"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
from typing import Callable, Optional, Tuple
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from ...modeling_flax_outputs import (FlaxBaseModelOutput, FlaxBaseModelOutputWithPooling, FlaxMaskedLMOutput, FlaxMultipleChoiceModelOutput, FlaxQuestionAnsweringModelOutput, FlaxSequenceClassifierOutput, FlaxTokenClassifierOutput)
from ...modeling_flax_utils import (ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, append_replace_return_docstrings, overwrite_call_docstring)
from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_albert import AlbertConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "albert/albert-base-v2"
_CONFIG_FOR_DOC = "AlbertConfig"
@flax.struct.dataclass
class FlaxAlbertForPreTrainingOutput(ModelOutput):
    """Args:"""
    prediction_logits: jnp.ndarray = None
    sop_logits: jnp.ndarray = None
    hidden_states: Optional[Tuple[jnp.ndarray]] = None
    attentions: Optional[Tuple[jnp.ndarray]] = None
ALBERT_START_DOCSTRING = r"""
    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
    This model is also a
    [flax.linen.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 ([`AlbertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
        dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
            The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
            `jax.numpy.bfloat16` (on TPUs).
            This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
            specified all the computation will be performed with the given `dtype`.
            *Note that this only specifies the dtype of the computation and does not influence the dtype of model
            parameters.*
            If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
            [`~FlaxPreTrainedModel.to_bf16`].
"""
ALBERT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`numpy.ndarray` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`numpy.ndarray` of shape `(0)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`numpy.ndarray` of shape `(0)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`numpy.ndarray` of shape `(0)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class FlaxAlbertEmbeddings(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.word_embeddings = nn.Embed(self.config.vocab_size, self.config.embedding_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        self.position_embeddings = nn.Embed(self.config.max_position_embeddings, self.config.embedding_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        self.token_type_embeddings = nn.Embed(self.config.type_vocab_size, self.config.embedding_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range))
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
    def __call__(self, input_ids, token_type_ids, position_ids, deterministic: bool = True):
        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"))
        hidden_states = inputs_embeds + token_type_embeddings + position_embeds
        hidden_states = self.LayerNorm(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        return hidden_states
class FlaxAlbertSelfAttention(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        if self.config.hidden_size % self.config.num_attention_heads != 0: raise ValueError("`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads`: {self.config.num_attention_heads}")
        self.query = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range))
        self.key = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range))
        self.value = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range))
        self.dense = nn.Dense(self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype)
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
    def __call__(self, hidden_states, attention_mask, deterministic=True, output_attentions: bool = False):
        head_dim = self.config.hidden_size // self.config.num_attention_heads
        query_states = self.query(hidden_states).reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim))
        value_states = self.value(hidden_states).reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim))
        key_states = self.key(hidden_states).reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim))
        if attention_mask is not None:
            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
            attention_bias = lax.select(attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype))
        else: attention_bias = None
        dropout_rng = None
        if not deterministic and self.config.attention_probs_dropout_prob > 0.0: dropout_rng = self.make_rng("dropout")
        attn_weights = dot_product_attention_weights(query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.config.attention_probs_dropout_prob, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None)
        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))
        projected_attn_output = self.dense(attn_output)
        projected_attn_output = self.dropout(projected_attn_output, deterministic=deterministic)
        layernormed_attn_output = self.LayerNorm(projected_attn_output + hidden_states)
        outputs = (layernormed_attn_output, attn_weights) if output_attentions else (layernormed_attn_output,)
        return outputs
class FlaxAlbertLayer(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.attention = FlaxAlbertSelfAttention(self.config, dtype=self.dtype)
        self.ffn = nn.Dense(self.config.intermediate_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype)
        self.activation = ACT2FN[self.config.hidden_act]
        self.ffn_output = nn.Dense(self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype)
        self.full_layer_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
    def __call__(self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False):
        attention_outputs = self.attention(hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions)
        attention_output = attention_outputs[0]
        ffn_output = self.ffn(attention_output)
        ffn_output = self.activation(ffn_output)
        ffn_output = self.ffn_output(ffn_output)
        ffn_output = self.dropout(ffn_output, deterministic=deterministic)
        hidden_states = self.full_layer_layer_norm(ffn_output + attention_output)
        outputs = (hidden_states,)
        if output_attentions: outputs += (attention_outputs[1],)
        return outputs
class FlaxAlbertLayerCollection(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self): self.layers = [FlaxAlbertLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.inner_group_num)]
    def __call__(self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False):
        layer_hidden_states = ()
        layer_attentions = ()
        for layer_index, albert_layer in enumerate(self.layers):
            layer_output = albert_layer(hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions)
            hidden_states = layer_output[0]
            if output_attentions: layer_attentions = layer_attentions + (layer_output[1],)
            if output_hidden_states: layer_hidden_states = layer_hidden_states + (hidden_states,)
        outputs = (hidden_states,)
        if output_hidden_states: outputs = outputs + (layer_hidden_states,)
        if output_attentions: outputs = outputs + (layer_attentions,)
        return outputs
class FlaxAlbertLayerCollections(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    layer_index: Optional[str] = None
    def setup(self): self.albert_layers = FlaxAlbertLayerCollection(self.config, dtype=self.dtype)
    def __call__(self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False):
        outputs = self.albert_layers(hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states)
        return outputs
class FlaxAlbertLayerGroups(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self): self.layers = [FlaxAlbertLayerCollections(self.config, name=str(i), layer_index=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_groups)]
    def __call__(self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        all_attentions = () if output_attentions else None
        all_hidden_states = (hidden_states,) if output_hidden_states else None
        for i in range(self.config.num_hidden_layers):
            group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups))
            layer_group_output = self.layers[group_idx](hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states)
            hidden_states = layer_group_output[0]
            if output_attentions: all_attentions = all_attentions + layer_group_output[-1]
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
        return FlaxBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions)
class FlaxAlbertEncoder(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.embedding_hidden_mapping_in = nn.Dense(self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype)
        self.albert_layer_groups = FlaxAlbertLayerGroups(self.config, dtype=self.dtype)
    def __call__(self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        hidden_states = self.embedding_hidden_mapping_in(hidden_states)
        return self.albert_layer_groups(hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states)
class FlaxAlbertOnlyMLMHead(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
    def setup(self):
        self.dense = nn.Dense(self.config.embedding_size, dtype=self.dtype)
        self.activation = ACT2FN[self.config.hidden_act]
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False)
        self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))
    def __call__(self, hidden_states, shared_embedding=None):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        if shared_embedding is not None: hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
        else: hidden_states = self.decoder(hidden_states)
        hidden_states += self.bias
        return hidden_states
class FlaxAlbertSOPHead(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.dropout = nn.Dropout(self.config.classifier_dropout_prob)
        self.classifier = nn.Dense(2, dtype=self.dtype)
    def __call__(self, pooled_output, deterministic=True):
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)
        return logits
class FlaxAlbertPreTrainedModel(FlaxPreTrainedModel):
    config_class = AlbertConfig
    base_model_prefix = "albert"
    module_class: nn.Module = None
    def __init__(self, config: AlbertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs):
        module = self.module_class(config=config, dtype=dtype, **kwargs)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        input_ids = jnp.zeros(input_shape, dtype="i4")
        token_type_ids = jnp.zeros_like(input_ids)
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
        attention_mask = jnp.ones_like(input_ids)
        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}
        random_params = self.module.init(rngs, input_ids, attention_mask, token_type_ids, position_ids, return_dict=False)["params"]
        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else: return random_params
    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.return_dict
        if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids)
        if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
        if attention_mask is None: attention_mask = jnp.ones_like(input_ids)
        rngs = {}
        if dropout_rng is not None: rngs["dropout"] = dropout_rng
        return self.module.apply({"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"),
        jnp.array(token_type_ids, dtype="i4"), jnp.array(position_ids, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs)
class FlaxAlbertModule(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    add_pooling_layer: bool = True
    def setup(self):
        self.embeddings = FlaxAlbertEmbeddings(self.config, dtype=self.dtype)
        self.encoder = FlaxAlbertEncoder(self.config, dtype=self.dtype)
        if self.add_pooling_layer:
            self.pooler = nn.Dense(self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, name="pooler")
            self.pooler_activation = nn.tanh
        else:
            self.pooler = None
            self.pooler_activation = None
    def __call__(self, input_ids, attention_mask, token_type_ids: Optional[np.ndarray] = None, position_ids: Optional[np.ndarray] = None, deterministic: bool = True,
    output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids)
        if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
        hidden_states = self.embeddings(input_ids, token_type_ids, position_ids, deterministic=deterministic)
        outputs = self.encoder(hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        if self.add_pooling_layer:
            pooled = self.pooler(hidden_states[:, 0])
            pooled = self.pooler_activation(pooled)
        else: pooled = None
        if not return_dict:
            if pooled is None: return (hidden_states,) + outputs[1:]
            return (hidden_states, pooled) + outputs[1:]
        return FlaxBaseModelOutputWithPooling(last_hidden_state=hidden_states, pooler_output=pooled, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("The bare Albert Model transformer outputting raw hidden-states without any specific head on top.", ALBERT_START_DOCSTRING)
class FlaxAlbertModel(FlaxAlbertPreTrainedModel): module_class = FlaxAlbertModule
append_call_sample_docstring(FlaxAlbertModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)
class FlaxAlbertForPreTrainingModule(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
        self.predictions = FlaxAlbertOnlyMLMHead(config=self.config, dtype=self.dtype)
        self.sop_classifier = FlaxAlbertSOPHead(config=self.config, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.albert(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        if self.config.tie_word_embeddings: shared_embedding = self.albert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else: shared_embedding = None
        hidden_states = outputs[0]
        pooled_output = outputs[1]
        prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding)
        sop_scores = self.sop_classifier(pooled_output, deterministic=deterministic)
        if not return_dict: return (prediction_scores, sop_scores) + outputs[2:]
        return FlaxAlbertForPreTrainingOutput(prediction_logits=prediction_scores, sop_logits=sop_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""
    Albert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
    `sentence order prediction (classification)` head.
    """, ALBERT_START_DOCSTRING)
class FlaxAlbertForPreTraining(FlaxAlbertPreTrainedModel): module_class = FlaxAlbertForPreTrainingModule
FLAX_ALBERT_FOR_PRETRAINING_DOCSTRING = """
    Returns:
    Example:
    ```python
    >>> from sapiens_transformers import AutoTokenizer, FlaxAlbertForPreTraining
    >>> tokenizer = AutoTokenizer.from_pretrained("albert/albert-base-v2")
    >>> model = FlaxAlbertForPreTraining.from_pretrained("albert/albert-base-v2")
    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
    >>> outputs = model(**inputs)
    >>> prediction_logits = outputs.prediction_logits
    >>> seq_relationship_logits = outputs.sop_logits
    ```
"""
overwrite_call_docstring(FlaxAlbertForPreTraining, ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_ALBERT_FOR_PRETRAINING_DOCSTRING)
append_replace_return_docstrings(FlaxAlbertForPreTraining, output_type=FlaxAlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
class FlaxAlbertForMaskedLMModule(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.albert = FlaxAlbertModule(config=self.config, add_pooling_layer=False, dtype=self.dtype)
        self.predictions = FlaxAlbertOnlyMLMHead(config=self.config, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.albert(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        if self.config.tie_word_embeddings: shared_embedding = self.albert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else: shared_embedding = None
        logits = self.predictions(hidden_states, shared_embedding=shared_embedding)
        if not return_dict: return (logits,) + outputs[1:]
        return FlaxMaskedLMOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""Albert Model with a `language modeling` head on top.""", ALBERT_START_DOCSTRING)
class FlaxAlbertForMaskedLM(FlaxAlbertPreTrainedModel): module_class = FlaxAlbertForMaskedLMModule
append_call_sample_docstring(FlaxAlbertForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC, revision="refs/pr/11")
class FlaxAlbertForSequenceClassificationModule(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
        classifier_dropout = (self.config.classifier_dropout_prob if self.config.classifier_dropout_prob is not None else self.config.hidden_dropout_prob)
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.albert(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)
        if not return_dict: return (logits,) + outputs[2:]
        return FlaxSequenceClassifierOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""
    Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
    output) e.g. for GLUE tasks.
    """, ALBERT_START_DOCSTRING)
class FlaxAlbertForSequenceClassification(FlaxAlbertPreTrainedModel): module_class = FlaxAlbertForSequenceClassificationModule
append_call_sample_docstring(FlaxAlbertForSequenceClassification, _CHECKPOINT_FOR_DOC, FlaxSequenceClassifierOutput, _CONFIG_FOR_DOC)
class FlaxAlbertForMultipleChoiceModule(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.classifier = nn.Dense(1, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        num_choices = input_ids.shape[1]
        input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None
        attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None
        token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None
        position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None
        outputs = self.albert(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.reshape(-1, num_choices)
        if not return_dict: return (reshaped_logits,) + outputs[2:]
        return FlaxMultipleChoiceModelOutput(logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""
    Albert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
    softmax) e.g. for RocStories/SWAG tasks.
    """, ALBERT_START_DOCSTRING)
class FlaxAlbertForMultipleChoice(FlaxAlbertPreTrainedModel): module_class = FlaxAlbertForMultipleChoiceModule
overwrite_call_docstring(FlaxAlbertForMultipleChoice, ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
append_call_sample_docstring(FlaxAlbertForMultipleChoice, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC)
class FlaxAlbertForTokenClassificationModule(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
        classifier_dropout = (self.config.classifier_dropout_prob if self.config.classifier_dropout_prob is not None else self.config.hidden_dropout_prob)
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.albert(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        logits = self.classifier(hidden_states)
        if not return_dict: return (logits,) + outputs[1:]
        return FlaxTokenClassifierOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""
    Albert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
    Named-Entity-Recognition (NER) tasks.
    """, ALBERT_START_DOCSTRING)
class FlaxAlbertForTokenClassification(FlaxAlbertPreTrainedModel): module_class = FlaxAlbertForTokenClassificationModule
append_call_sample_docstring(FlaxAlbertForTokenClassification, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC)
class FlaxAlbertForQuestionAnsweringModule(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
        self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.albert(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        logits = self.qa_outputs(hidden_states)
        start_logits, end_logits = logits.split(self.config.num_labels, axis=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)
        if not return_dict: return (start_logits, end_logits) + outputs[1:]
        return FlaxQuestionAnsweringModelOutput(start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""
    Albert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """, ALBERT_START_DOCSTRING)
class FlaxAlbertForQuestionAnswering(FlaxAlbertPreTrainedModel): module_class = FlaxAlbertForQuestionAnsweringModule
append_call_sample_docstring(FlaxAlbertForQuestionAnswering, _CHECKPOINT_FOR_DOC, FlaxQuestionAnsweringModelOutput, _CONFIG_FOR_DOC)
__all__ = ["FlaxAlbertPreTrainedModel", "FlaxAlbertModel", "FlaxAlbertForPreTraining", "FlaxAlbertForMaskedLM", "FlaxAlbertForSequenceClassification", "FlaxAlbertForMultipleChoice", "FlaxAlbertForTokenClassification", "FlaxAlbertForQuestionAnswering"]
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
