# 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.
""" TF 2.0 XXX model. """

####################################################
# In this template, replace all the XXX (various casings) with your model name
####################################################


import logging

import tensorflow as tf

from .configuration_xxx import XxxConfig
from .file_utils import (
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_callable,
)
from .modeling_tf_outputs import (
    TFBaseModelOutputWithPooling,
    TFMaskedLMOutput,
    TFMultipleChoiceModelOutput,
    TFQuestionAnsweringModelOutput,
    TFSequenceClassifierOutput,
    TFTokenClassifierOutput,
)
from .modeling_tf_utils import (
    TFMaskedLanguageModelingLoss,
    TFMultipleChoiceLoss,
    TFPreTrainedModel,
    TFQuestionAnsweringLoss,
    TFSequenceClassificationLoss,
    TFTokenClassificationLoss,
    get_initializer,
    shape_list,
)
from .tokenization_utils import BatchEncoding


logger = logging.getLogger(__name__)

_CONFIG_FOR_DOC = "XXXConfig"
_TOKENIZER_FOR_DOC = "XxxTokenizer"

####################################################
# This list contrains shortcut names for some of
# the pretrained weights provided with the models
####################################################
TF_XXX_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "xxx-base-uncased",
    "xxx-large-uncased",
]


####################################################
# TF 2.0 Models are constructed using Keras imperative API by sub-classing
# - tf.keras.layers.Layer for the layers and
# - TFPreTrainedModel for the models (itself a sub-class of tf.keras.Model)
####################################################

####################################################
# Here is an example of typical layer in a TF 2.0 model of the library
# The classes are usually identical to the PyTorch ones and prefixed with 'TF'.
#
# Note that class __init__ parameters includes **kwargs (send to 'super').
# This let us have a control on class scope and variable names:
# More precisely, we set the names of the class attributes (lower level layers) to
# to the equivalent attributes names in the PyTorch model so we can have equivalent
# class and scope structure between PyTorch and TF 2.0 models and easily load one in the other.
#
# See the conversion methods in modeling_tf_pytorch_utils.py for more details
####################################################

TFXxxAttention = tf.keras.layers.Layer

TFXxxIntermediate = tf.keras.layers.Layer

TFXxxOutput = tf.keras.layers.Layer


class TFXxxLayer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.attention = TFXxxAttention(config, name="attention")
        self.intermediate = TFXxxIntermediate(config, name="intermediate")
        self.transformer_output = TFXxxOutput(config, name="output")

    def call(self, inputs, training=False):
        hidden_states, attention_mask, head_mask = inputs

        attention_outputs = self.attention([hidden_states, attention_mask, head_mask], training=training)
        attention_output = attention_outputs[0]
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.transformer_output([intermediate_output, attention_output], training=training)
        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
        return outputs


####################################################
# The full model without a specific pretrained or finetuning head is
# provided as a tf.keras.layers.Layer usually called "TFXxxMainLayer"
####################################################
class TFXxxMainLayer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

    def get_input_embeddings(self):
        return self.embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value
        self.embeddings.vocab_size = value.shape[0]

    def _prune_heads(self, heads_to_prune):
        raise NotImplementedError  # Not implemented yet in the library for TF 2.0 models

    def call(
        self,
        inputs,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        training=False,
    ):
        if isinstance(inputs, (tuple, list)):
            input_ids = inputs[0]
            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
            token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
            position_ids = inputs[3] if len(inputs) > 3 else position_ids
            head_mask = inputs[4] if len(inputs) > 4 else head_mask
            inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
            output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
            output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
            return_dict = inputs[8] if len(inputs) > 8 else return_dict
            assert len(inputs) <= 9, "Too many inputs."
        elif isinstance(inputs, (dict, BatchEncoding)):
            input_ids = inputs.get("input_ids")
            attention_mask = inputs.get("attention_mask", attention_mask)
            token_type_ids = inputs.get("token_type_ids", token_type_ids)
            position_ids = inputs.get("position_ids", position_ids)
            head_mask = inputs.get("head_mask", head_mask)
            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
            output_attentions = inputs.get("output_attentions", output_attentions)
            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
            return_dict = inputs.get("return_dict", return_dict)
            assert len(inputs) <= 9, "Too many inputs."
        else:
            input_ids = inputs

        output_attentions = output_attentions if output_attentions is not None else self.output_attentions
        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
        return_dict = return_dict if return_dict is not None else self.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 = shape_list(input_ids)
        elif inputs_embeds is not None:
            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if attention_mask is None:
            attention_mask = tf.fill(input_shape, 1)
        if token_type_ids is None:
            token_type_ids = tf.fill(input_shape, 0)

        # We create a 3D attention mask from a 2D tensor mask.
        # Sizes are [batch_size, 1, 1, to_seq_length]
        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
        # this attention mask is more simple than the triangular masking of causal attention
        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
        extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
        # positions we want to attend and -10000.0 for masked positions.
        # Since we are adding it to the raw scores before the softmax, this is
        # effectively the same as removing these entirely.

        extended_attention_mask = tf.cast(extended_attention_mask, tf.float32)
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0

        # 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]
        if head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.num_hidden_layers
            # head_mask = tf.constant([0] * self.num_hidden_layers)

        embedding_output = self.embeddings(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
        encoder_outputs = self.encoder(
            embedding_output,
            extended_attention_mask,
            head_mask,
            output_attentions,
            output_hidden_states,
            return_dict,
            training=training,
        )

        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output)

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

        return TFBaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


####################################################
# TFXxxPreTrainedModel is a sub-class of tf.keras.Model
# which take care of loading and saving pretrained weights
# and various common utilities.
# Here you just need to specify a few (self-explanatory)
# pointers for your model.
####################################################
class TFXxxPreTrainedModel(TFPreTrainedModel):
    """An abstract class to handle weights initialization and
    a simple interface for downloading and loading pretrained models.
    """

    config_class = XxxConfig
    base_model_prefix = "transformer"


XXX_START_DOCSTRING = r"""
    The XXX model was proposed in
    `XXX: Pre-training of Deep Bidirectional Transformers for Language Understanding
    <https://arxiv.org/abs/1810.04805>`__ by....

    This model is a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ sub-class.
    Use it as a regular TF 2.0 Keras Model and
    refer to the TF 2.0 documentation for all matter related to general usage and behavior.

    .. note::

        TF 2.0 models accepts two formats as inputs:

            - having all inputs as keyword arguments (like PyTorch models), or
            - having all inputs as a list, tuple or dict in the first positional arguments.

        This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having
        all the tensors in the first argument of the model call function: :obj:`model(inputs)`.

        If you choose this second option, there are three possibilities you can use to gather all the input Tensors
        in the first positional argument :

        - a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
        - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
          :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
        - a dictionary with one or several input Tensors associated to the input names given in the docstring:
          :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`

    Parameters:
        config (:class:`~transformers.XxxConfig`): 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.
"""

XXX_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`{0}`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`transformers.XxxTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.__call__` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`{0}`, `optional`):
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

            `What are attention masks? <../glossary.html#attention-mask>`__
        token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`{0}`, `optional`):
            Segment token indices to indicate first and second portions of the inputs.
            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
            corresponds to a `sentence B` token

            `What are token type IDs? <../glossary.html#token-type-ids>`__
        position_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`{0}`, `optional`):
            Indices of positions of each input sequence tokens in the position embeddings.
            Selected in the range ``[0, config.max_position_embeddings - 1]``.

            `What are position IDs? <../glossary.html#position-ids>`__
        head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
        inputs_embeds (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, embedding_dim)`, `optional`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        training (:obj:`boolean`, `optional`, defaults to :obj:`False`):
            Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them
            (if set to :obj:`False`) for evaluation.
        output_attentions (:obj:`bool`, `optional`):
            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`):
            If set to ``True``, the hidden states of all layers are returned. See ``hidden_states`` under returned tensors for more detail.
        return_dict (:obj:`bool`, `optional`):
            If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a
            plain tuple.
"""


@add_start_docstrings(
    "The bare XXX Model transformer outputing raw hidden-states without any specific head on top.",
    XXX_START_DOCSTRING,
)
class TFXxxModel(TFXxxPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFXxxMainLayer(config, name="transformer")

    @add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="xxx-base-cased",
        output_type=TFBaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(self, inputs, **kwargs):
        outputs = self.transformer(inputs, **kwargs)
        return outputs


TFXxxMLMHead = tf.keras.layers.Layer


@add_start_docstrings("""Xxx Model with a `language modeling` head on top. """, XXX_START_DOCSTRING)
class TFXxxForMaskedLM(TFXxxPreTrainedModel, TFMaskedLanguageModelingLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.transformer = TFXxxMainLayer(config, name="transformer")
        self.mlm = TFXxxMLMHead(config, self.transformer.embeddings, name="mlm")

    @add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="xxx-base-cased",
        output_type=TFMaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        labels=None,
        training=False,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the masked language modeling loss.
            Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
            Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
            in ``[0, ..., config.vocab_size]``
        """
        return_dict = return_dict if return_dict is not None else self.transformer.return_dict
        if isinstance(inputs, (tuple, list)):
            labels = inputs[9] if len(inputs) > 9 else labels
            if len(inputs) > 9:
                inputs = inputs[:9]
        elif isinstance(inputs, (dict, BatchEncoding)):
            labels = inputs.pop("labels", labels)

        outputs = self.transformer(
            inputs,
            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,
            training=training,
        )

        sequence_output = outputs[0]
        prediction_scores = self.mlm(sequence_output, training=training)

        loss = None if labels is None else self.compute_loss(labels, prediction_scores)

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

        return TFMaskedLMOutput(
            loss=loss,
            logits=prediction_scores,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """XXX Model transformer with a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """,
    XXX_START_DOCSTRING,
)
class TFXxxForSequenceClassification(TFXxxPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels

        self.transformer = TFXxxMainLayer(config, name="transformer")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
        self.classifier = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )

    @add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="xxx-base-cased",
        output_type=TFSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        labels=None,
        training=False,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for computing the sequence classification/regression loss.
            Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
            If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.transformer.return_dict
        if isinstance(inputs, (tuple, list)):
            labels = inputs[9] if len(inputs) > 9 else labels
            if len(inputs) > 9:
                inputs = inputs[:9]
        elif isinstance(inputs, (dict, BatchEncoding)):
            labels = inputs.pop("labels", labels)

        outputs = self.transformer(
            inputs,
            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,
            training=training,
        )

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output, training=training)
        logits = self.classifier(pooled_output)

        loss = None if labels is None else self.compute_loss(labels, logits)

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

        return TFSequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """XXX 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. """,
    XXX_START_DOCSTRING,
)
class TFXxxForMultipleChoice(TFXxxPreTrainedModel, TFMultipleChoiceLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.transformer = TFXxxMainLayer(config, name="transformer")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
        self.classifier = tf.keras.layers.Dense(
            1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )

    @property
    def dummy_inputs(self):
        """Dummy inputs to build the network.

        Returns:
            tf.Tensor with dummy inputs
        """
        return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}

    @add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("(batch_size, num_choices, sequence_length)"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="xxx-base-cased",
        output_type=TFMultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        inputs,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        labels=None,
        training=False,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for computing the multiple choice classification loss.
            Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
            of the input tensors. (see `input_ids` above)s after the attention softmax, used to compute the weighted average in the self-attention
            heads.
        """
        if isinstance(inputs, (tuple, list)):
            input_ids = inputs[0]
            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
            token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
            position_ids = inputs[3] if len(inputs) > 3 else position_ids
            head_mask = inputs[4] if len(inputs) > 4 else head_mask
            inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
            output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
            output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
            return_dict = inputs[8] if len(inputs) > 8 else return_dict
            labels = inputs[9] if len(inputs) > 9 else labels
            assert len(inputs) <= 10, "Too many inputs."
        elif isinstance(inputs, (dict, BatchEncoding)):
            input_ids = inputs.get("input_ids")
            attention_mask = inputs.get("attention_mask", attention_mask)
            token_type_ids = inputs.get("token_type_ids", token_type_ids)
            position_ids = inputs.get("position_ids", position_ids)
            head_mask = inputs.get("head_mask", head_mask)
            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
            output_attentions = inputs.get("output_attentions", output_attentions)
            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
            return_dict = inputs.get("return_dict", return_dict)
            labels = inputs.get("labels", labels)
            assert len(inputs) <= 10, "Too many inputs."
        else:
            input_ids = inputs
        return_dict = return_dict if return_dict is not None else self.transformer.return_dict

        if input_ids is not None:
            num_choices = shape_list(input_ids)[1]
            seq_length = shape_list(input_ids)[2]
        else:
            num_choices = shape_list(inputs_embeds)[1]
            seq_length = shape_list(inputs_embeds)[2]

        flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
        flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
        flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
        flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
        flat_inputs_embeds = (
            tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
            if inputs_embeds is not None
            else None
        )

        flat_inputs = [
            flat_input_ids,
            flat_attention_mask,
            flat_token_type_ids,
            flat_position_ids,
            head_mask,
            flat_inputs_embeds,
            output_attentions,
            output_hidden_states,
            return_dict,
        ]

        outputs = self.transformer(flat_inputs, training=training)

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output, training=training)
        logits = self.classifier(pooled_output)
        reshaped_logits = tf.reshape(logits, (-1, num_choices))

        loss = None if labels is None else self.compute_loss(labels, reshaped_logits)

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

        return TFMultipleChoiceModelOutput(
            loss=loss,
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """XXX 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. """,
    XXX_START_DOCSTRING,
)
class TFXxxForTokenClassification(TFXxxPreTrainedModel, TFTokenClassificationLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels

        self.transformer = TFXxxMainLayer(config, name="transformer")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
        self.classifier = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )

    @add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="xxx-base-cased",
        output_type=TFTokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        labels=None,
        training=False,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the token classification loss.
            Indices should be in ``[0, ..., config.num_labels - 1]``.
        """
        return_dict = return_dict if return_dict is not None else self.transformer.return_dict
        if isinstance(inputs, (tuple, list)):
            labels = inputs[9] if len(inputs) > 9 else labels
            if len(inputs) > 9:
                inputs = inputs[:9]
        elif isinstance(inputs, (dict, BatchEncoding)):
            labels = inputs.pop("labels", labels)

        outputs = self.transformer(
            inputs,
            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,
            training=training,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output, training=training)
        logits = self.classifier(sequence_output)

        loss = None if labels is None else self.compute_loss(labels, logits)

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

        return TFTokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """XXX 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`). """,
    XXX_START_DOCSTRING,
)
class TFXxxForQuestionAnswering(TFXxxPreTrainedModel, TFQuestionAnsweringLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels

        self.transformer = TFXxxMainLayer(config, name="transformer")
        self.qa_outputs = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
        )

    @add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="xxx-base-cased",
        output_type=TFQuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        start_positions=None,
        end_positions=None,
        training=False,
    ):
        r"""
        start_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`).
            Position outside of the sequence are not taken into account for computing the loss.
        end_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`).
            Position outside of the sequence are not taken into account for computing the loss.
        """
        return_dict = return_dict if return_dict is not None else self.transformer.return_dict
        if isinstance(inputs, (tuple, list)):
            start_positions = inputs[9] if len(inputs) > 9 else start_positions
            end_positions = inputs[10] if len(inputs) > 10 else end_positions
            if len(inputs) > 9:
                inputs = inputs[:9]
        elif isinstance(inputs, (dict, BatchEncoding)):
            start_positions = inputs.pop("start_positions", start_positions)
            end_positions = inputs.pop("end_positions", start_positions)

        outputs = self.transformer(
            inputs,
            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,
            training=training,
        )

        sequence_output = outputs[0]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = tf.split(logits, 2, axis=-1)
        start_logits = tf.squeeze(start_logits, axis=-1)
        end_logits = tf.squeeze(end_logits, axis=-1)

        loss = None
        if start_positions is not None and end_positions is not None:
            labels = {"start_position": start_positions}
            labels["end_position"] = end_positions
            loss = self.compute_loss(labels, (start_logits, end_logits))

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TFQuestionAnsweringModelOutput(
            loss=loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )