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Grounded-Segment-Anything / transformers_4_35_0 /models /albert /modeling_flax_albert.py

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	# coding=utf-8
	# Copyright 2021 Google AI, Google Brain 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.attention import dot_product_attention_weights
	from flax.traverse_util import flatten_dict, unflatten_dict
	from jax import lax

	from ...modeling_flax_outputs import (
	FlaxBaseModelOutput,
	FlaxBaseModelOutputWithPooling,
	FlaxMaskedLMOutput,
	FlaxMultipleChoiceModelOutput,
	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_albert import AlbertConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "albert-base-v2"
	_CONFIG_FOR_DOC = "AlbertConfig"


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

	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).
	sop_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
	sop_logits: jnp.ndarray = None
	hidden_states: Optional[Tuple[jnp.ndarray]] = None
	attentions: Optional[Tuple[jnp.ndarray]] = None


	ALBERT_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 ([`AlbertConfig`]): 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`].
	"""

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

	"""


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

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

	def setup(self):
	self.word_embeddings = nn.Embed(
	self.config.vocab_size,
	self.config.embedding_size,
	embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
	)
	self.position_embeddings = nn.Embed(
	self.config.max_position_embeddings,
	self.config.embedding_size,
	embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
	)
	self.token_type_embeddings = nn.Embed(
	self.config.type_vocab_size,
	self.config.embedding_size,
	embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
	)
	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, 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 FlaxAlbertSelfAttention(nn.Module):
	config: AlbertConfig
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation

	def setup(self):
	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),
	)
	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, attention_mask, deterministic=True, output_attentions: bool = False):
	head_dim = self.config.hidden_size // self.config.num_attention_heads

	query_states = self.query(hidden_states).reshape(
	hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
	)
	value_states = self.value(hidden_states).reshape(
	hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
	)
	key_states = self.key(hidden_states).reshape(
	hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
	)

	# Convert the boolean attention mask to an attention bias.
	if attention_mask is not None:
	# attention mask in the form of attention bias
	attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
	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,
	)

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

	projected_attn_output = self.dense(attn_output)
	projected_attn_output = self.dropout(projected_attn_output, deterministic=deterministic)
	layernormed_attn_output = self.LayerNorm(projected_attn_output + hidden_states)
	outputs = (layernormed_attn_output, attn_weights) if output_attentions else (layernormed_attn_output,)
	return outputs


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

	def setup(self):
	self.attention = FlaxAlbertSelfAttention(self.config, dtype=self.dtype)
	self.ffn = 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]
	self.ffn_output = nn.Dense(
	self.config.hidden_size,
	kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
	dtype=self.dtype,
	)
	self.full_layer_layer_norm = 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,
	attention_mask,
	deterministic: bool = True,
	output_attentions: bool = False,
	):
	attention_outputs = self.attention(
	hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions
	)
	attention_output = attention_outputs[0]
	ffn_output = self.ffn(attention_output)
	ffn_output = self.activation(ffn_output)
	ffn_output = self.ffn_output(ffn_output)
	ffn_output = self.dropout(ffn_output, deterministic=deterministic)
	hidden_states = self.full_layer_layer_norm(ffn_output + attention_output)

	outputs = (hidden_states,)

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


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

	def setup(self):
	self.layers = [
	FlaxAlbertLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.inner_group_num)
	]

	def __call__(
	self,
	hidden_states,
	attention_mask,
	deterministic: bool = True,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	):
	layer_hidden_states = ()
	layer_attentions = ()

	for layer_index, albert_layer in enumerate(self.layers):
	layer_output = albert_layer(
	hidden_states,
	attention_mask,
	deterministic=deterministic,
	output_attentions=output_attentions,
	)
	hidden_states = layer_output[0]

	if output_attentions:
	layer_attentions = layer_attentions + (layer_output[1],)

	if output_hidden_states:
	layer_hidden_states = layer_hidden_states + (hidden_states,)

	outputs = (hidden_states,)
	if output_hidden_states:
	outputs = outputs + (layer_hidden_states,)
	if output_attentions:
	outputs = outputs + (layer_attentions,)
	return outputs # last-layer hidden state, (layer hidden states), (layer attentions)


	class FlaxAlbertLayerCollections(nn.Module):
	config: AlbertConfig
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation
	layer_index: Optional[str] = None

	def setup(self):
	self.albert_layers = FlaxAlbertLayerCollection(self.config, dtype=self.dtype)

	def __call__(
	self,
	hidden_states,
	attention_mask,
	deterministic: bool = True,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	):
	outputs = self.albert_layers(
	hidden_states,
	attention_mask,
	deterministic=deterministic,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	)
	return outputs


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

	def setup(self):
	self.layers = [
	FlaxAlbertLayerCollections(self.config, name=str(i), layer_index=str(i), dtype=self.dtype)
	for i in range(self.config.num_hidden_groups)
	]

	def __call__(
	self,
	hidden_states,
	attention_mask,
	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 = (hidden_states,) if output_hidden_states else None

	for i in range(self.config.num_hidden_layers):
	# Index of the hidden group
	group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups))
	layer_group_output = self.layers[group_idx](
	hidden_states,
	attention_mask,
	deterministic=deterministic,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	)
	hidden_states = layer_group_output[0]

	if output_attentions:
	all_attentions = all_attentions + layer_group_output[-1]

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

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


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

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

	def __call__(
	self,
	hidden_states,
	attention_mask,
	deterministic: bool = True,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	):
	hidden_states = self.embedding_hidden_mapping_in(hidden_states)
	return self.albert_layer_groups(
	hidden_states,
	attention_mask,
	deterministic=deterministic,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	)


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

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

	hidden_states += self.bias
	return hidden_states


	class FlaxAlbertSOPHead(nn.Module):
	config: AlbertConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.dropout = nn.Dropout(self.config.classifier_dropout_prob)
	self.classifier = nn.Dense(2, dtype=self.dtype)

	def __call__(self, pooled_output, deterministic=True):
	pooled_output = self.dropout(pooled_output, deterministic=deterministic)
	logits = self.classifier(pooled_output)
	return logits


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

	config_class = AlbertConfig
	base_model_prefix = "albert"
	module_class: nn.Module = None

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

	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)

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

	random_params = self.module.init(
	rngs, input_ids, attention_mask, token_type_ids, position_ids, return_dict=False
	)["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

	@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	def __call__(
	self,
	input_ids,
	attention_mask=None,
	token_type_ids=None,
	position_ids=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,
	):
	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)

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

	return self.module.apply(
	{"params": params or self.params},
	jnp.array(input_ids, dtype="i4"),
	jnp.array(attention_mask, dtype="i4"),
	jnp.array(token_type_ids, dtype="i4"),
	jnp.array(position_ids, dtype="i4"),
	not train,
	output_attentions,
	output_hidden_states,
	return_dict,
	rngs=rngs,
	)


	class FlaxAlbertModule(nn.Module):
	config: AlbertConfig
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation
	add_pooling_layer: bool = True

	def setup(self):
	self.embeddings = FlaxAlbertEmbeddings(self.config, dtype=self.dtype)
	self.encoder = FlaxAlbertEncoder(self.config, dtype=self.dtype)
	if self.add_pooling_layer:
	self.pooler = nn.Dense(
	self.config.hidden_size,
	kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
	dtype=self.dtype,
	name="pooler",
	)
	self.pooler_activation = nn.tanh
	else:
	self.pooler = None
	self.pooler_activation = None

	def __call__(
	self,
	input_ids,
	attention_mask,
	token_type_ids: Optional[np.ndarray] = None,
	position_ids: Optional[np.ndarray] = None,
	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, deterministic=deterministic)

	outputs = self.encoder(
	hidden_states,
	attention_mask,
	deterministic=deterministic,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	hidden_states = outputs[0]
	if self.add_pooling_layer:
	pooled = self.pooler(hidden_states[:, 0])
	pooled = self.pooler_activation(pooled)
	else:
	pooled = 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 FlaxBaseModelOutputWithPooling(
	last_hidden_state=hidden_states,
	pooler_output=pooled,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"The bare Albert Model transformer outputting raw hidden-states without any specific head on top.",
	ALBERT_START_DOCSTRING,
	)
	class FlaxAlbertModel(FlaxAlbertPreTrainedModel):
	module_class = FlaxAlbertModule


	append_call_sample_docstring(FlaxAlbertModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)


	class FlaxAlbertForPreTrainingModule(nn.Module):
	config: AlbertConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
	self.predictions = FlaxAlbertOnlyMLMHead(config=self.config, dtype=self.dtype)
	self.sop_classifier = FlaxAlbertSOPHead(config=self.config, dtype=self.dtype)

	def __call__(
	self,
	input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	deterministic: bool = True,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	):
	# Model
	outputs = self.albert(
	input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	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.albert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
	else:
	shared_embedding = None

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

	prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding)
	sop_scores = self.sop_classifier(pooled_output, deterministic=deterministic)

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

	return FlaxAlbertForPreTrainingOutput(
	prediction_logits=prediction_scores,
	sop_logits=sop_scores,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	Albert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
	`sentence order prediction (classification)` head.
	""",
	ALBERT_START_DOCSTRING,
	)
	class FlaxAlbertForPreTraining(FlaxAlbertPreTrainedModel):
	module_class = FlaxAlbertForPreTrainingModule


	FLAX_ALBERT_FOR_PRETRAINING_DOCSTRING = """
	Returns:

	Example:

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

	>>> tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
	>>> model = FlaxAlbertForPreTraining.from_pretrained("albert-base-v2")

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

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

	overwrite_call_docstring(
	FlaxAlbertForPreTraining,
	ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_ALBERT_FOR_PRETRAINING_DOCSTRING,
	)
	append_replace_return_docstrings(
	FlaxAlbertForPreTraining, output_type=FlaxAlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC
	)


	class FlaxAlbertForMaskedLMModule(nn.Module):
	config: AlbertConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.albert = FlaxAlbertModule(config=self.config, add_pooling_layer=False, dtype=self.dtype)
	self.predictions = FlaxAlbertOnlyMLMHead(config=self.config, dtype=self.dtype)

	def __call__(
	self,
	input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	deterministic: bool = True,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	):
	# Model
	outputs = self.albert(
	input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	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.albert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
	else:
	shared_embedding = None

	# Compute the prediction scores
	logits = self.predictions(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("""Albert Model with a `language modeling` head on top.""", ALBERT_START_DOCSTRING)
	class FlaxAlbertForMaskedLM(FlaxAlbertPreTrainedModel):
	module_class = FlaxAlbertForMaskedLMModule


	append_call_sample_docstring(FlaxAlbertForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC)


	class FlaxAlbertForSequenceClassificationModule(nn.Module):
	config: AlbertConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
	classifier_dropout = (
	self.config.classifier_dropout_prob
	if self.config.classifier_dropout_prob 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,
	deterministic: bool = True,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	):
	# Model
	outputs = self.albert(
	input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	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(
	"""
	Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
	output) e.g. for GLUE tasks.
	""",
	ALBERT_START_DOCSTRING,
	)
	class FlaxAlbertForSequenceClassification(FlaxAlbertPreTrainedModel):
	module_class = FlaxAlbertForSequenceClassificationModule


	append_call_sample_docstring(
	FlaxAlbertForSequenceClassification,
	_CHECKPOINT_FOR_DOC,
	FlaxSequenceClassifierOutput,
	_CONFIG_FOR_DOC,
	)


	class FlaxAlbertForMultipleChoiceModule(nn.Module):
	config: AlbertConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
	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,
	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.albert(
	input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	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(
	"""
	Albert 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.
	""",
	ALBERT_START_DOCSTRING,
	)
	class FlaxAlbertForMultipleChoice(FlaxAlbertPreTrainedModel):
	module_class = FlaxAlbertForMultipleChoiceModule


	overwrite_call_docstring(
	FlaxAlbertForMultipleChoice, ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
	)
	append_call_sample_docstring(
	FlaxAlbertForMultipleChoice,
	_CHECKPOINT_FOR_DOC,
	FlaxMultipleChoiceModelOutput,
	_CONFIG_FOR_DOC,
	)


	class FlaxAlbertForTokenClassificationModule(nn.Module):
	config: AlbertConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
	classifier_dropout = (
	self.config.classifier_dropout_prob
	if self.config.classifier_dropout_prob 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,
	deterministic: bool = True,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	):
	# Model
	outputs = self.albert(
	input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	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(
	"""
	Albert 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.
	""",
	ALBERT_START_DOCSTRING,
	)
	class FlaxAlbertForTokenClassification(FlaxAlbertPreTrainedModel):
	module_class = FlaxAlbertForTokenClassificationModule


	append_call_sample_docstring(
	FlaxAlbertForTokenClassification,
	_CHECKPOINT_FOR_DOC,
	FlaxTokenClassifierOutput,
	_CONFIG_FOR_DOC,
	)


	class FlaxAlbertForQuestionAnsweringModule(nn.Module):
	config: AlbertConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self):
	self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
	self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)

	def __call__(
	self,
	input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	deterministic: bool = True,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	):
	# Model
	outputs = self.albert(
	input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	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(
	"""
	Albert 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`).
	""",
	ALBERT_START_DOCSTRING,
	)
	class FlaxAlbertForQuestionAnswering(FlaxAlbertPreTrainedModel):
	module_class = FlaxAlbertForQuestionAnsweringModule


	append_call_sample_docstring(
	FlaxAlbertForQuestionAnswering,
	_CHECKPOINT_FOR_DOC,
	FlaxQuestionAnsweringModelOutput,
	_CONFIG_FOR_DOC,
	)