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	# coding=utf-8
	# Copyright 2021 The Google Flax Team Authors and The HuggingFace Inc. team.
	#
	# 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.

	from typing import Callable, Optional, Tuple

	import flax
	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,
	FlaxNextSentencePredictorOutput,
	FlaxQuestionAnsweringModelOutput,
	FlaxSequenceClassifierOutput,
	FlaxTokenClassifierOutput,
	)
	from ...modeling_flax_utils import (
	ACT2FN,
	FlaxPreTrainedModel,
	append_call_sample_docstring,
	append_replace_return_docstrings,
	overwrite_call_docstring,
	)
	from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging
	from .configuration_bert import BertConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "bert-base-uncased"
	_CONFIG_FOR_DOC = "BertConfig"

	remat = nn_partitioning.remat


	@flax.struct.dataclass
	class FlaxBertForPreTrainingOutput(ModelOutput):
	"""
	Output type of [`BertForPreTraining`].

	Args:
	prediction_logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
	Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
	seq_relationship_logits (`jnp.ndarray` of shape `(batch_size, 2)`):
	Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
	before SoftMax).
	hidden_states (`tuple(jnp.ndarray)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
	`(batch_size, sequence_length, hidden_size)`.

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

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

	prediction_logits: jnp.ndarray = None
	seq_relationship_logits: jnp.ndarray = None
	hidden_states: Optional[Tuple[jnp.ndarray]] = None
	attentions: Optional[Tuple[jnp.ndarray]] = None


	BERT_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 ([`BertConfig`]): 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.
	dtype (`jax.numpy.dtype`, optional, defaults to `jax.numpy.float32`):
	The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
	`jax.numpy.bfloat16` (on TPUs).

	This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
	specified all the computation will be performed with the given `dtype`.

	**Note that this only specifies the dtype of the computation and does not influence the dtype of model
	parameters.**

	If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
	[`~FlaxPreTrainedModel.to_bf16`].
	dtype (`jax.numpy.dtype`, optional, defaults to `jax.numpy.float32`):
	The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
	`jax.numpy.bfloat16` (on TPUs).

	This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
	specified all the computation will be performed with the given `dtype`.

	**Note that this only specifies the dtype of the computation and does not influence the dtype of model
	parameters.**

	If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
	[`~FlaxPreTrainedModel.to_bf16`].

	"""

	BERT_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.

	"""


	class FlaxBertEmbeddings(nn.Module):
	"""Construct the embeddings from word, position and token_type embeddings."""

	config: BertConfig
	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


	class FlaxBertSelfAttention(nn.Module):
	config: BertConfig
	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


	class FlaxBertSelfOutput(nn.Module):
	config: BertConfig
	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


	class FlaxBertAttention(nn.Module):
	config: BertConfig
	causal: bool = False
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.self = FlaxBertSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
	self.output = FlaxBertSelfOutput(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


	class FlaxBertIntermediate(nn.Module):
	config: BertConfig
	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


	class FlaxBertOutput(nn.Module):
	config: BertConfig
	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


	class FlaxBertLayer(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation

	def setup(self):
	self.attention = FlaxBertAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
	self.intermediate = FlaxBertIntermediate(self.config, dtype=self.dtype)
	self.output = FlaxBertOutput(self.config, dtype=self.dtype)
	if self.config.add_cross_attention:
	self.crossattention = FlaxBertAttention(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


	class FlaxBertLayerCollection(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation
	gradient_checkpointing: bool = False

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


	class FlaxBertEncoder(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation
	gradient_checkpointing: bool = False

	def setup(self):
	self.layer = FlaxBertLayerCollection(
	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,
	)


	class FlaxBertPooler(nn.Module):
	config: BertConfig
	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)


	class FlaxBertPredictionHeadTransform(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
	self.activation = ACT2FN[self.config.hidden_act]
	self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)

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


	class FlaxBertLMPredictionHead(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32
	bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros

	def setup(self):
	self.transform = FlaxBertPredictionHeadTransform(self.config, dtype=self.dtype)
	self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False)
	self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))

	def __call__(self, hidden_states, shared_embedding=None):
	hidden_states = self.transform(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


	class FlaxBertOnlyMLMHead(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.predictions = FlaxBertLMPredictionHead(self.config, dtype=self.dtype)

	def __call__(self, hidden_states, shared_embedding=None):
	hidden_states = self.predictions(hidden_states, shared_embedding=shared_embedding)
	return hidden_states


	class FlaxBertOnlyNSPHead(nn.Module):
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.seq_relationship = nn.Dense(2, dtype=self.dtype)

	def __call__(self, pooled_output):
	return self.seq_relationship(pooled_output)


	class FlaxBertPreTrainingHeads(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.predictions = FlaxBertLMPredictionHead(self.config, dtype=self.dtype)
	self.seq_relationship = nn.Dense(2, dtype=self.dtype)

	def __call__(self, hidden_states, pooled_output, shared_embedding=None):
	prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding)
	seq_relationship_score = self.seq_relationship(pooled_output)
	return prediction_scores, seq_relationship_score


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

	config_class = BertConfig
	base_model_prefix = "bert"
	module_class: nn.Module = None

	def __init__(
	self,
	config: BertConfig,
	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)

	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.zeros_like(input_ids)
	position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
	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(BERT_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 = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

	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 FlaxBertAttention 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


	class FlaxBertModule(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation
	add_pooling_layer: bool = True
	gradient_checkpointing: bool = False

	def setup(self):
	self.embeddings = FlaxBertEmbeddings(self.config, dtype=self.dtype)
	self.encoder = FlaxBertEncoder(
	self.config,
	dtype=self.dtype,
	gradient_checkpointing=self.gradient_checkpointing,
	)
	self.pooler = FlaxBertPooler(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 Bert Model transformer outputting raw hidden-states without any specific head on top.",
	BERT_START_DOCSTRING,
	)
	class FlaxBertModel(FlaxBertPreTrainedModel):
	module_class = FlaxBertModule


	append_call_sample_docstring(FlaxBertModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)


	class FlaxBertForPreTrainingModule(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32
	gradient_checkpointing: bool = False

	def setup(self):
	self.bert = FlaxBertModule(
	config=self.config,
	dtype=self.dtype,
	gradient_checkpointing=self.gradient_checkpointing,
	)
	self.cls = FlaxBertPreTrainingHeads(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.bert(
	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,
	)

	if self.config.tie_word_embeddings:
	shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
	else:
	shared_embedding = None

	hidden_states = outputs[0]
	pooled_output = outputs[1]

	prediction_scores, seq_relationship_score = self.cls(
	hidden_states, pooled_output, shared_embedding=shared_embedding
	)

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

	return FlaxBertForPreTrainingOutput(
	prediction_logits=prediction_scores,
	seq_relationship_logits=seq_relationship_score,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
	sentence prediction (classification)` head.
	""",
	BERT_START_DOCSTRING,
	)
	class FlaxBertForPreTraining(FlaxBertPreTrainedModel):
	module_class = FlaxBertForPreTrainingModule


	FLAX_BERT_FOR_PRETRAINING_DOCSTRING = """
	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, FlaxBertForPreTraining

	>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
	>>> model = FlaxBertForPreTraining.from_pretrained("bert-base-uncased")

	>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
	>>> outputs = model(**inputs)

	>>> prediction_logits = outputs.prediction_logits
	>>> seq_relationship_logits = outputs.seq_relationship_logits
	```
	"""

	overwrite_call_docstring(
	FlaxBertForPreTraining,
	BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_BERT_FOR_PRETRAINING_DOCSTRING,
	)
	append_replace_return_docstrings(
	FlaxBertForPreTraining, output_type=FlaxBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC
	)


	class FlaxBertForMaskedLMModule(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32
	gradient_checkpointing: bool = False

	def setup(self):
	self.bert = FlaxBertModule(
	config=self.config,
	add_pooling_layer=False,
	dtype=self.dtype,
	gradient_checkpointing=self.gradient_checkpointing,
	)
	self.cls = FlaxBertOnlyMLMHead(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.bert(
	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.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
	else:
	shared_embedding = None

	# Compute the prediction scores
	logits = self.cls(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("""Bert Model with a `language modeling` head on top.""", BERT_START_DOCSTRING)
	class FlaxBertForMaskedLM(FlaxBertPreTrainedModel):
	module_class = FlaxBertForMaskedLMModule


	append_call_sample_docstring(FlaxBertForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC)


	class FlaxBertForNextSentencePredictionModule(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32
	gradient_checkpointing: bool = False

	def setup(self):
	self.bert = FlaxBertModule(
	config=self.config,
	dtype=self.dtype,
	gradient_checkpointing=self.gradient_checkpointing,
	)
	self.cls = FlaxBertOnlyNSPHead(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,
	):
	return_dict = return_dict if return_dict is not None else self.config.return_dict

	# Model
	outputs = self.bert(
	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]
	seq_relationship_scores = self.cls(pooled_output)

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

	return FlaxNextSentencePredictorOutput(
	logits=seq_relationship_scores,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""Bert Model with a `next sentence prediction (classification)` head on top.""",
	BERT_START_DOCSTRING,
	)
	class FlaxBertForNextSentencePrediction(FlaxBertPreTrainedModel):
	module_class = FlaxBertForNextSentencePredictionModule


	FLAX_BERT_FOR_NEXT_SENT_PRED_DOCSTRING = """
	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, FlaxBertForNextSentencePrediction

	>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
	>>> model = FlaxBertForNextSentencePrediction.from_pretrained("bert-base-uncased")

	>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
	>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
	>>> encoding = tokenizer(prompt, next_sentence, return_tensors="jax")

	>>> outputs = model(**encoding)
	>>> logits = outputs.logits
	>>> assert logits[0, 0] < logits[0, 1] # next sentence was random
	```
	"""


	overwrite_call_docstring(
	FlaxBertForNextSentencePrediction,
	BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_BERT_FOR_NEXT_SENT_PRED_DOCSTRING,
	)
	append_replace_return_docstrings(
	FlaxBertForNextSentencePrediction, output_type=FlaxNextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC
	)


	class FlaxBertForSequenceClassificationModule(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32
	gradient_checkpointing: bool = False

	def setup(self):
	self.bert = FlaxBertModule(
	config=self.config,
	dtype=self.dtype,
	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.bert(
	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)

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

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


	@add_start_docstrings(
	"""
	Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
	output) e.g. for GLUE tasks.
	""",
	BERT_START_DOCSTRING,
	)
	class FlaxBertForSequenceClassification(FlaxBertPreTrainedModel):
	module_class = FlaxBertForSequenceClassificationModule


	append_call_sample_docstring(
	FlaxBertForSequenceClassification,
	_CHECKPOINT_FOR_DOC,
	FlaxSequenceClassifierOutput,
	_CONFIG_FOR_DOC,
	)


	class FlaxBertForMultipleChoiceModule(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32
	gradient_checkpointing: bool = False

	def setup(self):
	self.bert = FlaxBertModule(
	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.bert(
	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(
	"""
	Bert 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.
	""",
	BERT_START_DOCSTRING,
	)
	class FlaxBertForMultipleChoice(FlaxBertPreTrainedModel):
	module_class = FlaxBertForMultipleChoiceModule


	overwrite_call_docstring(
	FlaxBertForMultipleChoice, BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
	)
	append_call_sample_docstring(
	FlaxBertForMultipleChoice, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC
	)


	class FlaxBertForTokenClassificationModule(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32
	gradient_checkpointing: bool = False

	def setup(self):
	self.bert = FlaxBertModule(
	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.bert(
	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(
	"""
	Bert 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.
	""",
	BERT_START_DOCSTRING,
	)
	class FlaxBertForTokenClassification(FlaxBertPreTrainedModel):
	module_class = FlaxBertForTokenClassificationModule


	append_call_sample_docstring(
	FlaxBertForTokenClassification, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC
	)


	class FlaxBertForQuestionAnsweringModule(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32
	gradient_checkpointing: bool = False

	def setup(self):
	self.bert = FlaxBertModule(
	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.bert(
	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(
	"""
	Bert 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`).
	""",
	BERT_START_DOCSTRING,
	)
	class FlaxBertForQuestionAnswering(FlaxBertPreTrainedModel):
	module_class = FlaxBertForQuestionAnsweringModule


	append_call_sample_docstring(
	FlaxBertForQuestionAnswering,
	_CHECKPOINT_FOR_DOC,
	FlaxQuestionAnsweringModelOutput,
	_CONFIG_FOR_DOC,
	)


	class FlaxBertForCausalLMModule(nn.Module):
	config: BertConfig
	dtype: jnp.dtype = jnp.float32
	gradient_checkpointing: bool = False

	def setup(self):
	self.bert = FlaxBertModule(
	config=self.config,
	add_pooling_layer=False,
	dtype=self.dtype,
	gradient_checkpointing=self.gradient_checkpointing,
	)
	self.cls = FlaxBertOnlyMLMHead(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.bert(
	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.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
	else:
	shared_embedding = None

	# Compute the prediction scores
	logits = self.cls(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(
	"""
	Bert Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
	autoregressive tasks.
	""",
	BERT_START_DOCSTRING,
	)
	class FlaxBertForCausalLM(FlaxBertPreTrainedModel):
	module_class = FlaxBertForCausalLMModule

	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(
	FlaxBertForCausalLM,
	_CHECKPOINT_FOR_DOC,
	FlaxCausalLMOutputWithCrossAttentions,
	_CONFIG_FOR_DOC,
	)