roberta.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch RoBERTa model."""

import random
import math
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from transformers.activations import ACT2FN, gelu
from transformers.modeling_outputs import (
   BaseModelOutputWithPastAndCrossAttentions,
   BaseModelOutputWithPoolingAndCrossAttentions,
   CausalLMOutputWithCrossAttentions,
   MaskedLMOutput,
   MultipleChoiceModelOutput,
   QuestionAnsweringModelOutput,
   SequenceClassifierOutput,
   TokenClassifierOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from transformers.utils import (
   add_code_sample_docstrings,
   add_start_docstrings,
   add_start_docstrings_to_model_forward,
   logging,
   replace_return_docstrings,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "roberta-base"
_CONFIG_FOR_DOC = "RobertaConfig"

ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
   "roberta-base",
   "roberta-large",
   "roberta-large-mnli",
   "distilroberta-base",
   "roberta-base-openai-detector",
   "roberta-large-openai-detector",
   # See all RoBERTa models at https://huggingface.co/models?filter=roberta
]


class RobertaEmbeddings(nn.Module):
   """
   Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
   """

   # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
   def __init__(self, config):
       super().__init__()
       self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
       self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
       self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_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.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
       self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
       self.register_buffer(
           "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
       )

       # End copy
       self.padding_idx = config.pad_token_id
       self.position_embeddings = nn.Embedding(
           config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
       )

       # init neighboring entity token embedding
       k = config.k_size
       self.entity_embeddings_k = []
       for _ in range(k - 1):
           entity_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
           self.entity_embeddings_k.append(entity_embeddings)
       self.entity_embeddings_k = nn.ParameterList(self.entity_embeddings_k)

   def forward(
       self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0,
           entity_mask=None
   ):
       if position_ids is None:
           if input_ids is not None:
               # Create the position ids from the input token ids. Any padded tokens remain padded.
               position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
           else:
               position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)

       if input_ids is not None:
           input_shape = input_ids.size()
       else:
           input_shape = inputs_embeds.size()[:-1]

       seq_length = input_shape[1]

       # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
       # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
       # issue #5664
       if token_type_ids is None:
           if hasattr(self, "token_type_ids"):
               buffered_token_type_ids = self.token_type_ids[:, :seq_length]
               buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
               token_type_ids = buffered_token_type_ids_expanded
           else:
               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.to('cuda:0'))
       token_type_embeddings = self.token_type_embeddings(token_type_ids)

       embeddings = inputs_embeds + token_type_embeddings
       if self.position_embedding_type == "absolute":
           position_embeddings = self.position_embeddings(position_ids)
           embeddings += position_embeddings

       # Create the covex hull
       entity_mask = entity_mask.to('cuda:0').unsqueeze(-1)
       entity_inputs_embeds_k = []
       for self_entity_embeds in self.entity_embeddings_k:
           entity_inputs_embeds = self_entity_embeds(input_ids)
           entity_inputs_embeds_k.append(entity_inputs_embeds)
       k_size = len(self.entity_embeddings_k)

       if self.training:
           # Randomly sample entity token embedding within the convex hull during training.
           # Note that we use linear transformation of vertices to mimic sampling, so the distribution is not uniform.
           random_weight_list = []
           mul = 1
           for k in range(k_size):
               random_weight = torch.rand(entity_mask.size()[0], 1, 1).to('cuda:0') * mul
               random_weight_list.append(random_weight)
               mul -= random_weight
           random_weight_list.append(mul)
           mixed_embeddings = (1 - entity_mask) * embeddings + random_weight_list[0] * entity_mask * embeddings
           for k in range(k_size):
               me = random_weight_list[k + 1] * entity_mask * entity_inputs_embeds_k[k]
               mixed_embeddings += me
           embeddings = mixed_embeddings
       else:
           # Replace the entity token embedding with the center of the convex hull during inference. 
           k = k_size + 1
           mixed_embeddings = (1 - entity_mask) * embeddings + 1 / k * entity_mask * embeddings
           for entity_inputs_embeds in entity_inputs_embeds_k:
               mixed_embeddings += 1/k * entity_mask * entity_inputs_embeds
           embeddings = mixed_embeddings

       embeddings = self.LayerNorm(embeddings)
       embeddings = self.dropout(embeddings)
       return embeddings

   def create_position_ids_from_inputs_embeds(self, inputs_embeds):
       """
       We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.

       Args:
           inputs_embeds: torch.Tensor

       Returns: torch.Tensor
       """
       input_shape = inputs_embeds.size()[:-1]
       sequence_length = input_shape[1]

       position_ids = torch.arange(
           self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
       )
       return position_ids.unsqueeze(0).expand(input_shape)


# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta
class RobertaSelfAttention(nn.Module):
   def __init__(self, config, position_embedding_type=None):
       super().__init__()
       if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
           raise ValueError(
               f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
               f"heads ({config.num_attention_heads})"
           )

       self.num_attention_heads = config.num_attention_heads
       self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
       self.all_head_size = self.num_attention_heads * self.attention_head_size

       self.query = nn.Linear(config.hidden_size, self.all_head_size)
       self.key = nn.Linear(config.hidden_size, self.all_head_size)
       self.value = nn.Linear(config.hidden_size, self.all_head_size)

       self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
       self.position_embedding_type = position_embedding_type or getattr(
           config, "position_embedding_type", "absolute"
       )
       if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
           self.max_position_embeddings = config.max_position_embeddings
           self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)

       self.is_decoder = config.is_decoder

   def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
       new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
       x = x.view(new_x_shape)
       return x.permute(0, 2, 1, 3)

   def forward(
       self,
       hidden_states: torch.Tensor,
       attention_mask: Optional[torch.FloatTensor] = None,
       head_mask: Optional[torch.FloatTensor] = None,
       encoder_hidden_states: Optional[torch.FloatTensor] = None,
       encoder_attention_mask: Optional[torch.FloatTensor] = None,
       past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
       output_attentions: Optional[bool] = False,
   ) -> Tuple[torch.Tensor]:
       mixed_query_layer = self.query(hidden_states)

       # If this is instantiated as a cross-attention module, the keys
       # and values come from an encoder; the attention mask needs to be
       # such that the encoder's padding tokens are not attended to.
       is_cross_attention = encoder_hidden_states is not None

       if is_cross_attention and past_key_value is not None:
           # reuse k,v, cross_attentions
           key_layer = past_key_value[0]
           value_layer = past_key_value[1]
           attention_mask = encoder_attention_mask
       elif is_cross_attention:
           key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
           value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
           attention_mask = encoder_attention_mask
       elif past_key_value is not None:
           key_layer = self.transpose_for_scores(self.key(hidden_states))
           value_layer = self.transpose_for_scores(self.value(hidden_states))
           key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
           value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
       else:
           key_layer = self.transpose_for_scores(self.key(hidden_states))
           value_layer = self.transpose_for_scores(self.value(hidden_states))

       query_layer = self.transpose_for_scores(mixed_query_layer)

       use_cache = past_key_value is not None
       if self.is_decoder:
           # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
           # Further calls to cross_attention layer can then reuse all cross-attention
           # key/value_states (first "if" case)
           # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
           # all previous decoder key/value_states. Further calls to uni-directional self-attention
           # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
           # if encoder bi-directional self-attention `past_key_value` is always `None`
           past_key_value = (key_layer, 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))

       if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
           query_length, key_length = query_layer.shape[2], key_layer.shape[2]
           if use_cache:
               position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
                   -1, 1
               )
           else:
               position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
           position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
           distance = position_ids_l - position_ids_r

           positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
           positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility

           if self.position_embedding_type == "relative_key":
               relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
               attention_scores = attention_scores + relative_position_scores
           elif self.position_embedding_type == "relative_key_query":
               relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
               relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
               attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key

       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 RobertaModel forward() function)
           attention_scores = attention_scores + attention_mask

       # Normalize the attention scores to probabilities.
       attention_probs = nn.functional.softmax(attention_scores, dim=-1)

       # 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()
       new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
       context_layer = context_layer.view(new_context_layer_shape)

       outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

       if self.is_decoder:
           outputs = outputs + (past_key_value,)
       return outputs


# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
class RobertaSelfOutput(nn.Module):
   def __init__(self, config):
       super().__init__()
       self.dense = nn.Linear(config.hidden_size, config.hidden_size)
       self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
       self.dropout = nn.Dropout(config.hidden_dropout_prob)

   def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
       hidden_states = self.dense(hidden_states)
       hidden_states = self.dropout(hidden_states)
       hidden_states = self.LayerNorm(hidden_states + input_tensor)
       return hidden_states


# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta
class RobertaAttention(nn.Module):
   def __init__(self, config, position_embedding_type=None):
       super().__init__()
       self.self = RobertaSelfAttention(config, position_embedding_type=position_embedding_type)
       self.output = RobertaSelfOutput(config)
       self.pruned_heads = set()

   def prune_heads(self, heads):
       if len(heads) == 0:
           return
       heads, index = find_pruneable_heads_and_indices(
           heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
       )

       # Prune linear layers
       self.self.query = prune_linear_layer(self.self.query, index)
       self.self.key = prune_linear_layer(self.self.key, index)
       self.self.value = prune_linear_layer(self.self.value, index)
       self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

       # Update hyper params and store pruned heads
       self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
       self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
       self.pruned_heads = self.pruned_heads.union(heads)

   def forward(
       self,
       hidden_states: torch.Tensor,
       attention_mask: Optional[torch.FloatTensor] = None,
       head_mask: Optional[torch.FloatTensor] = None,
       encoder_hidden_states: Optional[torch.FloatTensor] = None,
       encoder_attention_mask: Optional[torch.FloatTensor] = None,
       past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
       output_attentions: Optional[bool] = False,
   ) -> Tuple[torch.Tensor]:
       self_outputs = self.self(
           hidden_states,
           attention_mask,
           head_mask,
           encoder_hidden_states,
           encoder_attention_mask,
           past_key_value,
           output_attentions,
       )
       attention_output = self.output(self_outputs[0], hidden_states)
       outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
       return outputs


# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class RobertaIntermediate(nn.Module):
   def __init__(self, config):
       super().__init__()
       self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
       if isinstance(config.hidden_act, str):
           self.intermediate_act_fn = ACT2FN[config.hidden_act]
       else:
           self.intermediate_act_fn = config.hidden_act

   def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
       hidden_states = self.dense(hidden_states)
       hidden_states = self.intermediate_act_fn(hidden_states)
       return hidden_states


# Copied from transformers.models.bert.modeling_bert.BertOutput
class RobertaOutput(nn.Module):
   def __init__(self, config):
       super().__init__()
       self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
       self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
       self.dropout = nn.Dropout(config.hidden_dropout_prob)

   def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
       hidden_states = self.dense(hidden_states)
       hidden_states = self.dropout(hidden_states)
       hidden_states = self.LayerNorm(hidden_states + input_tensor)
       return hidden_states


# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta
class RobertaLayer(nn.Module):
   def __init__(self, config):
       super().__init__()
       self.chunk_size_feed_forward = config.chunk_size_feed_forward
       self.seq_len_dim = 1
       self.attention = RobertaAttention(config)
       self.is_decoder = config.is_decoder
       self.add_cross_attention = config.add_cross_attention
       if self.add_cross_attention:
           if not self.is_decoder:
               raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
           self.crossattention = RobertaAttention(config, position_embedding_type="absolute")
       self.intermediate = RobertaIntermediate(config)
       self.output = RobertaOutput(config)

   def forward(
       self,
       hidden_states: torch.Tensor,
       attention_mask: Optional[torch.FloatTensor] = None,
       head_mask: Optional[torch.FloatTensor] = None,
       encoder_hidden_states: Optional[torch.FloatTensor] = None,
       encoder_attention_mask: Optional[torch.FloatTensor] = None,
       past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
       output_attentions: Optional[bool] = False,
   ) -> Tuple[torch.Tensor]:
       # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
       self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
       self_attention_outputs = self.attention(
           hidden_states,
           attention_mask,
           head_mask,
           output_attentions=output_attentions,
           past_key_value=self_attn_past_key_value,
       )
       attention_output = self_attention_outputs[0]

       # if decoder, the last output is tuple of self-attn cache
       if self.is_decoder:
           outputs = self_attention_outputs[1:-1]
           present_key_value = self_attention_outputs[-1]
       else:
           outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

       cross_attn_present_key_value = None
       if self.is_decoder and encoder_hidden_states is not None:
           if not hasattr(self, "crossattention"):
               raise ValueError(
                   f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
                   " by setting `config.add_cross_attention=True`"
               )

           # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
           cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
           cross_attention_outputs = self.crossattention(
               attention_output,
               attention_mask,
               head_mask,
               encoder_hidden_states,
               encoder_attention_mask,
               cross_attn_past_key_value,
               output_attentions,
           )
           attention_output = cross_attention_outputs[0]
           outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights

           # add cross-attn cache to positions 3,4 of present_key_value tuple
           cross_attn_present_key_value = cross_attention_outputs[-1]
           present_key_value = present_key_value + cross_attn_present_key_value

       layer_output = apply_chunking_to_forward(
           self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
       )
       outputs = (layer_output,) + outputs

       # if decoder, return the attn key/values as the last output
       if self.is_decoder:
           outputs = outputs + (present_key_value,)

       return outputs

   def feed_forward_chunk(self, attention_output):
       intermediate_output = self.intermediate(attention_output)
       layer_output = self.output(intermediate_output, attention_output)
       return layer_output


# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta
class RobertaEncoder(nn.Module):
   def __init__(self, config):
       super().__init__()
       self.config = config
       self.layer = nn.ModuleList([RobertaLayer(config) for _ in range(config.num_hidden_layers)])
       self.gradient_checkpointing = False

   def forward(
       self,
       hidden_states: torch.Tensor,
       attention_mask: Optional[torch.FloatTensor] = None,
       head_mask: Optional[torch.FloatTensor] = None,
       encoder_hidden_states: Optional[torch.FloatTensor] = None,
       encoder_attention_mask: Optional[torch.FloatTensor] = None,
       past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
       use_cache: Optional[bool] = None,
       output_attentions: Optional[bool] = False,
       output_hidden_states: Optional[bool] = False,
       return_dict: Optional[bool] = True,
   ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
       all_hidden_states = () if output_hidden_states else None
       all_self_attentions = () if output_attentions else None
       all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None

       next_decoder_cache = () if use_cache else None
       for i, layer_module in enumerate(self.layer):
           if output_hidden_states:
               all_hidden_states = all_hidden_states + (hidden_states,)

           layer_head_mask = head_mask[i] if head_mask is not None else None
           past_key_value = past_key_values[i] if past_key_values is not None else None

           if self.gradient_checkpointing and self.training:
               if use_cache:
                   logger.warning(
                       "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                   )
                   use_cache = False

               def create_custom_forward(module):
                   def custom_forward(*inputs):
                       return module(*inputs, past_key_value, output_attentions)

                   return custom_forward

               layer_outputs = torch.utils.checkpoint.checkpoint(
                   create_custom_forward(layer_module),
                   hidden_states,
                   attention_mask,
                   layer_head_mask,
                   encoder_hidden_states,
                   encoder_attention_mask,
               )
           else:
               layer_outputs = layer_module(
                   hidden_states,
                   attention_mask,
                   layer_head_mask,
                   encoder_hidden_states,
                   encoder_attention_mask,
                   past_key_value,
                   output_attentions,
               )

           hidden_states = layer_outputs[0]
           if use_cache:
               next_decoder_cache += (layer_outputs[-1],)
           if output_attentions:
               all_self_attentions = all_self_attentions + (layer_outputs[1],)
               if self.config.add_cross_attention:
                   all_cross_attentions = all_cross_attentions + (layer_outputs[2],)

       if output_hidden_states:
           all_hidden_states = all_hidden_states + (hidden_states,)

       if not return_dict:
           return tuple(
               v
               for v in [
                   hidden_states,
                   next_decoder_cache,
                   all_hidden_states,
                   all_self_attentions,
                   all_cross_attentions,
               ]
               if v is not None
           )
       return BaseModelOutputWithPastAndCrossAttentions(
           last_hidden_state=hidden_states,
           past_key_values=next_decoder_cache,
           hidden_states=all_hidden_states,
           attentions=all_self_attentions,
           cross_attentions=all_cross_attentions,
       )


# Copied from transformers.models.bert.modeling_bert.BertPooler
class RobertaPooler(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: torch.Tensor) -> torch.Tensor:
       # 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 RobertaPreTrainedModel(PreTrainedModel):
   """
   An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
   models.
   """

   config_class = RobertaConfig
   base_model_prefix = "roberta"
   supports_gradient_checkpointing = True
   _no_split_modules = []

   # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
   def _init_weights(self, module):
       """Initialize the weights"""
       if isinstance(module, nn.Linear):
           # 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 module.bias is not None:
               module.bias.data.zero_()
       elif isinstance(module, nn.Embedding):
           module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
           if module.padding_idx is not None:
               module.weight.data[module.padding_idx].zero_()
       elif isinstance(module, nn.LayerNorm):
           module.bias.data.zero_()
           module.weight.data.fill_(1.0)

   def _set_gradient_checkpointing(self, module, value=False):
       if isinstance(module, RobertaEncoder):
           module.gradient_checkpointing = value

   def update_keys_to_ignore(self, config, del_keys_to_ignore):
       """Remove some keys from ignore list"""
       if not config.tie_word_embeddings:
           # must make a new list, or the class variable gets modified!
           self._keys_to_ignore_on_save = [k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore]
           self._keys_to_ignore_on_load_missing = [
               k for k in self._keys_to_ignore_on_load_missing if k not in del_keys_to_ignore
           ]


ROBERTA_START_DOCSTRING = r"""

   This model inherits from [`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.

   Parameters:
       config ([`RobertaConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

ROBERTA_INPUTS_DOCSTRING = r"""
   Args:
       input_ids (`torch.LongTensor` of shape `({0})`):
           Indices of input sequence tokens in the vocabulary.

           Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
           [`PreTrainedTokenizer.__call__`] for details.

           [What are input IDs?](../glossary#input-ids)
       attention_mask (`torch.FloatTensor` of shape `({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#attention-mask)
       token_type_ids (`torch.LongTensor` of shape `({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.
           This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value
           >= 2. All the value in this tensor should be always < type_vocab_size.

           [What are token type IDs?](../glossary#token-type-ids)
       position_ids (`torch.LongTensor` of shape `({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#position-ids)
       head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(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 (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
           Optionally, instead of passing `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.
       output_attentions (`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 (`bool`, *optional*):
           Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
           more detail.
       return_dict (`bool`, *optional*):
           Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
   "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
   ROBERTA_START_DOCSTRING,
)
class RobertaModel(RobertaPreTrainedModel):
   """

   The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
   cross-attention is added between the self-attention layers, following the architecture described in *Attention is
   all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
   Kaiser and Illia Polosukhin.

   To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
   to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
   `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.

   .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762

   """

   _keys_to_ignore_on_load_missing = [r"position_ids"]

   # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta
   def __init__(self, config, add_pooling_layer=True):
       super().__init__(config)
       self.config = config

       self.embeddings = RobertaEmbeddings(config)
       self.encoder = RobertaEncoder(config)

       self.pooler = RobertaPooler(config) if add_pooling_layer else None

       # Initialize weights and apply final processing
       self.post_init()

   def get_input_embeddings(self):
       return self.embeddings.word_embeddings

   def set_input_embeddings(self, value):
       self.embeddings.word_embeddings = value

   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(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC,
       output_type=BaseModelOutputWithPoolingAndCrossAttentions,
       config_class=_CONFIG_FOR_DOC,
   )
   # Copied from transformers.models.bert.modeling_bert.BertModel.forward
   def forward(
       self,
       input_ids: Optional[torch.Tensor] = None,
       attention_mask: Optional[torch.Tensor] = None,
       token_type_ids: Optional[torch.Tensor] = None,
       position_ids: Optional[torch.Tensor] = None,
       head_mask: Optional[torch.Tensor] = None,
       inputs_embeds: Optional[torch.Tensor] = None,
       encoder_hidden_states: Optional[torch.Tensor] = None,
       encoder_attention_mask: Optional[torch.Tensor] = None,
       past_key_values: Optional[List[torch.FloatTensor]] = None,
       use_cache: Optional[bool] = None,
       output_attentions: Optional[bool] = None,
       output_hidden_states: Optional[bool] = None,
       return_dict: Optional[bool] = None,
       entity_mask: Optional[torch.Tensor] = None,
   ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
       r"""
       encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
           Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
           the model is configured as a decoder.
       encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
           Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
           the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

           - 1 for tokens that are **not masked**,
           - 0 for tokens that are **masked**.
       past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
           Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

           If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
           don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
           `decoder_input_ids` of shape `(batch_size, sequence_length)`.
       use_cache (`bool`, *optional*):
           If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
           `past_key_values`).
       """
       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 self.config.is_decoder:
           use_cache = use_cache if use_cache is not None else self.config.use_cache
       else:
           use_cache = False

       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")

       batch_size, seq_length = input_shape
       device = input_ids.device if input_ids is not None else inputs_embeds.device

       # past_key_values_length
       past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

       if attention_mask is None:
           attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)

       if token_type_ids is None:
           if hasattr(self.embeddings, "token_type_ids"):
               buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
               buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
               token_type_ids = buffered_token_type_ids_expanded
           else:
               token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

       # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
       # ourselves in which case we just need to make it broadcastable to all heads.
       extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)

       # If a 2D or 3D attention mask is provided for the cross-attention
       # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
       if self.config.is_decoder and encoder_hidden_states is not None:
           encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
           encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
           if encoder_attention_mask is None:
               encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
           encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
       else:
           encoder_extended_attention_mask = None

       # 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,
           past_key_values_length=past_key_values_length,
           entity_mask=entity_mask,
       )
       encoder_outputs = self.encoder(
           embedding_output,
           attention_mask=extended_attention_mask,
           head_mask=head_mask,
           encoder_hidden_states=encoder_hidden_states,
           encoder_attention_mask=encoder_extended_attention_mask,
           past_key_values=past_key_values,
           use_cache=use_cache,
           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 self.pooler is not None else None

       if not return_dict:
           return (sequence_output, pooled_output) + encoder_outputs[1:]

       return BaseModelOutputWithPoolingAndCrossAttentions(
           last_hidden_state=sequence_output,
           pooler_output=pooled_output,
           past_key_values=encoder_outputs.past_key_values,
           hidden_states=encoder_outputs.hidden_states,
           attentions=encoder_outputs.attentions,
           cross_attentions=encoder_outputs.cross_attentions,
       )


@add_start_docstrings(
   """RoBERTa Model with a `language modeling` head on top for CLM fine-tuning.""", ROBERTA_START_DOCSTRING
)
class RobertaForCausalLM(RobertaPreTrainedModel):
   _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
   _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
   _keys_to_ignore_on_load_unexpected = [r"pooler"]

   def __init__(self, config):
       super().__init__(config)

       if not config.is_decoder:
           logger.warning("If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`")

       self.roberta = RobertaModel(config, add_pooling_layer=False)
       self.lm_head = RobertaLMHead(config)

       # The LM head weights require special treatment only when they are tied with the word embeddings
       self.update_keys_to_ignore(config, ["lm_head.decoder.weight"])

       # Initialize weights and apply final processing
       self.post_init()

   def get_output_embeddings(self):
       return self.lm_head.decoder

   def set_output_embeddings(self, new_embeddings):
       self.lm_head.decoder = new_embeddings

   @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
   @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
   def forward(
       self,
       input_ids: Optional[torch.LongTensor] = None,
       attention_mask: Optional[torch.FloatTensor] = None,
       token_type_ids: Optional[torch.LongTensor] = None,
       position_ids: Optional[torch.LongTensor] = None,
       head_mask: Optional[torch.FloatTensor] = None,
       inputs_embeds: Optional[torch.FloatTensor] = None,
       encoder_hidden_states: Optional[torch.FloatTensor] = None,
       encoder_attention_mask: Optional[torch.FloatTensor] = None,
       labels: Optional[torch.LongTensor] = None,
       past_key_values: Tuple[Tuple[torch.FloatTensor]] = None,
       use_cache: Optional[bool] = None,
       output_attentions: Optional[bool] = None,
       output_hidden_states: Optional[bool] = None,
       return_dict: Optional[bool] = None,
   ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
       r"""
       encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
           Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
           the model is configured as a decoder.
       encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
           Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
           the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

           - 1 for tokens that are **not masked**,
           - 0 for tokens that are **masked**.

       labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
           Labels for computing the left-to-right language modeling loss (next word prediction). 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]`
       past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
           Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

           If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
           don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
           `decoder_input_ids` of shape `(batch_size, sequence_length)`.
       use_cache (`bool`, *optional*):
           If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
           `past_key_values`).

       Returns:

       Example:

       ```python
       >>> from transformers import AutoTokenizer, RobertaForCausalLM, AutoConfig
       >>> import torch

       >>> tokenizer = AutoTokenizer.from_pretrained("roberta-base")
       >>> config = AutoConfig.from_pretrained("roberta-base")
       >>> config.is_decoder = True
       >>> model = RobertaForCausalLM.from_pretrained("roberta-base", config=config)

       >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
       >>> outputs = model(**inputs)

       >>> prediction_logits = outputs.logits
       ```"""
       return_dict = return_dict if return_dict is not None else self.config.use_return_dict
       if labels is not None:
           use_cache = False

       outputs = self.roberta(
           input_ids,
           attention_mask=attention_mask,
           token_type_ids=token_type_ids,
           position_ids=position_ids,
           head_mask=head_mask,
           inputs_embeds=inputs_embeds,
           encoder_hidden_states=encoder_hidden_states,
           encoder_attention_mask=encoder_attention_mask,
           past_key_values=past_key_values,
           use_cache=use_cache,
           output_attentions=output_attentions,
           output_hidden_states=output_hidden_states,
           return_dict=return_dict,
       )

       sequence_output = outputs[0]
       prediction_scores = self.lm_head(sequence_output)

       lm_loss = None
       if labels is not None:
           # we are doing next-token prediction; shift prediction scores and input ids by one
           shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
           labels = labels[:, 1:].contiguous()
           loss_fct = CrossEntropyLoss()
           lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

       if not return_dict:
           output = (prediction_scores,) + outputs[2:]
           return ((lm_loss,) + output) if lm_loss is not None else output

       return CausalLMOutputWithCrossAttentions(
           loss=lm_loss,
           logits=prediction_scores,
           past_key_values=outputs.past_key_values,
           hidden_states=outputs.hidden_states,
           attentions=outputs.attentions,
           cross_attentions=outputs.cross_attentions,
       )

   def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
       input_shape = input_ids.shape
       # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
       if attention_mask is None:
           attention_mask = input_ids.new_ones(input_shape)

       # cut decoder_input_ids if past is used
       if past_key_values is not None:
           input_ids = input_ids[:, -1:]

       return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}

   def _reorder_cache(self, past_key_values, beam_idx):
       reordered_past = ()
       for layer_past in past_key_values:
           reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
       return reordered_past


@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top.""", ROBERTA_START_DOCSTRING)
class RobertaForMaskedLM(RobertaPreTrainedModel):
   _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
   _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
   _keys_to_ignore_on_load_unexpected = [r"pooler"]

   def __init__(self, config):
       super().__init__(config)

       if config.is_decoder:
           logger.warning(
               "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for "
               "bi-directional self-attention."
           )

       self.roberta = RobertaModel(config, add_pooling_layer=False)
       self.lm_head = RobertaLMHead(config)

       # The LM head weights require special treatment only when they are tied with the word embeddings
       self.update_keys_to_ignore(config, ["lm_head.decoder.weight"])

       # Initialize weights and apply final processing
       self.post_init()

   def get_output_embeddings(self):
       return self.lm_head.decoder

   def set_output_embeddings(self, new_embeddings):
       self.lm_head.decoder = new_embeddings

   @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC,
       output_type=MaskedLMOutput,
       config_class=_CONFIG_FOR_DOC,
       mask="<mask>",
       expected_output="' Paris'",
       expected_loss=0.1,
   )
   def forward(
       self,
       input_ids: Optional[torch.LongTensor] = None,
       attention_mask: Optional[torch.FloatTensor] = None,
       token_type_ids: Optional[torch.LongTensor] = None,
       position_ids: Optional[torch.LongTensor] = None,
       head_mask: Optional[torch.FloatTensor] = None,
       inputs_embeds: Optional[torch.FloatTensor] = None,
       encoder_hidden_states: Optional[torch.FloatTensor] = None,
       encoder_attention_mask: Optional[torch.FloatTensor] = None,
       labels: Optional[torch.LongTensor] = None,
       output_attentions: Optional[bool] = None,
       output_hidden_states: Optional[bool] = None,
       return_dict: Optional[bool] = None,
   ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
       r"""
       labels (`torch.LongTensor` of shape `(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]`
       kwargs (`Dict[str, any]`, optional, defaults to *{}*):
           Used to hide legacy arguments that have been deprecated.
       """
       return_dict = return_dict if return_dict is not None else self.config.use_return_dict

       outputs = self.roberta(
           input_ids,
           attention_mask=attention_mask,
           token_type_ids=token_type_ids,
           position_ids=position_ids,
           head_mask=head_mask,
           inputs_embeds=inputs_embeds,
           encoder_hidden_states=encoder_hidden_states,
           encoder_attention_mask=encoder_attention_mask,
           output_attentions=output_attentions,
           output_hidden_states=output_hidden_states,
           return_dict=return_dict,
       )
       sequence_output = outputs[0]
       prediction_scores = self.lm_head(sequence_output)

       masked_lm_loss = None
       if labels is not None:
           loss_fct = CrossEntropyLoss()
           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,
       )


class RobertaLMHead(nn.Module):
   """Roberta Head for masked language modeling."""

   def __init__(self, config):
       super().__init__()
       self.dense = nn.Linear(config.hidden_size, config.hidden_size)
       self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

       self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
       self.bias = nn.Parameter(torch.zeros(config.vocab_size))
       self.decoder.bias = self.bias

   def forward(self, features, **kwargs):
       x = self.dense(features)
       x = gelu(x)
       x = self.layer_norm(x)

       # project back to size of vocabulary with bias
       x = self.decoder(x)

       return x

   def _tie_weights(self):
       # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
       # For accelerate compatibility and to not break backward compatibility
       if self.decoder.bias.device.type == "meta":
           self.decoder.bias = self.bias
       else:
           self.bias = self.decoder.bias


@add_start_docstrings(
   """
   RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
   pooled output) e.g. for GLUE tasks.
   """,
   ROBERTA_START_DOCSTRING,
)
class RobertaForSequenceClassification(RobertaPreTrainedModel):
   _keys_to_ignore_on_load_missing = [r"position_ids"]

   def __init__(self, config):
       super().__init__(config)
       self.num_labels = config.num_labels
       self.config = config

       self.roberta = RobertaModel(config, add_pooling_layer=False)
       self.classifier = RobertaClassificationHead(config)

       # Initialize weights and apply final processing
       self.post_init()

   @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
   @add_code_sample_docstrings(
       checkpoint="cardiffnlp/twitter-roberta-base-emotion",
       output_type=SequenceClassifierOutput,
       config_class=_CONFIG_FOR_DOC,
       expected_output="'optimism'",
       expected_loss=0.08,
   )
   def forward(
       self,
       input_ids: Optional[torch.LongTensor] = None,
       attention_mask: Optional[torch.FloatTensor] = None,
       token_type_ids: Optional[torch.LongTensor] = None,
       position_ids: Optional[torch.LongTensor] = None,
       head_mask: Optional[torch.FloatTensor] = None,
       inputs_embeds: Optional[torch.FloatTensor] = None,
       labels: Optional[torch.LongTensor] = None,
       output_attentions: Optional[bool] = None,
       output_hidden_states: Optional[bool] = None,
       return_dict: Optional[bool] = None,
   ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
       r"""
       labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
           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).
       """
       return_dict = return_dict if return_dict is not None else self.config.use_return_dict

       outputs = self.roberta(
           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.classifier(sequence_output)

       loss = None
       if labels is not None:
           if self.config.problem_type is None:
               if self.num_labels == 1:
                   self.config.problem_type = "regression"
               elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                   self.config.problem_type = "single_label_classification"
               else:
                   self.config.problem_type = "multi_label_classification"

           if self.config.problem_type == "regression":
               loss_fct = MSELoss()
               if self.num_labels == 1:
                   loss = loss_fct(logits.squeeze(), labels.squeeze())
               else:
                   loss = loss_fct(logits, labels)
           elif self.config.problem_type == "single_label_classification":
               loss_fct = CrossEntropyLoss()
               loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
           elif self.config.problem_type == "multi_label_classification":
               loss_fct = BCEWithLogitsLoss()
               loss = loss_fct(logits, labels)

       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(
   """
   Roberta 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.
   """,
   ROBERTA_START_DOCSTRING,
)
class RobertaForMultipleChoice(RobertaPreTrainedModel):
   _keys_to_ignore_on_load_missing = [r"position_ids"]

   def __init__(self, config):
       super().__init__(config)

       self.roberta = RobertaModel(config)
       self.dropout = nn.Dropout(config.hidden_dropout_prob)
       self.classifier = nn.Linear(config.hidden_size, 1)

       # Initialize weights and apply final processing
       self.post_init()

   @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC,
       output_type=MultipleChoiceModelOutput,
       config_class=_CONFIG_FOR_DOC,
   )
   def forward(
       self,
       input_ids: Optional[torch.LongTensor] = None,
       token_type_ids: Optional[torch.LongTensor] = None,
       attention_mask: Optional[torch.FloatTensor] = None,
       labels: Optional[torch.LongTensor] = None,
       position_ids: Optional[torch.LongTensor] = None,
       head_mask: Optional[torch.FloatTensor] = None,
       inputs_embeds: Optional[torch.FloatTensor] = None,
       output_attentions: Optional[bool] = None,
       output_hidden_states: Optional[bool] = None,
       return_dict: Optional[bool] = None,
   ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
       r"""
       labels (`torch.LongTensor` of shape `(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]

       flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
       flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
       flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
       flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
       flat_inputs_embeds = (
           inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
           if inputs_embeds is not None
           else None
       )

       outputs = self.roberta(
           flat_input_ids,
           position_ids=flat_position_ids,
           token_type_ids=flat_token_type_ids,
           attention_mask=flat_attention_mask,
           head_mask=head_mask,
           inputs_embeds=flat_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(
   """
   Roberta 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.
   """,
   ROBERTA_START_DOCSTRING,
)
class RobertaForTokenClassification(RobertaPreTrainedModel):
   _keys_to_ignore_on_load_unexpected = [r"pooler"]
   _keys_to_ignore_on_load_missing = [r"position_ids"]

   def __init__(self, config):
       super().__init__(config)
       self.num_labels = config.num_labels

       self.roberta = RobertaModel(config, add_pooling_layer=False)
       classifier_dropout = (
           config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
       )
       self.dropout = nn.Dropout(classifier_dropout)
       self.classifier = nn.Linear(config.hidden_size, config.num_labels)

       # Initialize weights and apply final processing
       self.post_init()

   @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
   @add_code_sample_docstrings(
       checkpoint="Jean-Baptiste/roberta-large-ner-english",
       output_type=TokenClassifierOutput,
       config_class=_CONFIG_FOR_DOC,
       expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']",
       expected_loss=0.01,
   )
   def forward(
       self,
       input_ids: Optional[torch.LongTensor] = None,
       attention_mask: Optional[torch.FloatTensor] = None,
       token_type_ids: Optional[torch.LongTensor] = None,
       position_ids: Optional[torch.LongTensor] = None,
       head_mask: Optional[torch.FloatTensor] = None,
       inputs_embeds: Optional[torch.FloatTensor] = None,
       labels: Optional[torch.LongTensor] = None,
       output_attentions: Optional[bool] = None,
       output_hidden_states: Optional[bool] = None,
       return_dict: Optional[bool] = None,
   ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
       r"""
       labels (`torch.LongTensor` of shape `(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.roberta(
           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()
           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,
       )


class RobertaClassificationHead(nn.Module):
   """Head for sentence-level classification tasks."""

   def __init__(self, config):
       super().__init__()
       self.dense = nn.Linear(config.hidden_size, config.hidden_size)
       classifier_dropout = (
           config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
       )
       self.dropout = nn.Dropout(classifier_dropout)
       self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

   def forward(self, features, **kwargs):
       x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
       x = self.dropout(x)
       x = self.dense(x)
       x = torch.tanh(x)
       x = self.dropout(x)
       x = self.out_proj(x)
       return x


@add_start_docstrings(
   """
   Roberta 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`).
   """,
   ROBERTA_START_DOCSTRING,
)
class RobertaForQuestionAnswering(RobertaPreTrainedModel):
   _keys_to_ignore_on_load_unexpected = [r"pooler"]
   _keys_to_ignore_on_load_missing = [r"position_ids"]

   def __init__(self, config):
       super().__init__(config)
       self.num_labels = config.num_labels

       self.roberta = RobertaModel(config, add_pooling_layer=False)
       self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

       # Initialize weights and apply final processing
       self.post_init()

   @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
   @add_code_sample_docstrings(
       checkpoint="deepset/roberta-base-squad2",
       output_type=QuestionAnsweringModelOutput,
       config_class=_CONFIG_FOR_DOC,
       expected_output="' puppet'",
       expected_loss=0.86,
   )
   def forward(
       self,
       input_ids: Optional[torch.LongTensor] = None,
       attention_mask: Optional[torch.FloatTensor] = None,
       token_type_ids: Optional[torch.LongTensor] = None,
       position_ids: Optional[torch.LongTensor] = None,
       head_mask: Optional[torch.FloatTensor] = None,
       inputs_embeds: Optional[torch.FloatTensor] = None,
       start_positions: Optional[torch.LongTensor] = None,
       end_positions: Optional[torch.LongTensor] = None,
       output_attentions: Optional[bool] = None,
       output_hidden_states: Optional[bool] = None,
       return_dict: Optional[bool] = None,
   ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
       r"""
       start_positions (`torch.LongTensor` of shape `(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 (`torch.LongTensor` of shape `(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.roberta(
           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).contiguous()
       end_logits = end_logits.squeeze(-1).contiguous()

       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 = start_positions.clamp(0, ignored_index)
           end_positions = 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,
       )


def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
   """
   Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
   are ignored. This is modified from fairseq's `utils.make_positions`.

   Args:
       x: torch.Tensor x:

   Returns: torch.Tensor
   """
   # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
   mask = input_ids.ne(padding_idx).int()
   incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
   return incremental_indices.long() + padding_idx