text: # 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, Tuple
  
  import numpy as np
  
  import flax.linen as nn
  import jax
  import jax.numpy as jnp
  from flax.core.frozen_dict import FrozenDict
  from flax.linen import dot_product_attention
  from jax import lax
  from jax.random import PRNGKey
  
  from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, overwrite_call_docstring
  from ...utils import logging
  from .configuration_bert import BertConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "BertConfig"
  _TOKENIZER_FOR_DOC = "BertTokenizer"
  
  
  BERT_START_DOCSTRING = r"""
  
      This model inherits from :class:`~transformers.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 (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.
              Initializing with a config file does not load the weights associated with the model, only the
              configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
              weights.
  """
  
  BERT_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`numpy.ndarray` of shape :obj:`({0})`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`~transformers.BertTokenizer`. See
              :meth:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`numpy.ndarray` of shape :obj:`({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.html#attention-mask>`__
          token_type_ids (:obj:`numpy.ndarray` of shape :obj:`({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.html#token-type-ids>`__
          position_ids (:obj:`numpy.ndarray` of shape :obj:`({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 (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
  
  """
  
  
  class FlaxBertEmbeddings(nn.Module):
      """Construct the embeddings from word, position and token_type embeddings."""
  
      config: BertConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.word_embeddings = nn.Embed(
              self.config.vocab_size,
              self.config.hidden_size,
              embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
              dtype=self.dtype,
          )
          self.position_embeddings = nn.Embed(
              self.config.max_position_embeddings,
              self.config.hidden_size,
              embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
              dtype=self.dtype,
          )
          self.token_type_embeddings = nn.Embed(
              self.config.type_vocab_size,
              self.config.hidden_size,
              embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
              dtype=self.dtype,
          )
          self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
          self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
  
      def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
          batch_size, sequence_length = input_ids.shape
          # 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
          #        hidden_states = hidden_states.reshape((batch_size, sequence_length, -1))
  
          # Layer Norm
          hidden_states = self.LayerNorm(hidden_states)
          hidden_states = self.dropout(hidden_states, deterministic=deterministic)
          return hidden_states
  
  
  class FlaxBertSelfAttention(nn.Module):
      config: BertConfig
      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.dtype),
          )
          self.key = nn.Dense(
              self.config.hidden_size,
              dtype=self.dtype,
              kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
          )
          self.value = nn.Dense(
              self.config.hidden_size,
              dtype=self.dtype,
              kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
          )
  
      def __call__(self, hidden_states, attention_mask, deterministic=True):
          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, -1e10).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_output = dot_product_attention(
              query_states,
              key_states,
              value_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,
          )
  
          return attn_output.reshape(attn_output.shape[:2] + (-1,))
  
  
  class FlaxBertSelfOutput(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.dense = nn.Dense(
              self.config.hidden_size,
              kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
              dtype=self.dtype,
          )
          self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
          self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
  
      def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states, deterministic=deterministic)
          hidden_states = self.LayerNorm(hidden_states + input_tensor)
          return hidden_states
  
  
  class FlaxBertAttention(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.self = FlaxBertSelfAttention(self.config, dtype=self.dtype)
          self.output = FlaxBertSelfOutput(self.config, dtype=self.dtype)
  
      def __call__(self, hidden_states, attention_mask, deterministic=True):
          # 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_output = self.self(hidden_states, attention_mask, deterministic=deterministic)
          hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)
          return hidden_states
  
  
  class FlaxBertIntermediate(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.dense = nn.Dense(
              self.config.intermediate_size,
              kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
              dtype=self.dtype,
          )
          self.activation = ACT2FN[self.config.hidden_act]
  
      def __call__(self, hidden_states):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.activation(hidden_states)
          return hidden_states
  
  
  class FlaxBertOutput(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.dense = nn.Dense(
              self.config.hidden_size,
              kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
              dtype=self.dtype,
          )
          self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
          self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
  
      def __call__(self, hidden_states, attention_output, deterministic: bool = True):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states, deterministic=deterministic)
          hidden_states = self.LayerNorm(hidden_states + attention_output)
          return hidden_states
  
  
  class FlaxBertLayer(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.attention = FlaxBertAttention(self.config, dtype=self.dtype)
          self.intermediate = FlaxBertIntermediate(self.config, dtype=self.dtype)
          self.output = FlaxBertOutput(self.config, dtype=self.dtype)
  
      def __call__(self, hidden_states, attention_mask, deterministic: bool = True):
          attention_output = self.attention(hidden_states, attention_mask, deterministic=deterministic)
          hidden_states = self.intermediate(attention_output)
          hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic)
          return hidden_states
  
  
  class FlaxBertLayerCollection(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.layers = [
              FlaxBertLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
          ]
  
      def __call__(self, hidden_states, attention_mask, deterministic: bool = True):
          for i, layer in enumerate(self.layers):
              hidden_states = layer(hidden_states, attention_mask, deterministic=deterministic)
          return hidden_states
  
  
  class FlaxBertEncoder(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.layer = FlaxBertLayerCollection(self.config, dtype=self.dtype)
  
      def __call__(self, hidden_states, attention_mask, deterministic: bool = True):
          return self.layer(hidden_states, attention_mask, deterministic=deterministic)
  
  
  class FlaxBertPooler(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.dense = nn.Dense(
              self.config.hidden_size,
              kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
              dtype=self.dtype,
          )
  
      def __call__(self, hidden_states):
          cls_hidden_state = hidden_states[:, 0]
          cls_hidden_state = self.dense(cls_hidden_state)
          return nn.tanh(cls_hidden_state)
  
  
  class FlaxBertPredictionHeadTransform(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
          self.activation = ACT2FN[self.config.hidden_act]
          self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
  
      def __call__(self, hidden_states):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.activation(hidden_states)
          return self.LayerNorm(hidden_states)
  
  
  class FlaxBertLMPredictionHead(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
      bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
  
      def setup(self):
          self.transform = FlaxBertPredictionHeadTransform(self.config, dtype=self.dtype)
          self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False)
          self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))
  
      def __call__(self, hidden_states, shared_embedding=None):
          hidden_states = self.transform(hidden_states)
  
          if shared_embedding is not None:
              hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
          else:
              hidden_states = self.decoder(hidden_states)
  
          hidden_states += self.bias
          return hidden_states
  
  
  class FlaxBertOnlyMLMHead(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.predictions = FlaxBertLMPredictionHead(self.config, dtype=self.dtype)
  
      def __call__(self, hidden_states, shared_embedding=None):
          hidden_states = self.predictions(hidden_states, shared_embedding=shared_embedding)
          return hidden_states
  
  
  class FlaxBertOnlyNSPHead(nn.Module):
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.seq_relationship = nn.Dense(2, dtype=self.dtype)
  
      def __call__(self, pooled_output):
          return self.seq_relationship(pooled_output)
  
  
  class FlaxBertPreTrainingHeads(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.predictions = FlaxBertLMPredictionHead(self.config, dtype=self.dtype)
          self.seq_relationship = nn.Dense(2, dtype=self.dtype)
  
      def __call__(self, hidden_states, pooled_output, shared_embedding=None):
          prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding)
          seq_relationship_score = self.seq_relationship(pooled_output)
          return prediction_scores, seq_relationship_score
  
  
  class FlaxBertPreTrainedModel(FlaxPreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = BertConfig
      base_model_prefix = "bert"
      module_class: nn.Module = None
  
      def __init__(
          self, config: BertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, **kwargs
      ):
          module = self.module_class(config=config, dtype=dtype, **kwargs)
          super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype)
  
      def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
          # init input tensors
          input_ids = jnp.zeros(input_shape, dtype="i4")
          token_type_ids = jnp.ones_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}
  
          return self.module.init(rngs, input_ids, attention_mask, token_type_ids, position_ids)["params"]
  
      @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      def __call__(
          self,
          input_ids,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          params: dict = None,
          dropout_rng: PRNGKey = None,
          train: bool = False,
      ):
          # 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)
  
          # 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,
              rngs=rngs,
          )
  
  
  class FlaxBertModule(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
      add_pooling_layer: bool = True
  
      def setup(self):
          self.embeddings = FlaxBertEmbeddings(self.config, dtype=self.dtype)
          self.encoder = FlaxBertEncoder(self.config, dtype=self.dtype)
          self.pooler = FlaxBertPooler(self.config, dtype=self.dtype)
  
      def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True):
          hidden_states = self.embeddings(
              input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic
          )
          hidden_states = self.encoder(hidden_states, attention_mask, deterministic=deterministic)
  
          if not self.add_pooling_layer:
              return hidden_states
  
          pooled = self.pooler(hidden_states)
          return hidden_states, pooled
  
  
  @add_start_docstrings(
      "The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
      BERT_START_DOCSTRING,
  )
  class FlaxBertModel(FlaxBertPreTrainedModel):
      module_class = FlaxBertModule
  
  
  class FlaxBertForPreTrainingModule(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.bert = FlaxBertModule(config=self.config, dtype=self.dtype)
          self.cls = FlaxBertPreTrainingHeads(config=self.config, dtype=self.dtype)
  
      def __call__(
          self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, deterministic: bool = True
      ):
          # Model
          hidden_states, pooled_output = self.bert(
              input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic
          )
  
          if self.config.tie_word_embeddings:
              shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
          else:
              shared_embedding = None
  
          prediction_scores, seq_relationship_score = self.cls(
              hidden_states, pooled_output, shared_embedding=shared_embedding
          )
  
          return (prediction_scores, seq_relationship_score)
  
  
  @add_start_docstrings(
      """
      Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
      sentence prediction (classification)` head.
      """,
      BERT_START_DOCSTRING,
  )
  class FlaxBertForPreTraining(FlaxBertPreTrainedModel):
      module_class = FlaxBertForPreTrainingModule
  
  
  class FlaxBertForMaskedLMModule(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.bert = FlaxBertModule(config=self.config, add_pooling_layer=False, dtype=self.dtype)
          self.cls = FlaxBertOnlyMLMHead(config=self.config, dtype=self.dtype)
  
      def __call__(
          self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, deterministic: bool = True
      ):
          # Model
          hidden_states = self.bert(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic)
  
          if self.config.tie_word_embeddings:
              shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
          else:
              shared_embedding = None
  
          # Compute the prediction scores
          logits = self.cls(hidden_states, shared_embedding=shared_embedding)
  
          return (logits,)
  
  
  @add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
  class FlaxBertForMaskedLM(FlaxBertPreTrainedModel):
      module_class = FlaxBertForMaskedLMModule
  
  
  class FlaxBertForNextSentencePredictionModule(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.bert = FlaxBertModule(config=self.config, dtype=self.dtype)
          self.cls = FlaxBertOnlyNSPHead(dtype=self.dtype)
  
      def __call__(
          self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, deterministic: bool = True
      ):
          # Model
          _, pooled_output = self.bert(
              input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic
          )
  
          seq_relationship_scores = self.cls(pooled_output)
          return (seq_relationship_scores,)
  
  
  @add_start_docstrings(
      """Bert Model with a `next sentence prediction (classification)` head on top. """,
      BERT_START_DOCSTRING,
  )
  class FlaxBertForNextSentencePrediction(FlaxBertPreTrainedModel):
      module_class = FlaxBertForNextSentencePredictionModule
  
  
  class FlaxBertForSequenceClassificationModule(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.bert = FlaxBertModule(config=self.config, dtype=self.dtype)
          self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
          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, deterministic: bool = True
      ):
          # Model
          _, pooled_output = self.bert(
              input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic
          )
  
          pooled_output = self.dropout(pooled_output, deterministic=deterministic)
          logits = self.classifier(pooled_output)
  
          return (logits,)
  
  
  @add_start_docstrings(
      """
      Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
      output) e.g. for GLUE tasks.
      """,
      BERT_START_DOCSTRING,
  )
  class FlaxBertForSequenceClassification(FlaxBertPreTrainedModel):
      module_class = FlaxBertForSequenceClassificationModule
  
  
  class FlaxBertForMultipleChoiceModule(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.bert = FlaxBertModule(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=None, token_type_ids=None, position_ids=None, deterministic: 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
          _, pooled_output = self.bert(
              input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic
          )
  
          pooled_output = self.dropout(pooled_output, deterministic=deterministic)
          logits = self.classifier(pooled_output)
  
          reshaped_logits = logits.reshape(-1, num_choices)
  
          return (reshaped_logits,)
  
  
  @add_start_docstrings(
      """
      Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
      softmax) e.g. for RocStories/SWAG tasks.
      """,
      BERT_START_DOCSTRING,
  )
  class FlaxBertForMultipleChoice(FlaxBertPreTrainedModel):
      module_class = FlaxBertForMultipleChoiceModule
  
  
  # adapt docstring slightly for FlaxBertForMultipleChoice
  overwrite_call_docstring(
      FlaxBertForMultipleChoice, BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
  )
  
  
  class FlaxBertForTokenClassificationModule(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.bert = FlaxBertModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
          self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
          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, deterministic: bool = True
      ):
          # Model
          hidden_states = self.bert(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic)
  
          hidden_states = self.dropout(hidden_states, deterministic=deterministic)
          logits = self.classifier(hidden_states)
  
          return (logits,)
  
  
  @add_start_docstrings(
      """
      Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
      Named-Entity-Recognition (NER) tasks.
      """,
      BERT_START_DOCSTRING,
  )
  class FlaxBertForTokenClassification(FlaxBertPreTrainedModel):
      module_class = FlaxBertForTokenClassificationModule
  
  
  class FlaxBertForQuestionAnsweringModule(nn.Module):
      config: BertConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.bert = FlaxBertModule(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=None, token_type_ids=None, position_ids=None, deterministic: bool = True
      ):
          # Model
          hidden_states = self.bert(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic)
  
          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)
  
          return (start_logits, end_logits)
  
  
  @add_start_docstrings(
      """
      Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
      layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
      """,
      BERT_START_DOCSTRING,
  )
  class FlaxBertForQuestionAnswering(FlaxBertPreTrainedModel):
      module_class = FlaxBertForQuestionAnsweringModule
  