# 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 RoBERTa model. """


import tensorflow as tf

from ...activations_tf import get_tf_activation
from ...file_utils import (
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
)
from ...modeling_tf_outputs import (
    TFBaseModelOutput,
    TFBaseModelOutputWithPooling,
    TFMaskedLMOutput,
    TFMultipleChoiceModelOutput,
    TFQuestionAnsweringModelOutput,
    TFSequenceClassifierOutput,
    TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
    TFMaskedLanguageModelingLoss,
    TFMultipleChoiceLoss,
    TFPreTrainedModel,
    TFQuestionAnsweringLoss,
    TFSequenceClassificationLoss,
    TFTokenClassificationLoss,
    get_initializer,
    keras_serializable,
    shape_list,
)
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_roberta import RobertaConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "RobertaConfig"
_TOKENIZER_FOR_DOC = "RobertaTokenizer"

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


class TFRobertaEmbeddings(tf.keras.layers.Layer):
    """
    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
    """

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

        self.padding_idx = 1
        self.vocab_size = config.vocab_size
        self.hidden_size = config.hidden_size
        self.initializer_range = config.initializer_range
        self.position_embeddings = tf.keras.layers.Embedding(
            config.max_position_embeddings,
            config.hidden_size,
            embeddings_initializer=get_initializer(self.initializer_range),
            name="position_embeddings",
        )
        self.token_type_embeddings = tf.keras.layers.Embedding(
            config.type_vocab_size,
            config.hidden_size,
            embeddings_initializer=get_initializer(self.initializer_range),
            name="token_type_embeddings",
        )

        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)

    def build(self, input_shape):
        """Build shared word embedding layer """
        with tf.name_scope("word_embeddings"):
            # Create and initialize weights. The random normal initializer was chosen
            # arbitrarily, and works well.
            self.word_embeddings = self.add_weight(
                "weight",
                shape=[self.vocab_size, self.hidden_size],
                initializer=get_initializer(self.initializer_range),
            )

        super().build(input_shape)

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

        Args:
            x: tf.Tensor

        Returns: tf.Tensor
        """
        mask = tf.cast(tf.math.not_equal(x, self.padding_idx), dtype=tf.int32)
        incremental_indices = tf.math.cumsum(mask, axis=1) * mask

        return incremental_indices + self.padding_idx

    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: tf.Tensor

        Returns: tf.Tensor
        """
        seq_length = shape_list(inputs_embeds)[1]
        position_ids = tf.range(self.padding_idx + 1, seq_length + self.padding_idx + 1, dtype=tf.int32)[tf.newaxis, :]

        return position_ids

    def call(
        self,
        input_ids=None,
        position_ids=None,
        token_type_ids=None,
        inputs_embeds=None,
        mode="embedding",
        training=False,
    ):
        """
        Get token embeddings of inputs.

        Args:
            inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
            mode: string, a valid value is one of "embedding" and "linear".

        Returns:
            outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
            embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
            vocab_size].

        Raises:
            ValueError: if mode is not valid.

        Shared weights logic adapted from
        https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
        """
        if mode == "embedding":
            return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
        elif mode == "linear":
            return self._linear(input_ids)
        else:
            raise ValueError("mode {} is not valid.".format(mode))

    def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
        """Applies embedding based on inputs tensor."""
        assert not (input_ids is None and inputs_embeds is None)

        if position_ids is None:
            if input_ids is not None:
                # Create the position ids from the input token ids. Any padded tokens remain padded.
                position_ids = self.create_position_ids_from_input_ids(input_ids)
            else:
                position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)

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

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]

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

        if inputs_embeds is None:
            inputs_embeds = tf.gather(self.word_embeddings, input_ids)

        position_embeddings = tf.cast(self.position_embeddings(position_ids), inputs_embeds.dtype)
        token_type_embeddings = tf.cast(self.token_type_embeddings(token_type_ids), inputs_embeds.dtype)
        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings, training=training)

        return embeddings

    def _linear(self, inputs):
        """
        Computes logits by running inputs through a linear layer.

        Args:
            inputs: A float32 tensor with shape [batch_size, length, hidden_size]

        Returns:
            float32 tensor with shape [batch_size, length, vocab_size].
        """
        batch_size = shape_list(inputs)[0]
        length = shape_list(inputs)[1]
        x = tf.reshape(inputs, [-1, self.hidden_size])
        logits = tf.matmul(x, self.word_embeddings, transpose_b=True)

        return tf.reshape(logits, [batch_size, length, self.vocab_size])


# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler
class TFRobertaPooler(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            activation="tanh",
            name="dense",
        )

    def call(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)

        return pooled_output


# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention
class TFRobertaSelfAttention(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
            )

        self.num_attention_heads = config.num_attention_heads
        assert config.hidden_size % config.num_attention_heads == 0
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = tf.keras.layers.Dense(
            self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
        )
        self.key = tf.keras.layers.Dense(
            self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
        )
        self.value = tf.keras.layers.Dense(
            self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
        )
        self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)

    def transpose_for_scores(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))

        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, hidden_states, attention_mask, head_mask, output_attentions, training=False):
        batch_size = shape_list(hidden_states)[0]
        mixed_query_layer = self.query(hidden_states)
        mixed_key_layer = self.key(hidden_states)
        mixed_value_layer = self.value(hidden_states)
        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = tf.matmul(
            query_layer, key_layer, transpose_b=True
        )  # (batch size, num_heads, seq_len_q, seq_len_k)
        dk = tf.cast(shape_list(key_layer)[-1], attention_scores.dtype)  # scale attention_scores
        attention_scores = attention_scores / tf.math.sqrt(dk)

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

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

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs, training=training)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = tf.matmul(attention_probs, value_layer)
        context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
        context_layer = tf.reshape(
            context_layer, (batch_size, -1, self.all_head_size)
        )  # (batch_size, seq_len_q, all_head_size)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        return outputs


# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput
class TFRobertaSelfOutput(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)

    def call(self, hidden_states, input_tensor, training=False):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)

        return hidden_states


# Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Roberta
class TFRobertaAttention(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.self_attention = TFRobertaSelfAttention(config, name="self")
        self.dense_output = TFRobertaSelfOutput(config, name="output")

    def prune_heads(self, heads):
        raise NotImplementedError

    def call(self, input_tensor, attention_mask, head_mask, output_attentions, training=False):
        self_outputs = self.self_attention(
            input_tensor, attention_mask, head_mask, output_attentions, training=training
        )
        attention_output = self.dense_output(self_outputs[0], input_tensor, training=training)
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them

        return outputs


# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate
class TFRobertaIntermediate(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )

        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = get_tf_activation(config.hidden_act)
        else:
            self.intermediate_act_fn = config.hidden_act

    def call(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)

        return hidden_states


# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput
class TFRobertaOutput(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)

    def call(self, hidden_states, input_tensor, training=False):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)

        return hidden_states


# Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Roberta
class TFRobertaLayer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.attention = TFRobertaAttention(config, name="attention")
        self.intermediate = TFRobertaIntermediate(config, name="intermediate")
        self.bert_output = TFRobertaOutput(config, name="output")

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

        return outputs


# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Roberta
class TFRobertaEncoder(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.layer = [TFRobertaLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]

    def call(
        self,
        hidden_states,
        attention_mask,
        head_mask,
        output_attentions,
        output_hidden_states,
        return_dict,
        training=False,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_outputs = layer_module(
                hidden_states, attention_mask, head_mask[i], output_attentions, training=training
            )
            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)

        return TFBaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )


@keras_serializable
class TFRobertaMainLayer(tf.keras.layers.Layer):
    config_class = RobertaConfig

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

        self.num_hidden_layers = config.num_hidden_layers
        self.initializer_range = config.initializer_range
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.return_dict = config.use_return_dict
        self.encoder = TFRobertaEncoder(config, name="encoder")
        self.pooler = TFRobertaPooler(config, name="pooler")
        # The embeddings must be the last declaration in order to follow the weights order
        self.embeddings = TFRobertaEmbeddings(config, name="embeddings")

    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings
    def get_input_embeddings(self):
        return self.embeddings

    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings
    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value
        self.embeddings.vocab_size = value.shape[0]

    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads
    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
        """
        raise NotImplementedError

    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.call
    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)

        embedding_output = self.embeddings(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)

        # 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, embedding_output.dtype)
        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)

        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,
        )


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

    config_class = RobertaConfig
    base_model_prefix = "roberta"


ROBERTA_START_DOCSTRING = r"""

    This model inherits from :class:`~transformers.TFPreTrainedModel`. 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 `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. 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 :meth:`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 :obj:`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.RobertaConfig`): Model configuration class with all the parameters of the
            model. Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
            weights.
"""

ROBERTA_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.RobertaTokenizer`. See
            :func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` 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 tokens that are **masked**.

            `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]``:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (:obj:`tf.Tensor` of shape :obj:`({0}, hidden_size)`, `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 :obj:`input_ids` indices into associated
            vectors than the model's internal embedding lookup matrix.
        output_attentions (:obj:`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 (:obj:`bool`, `optional`):
            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
            more detail.
        return_dict (:obj:`bool`, `optional`):
            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
        training (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to use the model in training mode (some modules like dropout modules have different
            behaviors between training and evaluation).
"""


@add_start_docstrings(
    "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
    ROBERTA_START_DOCSTRING,
)
class TFRobertaModel(TFRobertaPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.roberta = TFRobertaMainLayer(config, name="roberta")

    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="roberta-base",
        output_type=TFBaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(self, inputs, **kwargs):
        outputs = self.roberta(inputs, **kwargs)
        return outputs


class TFRobertaLMHead(tf.keras.layers.Layer):
    """Roberta Head for masked language modeling."""

    def __init__(self, config, input_embeddings, **kwargs):
        super().__init__(**kwargs)

        self.vocab_size = config.vocab_size
        self.dense = tf.keras.layers.Dense(
            config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
        self.act = get_tf_activation("gelu")

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = input_embeddings

    def build(self, input_shape):
        self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")

        super().build(input_shape)

    def call(self, features):
        x = self.dense(features)
        x = self.act(x)
        x = self.layer_norm(x)

        # project back to size of vocabulary with bias
        x = self.decoder(x, mode="linear") + self.bias

        return x


@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING)
class TFRobertaForMaskedLM(TFRobertaPreTrainedModel, TFMaskedLanguageModelingLoss):

    authorized_missing_keys = [r"pooler"]

    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.roberta = TFRobertaMainLayer(config, name="roberta")
        self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head")

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

    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="roberta-base",
        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.roberta.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.roberta(
            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 = outputs[0]
        prediction_scores = self.lm_head(sequence_output)

        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,
        )


class TFRobertaClassificationHead(tf.keras.layers.Layer):
    """Head for sentence-level classification tasks."""

    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.dense = tf.keras.layers.Dense(
            config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            activation="tanh",
            name="dense",
        )
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
        self.out_proj = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
        )

    def call(self, features, training=False):
        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
        x = self.dropout(x, training=training)
        x = self.dense(x)
        x = self.dropout(x, training=training)
        x = self.out_proj(x)
        return x


@add_start_docstrings(
    """
    RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """,
    ROBERTA_START_DOCSTRING,
)
class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceClassificationLoss):

    authorized_missing_keys = [r"pooler"]

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

        self.roberta = TFRobertaMainLayer(config, name="roberta")
        self.classifier = TFRobertaClassificationHead(config, name="classifier")

    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="roberta-base",
        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.roberta.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.roberta(
            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.classifier(sequence_output, training=training)

        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(
    """
    Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
    softmax) e.g. for RocStories/SWAG tasks.
    """,
    ROBERTA_START_DOCSTRING,
)
class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.roberta = TFRobertaMainLayer(config, name="roberta")
        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_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="roberta-base",
        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 :obj:`num_choices` is the size of the second dimension of the input tensors. (See
            :obj:`input_ids` above)
        """
        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_attentions)
            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.roberta.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
        outputs = self.roberta(
            flat_input_ids,
            flat_attention_mask,
            flat_token_type_ids,
            flat_position_ids,
            head_mask,
            inputs_embeds,
            output_attentions,
            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)
        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(
    """
    RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
    Named-Entity-Recognition (NER) tasks.
    """,
    ROBERTA_START_DOCSTRING,
)
class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassificationLoss):

    authorized_missing_keys = [r"pooler"]

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

        self.roberta = TFRobertaMainLayer(config, name="roberta")
        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_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="roberta-base",
        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.roberta.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.roberta(
            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(
    """
    RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    ROBERTA_START_DOCSTRING,
)
class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnsweringLoss):

    authorized_missing_keys = [r"pooler"]

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

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

    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="roberta-base",
        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 (:obj:`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 (:obj:`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.roberta.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.roberta(
            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,
        )
