This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
Add bark. This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
# coding=utf-8
# Copyright 2019 Facebook AI Research 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 XLM-RoBERTa model."""


from __future__ import annotations

import math
import warnings
from typing import Optional, Tuple, Union

import numpy as np
import tensorflow as tf

from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithPastAndCrossAttentions,
    TFBaseModelOutputWithPoolingAndCrossAttentions,
    TFCausalLMOutputWithCrossAttentions,
    TFMaskedLMOutput,
    TFMultipleChoiceModelOutput,
    TFQuestionAnsweringModelOutput,
    TFSequenceClassifierOutput,
    TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
    TFCausalLanguageModelingLoss,
    TFMaskedLanguageModelingLoss,
    TFModelInputType,
    TFMultipleChoiceLoss,
    TFPreTrainedModel,
    TFQuestionAnsweringLoss,
    TFSequenceClassificationLoss,
    TFTokenClassificationLoss,
    get_initializer,
    keras_serializable,
    unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
)
from .configuration_xlm_roberta import XLMRobertaConfig


logger = logging.get_logger(__name__)

logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "xlm-roberta-base"
_CONFIG_FOR_DOC = "XLMRobertaConfig"

TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "xlm-roberta-base",
    "xlm-roberta-large",
    "joeddav/xlm-roberta-large-xnli",
    "cardiffnlp/twitter-xlm-roberta-base-sentiment",
    # See all XLM-RoBERTa models at https://huggingface.co/models?filter=xlm-roberta
]

XLM_ROBERTA_START_DOCSTRING = r"""

    This model inherits from [`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.

    <Tip>

    TensorFlow models and layers in `transformers` accept two formats as input:

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

    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, 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: `model(input_ids)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([input_ids, attention_mask])` or `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:
    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!

    </Tip>

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

XLM_ROBERTA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See
            [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input
            IDs?](../glossary#input-ids)
        attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids)
        head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
            config will be used instead.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
            used instead.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
            eager mode, in graph mode the value will always be set to True.
        training (`bool`, *optional*, defaults to `False`):
            Whether or not to use the model in training mode (some modules like dropout modules have different
            behaviors between training and evaluation).
"""


# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaEmbeddings with Roberta->XLMRoberta
class TFXLMRobertaEmbeddings(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.config = config
        self.hidden_size = config.hidden_size
        self.max_position_embeddings = config.max_position_embeddings
        self.initializer_range = config.initializer_range
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

    def build(self, input_shape: tf.TensorShape):
        with tf.name_scope("word_embeddings"):
            self.weight = self.add_weight(
                name="weight",
                shape=[self.config.vocab_size, self.hidden_size],
                initializer=get_initializer(self.initializer_range),
            )

        with tf.name_scope("token_type_embeddings"):
            self.token_type_embeddings = self.add_weight(
                name="embeddings",
                shape=[self.config.type_vocab_size, self.hidden_size],
                initializer=get_initializer(self.initializer_range),
            )

        with tf.name_scope("position_embeddings"):
            self.position_embeddings = self.add_weight(
                name="embeddings",
                shape=[self.max_position_embeddings, self.hidden_size],
                initializer=get_initializer(self.initializer_range),
            )

        super().build(input_shape)

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

        Args:
            input_ids: tf.Tensor
        Returns: tf.Tensor
        """
        mask = tf.cast(tf.math.not_equal(input_ids, self.padding_idx), dtype=input_ids.dtype)
        incremental_indices = (tf.math.cumsum(mask, axis=1) + past_key_values_length) * mask

        return incremental_indices + self.padding_idx

    def call(
        self,
        input_ids=None,
        position_ids=None,
        token_type_ids=None,
        inputs_embeds=None,
        past_key_values_length=0,
        training=False,
    ):
        """
        Applies embedding based on inputs tensor.

        Returns:
            final_embeddings (`tf.Tensor`): output embedding tensor.
        """
        assert not (input_ids is None and inputs_embeds is None)

        if input_ids is not None:
            check_embeddings_within_bounds(input_ids, self.config.vocab_size)
            inputs_embeds = tf.gather(params=self.weight, indices=input_ids)

        input_shape = shape_list(inputs_embeds)[:-1]

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

        if position_ids is None:
            if input_ids is not None:
                # Create the position ids from the input token ids. Any padded tokens remain padded.
                position_ids = self.create_position_ids_from_input_ids(
                    input_ids=input_ids, past_key_values_length=past_key_values_length
                )
            else:
                position_ids = tf.expand_dims(
                    tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1), axis=0
                )

        position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
        token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
        final_embeddings = self.LayerNorm(inputs=final_embeddings)
        final_embeddings = self.dropout(inputs=final_embeddings, training=training)

        return final_embeddings


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

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

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(inputs=first_token_tensor)

        return pooled_output


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

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

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

        self.query = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
        )
        self.key = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
        )
        self.value = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)

        self.is_decoder = config.is_decoder

    def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
        # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
        tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))

        # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
        return tf.transpose(tensor, perm=[0, 2, 1, 3])

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        head_mask: tf.Tensor,
        encoder_hidden_states: tf.Tensor,
        encoder_attention_mask: tf.Tensor,
        past_key_value: Tuple[tf.Tensor],
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        batch_size = shape_list(hidden_states)[0]
        mixed_query_layer = self.query(inputs=hidden_states)

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

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

        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)

        if self.is_decoder:
            # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_layer, value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        # (batch size, num_heads, seq_len_q, seq_len_k)
        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
        dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
        attention_scores = tf.divide(attention_scores, dk)

        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in TFXLMRobertaModel call() function)
            attention_scores = tf.add(attention_scores, attention_mask)

        # Normalize the attention scores to probabilities.
        attention_probs = stable_softmax(logits=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(inputs=attention_probs, training=training)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = tf.multiply(attention_probs, head_mask)

        attention_output = tf.matmul(attention_probs, value_layer)
        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])

        # (batch_size, seq_len_q, all_head_size)
        attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
        outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)

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


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

        self.dense = tf.keras.layers.Dense(
            units=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(rate=config.hidden_dropout_prob)

    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)

        return hidden_states


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

        self.self_attention = TFXLMRobertaSelfAttention(config, name="self")
        self.dense_output = TFXLMRobertaSelfOutput(config, name="output")

    def prune_heads(self, heads):
        raise NotImplementedError

    def call(
        self,
        input_tensor: tf.Tensor,
        attention_mask: tf.Tensor,
        head_mask: tf.Tensor,
        encoder_hidden_states: tf.Tensor,
        encoder_attention_mask: tf.Tensor,
        past_key_value: Tuple[tf.Tensor],
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        self_outputs = self.self_attention(
            hidden_states=input_tensor,
            attention_mask=attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = self.dense_output(
            hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
        )
        # add attentions (possibly with past_key_value) if we output them
        outputs = (attention_output,) + self_outputs[1:]

        return outputs


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

        self.dense = tf.keras.layers.Dense(
            units=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: tf.Tensor) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)

        return hidden_states


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

        self.dense = tf.keras.layers.Dense(
            units=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(rate=config.hidden_dropout_prob)

    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)

        return hidden_states


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

        self.attention = TFXLMRobertaAttention(config, name="attention")
        self.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder:
                raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
            self.crossattention = TFXLMRobertaAttention(config, name="crossattention")
        self.intermediate = TFXLMRobertaIntermediate(config, name="intermediate")
        self.bert_output = TFXLMRobertaOutput(config, name="output")

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

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

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

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

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

        intermediate_output = self.intermediate(hidden_states=attention_output)
        layer_output = self.bert_output(
            hidden_states=intermediate_output, input_tensor=attention_output, training=training
        )
        outputs = (layer_output,) + outputs  # add attentions if we output them

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

        return outputs


# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->XLMRoberta
class TFXLMRobertaEncoder(tf.keras.layers.Layer):
    def __init__(self, config: XLMRobertaConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.layer = [TFXLMRobertaLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        head_mask: tf.Tensor,
        encoder_hidden_states: tf.Tensor | None,
        encoder_attention_mask: tf.Tensor | None,
        past_key_values: Tuple[Tuple[tf.Tensor]] | None,
        use_cache: Optional[bool],
        output_attentions: bool,
        output_hidden_states: bool,
        return_dict: bool,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None

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

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

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

            if use_cache:
                next_decoder_cache += (layer_outputs[-1],)

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)
                if self.config.add_cross_attention and encoder_hidden_states is not None:
                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)

        # 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, all_cross_attentions] if v is not None
            )

        return TFBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_decoder_cache,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_attentions,
        )


@keras_serializable
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaMainLayer with Roberta->XLMRoberta
class TFXLMRobertaMainLayer(tf.keras.layers.Layer):
    config_class = XLMRobertaConfig

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

        self.config = config
        self.is_decoder = config.is_decoder

        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 = TFXLMRobertaEncoder(config, name="encoder")
        self.pooler = TFXLMRobertaPooler(config, name="pooler") if add_pooling_layer else None
        # The embeddings must be the last declaration in order to follow the weights order
        self.embeddings = TFXLMRobertaEmbeddings(config, name="embeddings")

    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings
    def get_input_embeddings(self) -> tf.keras.layers.Layer:
        return self.embeddings

    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings
    def set_input_embeddings(self, value: tf.Variable):
        self.embeddings.weight = value
        self.embeddings.vocab_size = shape_list(value)[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

    @unpack_inputs
    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.call
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
        encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
        past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
        if not self.config.is_decoder:
            use_cache = False

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = 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")

        batch_size, seq_length = input_shape

        if past_key_values is None:
            past_key_values_length = 0
            past_key_values = [None] * len(self.encoder.layer)
        else:
            past_key_values_length = shape_list(past_key_values[0][0])[-2]

        if attention_mask is None:
            attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1)

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

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            past_key_values_length=past_key_values_length,
            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.
        attention_mask_shape = shape_list(attention_mask)

        mask_seq_length = seq_length + past_key_values_length
        # Copied from `modeling_tf_t5.py`
        # Provided a padding mask of dimensions [batch_size, mask_seq_length]
        # - if the model is a decoder, apply a causal mask in addition to the padding mask
        # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
        if self.is_decoder:
            seq_ids = tf.range(mask_seq_length)
            causal_mask = tf.less_equal(
                tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)),
                seq_ids[None, :, None],
            )
            causal_mask = tf.cast(causal_mask, dtype=attention_mask.dtype)
            extended_attention_mask = causal_mask * attention_mask[:, None, :]
            attention_mask_shape = shape_list(extended_attention_mask)
            extended_attention_mask = tf.reshape(
                extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2])
            )
            if past_key_values[0] is not None:
                # attention_mask needs to be sliced to the shape `[batch_size, 1, from_seq_length - cached_seq_length, to_seq_length]
                extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :]
        else:
            extended_attention_mask = tf.reshape(
                attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1])
            )

        # 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, dtype=embedding_output.dtype)
        one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
        ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
        extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)

        # Copied from `modeling_tf_t5.py` with -1e9 -> -10000
        if self.is_decoder and encoder_attention_mask is not None:
            # If a 2D ou 3D attention mask is provided for the cross-attention
            # we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
            # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
            encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=extended_attention_mask.dtype)
            num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask))
            if num_dims_encoder_attention_mask == 3:
                encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
            if num_dims_encoder_attention_mask == 2:
                encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]

            # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
            # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
            # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask,
            #                                         tf.transpose(encoder_extended_attention_mask, perm=(-1, -2)))

            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        if head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.config.num_hidden_layers

        encoder_outputs = self.encoder(
            hidden_states=embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None

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

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


# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaPreTrainedModel with Roberta->XLMRoberta
class TFXLMRobertaPreTrainedModel(TFPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = XLMRobertaConfig
    base_model_prefix = "roberta"


@add_start_docstrings(
    "The bare XLM RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaModel with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaModel(TFXLMRobertaPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.roberta = TFXLMRobertaMainLayer(config, name="roberta")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutputWithPoolingAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
        encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
        past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: Optional[bool] = False,
    ) -> Union[Tuple, TFBaseModelOutputWithPoolingAndCrossAttentions]:
        r"""
        encoder_hidden_states  (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

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

        past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
            contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache (`bool`, *optional*, defaults to `True`):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`). Set to `False` during training, `True` during generation
        """
        outputs = self.roberta(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        return outputs


# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaLMHead with Roberta->XLMRoberta
class TFXLMRobertaLMHead(tf.keras.layers.Layer):
    """XLMRoberta Head for masked language modeling."""

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

        self.config = config
        self.hidden_size = config.hidden_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.config.vocab_size,), initializer="zeros", trainable=True, name="bias")

        super().build(input_shape)

    def get_output_embeddings(self):
        return self.decoder

    def set_output_embeddings(self, value):
        self.decoder.weight = value
        self.decoder.vocab_size = shape_list(value)[0]

    def get_bias(self):
        return {"bias": self.bias}

    def set_bias(self, value):
        self.bias = value["bias"]
        self.config.vocab_size = shape_list(value["bias"])[0]

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

        # project back to size of vocabulary with bias
        seq_length = shape_list(tensor=hidden_states)[1]
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
        hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True)
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size])
        hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)

        return hidden_states


@add_start_docstrings("""XLM RoBERTa Model with a `language modeling` head on top.""", XLM_ROBERTA_START_DOCSTRING)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForMaskedLM with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForMaskedLM(TFXLMRobertaPreTrainedModel, TFMaskedLanguageModelingLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"]

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

        self.roberta = TFXLMRobertaMainLayer(config, add_pooling_layer=False, name="roberta")
        self.lm_head = TFXLMRobertaLMHead(config, self.roberta.embeddings, name="lm_head")

    def get_lm_head(self):
        return self.lm_head

    def get_prefix_bias_name(self):
        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
        return self.name + "/" + self.lm_head.name

    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
        mask="<mask>",
        expected_output="' Paris'",
        expected_loss=0.1,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
        """
        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

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

        loss = None if labels is None else self.hf_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(
    "XLM-RoBERTa Model with a `language modeling` head on top for CLM fine-tuning.",
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForCausalLM with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForCausalLM(TFXLMRobertaPreTrainedModel, TFCausalLanguageModelingLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"]

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

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

        self.roberta = TFXLMRobertaMainLayer(config, add_pooling_layer=False, name="roberta")
        self.lm_head = TFXLMRobertaLMHead(config, input_embeddings=self.roberta.embeddings, name="lm_head")

    def get_lm_head(self):
        return self.lm_head

    def get_prefix_bias_name(self):
        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
        return self.name + "/" + self.lm_head.name

    # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.prepare_inputs_for_generation
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
        input_shape = input_ids.shape
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = tf.ones(input_shape)

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

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

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

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

        past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
            contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache (`bool`, *optional*, defaults to `True`):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`). Set to `False` during training, `True` during generation
        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
            config.vocab_size - 1]`.
        """
        outputs = self.roberta(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]
        logits = self.lm_head(hidden_states=sequence_output, training=training)
        loss = None

        if labels is not None:
            # shift labels to the left and cut last logit token
            shifted_logits = logits[:, :-1]
            labels = labels[:, 1:]
            loss = self.hf_compute_loss(labels=labels, logits=shifted_logits)

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

        return TFCausalLMOutputWithCrossAttentions(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaClassificationHead with Roberta->XLMRoberta
class TFXLMRobertaClassificationHead(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",
        )
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = tf.keras.layers.Dropout(classifier_dropout)
        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(
    """
    XLM 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.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForSequenceClassification with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForSequenceClassification(TFXLMRobertaPreTrainedModel, TFSequenceClassificationLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]

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

        self.roberta = TFXLMRobertaMainLayer(config, add_pooling_layer=False, name="roberta")
        self.classifier = TFXLMRobertaClassificationHead(config, name="classifier")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint="cardiffnlp/twitter-roberta-base-emotion",
        output_type=TFSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output="'optimism'",
        expected_loss=0.08,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output, training=training)

        loss = None if labels is None else self.hf_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(
    """
    XLM 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.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForMultipleChoice with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForMultipleChoice(TFXLMRobertaPreTrainedModel, TFMultipleChoiceLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"lm_head"]
    _keys_to_ignore_on_load_missing = [r"dropout"]

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

        self.roberta = TFXLMRobertaMainLayer(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"
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(
        XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
    )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` of shape `(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)
        """

        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.hf_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(
    """
    XLM 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.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForTokenClassification with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForTokenClassification(TFXLMRobertaPreTrainedModel, TFTokenClassificationLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]
    _keys_to_ignore_on_load_missing = [r"dropout"]

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

        self.roberta = TFXLMRobertaMainLayer(config, add_pooling_layer=False, name="roberta")
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = tf.keras.layers.Dropout(classifier_dropout)
        self.classifier = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint="ydshieh/roberta-large-ner-english",
        output_type=TFTokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']",
        expected_loss=0.01,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
        """
        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            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.hf_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(
    """
    XLM 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`).
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForQuestionAnswering with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForQuestionAnswering(TFXLMRobertaPreTrainedModel, TFQuestionAnsweringLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]

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

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

    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint="ydshieh/roberta-base-squad2",
        output_type=TFQuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output="' puppet'",
        expected_loss=0.86,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        start_positions: np.ndarray | tf.Tensor | None = None,
        end_positions: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
        r"""
        start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        """
        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            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.hf_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,
        )

# coding=utf-8
# Copyright 2019 Facebook AI Research 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 XLM-RoBERTa model."""


from __future__ import annotations

import math
import warnings
from typing import Optional, Tuple, Union

import numpy as np
import tensorflow as tf

from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithPastAndCrossAttentions,
    TFBaseModelOutputWithPoolingAndCrossAttentions,
    TFCausalLMOutputWithCrossAttentions,
    TFMaskedLMOutput,
    TFMultipleChoiceModelOutput,
    TFQuestionAnsweringModelOutput,
    TFSequenceClassifierOutput,
    TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
    TFCausalLanguageModelingLoss,
    TFMaskedLanguageModelingLoss,
    TFModelInputType,
    TFMultipleChoiceLoss,
    TFPreTrainedModel,
    TFQuestionAnsweringLoss,
    TFSequenceClassificationLoss,
    TFTokenClassificationLoss,
    get_initializer,
    keras_serializable,
    unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
)
from .configuration_xlm_roberta import XLMRobertaConfig


logger = logging.get_logger(__name__)

logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "xlm-roberta-base"
_CONFIG_FOR_DOC = "XLMRobertaConfig"

TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "xlm-roberta-base",
    "xlm-roberta-large",
    "joeddav/xlm-roberta-large-xnli",
    "cardiffnlp/twitter-xlm-roberta-base-sentiment",
    # See all XLM-RoBERTa models at https://huggingface.co/models?filter=xlm-roberta
]

XLM_ROBERTA_START_DOCSTRING = r"""

    This model inherits from [`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.

    <Tip>

    TensorFlow models and layers in `transformers` accept two formats as input:

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

    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, 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: `model(input_ids)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([input_ids, attention_mask])` or `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:
    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!

    </Tip>

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

XLM_ROBERTA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See
            [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input
            IDs?](../glossary#input-ids)
        attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids)
        head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
            config will be used instead.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
            used instead.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
            eager mode, in graph mode the value will always be set to True.
        training (`bool`, *optional*, defaults to `False`):
            Whether or not to use the model in training mode (some modules like dropout modules have different
            behaviors between training and evaluation).
"""


# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaEmbeddings with Roberta->XLMRoberta
class TFXLMRobertaEmbeddings(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.config = config
        self.hidden_size = config.hidden_size
        self.max_position_embeddings = config.max_position_embeddings
        self.initializer_range = config.initializer_range
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

    def build(self, input_shape: tf.TensorShape):
        with tf.name_scope("word_embeddings"):
            self.weight = self.add_weight(
                name="weight",
                shape=[self.config.vocab_size, self.hidden_size],
                initializer=get_initializer(self.initializer_range),
            )

        with tf.name_scope("token_type_embeddings"):
            self.token_type_embeddings = self.add_weight(
                name="embeddings",
                shape=[self.config.type_vocab_size, self.hidden_size],
                initializer=get_initializer(self.initializer_range),
            )

        with tf.name_scope("position_embeddings"):
            self.position_embeddings = self.add_weight(
                name="embeddings",
                shape=[self.max_position_embeddings, self.hidden_size],
                initializer=get_initializer(self.initializer_range),
            )

        super().build(input_shape)

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

        Args:
            input_ids: tf.Tensor
        Returns: tf.Tensor
        """
        mask = tf.cast(tf.math.not_equal(input_ids, self.padding_idx), dtype=input_ids.dtype)
        incremental_indices = (tf.math.cumsum(mask, axis=1) + past_key_values_length) * mask

        return incremental_indices + self.padding_idx

    def call(
        self,
        input_ids=None,
        position_ids=None,
        token_type_ids=None,
        inputs_embeds=None,
        past_key_values_length=0,
        training=False,
    ):
        """
        Applies embedding based on inputs tensor.

        Returns:
            final_embeddings (`tf.Tensor`): output embedding tensor.
        """
        assert not (input_ids is None and inputs_embeds is None)

        if input_ids is not None:
            check_embeddings_within_bounds(input_ids, self.config.vocab_size)
            inputs_embeds = tf.gather(params=self.weight, indices=input_ids)

        input_shape = shape_list(inputs_embeds)[:-1]

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

        if position_ids is None:
            if input_ids is not None:
                # Create the position ids from the input token ids. Any padded tokens remain padded.
                position_ids = self.create_position_ids_from_input_ids(
                    input_ids=input_ids, past_key_values_length=past_key_values_length
                )
            else:
                position_ids = tf.expand_dims(
                    tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1), axis=0
                )

        position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
        token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
        final_embeddings = self.LayerNorm(inputs=final_embeddings)
        final_embeddings = self.dropout(inputs=final_embeddings, training=training)

        return final_embeddings


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

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

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(inputs=first_token_tensor)

        return pooled_output


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

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

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

        self.query = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
        )
        self.key = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
        )
        self.value = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)

        self.is_decoder = config.is_decoder

    def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
        # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
        tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))

        # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
        return tf.transpose(tensor, perm=[0, 2, 1, 3])

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        head_mask: tf.Tensor,
        encoder_hidden_states: tf.Tensor,
        encoder_attention_mask: tf.Tensor,
        past_key_value: Tuple[tf.Tensor],
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        batch_size = shape_list(hidden_states)[0]
        mixed_query_layer = self.query(inputs=hidden_states)

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

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

        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)

        if self.is_decoder:
            # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_layer, value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        # (batch size, num_heads, seq_len_q, seq_len_k)
        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
        dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
        attention_scores = tf.divide(attention_scores, dk)

        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in TFXLMRobertaModel call() function)
            attention_scores = tf.add(attention_scores, attention_mask)

        # Normalize the attention scores to probabilities.
        attention_probs = stable_softmax(logits=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(inputs=attention_probs, training=training)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = tf.multiply(attention_probs, head_mask)

        attention_output = tf.matmul(attention_probs, value_layer)
        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])

        # (batch_size, seq_len_q, all_head_size)
        attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
        outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)

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


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

        self.dense = tf.keras.layers.Dense(
            units=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(rate=config.hidden_dropout_prob)

    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)

        return hidden_states


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

        self.self_attention = TFXLMRobertaSelfAttention(config, name="self")
        self.dense_output = TFXLMRobertaSelfOutput(config, name="output")

    def prune_heads(self, heads):
        raise NotImplementedError

    def call(
        self,
        input_tensor: tf.Tensor,
        attention_mask: tf.Tensor,
        head_mask: tf.Tensor,
        encoder_hidden_states: tf.Tensor,
        encoder_attention_mask: tf.Tensor,
        past_key_value: Tuple[tf.Tensor],
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        self_outputs = self.self_attention(
            hidden_states=input_tensor,
            attention_mask=attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = self.dense_output(
            hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
        )
        # add attentions (possibly with past_key_value) if we output them
        outputs = (attention_output,) + self_outputs[1:]

        return outputs


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

        self.dense = tf.keras.layers.Dense(
            units=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: tf.Tensor) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)

        return hidden_states


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

        self.dense = tf.keras.layers.Dense(
            units=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(rate=config.hidden_dropout_prob)

    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)

        return hidden_states


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

        self.attention = TFXLMRobertaAttention(config, name="attention")
        self.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder:
                raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
            self.crossattention = TFXLMRobertaAttention(config, name="crossattention")
        self.intermediate = TFXLMRobertaIntermediate(config, name="intermediate")
        self.bert_output = TFXLMRobertaOutput(config, name="output")

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

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

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

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

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

        intermediate_output = self.intermediate(hidden_states=attention_output)
        layer_output = self.bert_output(
            hidden_states=intermediate_output, input_tensor=attention_output, training=training
        )
        outputs = (layer_output,) + outputs  # add attentions if we output them

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

        return outputs


# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->XLMRoberta
class TFXLMRobertaEncoder(tf.keras.layers.Layer):
    def __init__(self, config: XLMRobertaConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.layer = [TFXLMRobertaLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        head_mask: tf.Tensor,
        encoder_hidden_states: tf.Tensor | None,
        encoder_attention_mask: tf.Tensor | None,
        past_key_values: Tuple[Tuple[tf.Tensor]] | None,
        use_cache: Optional[bool],
        output_attentions: bool,
        output_hidden_states: bool,
        return_dict: bool,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None

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

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

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

            if use_cache:
                next_decoder_cache += (layer_outputs[-1],)

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)
                if self.config.add_cross_attention and encoder_hidden_states is not None:
                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)

        # 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, all_cross_attentions] if v is not None
            )

        return TFBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_decoder_cache,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_attentions,
        )


@keras_serializable
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaMainLayer with Roberta->XLMRoberta
class TFXLMRobertaMainLayer(tf.keras.layers.Layer):
    config_class = XLMRobertaConfig

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

        self.config = config
        self.is_decoder = config.is_decoder

        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 = TFXLMRobertaEncoder(config, name="encoder")
        self.pooler = TFXLMRobertaPooler(config, name="pooler") if add_pooling_layer else None
        # The embeddings must be the last declaration in order to follow the weights order
        self.embeddings = TFXLMRobertaEmbeddings(config, name="embeddings")

    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings
    def get_input_embeddings(self) -> tf.keras.layers.Layer:
        return self.embeddings

    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings
    def set_input_embeddings(self, value: tf.Variable):
        self.embeddings.weight = value
        self.embeddings.vocab_size = shape_list(value)[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

    @unpack_inputs
    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.call
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
        encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
        past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
        if not self.config.is_decoder:
            use_cache = False

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = 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")

        batch_size, seq_length = input_shape

        if past_key_values is None:
            past_key_values_length = 0
            past_key_values = [None] * len(self.encoder.layer)
        else:
            past_key_values_length = shape_list(past_key_values[0][0])[-2]

        if attention_mask is None:
            attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1)

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

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            past_key_values_length=past_key_values_length,
            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.
        attention_mask_shape = shape_list(attention_mask)

        mask_seq_length = seq_length + past_key_values_length
        # Copied from `modeling_tf_t5.py`
        # Provided a padding mask of dimensions [batch_size, mask_seq_length]
        # - if the model is a decoder, apply a causal mask in addition to the padding mask
        # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
        if self.is_decoder:
            seq_ids = tf.range(mask_seq_length)
            causal_mask = tf.less_equal(
                tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)),
                seq_ids[None, :, None],
            )
            causal_mask = tf.cast(causal_mask, dtype=attention_mask.dtype)
            extended_attention_mask = causal_mask * attention_mask[:, None, :]
            attention_mask_shape = shape_list(extended_attention_mask)
            extended_attention_mask = tf.reshape(
                extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2])
            )
            if past_key_values[0] is not None:
                # attention_mask needs to be sliced to the shape `[batch_size, 1, from_seq_length - cached_seq_length, to_seq_length]
                extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :]
        else:
            extended_attention_mask = tf.reshape(
                attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1])
            )

        # 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, dtype=embedding_output.dtype)
        one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
        ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
        extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)

        # Copied from `modeling_tf_t5.py` with -1e9 -> -10000
        if self.is_decoder and encoder_attention_mask is not None:
            # If a 2D ou 3D attention mask is provided for the cross-attention
            # we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
            # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
            encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=extended_attention_mask.dtype)
            num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask))
            if num_dims_encoder_attention_mask == 3:
                encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
            if num_dims_encoder_attention_mask == 2:
                encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]

            # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
            # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
            # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask,
            #                                         tf.transpose(encoder_extended_attention_mask, perm=(-1, -2)))

            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        if head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.config.num_hidden_layers

        encoder_outputs = self.encoder(
            hidden_states=embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None

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

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


# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaPreTrainedModel with Roberta->XLMRoberta
class TFXLMRobertaPreTrainedModel(TFPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = XLMRobertaConfig
    base_model_prefix = "roberta"


@add_start_docstrings(
    "The bare XLM RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaModel with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaModel(TFXLMRobertaPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.roberta = TFXLMRobertaMainLayer(config, name="roberta")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutputWithPoolingAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
        encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
        past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: Optional[bool] = False,
    ) -> Union[Tuple, TFBaseModelOutputWithPoolingAndCrossAttentions]:
        r"""
        encoder_hidden_states  (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

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

        past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
            contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache (`bool`, *optional*, defaults to `True`):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`). Set to `False` during training, `True` during generation
        """
        outputs = self.roberta(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        return outputs


# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaLMHead with Roberta->XLMRoberta
class TFXLMRobertaLMHead(tf.keras.layers.Layer):
    """XLMRoberta Head for masked language modeling."""

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

        self.config = config
        self.hidden_size = config.hidden_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.config.vocab_size,), initializer="zeros", trainable=True, name="bias")

        super().build(input_shape)

    def get_output_embeddings(self):
        return self.decoder

    def set_output_embeddings(self, value):
        self.decoder.weight = value
        self.decoder.vocab_size = shape_list(value)[0]

    def get_bias(self):
        return {"bias": self.bias}

    def set_bias(self, value):
        self.bias = value["bias"]
        self.config.vocab_size = shape_list(value["bias"])[0]

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

        # project back to size of vocabulary with bias
        seq_length = shape_list(tensor=hidden_states)[1]
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
        hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True)
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size])
        hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)

        return hidden_states


@add_start_docstrings("""XLM RoBERTa Model with a `language modeling` head on top.""", XLM_ROBERTA_START_DOCSTRING)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForMaskedLM with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForMaskedLM(TFXLMRobertaPreTrainedModel, TFMaskedLanguageModelingLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"]

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

        self.roberta = TFXLMRobertaMainLayer(config, add_pooling_layer=False, name="roberta")
        self.lm_head = TFXLMRobertaLMHead(config, self.roberta.embeddings, name="lm_head")

    def get_lm_head(self):
        return self.lm_head

    def get_prefix_bias_name(self):
        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
        return self.name + "/" + self.lm_head.name

    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
        mask="<mask>",
        expected_output="' Paris'",
        expected_loss=0.1,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
        """
        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

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

        loss = None if labels is None else self.hf_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(
    "XLM-RoBERTa Model with a `language modeling` head on top for CLM fine-tuning.",
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForCausalLM with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForCausalLM(TFXLMRobertaPreTrainedModel, TFCausalLanguageModelingLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"]

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

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

        self.roberta = TFXLMRobertaMainLayer(config, add_pooling_layer=False, name="roberta")
        self.lm_head = TFXLMRobertaLMHead(config, input_embeddings=self.roberta.embeddings, name="lm_head")

    def get_lm_head(self):
        return self.lm_head

    def get_prefix_bias_name(self):
        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
        return self.name + "/" + self.lm_head.name

    # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.prepare_inputs_for_generation
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
        input_shape = input_ids.shape
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = tf.ones(input_shape)

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

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

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

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

        past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
            contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache (`bool`, *optional*, defaults to `True`):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`). Set to `False` during training, `True` during generation
        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
            config.vocab_size - 1]`.
        """
        outputs = self.roberta(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]
        logits = self.lm_head(hidden_states=sequence_output, training=training)
        loss = None

        if labels is not None:
            # shift labels to the left and cut last logit token
            shifted_logits = logits[:, :-1]
            labels = labels[:, 1:]
            loss = self.hf_compute_loss(labels=labels, logits=shifted_logits)

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

        return TFCausalLMOutputWithCrossAttentions(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaClassificationHead with Roberta->XLMRoberta
class TFXLMRobertaClassificationHead(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",
        )
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = tf.keras.layers.Dropout(classifier_dropout)
        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(
    """
    XLM 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.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForSequenceClassification with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForSequenceClassification(TFXLMRobertaPreTrainedModel, TFSequenceClassificationLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]

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

        self.roberta = TFXLMRobertaMainLayer(config, add_pooling_layer=False, name="roberta")
        self.classifier = TFXLMRobertaClassificationHead(config, name="classifier")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint="cardiffnlp/twitter-roberta-base-emotion",
        output_type=TFSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output="'optimism'",
        expected_loss=0.08,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output, training=training)

        loss = None if labels is None else self.hf_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(
    """
    XLM 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.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForMultipleChoice with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForMultipleChoice(TFXLMRobertaPreTrainedModel, TFMultipleChoiceLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"lm_head"]
    _keys_to_ignore_on_load_missing = [r"dropout"]

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

        self.roberta = TFXLMRobertaMainLayer(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"
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(
        XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
    )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` of shape `(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)
        """

        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.hf_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(
    """
    XLM 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.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForTokenClassification with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForTokenClassification(TFXLMRobertaPreTrainedModel, TFTokenClassificationLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]
    _keys_to_ignore_on_load_missing = [r"dropout"]

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

        self.roberta = TFXLMRobertaMainLayer(config, add_pooling_layer=False, name="roberta")
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = tf.keras.layers.Dropout(classifier_dropout)
        self.classifier = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint="ydshieh/roberta-large-ner-english",
        output_type=TFTokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']",
        expected_loss=0.01,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
        """
        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            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.hf_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(
    """
    XLM 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`).
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForQuestionAnswering with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class TFXLMRobertaForQuestionAnswering(TFXLMRobertaPreTrainedModel, TFQuestionAnsweringLoss):
    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
    _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]

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

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

    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint="ydshieh/roberta-base-squad2",
        output_type=TFQuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output="' puppet'",
        expected_loss=0.86,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        start_positions: np.ndarray | tf.Tensor | None = None,
        end_positions: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
        r"""
        start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        """
        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            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.hf_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,
        )
