeze(-1)
  
          total_loss = None
          if start_positions is not None and end_positions is not None:
              # If we are on multi-GPU, split add a dimension
              if len(start_positions.size()) > 1:
                  start_positions = start_positions.squeeze(-1)
              if len(end_positions.size()) > 1:
                  end_positions = end_positions.squeeze(-1)
              # sometimes the start/end positions are outside our model inputs, we ignore these terms
              ignored_index = start_logits.size(1)
              start_positions.clamp_(0, ignored_index)
              end_positions.clamp_(0, ignored_index)
  
              loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
              start_loss = loss_fct(start_logits, start_positions)
              end_loss = loss_fct(end_logits, end_positions)
              total_loss = (start_loss + end_loss) / 2
  
          if not return_dict:
              output = (start_logits, end_logits) + outputs[2:]
              return ((total_loss,) + output) if total_loss is not None else output
  
          return QuestionAnsweringModelOutput(
              loss=total_loss,
              start_logits=start_logits,
              end_logits=end_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  