modeling_flax_xlm_roberta.py

# coding=utf-8
# Copyright 2022 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.
"""Flax XLM-RoBERTa model."""

from typing import Callable, Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax

from ...modeling_flax_outputs import (
    FlaxBaseModelOutputWithPastAndCrossAttentions,
    FlaxBaseModelOutputWithPooling,
    FlaxBaseModelOutputWithPoolingAndCrossAttentions,
    FlaxCausalLMOutputWithCrossAttentions,
    FlaxMaskedLMOutput,
    FlaxMultipleChoiceModelOutput,
    FlaxQuestionAnsweringModelOutput,
    FlaxSequenceClassifierOutput,
    FlaxTokenClassifierOutput,
)
from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, overwrite_call_docstring
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_xlm_roberta import XLMRobertaConfig


logger = logging.get_logger(__name__)

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

remat = nn_partitioning.remat

FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "xlm-roberta-base",
    "xlm-roberta-large",
    # See all XLM-RoBERTa models at https://huggingface.co/models?filter=xlm-roberta
]


# Copied from transformers.models.roberta.modeling_flax_roberta.create_position_ids_from_input_ids
def create_position_ids_from_input_ids(input_ids, padding_idx):
    """
    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: jnp.ndarray
        padding_idx: int

    Returns: jnp.ndarray
    """
    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
    mask = (input_ids != padding_idx).astype("i4")

    if mask.ndim > 2:
        mask = mask.reshape((-1, mask.shape[-1]))
        incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask
        incremental_indices = incremental_indices.reshape(input_ids.shape)
    else:
        incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask

    return incremental_indices.astype("i4") + padding_idx


XLM_ROBERTA_START_DOCSTRING = r"""

    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

    This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
    general usage and behavior.

    Finally, this model supports inherent JAX features such as:

    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    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 [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
"""

XLM_ROBERTA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`numpy.ndarray` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`numpy.ndarray` 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.ndarray` 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.ndarray` 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]`.
        head_mask (`numpy.ndarray` of shape `({0})`, `optional):
            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

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

        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings with Bert->XLMRoberta
class FlaxXLMRobertaEmbeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.word_embeddings = nn.Embed(
            self.config.vocab_size,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.position_embeddings = nn.Embed(
            self.config.max_position_embeddings,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.token_type_embeddings = nn.Embed(
            self.config.type_vocab_size,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
        # Embed
        inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
        position_embeds = self.position_embeddings(position_ids.astype("i4"))
        token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4"))

        # Sum all embeddings
        hidden_states = inputs_embeds + token_type_embeddings + position_embeds

        # Layer Norm
        hidden_states = self.LayerNorm(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfAttention with Bert->XLMRoberta
class FlaxXLMRobertaSelfAttention(nn.Module):
    config: XLMRobertaConfig
    causal: bool = False
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.head_dim = self.config.hidden_size // self.config.num_attention_heads
        if self.config.hidden_size % self.config.num_attention_heads != 0:
            raise ValueError(
                "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` "
                "                   : {self.config.num_attention_heads}"
            )

        self.query = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.key = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.value = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )

        if self.causal:
            self.causal_mask = make_causal_mask(
                jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
            )

    def _split_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim))

    def _merge_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,))

    @nn.compact
    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache
    def _concatenate_to_cache(self, key, value, query, attention_mask):
        """
        This function takes projected key, value states from a single input token and concatenates the states to cached
        states from previous steps. This function is slighly adapted from the official Flax repository:
        https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
        """
        # detect if we're initializing by absence of existing cache data.
        is_initialized = self.has_variable("cache", "cached_key")
        cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
        cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))

        if is_initialized:
            *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
            # update key, value caches with our new 1d spatial slices
            cur_index = cache_index.value
            indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
            key = lax.dynamic_update_slice(cached_key.value, key, indices)
            value = lax.dynamic_update_slice(cached_value.value, value, indices)
            cached_key.value = key
            cached_value.value = value
            num_updated_cache_vectors = query.shape[1]
            cache_index.value = cache_index.value + num_updated_cache_vectors
            # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
            pad_mask = jnp.broadcast_to(
                jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
                tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
            )
            attention_mask = combine_masks(pad_mask, attention_mask)
        return key, value, attention_mask

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        key_value_states: Optional[jnp.array] = None,
        init_cache: bool = False,
        deterministic=True,
        output_attentions: bool = False,
    ):
        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        batch_size = hidden_states.shape[0]

        # get query proj
        query_states = self.query(hidden_states)
        # get key, value proj
        if is_cross_attention:
            # cross_attentions
            key_states = self.key(key_value_states)
            value_states = self.value(key_value_states)
        else:
            # self_attention
            key_states = self.key(hidden_states)
            value_states = self.value(hidden_states)

        query_states = self._split_heads(query_states)
        key_states = self._split_heads(key_states)
        value_states = self._split_heads(value_states)

        # handle cache prepare causal attention mask
        if self.causal:
            query_length, key_length = query_states.shape[1], key_states.shape[1]
            if self.has_variable("cache", "cached_key"):
                mask_shift = self.variables["cache"]["cache_index"]
                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
                causal_mask = lax.dynamic_slice(
                    self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
                )
            else:
                causal_mask = self.causal_mask[:, :, :query_length, :key_length]
            causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])

        # combine masks if needed
        if attention_mask is not None and self.causal:
            attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
            attention_mask = combine_masks(attention_mask, causal_mask)
        elif self.causal:
            attention_mask = causal_mask
        elif attention_mask is not None:
            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))

        # During fast autoregressive decoding, we feed one position at a time,
        # and cache the keys and values step by step.
        if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
            key_states, value_states, attention_mask = self._concatenate_to_cache(
                key_states, value_states, query_states, attention_mask
            )

        # Convert the boolean attention mask to an attention bias.
        if attention_mask is not None:
            # attention mask in the form of attention bias
            attention_bias = lax.select(
                attention_mask > 0,
                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
                jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
            )
        else:
            attention_bias = None

        dropout_rng = None
        if not deterministic and self.config.attention_probs_dropout_prob > 0.0:
            dropout_rng = self.make_rng("dropout")

        attn_weights = dot_product_attention_weights(
            query_states,
            key_states,
            bias=attention_bias,
            dropout_rng=dropout_rng,
            dropout_rate=self.config.attention_probs_dropout_prob,
            broadcast_dropout=True,
            deterministic=deterministic,
            dtype=self.dtype,
            precision=None,
        )

        # Mask heads if we want to
        if layer_head_mask is not None:
            attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask)

        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))

        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->XLMRoberta
class FlaxXLMRobertaSelfOutput(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertAttention with Bert->XLMRoberta
class FlaxXLMRobertaAttention(nn.Module):
    config: XLMRobertaConfig
    causal: bool = False
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.self = FlaxXLMRobertaSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
        self.output = FlaxXLMRobertaSelfOutput(self.config, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        key_value_states=None,
        init_cache=False,
        deterministic=True,
        output_attentions: bool = False,
    ):
        # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
        # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
        # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
        attn_outputs = self.self(
            hidden_states,
            attention_mask,
            layer_head_mask=layer_head_mask,
            key_value_states=key_value_states,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
        )
        attn_output = attn_outputs[0]
        hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_outputs[1],)

        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->XLMRoberta
class FlaxXLMRobertaIntermediate(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.intermediate_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.activation = ACT2FN[self.config.hidden_act]

    def __call__(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->XLMRoberta
class FlaxXLMRobertaOutput(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)

    def __call__(self, hidden_states, attention_output, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + attention_output)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayer with Bert->XLMRoberta
class FlaxXLMRobertaLayer(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.attention = FlaxXLMRobertaAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
        self.intermediate = FlaxXLMRobertaIntermediate(self.config, dtype=self.dtype)
        self.output = FlaxXLMRobertaOutput(self.config, dtype=self.dtype)
        if self.config.add_cross_attention:
            self.crossattention = FlaxXLMRobertaAttention(self.config, causal=False, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
    ):
        # Self Attention
        attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            layer_head_mask=layer_head_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
        )
        attention_output = attention_outputs[0]

        # Cross-Attention Block
        if encoder_hidden_states is not None:
            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask=encoder_attention_mask,
                layer_head_mask=layer_head_mask,
                key_value_states=encoder_hidden_states,
                deterministic=deterministic,
                output_attentions=output_attentions,
            )
            attention_output = cross_attention_outputs[0]

        hidden_states = self.intermediate(attention_output)
        hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attention_outputs[1],)
            if encoder_hidden_states is not None:
                outputs += (cross_attention_outputs[1],)
        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection with Bert->XLMRoberta
class FlaxXLMRobertaLayerCollection(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        if self.gradient_checkpointing:
            FlaxXLMRobertaCheckpointLayer = remat(FlaxXLMRobertaLayer, static_argnums=(5, 6, 7))
            self.layers = [
                FlaxXLMRobertaCheckpointLayer(self.config, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_hidden_layers)
            ]
        else:
            self.layers = [
                FlaxXLMRobertaLayer(self.config, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_hidden_layers)
            ]

    def __call__(
        self,
        hidden_states,
        attention_mask,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None

        # Check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            if head_mask.shape[0] != (len(self.layers)):
                raise ValueError(
                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for                  "
                    f"       {head_mask.shape[0]}."
                )

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

            layer_outputs = layer(
                hidden_states,
                attention_mask,
                head_mask[i] if head_mask is not None else None,
                encoder_hidden_states,
                encoder_attention_mask,
                init_cache,
                deterministic,
                output_attentions,
            )

            hidden_states = layer_outputs[0]

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

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions)

        if not return_dict:
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_attentions,
        )


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->XLMRoberta
class FlaxXLMRobertaEncoder(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        self.layer = FlaxXLMRobertaLayerCollection(
            self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )

    def __call__(
        self,
        hidden_states,
        attention_mask,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        return self.layer(
            hidden_states,
            attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPooler with Bert->XLMRoberta
class FlaxXLMRobertaPooler(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )

    def __call__(self, hidden_states):
        cls_hidden_state = hidden_states[:, 0]
        cls_hidden_state = self.dense(cls_hidden_state)
        return nn.tanh(cls_hidden_state)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaLMHead with Roberta->XLMRoberta
class FlaxXLMRobertaLMHead(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.decoder = nn.Dense(
            self.config.vocab_size,
            dtype=self.dtype,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))

    def __call__(self, hidden_states, shared_embedding=None):
        hidden_states = self.dense(hidden_states)
        hidden_states = ACT2FN["gelu"](hidden_states)
        hidden_states = self.layer_norm(hidden_states)

        if shared_embedding is not None:
            hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
        else:
            hidden_states = self.decoder(hidden_states)

        bias = jnp.asarray(self.bias, self.dtype)
        hidden_states += bias
        return hidden_states


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaClassificationHead with Roberta->XLMRoberta
class FlaxXLMRobertaClassificationHead(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        classifier_dropout = (
            self.config.classifier_dropout
            if self.config.classifier_dropout is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.out_proj = nn.Dense(
            self.config.num_labels,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )

    def __call__(self, hidden_states, deterministic=True):
        hidden_states = hidden_states[:, 0, :]  # take <s> token (equiv. to [CLS])
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.dense(hidden_states)
        hidden_states = nn.tanh(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.out_proj(hidden_states)
        return hidden_states


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaPreTrainedModel with Roberta->XLMRoberta, roberta->xlm-roberta, ROBERTA->XLM_ROBERTA
class FlaxXLMRobertaPreTrainedModel(FlaxPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = XLMRobertaConfig
    base_model_prefix = "xlm-roberta"

    module_class: nn.Module = None

    def __init__(
        self,
        config: XLMRobertaConfig,
        input_shape: Tuple = (1, 1),
        seed: int = 0,
        dtype: jnp.dtype = jnp.float32,
        _do_init: bool = True,
        gradient_checkpointing: bool = False,
        **kwargs,
    ):
        module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing
    def enable_gradient_checkpointing(self):
        self._module = self.module_class(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=True,
        )

    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        # init input tensors
        input_ids = jnp.zeros(input_shape, dtype="i4")
        token_type_ids = jnp.ones_like(input_ids)
        position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id)
        attention_mask = jnp.ones_like(input_ids)
        head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}

        if self.config.add_cross_attention:
            encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,))
            encoder_attention_mask = attention_mask
            module_init_outputs = self.module.init(
                rngs,
                input_ids,
                attention_mask,
                token_type_ids,
                position_ids,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                return_dict=False,
            )
        else:
            module_init_outputs = self.module.init(
                rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False
            )

        random_params = module_init_outputs["params"]

        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys:
                params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else:
            return random_params

    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderPreTrainedModel.init_cache
    def init_cache(self, batch_size, max_length):
        r"""
        Args:
            batch_size (`int`):
                batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
            max_length (`int`):
                maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
                cache.
        """
        # init input variables to retrieve cache
        input_ids = jnp.ones((batch_size, max_length), dtype="i4")
        attention_mask = jnp.ones_like(input_ids, dtype="i4")
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        init_variables = self.module.init(
            jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
        )
        return unfreeze(init_variables["cache"])

    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        params: dict = None,
        dropout_rng: jax.random.PRNGKey = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        past_key_values: dict = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        # init input tensors if not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        if position_ids is None:
            position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id)

        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)

        if head_mask is None:
            head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        inputs = {"params": params or self.params}

        if self.config.add_cross_attention:
            # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed
            # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be
            # changed by FlaxXLMRobertaAttention module
            if past_key_values:
                inputs["cache"] = past_key_values
                mutable = ["cache"]
            else:
                mutable = False

            outputs = self.module.apply(
                inputs,
                jnp.array(input_ids, dtype="i4"),
                jnp.array(attention_mask, dtype="i4"),
                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
                position_ids=jnp.array(position_ids, dtype="i4"),
                head_mask=jnp.array(head_mask, dtype="i4"),
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                deterministic=not train,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                rngs=rngs,
                mutable=mutable,
            )

            # add updated cache to model output
            if past_key_values is not None and return_dict:
                outputs, past_key_values = outputs
                outputs["past_key_values"] = unfreeze(past_key_values["cache"])
                return outputs
            elif past_key_values is not None and not return_dict:
                outputs, past_key_values = outputs
                outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]

        else:
            outputs = self.module.apply(
                inputs,
                jnp.array(input_ids, dtype="i4"),
                jnp.array(attention_mask, dtype="i4"),
                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
                position_ids=jnp.array(position_ids, dtype="i4"),
                head_mask=jnp.array(head_mask, dtype="i4"),
                deterministic=not train,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                rngs=rngs,
            )

        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->XLMRoberta
class FlaxXLMRobertaModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    add_pooling_layer: bool = True
    gradient_checkpointing: bool = False

    def setup(self):
        self.embeddings = FlaxXLMRobertaEmbeddings(self.config, dtype=self.dtype)
        self.encoder = FlaxXLMRobertaEncoder(
            self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.pooler = FlaxXLMRobertaPooler(self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids: Optional[jnp.ndarray] = None,
        position_ids: Optional[jnp.ndarray] = None,
        head_mask: Optional[jnp.ndarray] = None,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # make sure `token_type_ids` is correctly initialized when not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        # make sure `position_ids` is correctly initialized when not passed
        if position_ids is None:
            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        hidden_states = self.embeddings(
            input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic
        )
        outputs = self.encoder(
            hidden_states,
            attention_mask,
            head_mask=head_mask,
            deterministic=deterministic,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = outputs[0]
        pooled = self.pooler(hidden_states) if self.add_pooling_layer else None

        if not return_dict:
            # if pooled is None, don't return it
            if pooled is None:
                return (hidden_states,) + outputs[1:]
            return (hidden_states, pooled) + outputs[1:]

        return FlaxBaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=hidden_states,
            pooler_output=pooled,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


@add_start_docstrings(
    "The bare XLM RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
    XLM_ROBERTA_START_DOCSTRING,
)
class FlaxXLMRobertaModel(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaModule


append_call_sample_docstring(FlaxXLMRobertaModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForMaskedLMModule with Roberta->XLMRoberta
class FlaxXLMRobertaForMaskedLMModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            add_pooling_layer=False,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.lm_head = FlaxXLMRobertaLMHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.roberta.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        # Compute the prediction scores
        logits = self.lm_head(hidden_states, shared_embedding=shared_embedding)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxMaskedLMOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings("""XLM RoBERTa Model with a `language modeling` head on top.""", XLM_ROBERTA_START_DOCSTRING)
class FlaxXLMRobertaForMaskedLM(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForMaskedLMModule


append_call_sample_docstring(
    FlaxXLMRobertaForMaskedLM,
    _CHECKPOINT_FOR_DOC,
    FlaxBaseModelOutputWithPooling,
    _CONFIG_FOR_DOC,
    mask="<mask>",
)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForSequenceClassificationModule with Roberta->XLMRoberta
class FlaxXLMRobertaForSequenceClassificationModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            add_pooling_layer=False,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.classifier = FlaxXLMRobertaClassificationHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        logits = self.classifier(sequence_output, deterministic=deterministic)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxSequenceClassifierOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@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,
)
class FlaxXLMRobertaForSequenceClassification(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForSequenceClassificationModule


append_call_sample_docstring(
    FlaxXLMRobertaForSequenceClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxSequenceClassifierOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForMultipleChoiceModule with Bert->XLMRoberta, with self.bert->self.roberta
class FlaxXLMRobertaForMultipleChoiceModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.classifier = nn.Dense(1, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        num_choices = input_ids.shape[1]
        input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None
        attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None
        token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None
        position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None

        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)

        reshaped_logits = logits.reshape(-1, num_choices)

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

        return FlaxMultipleChoiceModelOutput(
            logits=reshaped_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,
)
class FlaxXLMRobertaForMultipleChoice(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForMultipleChoiceModule


overwrite_call_docstring(
    FlaxXLMRobertaForMultipleChoice, XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
append_call_sample_docstring(
    FlaxXLMRobertaForMultipleChoice,
    _CHECKPOINT_FOR_DOC,
    FlaxMultipleChoiceModelOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForTokenClassificationModule with Bert->XLMRoberta, with self.bert->self.roberta
class FlaxXLMRobertaForTokenClassificationModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            add_pooling_layer=False,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        classifier_dropout = (
            self.config.classifier_dropout
            if self.config.classifier_dropout is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        logits = self.classifier(hidden_states)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxTokenClassifierOutput(
            logits=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,
)
class FlaxXLMRobertaForTokenClassification(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForTokenClassificationModule


append_call_sample_docstring(
    FlaxXLMRobertaForTokenClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxTokenClassifierOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForQuestionAnsweringModule with Bert->XLMRoberta, with self.bert->self.roberta
class FlaxXLMRobertaForQuestionAnsweringModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            add_pooling_layer=False,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]

        logits = self.qa_outputs(hidden_states)
        start_logits, end_logits = logits.split(self.config.num_labels, axis=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)

        if not return_dict:
            return (start_logits, end_logits) + outputs[1:]

        return FlaxQuestionAnsweringModelOutput(
            start_logits=start_logits,
            end_logits=end_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,
)
class FlaxXLMRobertaForQuestionAnswering(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForQuestionAnsweringModule


append_call_sample_docstring(
    FlaxXLMRobertaForQuestionAnswering,
    _CHECKPOINT_FOR_DOC,
    FlaxQuestionAnsweringModelOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForCausalLMModule with Roberta->XLMRoberta
class FlaxXLMRobertaForCausalLMModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            add_pooling_layer=False,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.lm_head = FlaxXLMRobertaLMHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        position_ids,
        token_type_ids: Optional[jnp.ndarray] = None,
        head_mask: Optional[jnp.ndarray] = None,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.roberta.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        # Compute the prediction scores
        logits = self.lm_head(hidden_states, shared_embedding=shared_embedding)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxCausalLMOutputWithCrossAttentions(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


@add_start_docstrings(
    """
    XLM Roberta Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
    autoregressive tasks.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForCausalLM with Roberta->XLMRoberta
class FlaxXLMRobertaForCausalLM(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForCausalLMModule

    def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
        # initializing the cache
        batch_size, seq_length = input_ids.shape

        past_key_values = self.init_cache(batch_size, max_length)
        # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
        # But since the decoder uses a causal mask, those positions are masked anyway.
        # Thus, we can create a single static attention_mask here, which is more efficient for compilation
        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
        if attention_mask is not None:
            position_ids = attention_mask.cumsum(axis=-1) - 1
            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
        else:
            position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))

        return {
            "past_key_values": past_key_values,
            "attention_mask": extended_attention_mask,
            "position_ids": position_ids,
        }

    def update_inputs_for_generation(self, model_outputs, model_kwargs):
        model_kwargs["past_key_values"] = model_outputs.past_key_values
        model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
        return model_kwargs


append_call_sample_docstring(
    FlaxXLMRobertaForCausalLM,
    _CHECKPOINT_FOR_DOC,
    FlaxCausalLMOutputWithCrossAttentions,
    _CONFIG_FOR_DOC,
)