code: # coding=utf-8
  # Copyright 2018 Google AI, Google Brain 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 ALBERT model. """
  
  import logging
  import math
  import os
  
  import torch
  import torch.nn as nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from transformers.configuration_albert import AlbertConfig
  from transformers.modeling_bert import ACT2FN, BertEmbeddings, BertSelfAttention, prune_linear_layer
  from transformers.modeling_utils import PreTrainedModel
  
  from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
  
  
  logger = logging.getLogger(__name__)
  
  
  ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
      "albert-base-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-pytorch_model.bin",
      "albert-large-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-pytorch_model.bin",
      "albert-xlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-pytorch_model.bin",
      "albert-xxlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-pytorch_model.bin",
      "albert-base-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-pytorch_model.bin",
      "albert-large-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-pytorch_model.bin",
      "albert-xlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-pytorch_model.bin",
      "albert-xxlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-pytorch_model.bin",
  }
  
  
  def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
      """ Load tf checkpoints in a pytorch model."""
      try:
          import re
          import numpy as np
          import tensorflow as tf
      except ImportError:
          logger.error(
              "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
              "https://www.tensorflow.org/install/ for installation instructions."
          )
          raise
      tf_path = os.path.abspath(tf_checkpoint_path)
      logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
      # Load weights from TF model
      init_vars = tf.train.list_variables(tf_path)
      names = []
      arrays = []
      for name, shape in init_vars:
          logger.info("Loading TF weight {} with shape {}".format(name, shape))
          array = tf.train.load_variable(tf_path, name)
          names.append(name)
          arrays.append(array)
  
      for name, array in zip(names, arrays):
          print(name)
  
      for name, array in zip(names, arrays):
          original_name = name
  
          # If saved from the TF HUB module
          name = name.replace("module/", "")
  
          # Renaming and simplifying
          name = name.replace("ffn_1", "ffn")
          name = name.replace("bert/", "albert/")
          name = name.replace("attention_1", "attention")
          name = name.replace("transform/", "")
          name = name.replace("LayerNorm_1", "full_layer_layer_norm")
          name = name.replace("LayerNorm", "attention/LayerNorm")
          name = name.replace("transformer/", "")
  
          # The feed forward layer had an 'intermediate' step which has been abstracted away
          name = name.replace("intermediate/dense/", "")
          name = name.replace("ffn/intermediate/output/dense/", "ffn_output/")
  
          # ALBERT attention was split between self and output which have been abstracted away
          name = name.replace("/output/", "/")
          name = name.replace("/self/", "/")
  
          # The pooler is a linear layer
          name = name.replace("pooler/dense", "pooler")
  
          # The classifier was simplified to predictions from cls/predictions
          name = name.replace("cls/predictions", "predictions")
          name = name.replace("predictions/attention", "predictions")
  
          # Naming was changed to be more explicit
          name = name.replace("embeddings/attention", "embeddings")
          name = name.replace("inner_group_", "albert_layers/")
          name = name.replace("group_", "albert_layer_groups/")
  
          # Classifier
          if len(name.split("/")) == 1 and ("output_bias" in name or "output_weights" in name):
              name = "classifier/" + name
  
          # No ALBERT model currently handles the next sentence prediction task
          if "seq_relationship" in name:
              continue
  
          name = name.split("/")
  
          # Ignore the gradients applied by the LAMB/ADAM optimizers.
          if "adam_m" in name or "adam_v" in name or "global_step" in name:
              logger.info("Skipping {}".format("/".join(name)))
              continue
  
          pointer = model
          for m_name in name:
              if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
                  scope_names = re.split(r"_(\d+)", m_name)
              else:
                  scope_names = [m_name]
  
              if scope_names[0] == "kernel" or scope_names[0] == "gamma":
                  pointer = getattr(pointer, "weight")
              elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
                  pointer = getattr(pointer, "bias")
              elif scope_names[0] == "output_weights":
                  pointer = getattr(pointer, "weight")
              elif scope_names[0] == "squad":
                  pointer = getattr(pointer, "classifier")
              else:
                  try:
                      pointer = getattr(pointer, scope_names[0])
                  except AttributeError:
                      logger.info("Skipping {}".format("/".join(name)))
                      continue
              if len(scope_names) >= 2:
                  num = int(scope_names[1])
                  pointer = pointer[num]
  
          if m_name[-11:] == "_embeddings":
              pointer = getattr(pointer, "weight")
          elif m_name == "kernel":
              array = np.transpose(array)
          try:
              assert pointer.shape == array.shape
          except AssertionError as e:
              e.args += (pointer.shape, array.shape)
              raise
          print("Initialize PyTorch weight {} from {}".format(name, original_name))
          pointer.data = torch.from_numpy(array)
  
      return model
  
  
  class AlbertEmbeddings(BertEmbeddings):
      """
      Construct the embeddings from word, position and token_type embeddings.
      """
  
      def __init__(self, config):
          super().__init__(config)
  
          self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=0)
          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 = torch.nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
  
  
  class AlbertAttention(BertSelfAttention):
      def __init__(self, config):
          super().__init__(config)
  
          self.output_attentions = config.output_attentions
          self.num_attention_heads = config.num_attention_heads
          self.hidden_size = config.hidden_size
          self.attention_head_size = config.hidden_size // config.num_attention_heads
          self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.pruned_heads = set()
  
      def prune_heads(self, heads):
          if len(heads) == 0:
              return
          mask = torch.ones(self.num_attention_heads, self.attention_head_size)
          heads = set(heads) - self.pruned_heads  # Convert to set and emove already pruned heads
          for head in heads:
              # Compute how many pruned heads are before the head and move the index accordingly
              head = head - sum(1 if h < head else 0 for h in self.pruned_heads)
              mask[head] = 0
          mask = mask.view(-1).contiguous().eq(1)
          index = torch.arange(len(mask))[mask].long()
  
          # Prune linear layers
          self.query = prune_linear_layer(self.query, index)
          self.key = prune_linear_layer(self.key, index)
          self.value = prune_linear_layer(self.value, index)
          self.dense = prune_linear_layer(self.dense, index, dim=1)
  
          # Update hyper params and store pruned heads
          self.num_attention_heads = self.num_attention_heads - len(heads)
          self.all_head_size = self.attention_head_size * self.num_attention_heads
          self.pruned_heads = self.pruned_heads.union(heads)
  
      def forward(self, input_ids, attention_mask=None, head_mask=None):
          mixed_query_layer = self.query(input_ids)
          mixed_key_layer = self.key(input_ids)
          mixed_value_layer = self.value(input_ids)
  
          query_layer = self.transpose_for_scores(mixed_query_layer)
          key_layer = self.transpose_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_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
          attention_scores = attention_scores / math.sqrt(self.attention_head_size)
          if attention_mask is not None:
              # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
              attention_scores = attention_scores + attention_mask
  
          # Normalize the attention scores to probabilities.
          attention_probs = nn.Softmax(dim=-1)(attention_scores)
  
          # This is actually dropping out entire tokens to attend to, which might
          # seem a bit unusual, but is taken from the original Transformer paper.
          attention_probs = self.dropout(attention_probs)
  
          # Mask heads if we want to
          if head_mask is not None:
              attention_probs = attention_probs * head_mask
  
          context_layer = torch.matmul(attention_probs, value_layer)
  
          context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
  
          # Should find a better way to do this
          w = (
              self.dense.weight.t()
              .view(self.num_attention_heads, self.attention_head_size, self.hidden_size)
              .to(context_layer.dtype)
          )
          b = self.dense.bias.to(context_layer.dtype)
  
          projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b
          projected_context_layer_dropout = self.dropout(projected_context_layer)
          layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer_dropout)
          return (layernormed_context_layer, attention_probs) if self.output_attentions else (layernormed_context_layer,)
  
  
  class AlbertLayer(nn.Module):
      def __init__(self, config):
          super().__init__()
  
          self.config = config
          self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.attention = AlbertAttention(config)
          self.ffn = nn.Linear(config.hidden_size, config.intermediate_size)
          self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size)
          self.activation = ACT2FN[config.hidden_act]
  
      def forward(self, hidden_states, attention_mask=None, head_mask=None):
          attention_output = self.attention(hidden_states, attention_mask, head_mask)
          ffn_output = self.ffn(attention_output[0])
          ffn_output = self.activation(ffn_output)
          ffn_output = self.ffn_output(ffn_output)
          hidden_states = self.full_layer_layer_norm(ffn_output + attention_output[0])
  
          return (hidden_states,) + attention_output[1:]  # add attentions if we output them
  
  
  class AlbertLayerGroup(nn.Module):
      def __init__(self, config):
          super().__init__()
  
          self.output_attentions = config.output_attentions
          self.output_hidden_states = config.output_hidden_states
          self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)])
  
      def forward(self, hidden_states, attention_mask=None, head_mask=None):
          layer_hidden_states = ()
          layer_attentions = ()
  
          for layer_index, albert_layer in enumerate(self.albert_layers):
              layer_output = albert_layer(hidden_states, attention_mask, head_mask[layer_index])
              hidden_states = layer_output[0]
  
              if self.output_attentions:
                  layer_attentions = layer_attentions + (layer_output[1],)
  
              if self.output_hidden_states:
                  layer_hidden_states = layer_hidden_states + (hidden_states,)
  
          outputs = (hidden_states,)
          if self.output_hidden_states:
              outputs = outputs + (layer_hidden_states,)
          if self.output_attentions:
              outputs = outputs + (layer_attentions,)
          return outputs  # last-layer hidden state, (layer hidden states), (layer attentions)
  
  
  class AlbertTransformer(nn.Module):
      def __init__(self, config):
          super().__init__()
  
          self.config = config
          self.output_attentions = config.output_attentions
          self.output_hidden_states = config.output_hidden_states
          self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)
          self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)])
  
      def forward(self, hidden_states, attention_mask=None, head_mask=None):
          hidden_states = self.embedding_hidden_mapping_in(hidden_states)
  
          all_attentions = ()
  
          if self.output_hidden_states:
              all_hidden_states = (hidden_states,)
  
          for i in range(self.config.num_hidden_layers):
              # Number of layers in a hidden group
              layers_per_group = int(self.config.num_hidden_layers / self.config.num_hidden_groups)
  
              # Index of the hidden group
              group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups))
  
              layer_group_output = self.albert_layer_groups[group_idx](
                  hidden_states,
                  attention_mask,
                  head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group],
              )
              hidden_states = layer_group_output[0]
  
              if self.output_attentions:
                  all_attentions = all_attentions + layer_group_output[-1]
  
              if self.output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
  
          outputs = (hidden_states,)
          if self.output_hidden_states:
              outputs = outputs + (all_hidden_states,)
          if self.output_attentions:
              outputs = outputs + (all_attentions,)
          return outputs  # last-layer hidden state, (all hidden states), (all attentions)
  
  
  class AlbertPreTrainedModel(PreTrainedModel):
      """ An abstract class to handle weights initialization and
          a simple interface for downloading and loading pretrained models.
      """
  
      config_class = AlbertConfig
      pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
      base_model_prefix = "albert"
  
      def _init_weights(self, module):
          """ Initialize the weights.
          """
          if isinstance(module, (nn.Linear, nn.Embedding)):
              # Slightly different from the TF version which uses truncated_normal for initialization
              # cf https://github.com/pytorch/pytorch/pull/5617
              module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
              if isinstance(module, (nn.Linear)) and module.bias is not None:
                  module.bias.data.zero_()
          elif isinstance(module, nn.LayerNorm):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0)
  
  
  ALBERT_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.
  
      Args:
          config (:class:`~transformers.AlbertConfig`): 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.
  """
  
  ALBERT_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.AlbertTokenizer`.
              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>`__
          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>`_
          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 ALBERT Model transformer outputting raw hidden-states without any specific head on top.",
      ALBERT_START_DOCSTRING,
  )
  class AlbertModel(AlbertPreTrainedModel):
  
      config_class = AlbertConfig
      pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
      load_tf_weights = load_tf_weights_in_albert
      base_model_prefix = "albert"
  
      def __init__(self, config):
          super().__init__(config)
  
          self.config = config
          self.embeddings = AlbertEmbeddings(config)
          self.encoder = AlbertTransformer(config)
          self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
          self.pooler_activation = nn.Tanh()
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embeddings.word_embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.word_embeddings = value
  
      def _resize_token_embeddings(self, new_num_tokens):
          old_embeddings = self.embeddings.word_embeddings
          new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
          self.embeddings.word_embeddings = new_embeddings
          return self.embeddings.word_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}
              ALBERT has a different architecture in that its layers are shared across groups, which then has inner groups.
              If an ALBERT model has 12 hidden layers and 2 hidden groups, with two inner groups, there
              is a total of 4 different layers.
  
              These layers are flattened: the indices [0,1] correspond to the two inner groups of the first hidden layer,
              while [2,3] correspond to the two inner groups of the second hidden layer.
  
              Any layer with in index other than [0,1,2,3] will result in an error.
              See base class PreTrainedModel for more information about head pruning
          """
          for layer, heads in heads_to_prune.items():
              group_idx = int(layer / self.config.inner_group_num)
              inner_group_idx = int(layer - group_idx * self.config.inner_group_num)
              self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads)
  
      @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
      ):
          r"""
      Return:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) 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.
          pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
              Last layer hidden-state of the first token of the sequence (classification token)
              further processed by a Linear layer and a Tanh activation function. The Linear
              layer weights are trained from the next sentence prediction (classification)
              objective during pre-training.
  
              This output is usually *not* a good summary
              of the semantic content of the input, you're often better with averaging or pooling
              the sequence of hidden-states for the whole input sequence.
          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.
  
      Example::
  
          from transformers import AlbertModel, AlbertTokenizer
          import torch
  
          tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
          model = AlbertModel.from_pretrained('albert-base-v2')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids)
          last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
  
          """
  
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
          elif input_ids is not None:
              input_shape = input_ids.size()
          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 = attention_mask.unsqueeze(1).unsqueeze(2)
          extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
          extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
          if head_mask is not None:
              if head_mask.dim() == 1:
                  head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
                  head_mask = head_mask.expand(self.config.num_hidden_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.config.num_hidden_layers
  
          embedding_output = self.embeddings(
              input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
          )
          encoder_outputs = self.encoder(embedding_output, extended_attention_mask, head_mask=head_mask)
  
          sequence_output = encoder_outputs[0]
  
          pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0]))
  
          outputs = (sequence_output, pooled_output) + encoder_outputs[
              1:
          ]  # add hidden_states and attentions if they are here
          return outputs
  
  
  class AlbertMLMHead(nn.Module):
      def __init__(self, config):
          super().__init__()
  
          self.LayerNorm = nn.LayerNorm(config.embedding_size)
          self.bias = nn.Parameter(torch.zeros(config.vocab_size))
          self.dense = nn.Linear(config.hidden_size, config.embedding_size)
          self.decoder = nn.Linear(config.embedding_size, config.vocab_size)
          self.activation = ACT2FN[config.hidden_act]
  
          # 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.dense(hidden_states)
          hidden_states = self.activation(hidden_states)
          hidden_states = self.LayerNorm(hidden_states)
          hidden_states = self.decoder(hidden_states)
  
          prediction_scores = hidden_states + self.bias
  
          return prediction_scores
  
  
  @add_start_docstrings(
      "Albert Model with a `language modeling` head on top.", ALBERT_START_DOCSTRING,
  )
  class AlbertForMaskedLM(AlbertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.albert = AlbertModel(config)
          self.predictions = AlbertMLMHead(config)
  
          self.init_weights()
          self.tie_weights()
  
      def tie_weights(self):
          self._tie_or_clone_weights(self.predictions.decoder, self.albert.embeddings.word_embeddings)
  
      def get_output_embeddings(self):
          return self.predictions.decoder
  
      @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          masked_lm_labels=None,
      ):
          r"""
          masked_lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              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]``
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs:
          loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
              Masked 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.
  
      Example::
  
          from transformers import AlbertTokenizer, AlbertForMaskedLM
          import torch
  
          tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
          model = AlbertForMaskedLM.from_pretrained('albert-base-v2')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, masked_lm_labels=input_ids)
          loss, prediction_scores = outputs[:2]
  
          """
          outputs = self.albert(
              input_ids=input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
          )
          sequence_outputs = outputs[0]
  
          prediction_scores = self.predictions(sequence_outputs)
  
          outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here
          if masked_lm_labels is not None:
              loss_fct = CrossEntropyLoss()
              masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
              outputs = (masked_lm_loss,) + outputs
  
          return outputs
  
  
  @add_start_docstrings(
      """Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of
      the pooled output) e.g. for GLUE tasks. """,
      ALBERT_START_DOCSTRING,
  )
  class AlbertForSequenceClassification(AlbertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.albert = AlbertModel(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_callable(ALBERT_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=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 ``[0, ..., config.num_labels - 1]``.
              If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
              If ``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.AlbertConfig`) and inputs:
          loss: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
              Classification (or regression if config.num_labels==1) loss.
          logits ``torch.FloatTensor`` of shape ``(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 AlbertTokenizer, AlbertForSequenceClassification
              import torch
  
              tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
              model = AlbertForSequenceClassification.from_pretrained('albert-base-v2')
              input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
              labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
              outputs = model(input_ids, labels=labels)
              loss, logits = outputs[:2]
  
          """
  
          outputs = self.albert(
              input_ids=input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
          )
  
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
  
          outputs = (logits,) + outputs[2:]  # add hidden states and attention 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  # (loss), logits, (hidden_states), (attentions)
  
  
  @add_start_docstrings(
      """Albert 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`). """,
      ALBERT_START_DOCSTRING,
  )
  class AlbertForQuestionAnswering(AlbertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.albert = AlbertModel(config)
          self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING)
      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,
      ):
          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.AlbertConfig`) and inputs:
          loss: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
              Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
          start_scores ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
              Span-start scores (before SoftMax).
          end_scores: ``torch.FloatTensor`` of shape ``(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::
  
          # The checkpoint albert-base-v2 is not fine-tuned for question answering. Please see the
          # examples/run_squad.py example to see how to fine-tune a model to a question answering task.
  
          from transformers import AlbertTokenizer, AlbertForQuestionAnswering
          import torch
  
          tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
          model = AlbertForQuestionAnswering.from_pretrained('albert-base-v2')
          question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
          input_dict = tokenizer.encode_plus(question, text, return_tensors='pt')
          start_scores, end_scores = model(**input_dict)
  
          """
  
          outputs = self.albert(
              input_ids=input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
          )
  
          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)
  
          outputs = (start_logits, end_logits,) + outputs[2:]
          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
  
          return outputs  # (loss), start_logits, end_logits, (hidden_states), (attentions)
  
apis: torch.ones torch.nn.Linear torch.einsum numpy.transpose torch.nn.MSELoss tensorflow.train.list_variables torch.nn.Softmax torch.nn.Embedding torch.nn.Tanh torch.nn.CrossEntropyLoss torch.from_numpy torch.nn.LayerNorm torch.zeros tensorflow.train.load_variable torch.nn.Dropout torch.matmul