"""
    #################################################################################################################################################################################################
    # 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.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, 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_roformer import RoFormerConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "junnyu/roformer_chinese_base"
_CONFIG_FOR_DOC = "RoFormerConfig"
ROFORMER_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 ([`RoFormerConfig`]): 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`].
"""
ROFORMER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`numpy.ndarray` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`numpy.ndarray` of shape `(0)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`numpy.ndarray` of shape `(0)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`numpy.ndarray` of shape `(0)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
        head_mask (`numpy.ndarray` of shape `(0)`, `optional):
            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
def create_sinusoidal_positions(n_pos, dim):
    position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
    sentinel = dim // 2 + dim % 2
    out = np.zeros_like(position_enc)
    out[:, 0:sentinel] = np.sin(position_enc[:, 0::2])
    out[:, sentinel:] = np.cos(position_enc[:, 1::2])
    return jnp.array(out)
class FlaxRoFormerEmbeddings(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    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))
        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))
        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, attention_mask, deterministic: bool = True):
        inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
        token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4"))
        hidden_states = inputs_embeds + token_type_embeddings
        hidden_states = self.LayerNorm(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        return hidden_states
class FlaxRoFormerSelfAttention(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self) -> None:
        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.rotary_value = self.config.rotary_value
    def __call__(self, hidden_states, attention_mask, sinusoidal_pos, layer_head_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 sinusoidal_pos is not None:
            if self.rotary_value: query_states, key_states, value_states = self.apply_rotary_position_embeddings(sinusoidal_pos, query_states, key_states, value_states)
            else: query_states, key_states = self.apply_rotary_position_embeddings(sinusoidal_pos, query_states, key_states)
        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)
        if layer_head_mask is not None: attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask)
        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))
        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
        return outputs
    @staticmethod
    def apply_rotary_position_embeddings(sinusoidal_pos, query_layer, key_layer, value_layer=None):
        sin, cos = sinusoidal_pos.split(2, axis=-1)
        sin_pos = jnp.stack([sin, sin], axis=-1).reshape(sinusoidal_pos.shape)
        cos_pos = jnp.stack([cos, cos], axis=-1).reshape(sinusoidal_pos.shape)
        def rotate_layer(layer, sin_pos, cos_pos):
            rotate_half_layer = jnp.stack([-layer[..., 1::2], layer[..., ::2]], axis=-1).reshape(layer.shape)
            rotary_matrix_cos = jnp.einsum("bslh,...sh->bslh", layer, cos_pos)
            rotary_matrix_sin = jnp.einsum("bslh,...sh->bslh", rotate_half_layer, sin_pos)
            return rotary_matrix_cos + rotary_matrix_sin
        query_layer = rotate_layer(query_layer, sin_pos, cos_pos)
        key_layer = rotate_layer(key_layer, sin_pos, cos_pos)
        if value_layer is not None:
            value_layer = rotate_layer(value_layer, sin_pos, cos_pos)
            return query_layer, key_layer, value_layer
        return query_layer, key_layer
class FlaxRoFormerSelfOutput(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.dense = nn.Dense(self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype)
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
    def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class FlaxRoFormerAttention(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.self = FlaxRoFormerSelfAttention(self.config, dtype=self.dtype)
        self.output = FlaxRoFormerSelfOutput(self.config, dtype=self.dtype)
    def __call__(self, hidden_states, attention_mask, sinusoidal_pos, layer_head_mask, deterministic=True, output_attentions: bool = False):
        attn_outputs = self.self(hidden_states, attention_mask, sinusoidal_pos, layer_head_mask=layer_head_mask, deterministic=deterministic, output_attentions=output_attentions)
        attn_output = attn_outputs[0]
        hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)
        outputs = (hidden_states,)
        if output_attentions: outputs += (attn_outputs[1],)
        return outputs
class FlaxRoFormerIntermediate(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.dense = nn.Dense(self.config.intermediate_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype)
        self.activation = ACT2FN[self.config.hidden_act]
    def __call__(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states
class FlaxRoFormerOutput(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.dense = nn.Dense(self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
    def __call__(self, hidden_states, attention_output, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + attention_output)
        return hidden_states
class FlaxRoFormerLayer(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.attention = FlaxRoFormerAttention(self.config, dtype=self.dtype)
        self.intermediate = FlaxRoFormerIntermediate(self.config, dtype=self.dtype)
        self.output = FlaxRoFormerOutput(self.config, dtype=self.dtype)
    def __call__(self, hidden_states, attention_mask, sinusiodal_pos, layer_head_mask, deterministic: bool = True, output_attentions: bool = False):
        attention_outputs = self.attention(hidden_states, attention_mask, sinusiodal_pos, layer_head_mask=layer_head_mask, deterministic=deterministic, output_attentions=output_attentions)
        attention_output = attention_outputs[0]
        hidden_states = self.intermediate(attention_output)
        hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic)
        outputs = (hidden_states,)
        if output_attentions: outputs += (attention_outputs[1],)
        return outputs
class FlaxRoFormerLayerCollection(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self): self.layers = [FlaxRoFormerLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)]
    def __call__(self, hidden_states, attention_mask, sinusoidal_pos, head_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 = () if output_hidden_states else None
        if head_mask is not None:
            if head_mask.shape[0] != (len(self.layers)): raise ValueError(f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.shape[0]}.")
        for i, layer in enumerate(self.layers):
            if output_hidden_states: all_hidden_states += (hidden_states,)
            layer_outputs = layer(hidden_states, attention_mask, sinusoidal_pos, layer_head_mask=head_mask[i] if head_mask is not None else None, deterministic=deterministic,
            output_attentions=output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions: all_attentions += (layer_outputs[1],)
        if output_hidden_states: all_hidden_states += (hidden_states,)
        outputs = (hidden_states,)
        if not return_dict: return tuple(v for v in outputs if v is not None)
        return FlaxBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions)
class FlaxRoFormerEncoder(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.embed_positions = create_sinusoidal_positions(self.config.max_position_embeddings, self.config.hidden_size // self.config.num_attention_heads)
        self.layer = FlaxRoFormerLayerCollection(self.config, dtype=self.dtype)
    def __call__(self, hidden_states, attention_mask, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        sinusoidal_pos = self.embed_positions[: hidden_states.shape[1], :]
        return self.layer(hidden_states, attention_mask, sinusoidal_pos, head_mask, deterministic=deterministic, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
class FlaxRoFormerPredictionHeadTransform(nn.Module):
    config: RoFormerConfig
    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 FlaxRoFormerLMPredictionHead(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
    def setup(self):
        self.transform = FlaxRoFormerPredictionHeadTransform(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)
        bias = jnp.asarray(self.bias, self.dtype)
        hidden_states += bias
        return hidden_states
class FlaxRoFormerOnlyMLMHead(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self): self.predictions = FlaxRoFormerLMPredictionHead(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 FlaxRoFormerClassificationHead(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range))
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.out_proj = nn.Dense(self.config.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range))
        self.activation = ACT2FN[self.config.hidden_act]
    def __call__(self, hidden_states, deterministic=True):
        hidden_states = hidden_states[:, 0, :]
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.out_proj(hidden_states)
        return hidden_states
class FlaxRoFormerPreTrainedModel(FlaxPreTrainedModel):
    config_class = RoFormerConfig
    base_model_prefix = "roformer"
    module_class: nn.Module = None
    def __init__(self, config: RoFormerConfig, 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)
        attention_mask = jnp.ones_like(input_ids)
        head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))
        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}
        random_params = self.module.init(rngs, input_ids, attention_mask, token_type_ids, head_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(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(self, input_ids, attention_mask=None, token_type_ids=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 token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids)
        if attention_mask is None: attention_mask = jnp.ones_like(input_ids)
        if head_mask is None: head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))
        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(head_mask, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs)
class FlaxRoFormerModule(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.embeddings = FlaxRoFormerEmbeddings(self.config, dtype=self.dtype)
        self.encoder = FlaxRoFormerEncoder(self.config, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        hidden_states = self.embeddings(input_ids, token_type_ids, attention_mask, deterministic=deterministic)
        outputs = self.encoder(hidden_states, attention_mask, head_mask=head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        if not return_dict: return (hidden_states,) + outputs[1:]
        return FlaxBaseModelOutput(last_hidden_state=hidden_states, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("The bare RoFormer Model transformer outputting raw hidden-states without any specific head on top.", ROFORMER_START_DOCSTRING)
class FlaxRoFormerModel(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerModule
append_call_sample_docstring(FlaxRoFormerModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC)
class FlaxRoFormerForMaskedLMModule(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype)
        self.cls = FlaxRoFormerOnlyMLMHead(config=self.config, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.roformer(input_ids, attention_mask, token_type_ids, head_mask, 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.roformer.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else: shared_embedding = None
        logits = self.cls(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("RoFormer Model with a `language modeling` head on top.", ROFORMER_START_DOCSTRING)
class FlaxRoFormerForMaskedLM(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerForMaskedLMModule
append_call_sample_docstring(FlaxRoFormerForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC, mask="<mask>")
class FlaxRoFormerForSequenceClassificationModule(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype)
        self.classifier = FlaxRoFormerClassificationHead(config=self.config, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.roformer(input_ids, attention_mask, token_type_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output, deterministic=deterministic)
        if not return_dict: return (logits,) + outputs[1:]
        return FlaxSequenceClassifierOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("RoFormer Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", ROFORMER_START_DOCSTRING)
class FlaxRoFormerForSequenceClassification(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerForSequenceClassificationModule
append_call_sample_docstring(FlaxRoFormerForSequenceClassification, _CHECKPOINT_FOR_DOC, FlaxSequenceClassifierOutput, _CONFIG_FOR_DOC)
class FlaxRoFormerForMultipleChoiceModule(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.roformer = FlaxRoFormerModule(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, head_mask, 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])
        attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1])
        token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1])
        outputs = self.roformer(input_ids, attention_mask, token_type_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        pooled_output = hidden_states[:, -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("RoFormer 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.", ROFORMER_START_DOCSTRING)
class FlaxRoFormerForMultipleChoice(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerForMultipleChoiceModule
overwrite_call_docstring(FlaxRoFormerForMultipleChoice, ROFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
append_call_sample_docstring(FlaxRoFormerForMultipleChoice, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC)
class FlaxRoFormerForTokenClassificationModule(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.roformer = FlaxRoFormerModule(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, token_type_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.roformer(input_ids, attention_mask, token_type_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        logits = self.classifier(hidden_states)
        if not return_dict: return (logits,) + outputs[1:]
        return FlaxTokenClassifierOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("RoFormer 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.", ROFORMER_START_DOCSTRING)
class FlaxRoFormerForTokenClassification(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerForTokenClassificationModule
append_call_sample_docstring(FlaxRoFormerForTokenClassification, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC)
class FlaxRoFormerForQuestionAnsweringModule(nn.Module):
    config: RoFormerConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype)
        self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.roformer(input_ids, attention_mask, token_type_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        logits = self.qa_outputs(hidden_states)
        start_logits, end_logits = logits.split(self.config.num_labels, axis=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)
        if not return_dict: return (start_logits, end_logits) + outputs[1:]
        return FlaxQuestionAnsweringModelOutput(start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("RoFormer 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`).", ROFORMER_START_DOCSTRING)
class FlaxRoFormerForQuestionAnswering(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerForQuestionAnsweringModule
append_call_sample_docstring(FlaxRoFormerForQuestionAnswering, _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.                                                                 #
    #################################################################################################################################################################################################
"""
