# coding=utf-8
# Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao,
# Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team.
# Copyright (c) 20121, 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 I-BERT model."""

import math
from typing import Optional, Tuple, Union

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

from ...activations import gelu
from ...modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    BaseModelOutputWithPoolingAndCrossAttentions,
    MaskedLMOutput,
    MultipleChoiceModelOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_ibert import IBertConfig
from .quant_modules import IntGELU, IntLayerNorm, IntSoftmax, QuantAct, QuantEmbedding, QuantLinear


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "kssteven/ibert-roberta-base"
_CONFIG_FOR_DOC = "IBertConfig"

IBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "kssteven/ibert-roberta-base",
    "kssteven/ibert-roberta-large",
    "kssteven/ibert-roberta-large-mnli",
]


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

    def __init__(self, config):
        super().__init__()
        self.quant_mode = config.quant_mode
        self.embedding_bit = 8
        self.embedding_act_bit = 16
        self.act_bit = 8
        self.ln_input_bit = 22
        self.ln_output_bit = 32

        self.word_embeddings = QuantEmbedding(
            config.vocab_size,
            config.hidden_size,
            padding_idx=config.pad_token_id,
            weight_bit=self.embedding_bit,
            quant_mode=self.quant_mode,
        )
        self.token_type_embeddings = QuantEmbedding(
            config.type_vocab_size, config.hidden_size, weight_bit=self.embedding_bit, quant_mode=self.quant_mode
        )

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer(
            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
        )
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")

        # End copy
        self.padding_idx = config.pad_token_id
        self.position_embeddings = QuantEmbedding(
            config.max_position_embeddings,
            config.hidden_size,
            padding_idx=self.padding_idx,
            weight_bit=self.embedding_bit,
            quant_mode=self.quant_mode,
        )

        # Integer-only addition between embeddings
        self.embeddings_act1 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode)
        self.embeddings_act2 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode)

        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        self.LayerNorm = IntLayerNorm(
            config.hidden_size,
            eps=config.layer_norm_eps,
            output_bit=self.ln_output_bit,
            quant_mode=self.quant_mode,
            force_dequant=config.force_dequant,
        )
        self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(
        self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
    ):
        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
                ).to(input_ids.device)
            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]

        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)

        if inputs_embeds is None:
            inputs_embeds, inputs_embeds_scaling_factor = self.word_embeddings(input_ids)
        else:
            inputs_embeds_scaling_factor = None
        token_type_embeddings, token_type_embeddings_scaling_factor = self.token_type_embeddings(token_type_ids)

        embeddings, embeddings_scaling_factor = self.embeddings_act1(
            inputs_embeds,
            inputs_embeds_scaling_factor,
            identity=token_type_embeddings,
            identity_scaling_factor=token_type_embeddings_scaling_factor,
        )

        if self.position_embedding_type == "absolute":
            position_embeddings, position_embeddings_scaling_factor = self.position_embeddings(position_ids)
            embeddings, embeddings_scaling_factor = self.embeddings_act1(
                embeddings,
                embeddings_scaling_factor,
                identity=position_embeddings,
                identity_scaling_factor=position_embeddings_scaling_factor,
            )

        embeddings, embeddings_scaling_factor = self.LayerNorm(embeddings, embeddings_scaling_factor)
        embeddings = self.dropout(embeddings)
        embeddings, embeddings_scaling_factor = self.output_activation(embeddings, embeddings_scaling_factor)
        return embeddings, embeddings_scaling_factor

    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)


class IBertSelfAttention(nn.Module):
    def __init__(self, config):
        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.quant_mode = config.quant_mode
        self.weight_bit = 8
        self.bias_bit = 32
        self.act_bit = 8

        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

        # Q, K, V Linear layers
        self.query = QuantLinear(
            config.hidden_size,
            self.all_head_size,
            bias=True,
            weight_bit=self.weight_bit,
            bias_bit=self.bias_bit,
            quant_mode=self.quant_mode,
            per_channel=True,
        )
        self.key = QuantLinear(
            config.hidden_size,
            self.all_head_size,
            bias=True,
            weight_bit=self.weight_bit,
            bias_bit=self.bias_bit,
            quant_mode=self.quant_mode,
            per_channel=True,
        )
        self.value = QuantLinear(
            config.hidden_size,
            self.all_head_size,
            bias=True,
            weight_bit=self.weight_bit,
            bias_bit=self.bias_bit,
            quant_mode=self.quant_mode,
            per_channel=True,
        )

        # Requantization (32bit -> 8bit) for Q, K, V activations
        self.query_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
        self.key_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
        self.value_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
        self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        if self.position_embedding_type != "absolute":
            raise ValueError("I-BERT only supports 'absolute' for `config.position_embedding_type`")

        self.softmax = IntSoftmax(self.act_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant)

    def transpose_for_scores(self, x):
        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,
        hidden_states_scaling_factor,
        attention_mask=None,
        head_mask=None,
        output_attentions=False,
    ):
        # Projection
        mixed_query_layer, mixed_query_layer_scaling_factor = self.query(hidden_states, hidden_states_scaling_factor)
        mixed_key_layer, mixed_key_layer_scaling_factor = self.key(hidden_states, hidden_states_scaling_factor)
        mixed_value_layer, mixed_value_layer_scaling_factor = self.value(hidden_states, hidden_states_scaling_factor)

        # Requantization
        query_layer, query_layer_scaling_factor = self.query_activation(
            mixed_query_layer, mixed_query_layer_scaling_factor
        )
        key_layer, key_layer_scaling_factor = self.key_activation(mixed_key_layer, mixed_key_layer_scaling_factor)
        value_layer, value_layer_scaling_factor = self.value_activation(
            mixed_value_layer, mixed_value_layer_scaling_factor
        )

        # Transpose
        query_layer = self.transpose_for_scores(query_layer)
        key_layer = self.transpose_for_scores(key_layer)
        value_layer = self.transpose_for_scores(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))
        scale = math.sqrt(self.attention_head_size)
        attention_scores = attention_scores / scale
        if self.quant_mode:
            attention_scores_scaling_factor = query_layer_scaling_factor * key_layer_scaling_factor / scale
        else:
            attention_scores_scaling_factor = None

        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in IBertModel forward() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs, attention_probs_scaling_factor = self.softmax(
            attention_scores, attention_scores_scaling_factor
        )

        # 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)
        if attention_probs_scaling_factor is not None:
            context_layer_scaling_factor = attention_probs_scaling_factor * value_layer_scaling_factor
        else:
            context_layer_scaling_factor = None

        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)

        # requantization: 32-bit -> 8-bit
        context_layer, context_layer_scaling_factor = self.output_activation(
            context_layer, context_layer_scaling_factor
        )

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        output_scaling_factor = (
            (context_layer_scaling_factor, attention_probs_scaling_factor)
            if output_attentions
            else (context_layer_scaling_factor,)
        )

        return outputs, output_scaling_factor


class IBertSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.quant_mode = config.quant_mode
        self.act_bit = 8
        self.weight_bit = 8
        self.bias_bit = 32
        self.ln_input_bit = 22
        self.ln_output_bit = 32

        self.dense = QuantLinear(
            config.hidden_size,
            config.hidden_size,
            bias=True,
            weight_bit=self.weight_bit,
            bias_bit=self.bias_bit,
            quant_mode=self.quant_mode,
            per_channel=True,
        )
        self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode)
        self.LayerNorm = IntLayerNorm(
            config.hidden_size,
            eps=config.layer_norm_eps,
            output_bit=self.ln_output_bit,
            quant_mode=self.quant_mode,
            force_dequant=config.force_dequant,
        )
        self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor):
        hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
        hidden_states = self.dropout(hidden_states)
        hidden_states, hidden_states_scaling_factor = self.ln_input_act(
            hidden_states,
            hidden_states_scaling_factor,
            identity=input_tensor,
            identity_scaling_factor=input_tensor_scaling_factor,
        )
        hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor)

        hidden_states, hidden_states_scaling_factor = self.output_activation(
            hidden_states, hidden_states_scaling_factor
        )
        return hidden_states, hidden_states_scaling_factor


class IBertAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.quant_mode = config.quant_mode
        self.self = IBertSelfAttention(config)
        self.output = IBertSelfOutput(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,
        hidden_states_scaling_factor,
        attention_mask=None,
        head_mask=None,
        output_attentions=False,
    ):
        self_outputs, self_outputs_scaling_factor = self.self(
            hidden_states,
            hidden_states_scaling_factor,
            attention_mask,
            head_mask,
            output_attentions,
        )
        attention_output, attention_output_scaling_factor = self.output(
            self_outputs[0], self_outputs_scaling_factor[0], hidden_states, hidden_states_scaling_factor
        )
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        outputs_scaling_factor = (attention_output_scaling_factor,) + self_outputs_scaling_factor[1:]
        return outputs, outputs_scaling_factor


class IBertIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.quant_mode = config.quant_mode
        self.act_bit = 8
        self.weight_bit = 8
        self.bias_bit = 32
        self.dense = QuantLinear(
            config.hidden_size,
            config.intermediate_size,
            bias=True,
            weight_bit=self.weight_bit,
            bias_bit=self.bias_bit,
            quant_mode=self.quant_mode,
            per_channel=True,
        )
        if config.hidden_act != "gelu":
            raise ValueError("I-BERT only supports 'gelu' for `config.hidden_act`")
        self.intermediate_act_fn = IntGELU(quant_mode=self.quant_mode, force_dequant=config.force_dequant)
        self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)

    def forward(self, hidden_states, hidden_states_scaling_factor):
        hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
        hidden_states, hidden_states_scaling_factor = self.intermediate_act_fn(
            hidden_states, hidden_states_scaling_factor
        )

        # Requantization: 32bit -> 8-bit
        hidden_states, hidden_states_scaling_factor = self.output_activation(
            hidden_states, hidden_states_scaling_factor
        )
        return hidden_states, hidden_states_scaling_factor


class IBertOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.quant_mode = config.quant_mode
        self.act_bit = 8
        self.weight_bit = 8
        self.bias_bit = 32
        self.ln_input_bit = 22
        self.ln_output_bit = 32

        self.dense = QuantLinear(
            config.intermediate_size,
            config.hidden_size,
            bias=True,
            weight_bit=self.weight_bit,
            bias_bit=self.bias_bit,
            quant_mode=self.quant_mode,
            per_channel=True,
        )
        self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode)
        self.LayerNorm = IntLayerNorm(
            config.hidden_size,
            eps=config.layer_norm_eps,
            output_bit=self.ln_output_bit,
            quant_mode=self.quant_mode,
            force_dequant=config.force_dequant,
        )
        self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor):
        hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
        hidden_states = self.dropout(hidden_states)
        hidden_states, hidden_states_scaling_factor = self.ln_input_act(
            hidden_states,
            hidden_states_scaling_factor,
            identity=input_tensor,
            identity_scaling_factor=input_tensor_scaling_factor,
        )
        hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor)

        hidden_states, hidden_states_scaling_factor = self.output_activation(
            hidden_states, hidden_states_scaling_factor
        )
        return hidden_states, hidden_states_scaling_factor


class IBertLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.quant_mode = config.quant_mode
        self.act_bit = 8

        self.seq_len_dim = 1
        self.attention = IBertAttention(config)
        self.intermediate = IBertIntermediate(config)
        self.output = IBertOutput(config)

        self.pre_intermediate_act = QuantAct(self.act_bit, quant_mode=self.quant_mode)
        self.pre_output_act = QuantAct(self.act_bit, quant_mode=self.quant_mode)

    def forward(
        self,
        hidden_states,
        hidden_states_scaling_factor,
        attention_mask=None,
        head_mask=None,
        output_attentions=False,
    ):
        self_attention_outputs, self_attention_outputs_scaling_factor = self.attention(
            hidden_states,
            hidden_states_scaling_factor,
            attention_mask,
            head_mask,
            output_attentions=output_attentions,
        )
        attention_output = self_attention_outputs[0]
        attention_output_scaling_factor = self_attention_outputs_scaling_factor[0]

        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        layer_output, layer_output_scaling_factor = self.feed_forward_chunk(
            attention_output, attention_output_scaling_factor
        )
        outputs = (layer_output,) + outputs

        return outputs

    def feed_forward_chunk(self, attention_output, attention_output_scaling_factor):
        attention_output, attention_output_scaling_factor = self.pre_intermediate_act(
            attention_output, attention_output_scaling_factor
        )
        intermediate_output, intermediate_output_scaling_factor = self.intermediate(
            attention_output, attention_output_scaling_factor
        )

        intermediate_output, intermediate_output_scaling_factor = self.pre_output_act(
            intermediate_output, intermediate_output_scaling_factor
        )
        layer_output, layer_output_scaling_factor = self.output(
            intermediate_output, intermediate_output_scaling_factor, attention_output, attention_output_scaling_factor
        )
        return layer_output, layer_output_scaling_factor


class IBertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.quant_mode = config.quant_mode
        self.layer = nn.ModuleList([IBertLayer(config) for _ in range(config.num_hidden_layers)])

    def forward(
        self,
        hidden_states,
        hidden_states_scaling_factor,
        attention_mask=None,
        head_mask=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = None  # `config.add_cross_attention` is not supported
        next_decoder_cache = None  # `config.use_cache` is not supported

        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

            layer_outputs = layer_module(
                hidden_states,
                hidden_states_scaling_factor,
                attention_mask,
                layer_head_mask,
                output_attentions,
            )

            hidden_states = layer_outputs[0]
            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)

        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,
        )


class IBertPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.quant_mode = config.quant_mode
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class IBertPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = IBertConfig
    base_model_prefix = "ibert"

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (QuantLinear, 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, (QuantEmbedding, 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, (IntLayerNorm, nn.LayerNorm)):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    def resize_token_embeddings(self, new_num_tokens=None):
        raise NotImplementedError("`resize_token_embeddings` is not supported for I-BERT.")


IBERT_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 ([`IBertConfig`]): 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.
"""

IBERT_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.

            [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 I-BERT Model transformer outputting raw hidden-states without any specific head on top.",
    IBERT_START_DOCSTRING,
)
class IBertModel(IBertPreTrainedModel):
    """

    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](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

    """

    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.quant_mode = config.quant_mode

        self.embeddings = IBertEmbeddings(config)
        self.encoder = IBertEncoder(config)

        self.pooler = IBertPooler(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(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPoolingAndCrossAttentions,
        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,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[BaseModelOutputWithPoolingAndCrossAttentions, Tuple[torch.FloatTensor]]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

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

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

        # 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, embedding_output_scaling_factor = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
        )
        encoder_outputs = self.encoder(
            embedding_output,
            embedding_output_scaling_factor,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if 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("""I-BERT Model with a `language modeling` head on top.""", IBERT_START_DOCSTRING)
class IBertForMaskedLM(IBertPreTrainedModel):
    _tied_weights_keys = ["lm_head.decoder.bias", "lm_head.decoder.weight"]

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

        self.ibert = IBertModel(config, add_pooling_layer=False)
        self.lm_head = IBertLMHead(config)

        # 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(IBERT_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>",
    )
    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[MaskedLMOutput, Tuple[torch.FloatTensor]]:
        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.ibert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = outputs[0]
        prediction_scores = self.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 IBertLMHead(nn.Module):
    """I-BERT 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)
        self.bias = self.decoder.bias


@add_start_docstrings(
    """
    I-BERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
    output) e.g. for GLUE tasks.
    """,
    IBERT_START_DOCSTRING,
)
class IBertForSequenceClassification(IBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.ibert = IBertModel(config, add_pooling_layer=False)
        self.classifier = IBertClassificationHead(config)

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

    @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        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,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
        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.ibert(
            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(
    """
    I-BERT 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.
    """,
    IBERT_START_DOCSTRING,
)
class IBertForMultipleChoice(IBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.ibert = IBertModel(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(IBERT_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[MultipleChoiceModelOutput, Tuple[torch.FloatTensor]]:
        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.ibert(
            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(
    """
    I-BERT 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.
    """,
    IBERT_START_DOCSTRING,
)
class IBertForTokenClassification(IBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.ibert = IBertModel(config, add_pooling_layer=False)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        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(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TokenClassifierOutput,
        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,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[TokenClassifierOutput, Tuple[torch.FloatTensor]]:
        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.ibert(
            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 IBertClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

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


@add_start_docstrings(
    """
    I-BERT 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`).
    """,
    IBERT_START_DOCSTRING,
)
class IBertForQuestionAnswering(IBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.ibert = IBertModel(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(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=QuestionAnsweringModelOutput,
        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,
        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[QuestionAnsweringModelOutput, Tuple[torch.FloatTensor]]:
        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.ibert(
            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:
    input_ids (`torch.LongTensor`):
           Indices of input sequence tokens in the vocabulary.

    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
