# coding=utf-8
# Copyright 2018 DPR Authors
#
# 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 DPR model for Open Domain Question Answering."""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
from torch import Tensor, nn

from .configuration_dpr import DPRConfig
from .file_utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_callable, replace_return_docstrings
from .modeling_bert import BertModel
from .modeling_outputs import BaseModelOutputWithPooling
from .modeling_utils import PreTrainedModel
from .utils import logging


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "DPRConfig"

DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/dpr-ctx_encoder-single-nq-base",
]
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/dpr-question_encoder-single-nq-base",
]
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/dpr-reader-single-nq-base",
]


##########
# Outputs
##########


@dataclass
class DPRContextEncoderOutput(ModelOutput):
    """
    Class for outputs of :class:`~transformers.DPRQuestionEncoder`.

    Args:
        pooler_output: (:obj:``torch.FloatTensor`` of shape ``(batch_size, embeddings_size)``):
            The DPR encoder outputs the `pooler_output` that corresponds to the context representation.
            Last layer hidden-state of the first token of the sequence (classification token)
            further processed by a Linear layer. This output is to be used to embed contexts for
            nearest neighbors queries with questions embeddings.
        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    pooler_output: torch.FloatTensor
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


@dataclass
class DPRQuestionEncoderOutput(ModelOutput):
    """
    Class for outputs of :class:`~transformers.DPRQuestionEncoder`.

    Args:
        pooler_output: (:obj:``torch.FloatTensor`` of shape ``(batch_size, embeddings_size)``):
            The DPR encoder outputs the `pooler_output` that corresponds to the question representation.
            Last layer hidden-state of the first token of the sequence (classification token)
            further processed by a Linear layer. This output is to be used to embed questions for
            nearest neighbors queries with context embeddings.
        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    pooler_output: torch.FloatTensor
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


@dataclass
class DPRReaderOutput(ModelOutput):
    """
    Class for outputs of :class:`~transformers.DPRQuestionEncoder`.

    Args:
        start_logits: (:obj:``torch.FloatTensor`` of shape ``(n_passages, sequence_length)``):
            Logits of the start index of the span for each passage.
        end_logits: (:obj:``torch.FloatTensor`` of shape ``(n_passages, sequence_length)``):
            Logits of the end index of the span for each passage.
        relevance_logits: (:obj:`torch.FloatTensor`` of shape ``(n_passages, )``):
            Outputs of the QA classifier of the DPRReader that corresponds to the scores of each passage
            to answer the question, compared to all the other passages.
        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    start_logits: torch.FloatTensor
    end_logits: torch.FloatTensor = None
    relevance_logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


class DPREncoder(PreTrainedModel):

    base_model_prefix = "bert_model"

    def __init__(self, config: DPRConfig):
        super().__init__(config)
        self.bert_model = BertModel(config)
        assert self.bert_model.config.hidden_size > 0, "Encoder hidden_size can't be zero"
        self.projection_dim = config.projection_dim
        if self.projection_dim > 0:
            self.encode_proj = nn.Linear(self.bert_model.config.hidden_size, config.projection_dim)
        self.init_weights()

    def forward(
        self,
        input_ids: Tensor,
        attention_mask: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = False,
    ) -> Union[BaseModelOutputWithPooling, Tuple[Tensor, ...]]:
        outputs = self.bert_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output, pooled_output = outputs[:2]
        pooled_output = sequence_output[:, 0, :]
        if self.projection_dim > 0:
            pooled_output = self.encode_proj(pooled_output)

        if not return_dict:
            return (sequence_output, pooled_output) + outputs[2:]

        return BaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    @property
    def embeddings_size(self) -> int:
        if self.projection_dim > 0:
            return self.encode_proj.out_features
        return self.bert_model.config.hidden_size

    def init_weights(self):
        self.bert_model.init_weights()
        if self.projection_dim > 0:
            self.encode_proj.apply(self.bert_model._init_weights)


class DPRSpanPredictor(PreTrainedModel):

    base_model_prefix = "encoder"

    def __init__(self, config: DPRConfig):
        super().__init__(config)
        self.encoder = DPREncoder(config)
        self.qa_outputs = nn.Linear(self.encoder.embeddings_size, 2)
        self.qa_classifier = nn.Linear(self.encoder.embeddings_size, 1)
        self.init_weights()

    def forward(
        self,
        input_ids: Tensor,
        attention_mask: Tensor,
        inputs_embeds: Optional[Tensor] = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = False,
    ) -> Union[DPRReaderOutput, Tuple[Tensor, ...]]:
        # notations: N - number of questions in a batch, M - number of passages per questions, L - sequence length
        n_passages, sequence_length = input_ids.size() if input_ids is not None else inputs_embeds.size()[:2]
        # feed encoder
        outputs = self.encoder(
            input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = outputs[0]

        # compute logits
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)
        relevance_logits = self.qa_classifier(sequence_output[:, 0, :])

        # resize
        start_logits = start_logits.view(n_passages, sequence_length)
        end_logits = end_logits.view(n_passages, sequence_length)
        relevance_logits = relevance_logits.view(n_passages)

        if not return_dict:
            return (start_logits, end_logits, relevance_logits) + outputs[2:]

        return DPRReaderOutput(
            start_logits=start_logits,
            end_logits=end_logits,
            relevance_logits=relevance_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def init_weights(self):
        self.encoder.init_weights()


##################
# PreTrainedModel
##################


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

    config_class = DPRConfig
    load_tf_weights = None
    base_model_prefix = "ctx_encoder"

    def init_weights(self):
        self.ctx_encoder.init_weights()


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

    config_class = DPRConfig
    load_tf_weights = None
    base_model_prefix = "question_encoder"

    def init_weights(self):
        self.question_encoder.init_weights()


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

    config_class = DPRConfig
    load_tf_weights = None
    base_model_prefix = "span_predictor"

    def init_weights(self):
        self.span_predictor.encoder.init_weights()
        self.span_predictor.qa_classifier.apply(self.span_predictor.encoder.bert_model._init_weights)
        self.span_predictor.qa_outputs.apply(self.span_predictor.encoder.bert_model._init_weights)


###############
# Actual Models
###############


DPR_START_DOCSTRING = r"""

    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
    usage and behavior.

    Parameters:
        config (:class:`~transformers.DPRConfig`): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the configuration.
            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""

DPR_ENCODERS_INPUTS_DOCSTRING = r"""
    Args:
        input_ids: (:obj:``torch.LongTensor`` of shape ``(batch_size, sequence_length)``):
            Indices of input sequence tokens in the vocabulary.
            To match pre-training, DPR input sequence should be formatted with [CLS] and [SEP] tokens as follows:

            (a) For sequence pairs (for a pair title+text for example):

                ``tokens:         [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``

                ``token_type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1``

            (b) For single sequences (for a question for example):

                ``tokens:         [CLS] the dog is hairy . [SEP]``

                ``token_type_ids:   0   0   0   0  0     0   0``

            DPR is a model with absolute position embeddings so it's usually advised to pad the inputs on
            the right rather than the left.

            Indices can be obtained using :class:`transformers.DPRTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
        attention_mask: (:obj:``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
        token_type_ids: (:obj:``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`, defaults to :obj:`None`):
            Segment token indices to indicate first and second portions of the inputs.
            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
            corresponds to a `sentence B` token
        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
        output_attentions (:obj:`bool`, `optional`, defaults to :obj:`None`):
            If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
        output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`None`):
            If set to ``True``, the hidden states tensors of all layers are returned. See ``hidden_states`` under returned tensors for more detail.
        return_dict (:obj:`bool`, `optional`, defaults to :obj:`None`):
            If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a
            plain tuple.
"""

DPR_READER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids: (:obj:``torch.LongTensor`` of shape ``(n_passages, sequence_length)``):
            Indices of input sequence tokens in the vocabulary.
            It has to be a sequence triplet with 1) the question and 2) the passages titles and 3) the passages texts
            To match pre-training, DPR `input_ids` sequence should be formatted with [CLS] and [SEP] with the format:

                [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

            DPR is a model with absolute position embeddings so it's usually advised to pad the inputs on
            the right rather than the left.

            Indices can be obtained using :class:`transformers.DPRReaderTokenizer`.
            See :class:`transformers.DPRReaderTokenizer` for more details
        attention_mask: (:obj:torch.FloatTensor``, of shape ``(n_passages, sequence_length)``, `optional`, defaults to :obj:`None):
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(n_passages, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
        output_attentions (:obj:`bool`, `optional`, defaults to :obj:`None`):
            If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
        output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`None`):
            If set to ``True``, the hidden states tensors of all layers are returned. See ``hidden_states`` under returned tensors for more detail.
        return_dict (:obj:`bool`, `optional`, defaults to :obj:`None`):
            If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a
            plain tuple.
"""


@add_start_docstrings(
    "The bare DPRContextEncoder transformer outputting pooler outputs as context representations.",
    DPR_START_DOCSTRING,
)
class DPRContextEncoder(DPRPretrainedContextEncoder):
    def __init__(self, config: DPRConfig):
        super().__init__(config)
        self.config = config
        self.ctx_encoder = DPREncoder(config)
        self.init_weights()

    @add_start_docstrings_to_callable(DPR_ENCODERS_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=DPRContextEncoderOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ) -> Union[DPRContextEncoderOutput, Tuple[Tensor, ...]]:
        r"""
        Return:

        Examples::

            from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
            tokenizer = DPRContextEncoderTokenizer.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base')
            model = DPRContextEncoder.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base', return_dict=True)
            input_ids = tokenizer("Hello, is my dog cute ?", return_tensors='pt')["input_ids"]
            embeddings = model(input_ids).pooler_output
        """

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = (
                torch.ones(input_shape, device=device)
                if input_ids is None
                else (input_ids != self.config.pad_token_id)
            )
        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        outputs = self.ctx_encoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            return outputs[1:]
        return DPRContextEncoderOutput(
            pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions
        )


@add_start_docstrings(
    "The bare DPRQuestionEncoder transformer outputting pooler outputs as question representations.",
    DPR_START_DOCSTRING,
)
class DPRQuestionEncoder(DPRPretrainedQuestionEncoder):
    def __init__(self, config: DPRConfig):
        super().__init__(config)
        self.config = config
        self.question_encoder = DPREncoder(config)
        self.init_weights()

    @add_start_docstrings_to_callable(DPR_ENCODERS_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=DPRQuestionEncoderOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ) -> Union[DPRQuestionEncoderOutput, Tuple[Tensor, ...]]:
        r"""
        Return:

        Examples::

            from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer
            tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
            model = DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base', return_dict=True)
            input_ids = tokenizer("Hello, is my dog cute ?", return_tensors='pt')["input_ids"]
            embeddings = model(input_ids).pooler_output
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = (
                torch.ones(input_shape, device=device)
                if input_ids is None
                else (input_ids != self.config.pad_token_id)
            )
        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        outputs = self.question_encoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            return outputs[1:]
        return DPRQuestionEncoderOutput(
            pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions
        )


@add_start_docstrings(
    "The bare DPRReader transformer outputting span predictions.",
    DPR_START_DOCSTRING,
)
class DPRReader(DPRPretrainedReader):
    def __init__(self, config: DPRConfig):
        super().__init__(config)
        self.config = config
        self.span_predictor = DPRSpanPredictor(config)
        self.init_weights()

    @add_start_docstrings_to_callable(DPR_READER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=DPRReaderOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        output_attentions: bool = None,
        output_hidden_states: bool = None,
        return_dict=None,
    ) -> Union[DPRReaderOutput, Tuple[Tensor, ...]]:
        r"""
        Return:

        Examples::

            from transformers import DPRReader, DPRReaderTokenizer
            tokenizer = DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base')
            model = DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base', return_dict=True)
            encoded_inputs = tokenizer(
                    questions=["What is love ?"],
                    titles=["Haddaway"],
                    texts=["'What Is Love' is a song recorded by the artist Haddaway"],
                    return_tensors='pt'
                )
            outputs = model(**encoded_inputs)
            start_logits = outputs.stat_logits
            end_logits = outputs.end_logits
            relevance_logits = outputs.relevance_logits

        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = torch.ones(input_shape, device=device)

        return self.span_predictor(
            input_ids,
            attention_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
