modeling_flax_electra.py

# 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 (
    FlaxBaseModelOutput,
    FlaxBaseModelOutputWithPastAndCrossAttentions,
    FlaxCausalLMOutputWithCrossAttentions,
    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_electra import ElectraConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "google/electra-small-discriminator"
_CONFIG_FOR_DOC = "ElectraConfig"

remat = nn_partitioning.remat


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

    Args:
        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).
        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.
    """

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


ELECTRA_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.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
    regular Flax 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 ([`ElectraConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

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

    config: ElectraConfig
    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)

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings.__call__
    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->Electra
class FlaxElectraSelfAttention(nn.Module):
    config: ElectraConfig
    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.ndarray] = 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->Electra
class FlaxElectraSelfOutput(nn.Module):
    config: ElectraConfig
    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->Electra
class FlaxElectraAttention(nn.Module):
    config: ElectraConfig
    causal: bool = False
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.self = FlaxElectraSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
        self.output = FlaxElectraSelfOutput(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->Electra
class FlaxElectraIntermediate(nn.Module):
    config: ElectraConfig
    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->Electra
class FlaxElectraOutput(nn.Module):
    config: ElectraConfig
    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->Electra
class FlaxElectraLayer(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.attention = FlaxElectraAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
        self.intermediate = FlaxElectraIntermediate(self.config, dtype=self.dtype)
        self.output = FlaxElectraOutput(self.config, dtype=self.dtype)
        if self.config.add_cross_attention:
            self.crossattention = FlaxElectraAttention(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->Electra
class FlaxElectraLayerCollection(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        if self.gradient_checkpointing:
            FlaxElectraCheckpointLayer = remat(FlaxElectraLayer, static_argnums=(5, 6, 7))
            self.layers = [
                FlaxElectraCheckpointLayer(self.config, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_hidden_layers)
            ]
        else:
            self.layers = [
                FlaxElectraLayer(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->Electra
class FlaxElectraEncoder(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        self.layer = FlaxElectraLayerCollection(
            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 FlaxElectraGeneratorPredictions(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dense = nn.Dense(self.config.embedding_size, dtype=self.dtype)

    def __call__(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = ACT2FN[self.config.hidden_act](hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


class FlaxElectraDiscriminatorPredictions(nn.Module):
    """Prediction module for the discriminator, made up of two dense layers."""

    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
        self.dense_prediction = nn.Dense(1, dtype=self.dtype)

    def __call__(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = ACT2FN[self.config.hidden_act](hidden_states)
        hidden_states = self.dense_prediction(hidden_states).squeeze(-1)
        return hidden_states


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

    config_class = ElectraConfig
    base_model_prefix = "electra"
    module_class: nn.Module = None

    def __init__(
        self,
        config: ElectraConfig,
        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,
        )

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.init_weights
    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(ELECTRA_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.ones_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 FlaxElectraAttention 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 FlaxElectraModule(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        self.embeddings = FlaxElectraEmbeddings(self.config, dtype=self.dtype)
        if self.config.embedding_size != self.config.hidden_size:
            self.embeddings_project = nn.Dense(self.config.hidden_size, dtype=self.dtype)
        self.encoder = FlaxElectraEncoder(
            self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask: Optional[np.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,
    ):
        embeddings = self.embeddings(
            input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic
        )
        if hasattr(self, "embeddings_project"):
            embeddings = self.embeddings_project(embeddings)

        return self.encoder(
            embeddings,
            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,
        )


@add_start_docstrings(
    "The bare Electra Model transformer outputting raw hidden-states without any specific head on top.",
    ELECTRA_START_DOCSTRING,
)
class FlaxElectraModel(FlaxElectraPreTrainedModel):
    module_class = FlaxElectraModule


append_call_sample_docstring(FlaxElectraModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC)


class FlaxElectraTiedDense(nn.Module):
    embedding_size: int
    dtype: jnp.dtype = jnp.float32
    precision = None
    bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros

    def setup(self):
        self.bias = self.param("bias", self.bias_init, (self.embedding_size,))

    def __call__(self, x, kernel):
        x = jnp.asarray(x, self.dtype)
        kernel = jnp.asarray(kernel, self.dtype)
        y = lax.dot_general(
            x,
            kernel,
            (((x.ndim - 1,), (0,)), ((), ())),
            precision=self.precision,
        )
        bias = jnp.asarray(self.bias, self.dtype)
        return y + bias


class FlaxElectraForMaskedLMModule(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.electra = FlaxElectraModule(
            config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.generator_predictions = FlaxElectraGeneratorPredictions(config=self.config, dtype=self.dtype)
        if self.config.tie_word_embeddings:
            self.generator_lm_head = FlaxElectraTiedDense(self.config.vocab_size, dtype=self.dtype)
        else:
            self.generator_lm_head = nn.Dense(self.config.vocab_size, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        outputs = self.electra(
            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]
        prediction_scores = self.generator_predictions(hidden_states)

        if self.config.tie_word_embeddings:
            shared_embedding = self.electra.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
            prediction_scores = self.generator_lm_head(prediction_scores, shared_embedding.T)
        else:
            prediction_scores = self.generator_lm_head(prediction_scores)

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

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


@add_start_docstrings("""Electra Model with a `language modeling` head on top.""", ELECTRA_START_DOCSTRING)
class FlaxElectraForMaskedLM(FlaxElectraPreTrainedModel):
    module_class = FlaxElectraForMaskedLMModule


append_call_sample_docstring(FlaxElectraForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC)


class FlaxElectraForPreTrainingModule(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.electra = FlaxElectraModule(
            config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.discriminator_predictions = FlaxElectraDiscriminatorPredictions(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.electra(
            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.discriminator_predictions(hidden_states)

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

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


@add_start_docstrings(
    """
    Electra model with a binary classification head on top as used during pretraining for identifying generated tokens.

    It is recommended to load the discriminator checkpoint into that model.
    """,
    ELECTRA_START_DOCSTRING,
)
class FlaxElectraForPreTraining(FlaxElectraPreTrainedModel):
    module_class = FlaxElectraForPreTrainingModule


FLAX_ELECTRA_FOR_PRETRAINING_DOCSTRING = """
    Returns:

    Example:

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

    >>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
    >>> model = FlaxElectraForPreTraining.from_pretrained("google/electra-small-discriminator")

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

    >>> prediction_logits = outputs.logits
    ```
"""

overwrite_call_docstring(
    FlaxElectraForPreTraining,
    ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_ELECTRA_FOR_PRETRAINING_DOCSTRING,
)
append_replace_return_docstrings(
    FlaxElectraForPreTraining, output_type=FlaxElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC
)


class FlaxElectraForTokenClassificationModule(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.electra = FlaxElectraModule(
            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(classifier_dropout)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.electra(
            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(
    """
    Electra model with a token classification head on top.

    Both the discriminator and generator may be loaded into this model.
    """,
    ELECTRA_START_DOCSTRING,
)
class FlaxElectraForTokenClassification(FlaxElectraPreTrainedModel):
    module_class = FlaxElectraForTokenClassificationModule


append_call_sample_docstring(
    FlaxElectraForTokenClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxTokenClassifierOutput,
    _CONFIG_FOR_DOC,
)


def identity(x, **kwargs):
    return x


class FlaxElectraSequenceSummary(nn.Module):
    r"""
    Compute a single vector summary of a sequence hidden states.

    Args:
        config ([`PretrainedConfig`]):
            The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
            config class of your model for the default values it uses):

            - **summary_use_proj** (`bool`) -- Add a projection after the vector extraction.
            - **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes
              (otherwise to `config.hidden_size`).
            - **summary_activation** (`Optional[str]`) -- Set to `"tanh"` to add a tanh activation to the output,
              another string or `None` will add no activation.
            - **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation.
            - **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation.
    """
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.summary = identity
        if hasattr(self.config, "summary_use_proj") and self.config.summary_use_proj:
            if (
                hasattr(self.config, "summary_proj_to_labels")
                and self.config.summary_proj_to_labels
                and self.config.num_labels > 0
            ):
                num_classes = self.config.num_labels
            else:
                num_classes = self.config.hidden_size
            self.summary = nn.Dense(num_classes, dtype=self.dtype)

        activation_string = getattr(self.config, "summary_activation", None)
        self.activation = ACT2FN[activation_string] if activation_string else lambda x: x  # noqa F407

        self.first_dropout = identity
        if hasattr(self.config, "summary_first_dropout") and self.config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(self.config.summary_first_dropout)

        self.last_dropout = identity
        if hasattr(self.config, "summary_last_dropout") and self.config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(self.config.summary_last_dropout)

    def __call__(self, hidden_states, cls_index=None, deterministic: bool = True):
        """
        Compute a single vector summary of a sequence hidden states.

        Args:
            hidden_states (`jnp.ndarray` of shape `[batch_size, seq_len, hidden_size]`):
                The hidden states of the last layer.
            cls_index (`jnp.ndarray` of shape `[batch_size]` or `[batch_size, ...]` where ... are optional leading dimensions of `hidden_states`, *optional*):
                Used if `summary_type == "cls_index"` and takes the last token of the sequence as classification token.

        Returns:
            `jnp.ndarray`: The summary of the sequence hidden states.
        """
        # NOTE: this doest "first" type summary always
        output = hidden_states[:, 0]
        output = self.first_dropout(output, deterministic=deterministic)
        output = self.summary(output)
        output = self.activation(output)
        output = self.last_dropout(output, deterministic=deterministic)
        return output


class FlaxElectraForMultipleChoiceModule(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.electra = FlaxElectraModule(
            config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.sequence_summary = FlaxElectraSequenceSummary(config=self.config, dtype=self.dtype)
        self.classifier = nn.Dense(1, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        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.electra(
            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]
        pooled_output = self.sequence_summary(hidden_states, deterministic=deterministic)
        logits = self.classifier(pooled_output)

        reshaped_logits = logits.reshape(-1, num_choices)

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

        return FlaxMultipleChoiceModelOutput(
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    ELECTRA 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.
    """,
    ELECTRA_START_DOCSTRING,
)
class FlaxElectraForMultipleChoice(FlaxElectraPreTrainedModel):
    module_class = FlaxElectraForMultipleChoiceModule


# adapt docstring slightly for FlaxElectraForMultipleChoice
overwrite_call_docstring(
    FlaxElectraForMultipleChoice, ELECTRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
append_call_sample_docstring(
    FlaxElectraForMultipleChoice,
    _CHECKPOINT_FOR_DOC,
    FlaxMultipleChoiceModelOutput,
    _CONFIG_FOR_DOC,
)


class FlaxElectraForQuestionAnsweringModule(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.electra = FlaxElectraModule(
            config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.electra(
            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(
    """
    ELECTRA 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`).
    """,
    ELECTRA_START_DOCSTRING,
)
class FlaxElectraForQuestionAnswering(FlaxElectraPreTrainedModel):
    module_class = FlaxElectraForQuestionAnsweringModule


append_call_sample_docstring(
    FlaxElectraForQuestionAnswering,
    _CHECKPOINT_FOR_DOC,
    FlaxQuestionAnsweringModelOutput,
    _CONFIG_FOR_DOC,
)


class FlaxElectraClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""

    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32

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

    def __call__(self, hidden_states, deterministic: bool = True):
        x = hidden_states[:, 0, :]  # take <s> token (equiv. to [CLS])
        x = self.dropout(x, deterministic=deterministic)
        x = self.dense(x)
        x = ACT2FN["gelu"](x)  # although BERT uses tanh here, it seems Electra authors used gelu
        x = self.dropout(x, deterministic=deterministic)
        x = self.out_proj(x)
        return x


class FlaxElectraForSequenceClassificationModule(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.electra = FlaxElectraModule(
            config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.classifier = FlaxElectraClassificationHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.electra(
            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.classifier(hidden_states, 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(
    """
    Electra Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """,
    ELECTRA_START_DOCSTRING,
)
class FlaxElectraForSequenceClassification(FlaxElectraPreTrainedModel):
    module_class = FlaxElectraForSequenceClassificationModule


append_call_sample_docstring(
    FlaxElectraForSequenceClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxSequenceClassifierOutput,
    _CONFIG_FOR_DOC,
)


class FlaxElectraForCausalLMModule(nn.Module):
    config: ElectraConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.electra = FlaxElectraModule(
            config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.generator_predictions = FlaxElectraGeneratorPredictions(config=self.config, dtype=self.dtype)
        if self.config.tie_word_embeddings:
            self.generator_lm_head = FlaxElectraTiedDense(self.config.vocab_size, dtype=self.dtype)
        else:
            self.generator_lm_head = nn.Dense(self.config.vocab_size, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask: Optional[jnp.ndarray] = None,
        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,
    ):
        outputs = self.electra(
            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]
        prediction_scores = self.generator_predictions(hidden_states)

        if self.config.tie_word_embeddings:
            shared_embedding = self.electra.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
            prediction_scores = self.generator_lm_head(prediction_scores, shared_embedding.T)
        else:
            prediction_scores = self.generator_lm_head(prediction_scores)

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

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


@add_start_docstrings(
    """
    Electra Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
    autoregressive tasks.
    """,
    ELECTRA_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForCausalLM with Bert->Electra
class FlaxElectraForCausalLM(FlaxElectraPreTrainedModel):
    module_class = FlaxElectraForCausalLMModule

    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(
    FlaxElectraForCausalLM,
    _CHECKPOINT_FOR_DOC,
    FlaxCausalLMOutputWithCrossAttentions,
    _CONFIG_FOR_DOC,
)