"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import math
from typing import Callable, Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from ...modeling_flax_outputs import (FlaxBaseModelOutput, FlaxMaskedLMOutput, FlaxMultipleChoiceModelOutput, FlaxQuestionAnsweringModelOutput, FlaxSequenceClassifierOutput, FlaxTokenClassifierOutput)
from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, overwrite_call_docstring
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_distilbert import DistilBertConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "distilbert-base-uncased"
_CONFIG_FOR_DOC = "DistilBertConfig"
FLAX_DISTILBERT_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.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as
    a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
    behavior.
    Finally, this model supports inherent JAX features such as:
    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
    Parameters:
        config ([`DistilBertConfig`]): 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.
"""
DISTILBERT_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)
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
def get_angles(pos, i, d_model):
    angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model))
    return pos * angle_rates
def positional_encoding(position, d_model):
    angle_rads = get_angles(np.arange(position)[:, np.newaxis], np.arange(d_model)[np.newaxis, :], d_model)
    angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])
    angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])
    pos_encoding = angle_rads[np.newaxis, ...]
    return jnp.array(pos_encoding)
class FlaxEmbeddings(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.word_embeddings = nn.Embed(self.config.vocab_size, self.config.dim, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        if not self.config.sinusoidal_pos_embds: self.position_embeddings = nn.Embed(self.config.max_position_embeddings, self.config.dim, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        else: self.pos_encoding = positional_encoding(self.config.max_position_embeddings, self.config.dim)
        self.LayerNorm = nn.LayerNorm(epsilon=1e-12, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.dropout)
    def __call__(self, input_ids, deterministic: bool = True):
        batch_size, seq_length = input_ids.shape
        inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
        if not self.config.sinusoidal_pos_embds:
            position_ids = jnp.arange(seq_length).astype("i4")
            position_ids = jnp.broadcast_to(position_ids, shape=(batch_size, seq_length))
            position_embeds = self.position_embeddings(position_ids.astype("i4"))
        else:
            position_embeds = self.pos_encoding[:, :seq_length, :]
            position_embeds = position_embeds.astype(inputs_embeds.dtype)
        hidden_states = inputs_embeds + position_embeds
        hidden_states = self.LayerNorm(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        return hidden_states
class FlaxMultiHeadSelfAttention(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.n_heads = self.config.n_heads
        self.dim = self.config.dim
        self.dropout = nn.Dropout(rate=self.config.attention_dropout)
        if not (self.dim % self.n_heads == 0): raise ValueError(f"Hidden size {self.dim} not dividable by number of heads {self.n_heads}")
        self.q_lin = nn.Dense(self.dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        self.k_lin = nn.Dense(self.dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        self.v_lin = nn.Dense(self.dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        self.out_lin = nn.Dense(self.dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
    def __call__(self, query, key, value, mask, deterministic: bool = True, output_attentions: bool = False):
        bs, q_len, dim = query.shape
        k_len = key.shape[1]
        dim_per_head = self.dim // self.n_heads
        mask_reshp = (bs, 1, 1, k_len)
        def shape(x): return x.reshape(bs, -1, self.n_heads, dim_per_head).transpose(0, 2, 1, 3)
        def unshape(x): return x.transpose(0, 2, 1, 3).reshape(bs, -1, self.n_heads * dim_per_head)
        q = shape(self.q_lin(query))
        k = shape(self.k_lin(key))
        v = shape(self.v_lin(value))
        q = q / math.sqrt(dim_per_head)
        scores = jnp.matmul(q, k.transpose(0, 1, 3, 2))
        mask = jnp.reshape(mask, mask_reshp)
        mask = mask.astype(scores.dtype)
        scores = scores - 1e30 * (1.0 - mask)
        weights = nn.softmax(scores, axis=-1)
        weights = self.dropout(weights, deterministic=deterministic)
        context = jnp.matmul(weights, v)
        context = unshape(context)
        context = self.out_lin(context)
        if output_attentions: return (context, weights)
        else: return (context,)
class FlaxFFN(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.dropout = nn.Dropout(rate=self.config.dropout)
        self.chunk_size_feed_forward = self.config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.lin1 = nn.Dense(self.config.hidden_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        self.lin2 = nn.Dense(self.config.dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        self.activation = ACT2FN[self.config.activation]
    def __call__(self, hidden_states, deterministic: bool = True):
        hidden_states = self.lin1(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.lin2(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        return hidden_states
class FlaxTransformerBlock(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        assert (self.config.dim % self.config.n_heads == 0), f"Hidden size {self.config.dim} not dividable by number of heads {self.config.n_heads}"
        self.attention = FlaxMultiHeadSelfAttention(self.config, dtype=self.dtype)
        self.sa_layer_norm = nn.LayerNorm(epsilon=1e-12, dtype=self.dtype)
        self.ffn = FlaxFFN(self.config, dtype=self.dtype)
        self.output_layer_norm = nn.LayerNorm(epsilon=1e-12, dtype=self.dtype)
    def __call__(self, hidden_states, attn_mask, output_attentions: bool = False, deterministic: bool = True):
        sa_output = self.attention(query=hidden_states, key=hidden_states, value=hidden_states, mask=attn_mask, output_attentions=output_attentions, deterministic=deterministic)
        if output_attentions: sa_output, sa_weights = sa_output
        else:
            assert type(sa_output) is tuple
            sa_output = sa_output[0]
        sa_output = self.sa_layer_norm(sa_output + hidden_states)
        ffn_output = self.ffn(sa_output, deterministic=deterministic)
        ffn_output = self.output_layer_norm(ffn_output + sa_output)
        output = (ffn_output,)
        if output_attentions: output = (sa_weights,) + output
        return output
class FlaxTransformer(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self): self.layers = [FlaxTransformerBlock(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.n_layers)]
    def __call__(self, hidden_states, attention_mask, output_attentions: bool = False, output_hidden_states: bool = False, deterministic: bool = True, return_dict: bool = False):
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for layer_module in self.layers:
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_outputs = layer_module(hidden_states=hidden_states, attn_mask=attention_mask, output_attentions=output_attentions, deterministic=deterministic)
            hidden_states = layer_outputs[-1]
            if output_attentions:
                assert len(layer_outputs) == 2
                attentions = layer_outputs[0]
                all_attentions = all_attentions + (attentions,)
            else: assert len(layer_outputs) == 1
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_attentions, all_hidden_states] if v is not None)
        return FlaxBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions)
class FlaxTransformerEncoder(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self): self.layer = FlaxTransformer(self.config, dtype=self.dtype)
    def __call__(self, hidden_states, attention_mask, output_attentions: bool = False, output_hidden_states: bool = False, deterministic: bool = True, return_dict: bool = False): return self.layer(hidden_states=hidden_states,
    attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, deterministic=deterministic, return_dict=return_dict)
class FlaxDistilBertLMDecoder(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
    def setup(self): self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))
    def __call__(self, inputs, kernel):
        inputs = jnp.asarray(inputs, self.dtype)
        kernel = jnp.asarray(kernel, self.dtype)
        y = lax.dot_general(inputs, kernel, (((inputs.ndim - 1,), (0,)), ((), ())))
        bias = jnp.asarray(self.bias, self.dtype)
        y = y + bias
        return y
class FlaxDistilBertPreTrainedModel(FlaxPreTrainedModel):
    config_class = DistilBertConfig
    base_model_prefix = "distilbert"
    module_class: nn.Module = None
    def __init__(self, config: DistilBertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs):
        module = self.module_class(config=config, dtype=dtype, **kwargs)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        input_ids = jnp.zeros(input_shape, dtype="i4")
        attention_mask = jnp.ones_like(input_ids)
        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}
        random_params = self.module.init(rngs, input_ids, attention_mask, return_dict=False)["params"]
        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else: return random_params
    @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(self, input_ids, attention_mask=None, head_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):
        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
        if attention_mask is None: attention_mask = jnp.ones_like(input_ids)
        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"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs)
class FlaxDistilBertModule(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.embeddings = FlaxEmbeddings(self.config, dtype=self.dtype)
        self.transformer = FlaxTransformerEncoder(self.config, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        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
        input_embeds = self.embeddings(input_ids, deterministic=deterministic)
        return self.transformer(hidden_states=input_embeds, attention_mask=attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
@add_start_docstrings("The bare DistilBert Model transformer outputting raw hidden-states without any specific head on top.", FLAX_DISTILBERT_START_DOCSTRING)
class FlaxDistilBertModel(FlaxDistilBertPreTrainedModel): module_class = FlaxDistilBertModule
append_call_sample_docstring(FlaxDistilBertModel, _CHECKPOINT_FOR_DOC, None, _CONFIG_FOR_DOC)
class FlaxDistilBertForMaskedLMModule(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.distilbert = FlaxDistilBertModule(self.config, dtype=self.dtype)
        self.vocab_transform = nn.Dense(self.config.dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        self.vocab_layer_norm = nn.LayerNorm(epsilon=1e-12, dtype=self.dtype)
        if self.config.tie_word_embeddings: self.vocab_projector = FlaxDistilBertLMDecoder(self.config, dtype=self.dtype)
        else: self.vocab_projector = nn.Dense(self.config.vocab_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
    def __call__(self, input_ids, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        dlbrt_output = self.distilbert(input_ids=input_ids, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, deterministic=deterministic, return_dict=return_dict)
        hidden_states = dlbrt_output[0]
        prediction_logits = self.vocab_transform(hidden_states)
        prediction_logits = ACT2FN[self.config.activation](prediction_logits)
        prediction_logits = self.vocab_layer_norm(prediction_logits)
        if self.config.tie_word_embeddings:
            shared_embedding = self.distilbert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
            prediction_logits = self.vocab_projector(prediction_logits, shared_embedding.T)
        else: prediction_logits = self.vocab_projector(prediction_logits)
        if not return_dict:
            output = (prediction_logits,) + dlbrt_output[1:]
            return output
        return FlaxMaskedLMOutput(logits=prediction_logits, hidden_states=dlbrt_output.hidden_states, attentions=dlbrt_output.attentions)
@add_start_docstrings("""DistilBert Model with a `language modeling` head on top.""", FLAX_DISTILBERT_START_DOCSTRING)
class FlaxDistilBertForMaskedLM(FlaxDistilBertPreTrainedModel): module_class = FlaxDistilBertForMaskedLMModule
append_call_sample_docstring(FlaxDistilBertForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC)
class FlaxDistilBertForSequenceClassificationModule(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.distilbert = FlaxDistilBertModule(config=self.config, dtype=self.dtype)
        self.pre_classifier = nn.Dense(self.config.dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        self.dropout = nn.Dropout(rate=self.config.seq_classif_dropout)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        distilbert_output = self.distilbert(input_ids, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_state = distilbert_output[0]
        pooled_output = hidden_state[:, 0]
        pooled_output = self.pre_classifier(pooled_output)
        pooled_output = ACT2FN["relu"](pooled_output)
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)
        if not return_dict: return (logits,) + distilbert_output[1:]
        return FlaxSequenceClassifierOutput(logits=logits, hidden_states=distilbert_output.hidden_states, attentions=distilbert_output.attentions)
@add_start_docstrings("DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", FLAX_DISTILBERT_START_DOCSTRING)
class FlaxDistilBertForSequenceClassification(FlaxDistilBertPreTrainedModel): module_class = FlaxDistilBertForSequenceClassificationModule
append_call_sample_docstring(FlaxDistilBertForSequenceClassification, _CHECKPOINT_FOR_DOC, FlaxSequenceClassifierOutput, _CONFIG_FOR_DOC)
class FlaxDistilBertForMultipleChoiceModule(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.distilbert = FlaxDistilBertModule(config=self.config, dtype=self.dtype)
        self.pre_classifier = nn.Dense(self.config.dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        self.dropout = nn.Dropout(rate=self.config.seq_classif_dropout)
        self.classifier = nn.Dense(1, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        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
        outputs = self.distilbert(input_ids, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_state = outputs[0]
        pooled_output = hidden_state[:, 0]
        pooled_output = self.pre_classifier(pooled_output)
        pooled_output = ACT2FN["relu"](pooled_output)
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.reshape(-1, num_choices)
        if not return_dict: return (reshaped_logits,) + outputs[2:]
        return FlaxMultipleChoiceModelOutput(logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("DistilBert 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.", FLAX_DISTILBERT_START_DOCSTRING)
class FlaxDistilBertForMultipleChoice(FlaxDistilBertPreTrainedModel): module_class = FlaxDistilBertForMultipleChoiceModule
overwrite_call_docstring(FlaxDistilBertForMultipleChoice, DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
append_call_sample_docstring(FlaxDistilBertForMultipleChoice, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC)
class FlaxDistilBertForTokenClassificationModule(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.distilbert = FlaxDistilBertModule(config=self.config, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.dropout)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.distilbert(input_ids, attention_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("DistilBert 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.", FLAX_DISTILBERT_START_DOCSTRING)
class FlaxDistilBertForTokenClassification(FlaxDistilBertPreTrainedModel): module_class = FlaxDistilBertForTokenClassificationModule
append_call_sample_docstring(FlaxDistilBertForTokenClassification, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC)
class FlaxDistilBertForQuestionAnsweringModule(nn.Module):
    config: DistilBertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.distilbert = FlaxDistilBertModule(config=self.config, dtype=self.dtype)
        self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)
        assert self.config.num_labels == 2
        self.dropout = nn.Dropout(rate=self.config.qa_dropout)
    def __call__(self, input_ids, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        distilbert_output = self.distilbert(input_ids, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = distilbert_output[0]
        hidden_states = self.dropout(hidden_states, 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)
        if not return_dict: return (start_logits, end_logits) + distilbert_output[1:]
        return FlaxQuestionAnsweringModelOutput(start_logits=start_logits, end_logits=end_logits, hidden_states=distilbert_output.hidden_states, attentions=distilbert_output.attentions)
@add_start_docstrings("DistilBert 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`).", FLAX_DISTILBERT_START_DOCSTRING)
class FlaxDistilBertForQuestionAnswering(FlaxDistilBertPreTrainedModel): module_class = FlaxDistilBertForQuestionAnsweringModule
append_call_sample_docstring(FlaxDistilBertForQuestionAnswering, _CHECKPOINT_FOR_DOC, FlaxQuestionAnsweringModelOutput, _CONFIG_FOR_DOC)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
