"""
    #################################################################################################################################################################################################
    # 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 import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from ...modeling_flax_outputs import (FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxBaseModelOutputWithPooling, FlaxBaseModelOutputWithPoolingAndCrossAttentions,
FlaxCausalLMOutputWithCrossAttentions, 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_roberta_prelayernorm import RobertaPreLayerNormConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "andreasmadsen/efficient_mlm_m0.40"
_CONFIG_FOR_DOC = "RobertaPreLayerNormConfig"
remat = nn_partitioning.remat
def create_position_ids_from_input_ids(input_ids, padding_idx):
    mask = (input_ids != padding_idx).astype("i4")
    if mask.ndim > 2:
        mask = mask.reshape((-1, mask.shape[-1]))
        incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask
        incremental_indices = incremental_indices.reshape(input_ids.shape)
    else: incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask
    return incremental_indices.astype("i4") + padding_idx
ROBERTA_PRELAYERNORM_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 ([`RobertaPreLayerNormConfig`]): 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.
"""
ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`numpy.ndarray` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`numpy.ndarray` of shape `(0)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`numpy.ndarray` of shape `(0)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`numpy.ndarray` of shape `(0)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
        head_mask (`numpy.ndarray` of shape `(0)`, `optional):
            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class FlaxRobertaPreLayerNormEmbeddings(nn.Module):
    config: RobertaPreLayerNormConfig
    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), dtype=self.dtype)
        self.position_embeddings = nn.Embed(self.config.max_position_embeddings, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), dtype=self.dtype)
        self.token_type_embeddings = nn.Embed(self.config.type_vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), dtype=self.dtype)
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
    def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
        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 FlaxRobertaPreLayerNormSelfAttention(nn.Module):
    config: RobertaPreLayerNormConfig
    causal: bool = False
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.head_dim = self.config.hidden_size // self.config.num_attention_heads
        if self.config.hidden_size % self.config.num_attention_heads != 0: raise ValueError("`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads`: {self.config.num_attention_heads}")
        self.query = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range))
        self.key = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range))
        self.value = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range))
        if self.causal: self.causal_mask = make_causal_mask(jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool")
    def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim))
    def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,))
    @nn.compact
    def _concatenate_to_cache(self, key, value, query, attention_mask):
        is_initialized = self.has_variable("cache", "cached_key")
        cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
        cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
        if is_initialized:
            *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
            cur_index = cache_index.value
            indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
            key = lax.dynamic_update_slice(cached_key.value, key, indices)
            value = lax.dynamic_update_slice(cached_value.value, value, indices)
            cached_key.value = key
            cached_value.value = value
            num_updated_cache_vectors = query.shape[1]
            cache_index.value = cache_index.value + num_updated_cache_vectors
            pad_mask = jnp.broadcast_to(jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length))
            attention_mask = combine_masks(pad_mask, attention_mask)
        return key, value, attention_mask
    def __call__(self, hidden_states, attention_mask, layer_head_mask, key_value_states: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic=True, output_attentions: bool = False):
        is_cross_attention = key_value_states is not None
        batch_size = hidden_states.shape[0]
        query_states = self.query(hidden_states)
        if is_cross_attention:
            key_states = self.key(key_value_states)
            value_states = self.value(key_value_states)
        else:
            key_states = self.key(hidden_states)
            value_states = self.value(hidden_states)
        query_states = self._split_heads(query_states)
        key_states = self._split_heads(key_states)
        value_states = self._split_heads(value_states)
        if self.causal:
            query_length, key_length = query_states.shape[1], key_states.shape[1]
            if self.has_variable("cache", "cached_key"):
                mask_shift = self.variables["cache"]["cache_index"]
                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
                causal_mask = lax.dynamic_slice(self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length))
            else: causal_mask = self.causal_mask[:, :, :query_length, :key_length]
            causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
        if attention_mask is not None and self.causal:
            attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
            attention_mask = combine_masks(attention_mask, causal_mask)
        elif self.causal: attention_mask = causal_mask
        elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
        if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache(key_states, value_states, query_states, attention_mask)
        if attention_mask is not None: 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
class FlaxRobertaPreLayerNormSelfOutput(nn.Module):
    config: RobertaPreLayerNormConfig
    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)
    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 = hidden_states + input_tensor
        return hidden_states
class FlaxRobertaPreLayerNormAttention(nn.Module):
    config: RobertaPreLayerNormConfig
    causal: bool = False
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.self = FlaxRobertaPreLayerNormSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
        self.output = FlaxRobertaPreLayerNormSelfOutput(self.config, dtype=self.dtype)
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
    def __call__(self, hidden_states, attention_mask, layer_head_mask, key_value_states=None, init_cache=False, deterministic=True, output_attentions: bool = False):
        hidden_states_pre_layer_norm = self.LayerNorm(hidden_states)
        attn_outputs = self.self(hidden_states_pre_layer_norm, attention_mask, layer_head_mask=layer_head_mask, key_value_states=key_value_states, init_cache=init_cache,
        deterministic=deterministic, output_attentions=output_attentions)
        attn_output = attn_outputs[0]
        hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)
        outputs = (hidden_states,)
        if output_attentions: outputs += (attn_outputs[1],)
        return outputs
class FlaxRobertaPreLayerNormIntermediate(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dense = nn.Dense(self.config.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.LayerNorm(hidden_states)
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states
class FlaxRobertaPreLayerNormOutput(nn.Module):
    config: RobertaPreLayerNormConfig
    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)
    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 = hidden_states + attention_output
        return hidden_states
class FlaxRobertaPreLayerNormLayer(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.attention = FlaxRobertaPreLayerNormAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
        self.intermediate = FlaxRobertaPreLayerNormIntermediate(self.config, dtype=self.dtype)
        self.output = FlaxRobertaPreLayerNormOutput(self.config, dtype=self.dtype)
        if self.config.add_cross_attention: self.crossattention = FlaxRobertaPreLayerNormAttention(self.config, causal=False, dtype=self.dtype)
    def __call__(self, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None,
    init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False):
        attention_outputs = self.attention(hidden_states, attention_mask, layer_head_mask=layer_head_mask, init_cache=init_cache, deterministic=deterministic,
        output_attentions=output_attentions)
        attention_output = attention_outputs[0]
        if encoder_hidden_states is not None:
            cross_attention_outputs = self.crossattention(attention_output, attention_mask=encoder_attention_mask, layer_head_mask=layer_head_mask,
            key_value_states=encoder_hidden_states, deterministic=deterministic, output_attentions=output_attentions)
            attention_output = cross_attention_outputs[0]
        hidden_states = self.intermediate(attention_output)
        hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic)
        outputs = (hidden_states,)
        if output_attentions:
            outputs += (attention_outputs[1],)
            if encoder_hidden_states is not None: outputs += (cross_attention_outputs[1],)
        return outputs
class FlaxRobertaPreLayerNormLayerCollection(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False
    def setup(self):
        if self.gradient_checkpointing:
            FlaxRobertaPreLayerNormCheckpointLayer = remat(FlaxRobertaPreLayerNormLayer, static_argnums=(5, 6, 7))
            self.layers = [FlaxRobertaPreLayerNormCheckpointLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)]
        else: self.layers = [FlaxRobertaPreLayerNormLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)]
    def __call__(self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None,
    init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        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, head_mask[i] if head_mask is not None else None, encoder_hidden_states, encoder_attention_mask,
            init_cache, deterministic, output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions:
                all_attentions += (layer_outputs[1],)
                if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],)
        if output_hidden_states: all_hidden_states += (hidden_states,)
        outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions)
        if not return_dict: return tuple(v for v in outputs if v is not None)
        return FlaxBaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions)
class FlaxRobertaPreLayerNormEncoder(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False
    def setup(self): self.layer = FlaxRobertaPreLayerNormLayerCollection(self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
    def __call__(self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None,
    init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        return self.layer(hidden_states, attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask,
        init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
class FlaxRobertaPreLayerNormPooler(nn.Module):
    config: RobertaPreLayerNormConfig
    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)
    def __call__(self, hidden_states):
        cls_hidden_state = hidden_states[:, 0]
        cls_hidden_state = self.dense(cls_hidden_state)
        return nn.tanh(cls_hidden_state)
class FlaxRobertaPreLayerNormLMHead(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
    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.layer_norm = 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, kernel_init=jax.nn.initializers.normal(self.config.initializer_range))
        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 = ACT2FN["gelu"](hidden_states)
        hidden_states = self.layer_norm(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 FlaxRobertaPreLayerNormClassificationHead(nn.Module):
    config: RobertaPreLayerNormConfig
    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))
        classifier_dropout = (self.config.classifier_dropout if self.config.classifier_dropout is not None else self.config.hidden_dropout_prob)
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.out_proj = nn.Dense(self.config.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range))
    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 = nn.tanh(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.out_proj(hidden_states)
        return hidden_states
class FlaxRobertaPreLayerNormPreTrainedModel(FlaxPreTrainedModel):
    config_class = RobertaPreLayerNormConfig
    base_model_prefix = "roberta_prelayernorm"
    module_class: nn.Module = None
    def __init__(self, config: RobertaPreLayerNormConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32,
    _do_init: bool = True, gradient_checkpointing: bool = False, **kwargs):
        module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
    def enable_gradient_checkpointing(self): self._module = self.module_class(config=self.config, dtype=self.dtype, gradient_checkpointing=True)
    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.ones_like(input_ids)
        position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id)
        attention_mask = jnp.ones_like(input_ids)
        head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))
        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}
        if self.config.add_cross_attention:
            encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,))
            encoder_attention_mask = attention_mask
            module_init_outputs = self.module.init(rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states,
            encoder_attention_mask, return_dict=False)
        else: module_init_outputs = self.module.init(rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False)
        random_params = module_init_outputs["params"]
        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else: return random_params
    def init_cache(self, batch_size, max_length):
        input_ids = jnp.ones((batch_size, max_length), dtype="i4")
        attention_mask = jnp.ones_like(input_ids, dtype="i4")
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
        init_variables = self.module.init(jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True)
        return unfreeze(init_variables["cache"])
    @add_start_docstrings_to_model_forward(ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None,
    params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, past_key_values: dict = None):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.return_dict
        if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids)
        if position_ids is None: position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id)
        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
        inputs = {"params": params or self.params}
        if self.config.add_cross_attention:
            if past_key_values:
                inputs["cache"] = past_key_values
                mutable = ["cache"]
            else: mutable = False
            outputs = self.module.apply(inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"),
            position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask, deterministic=not train, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
            return_dict=return_dict, rngs=rngs, mutable=mutable)
            if past_key_values is not None and return_dict:
                outputs, past_key_values = outputs
                outputs["past_key_values"] = unfreeze(past_key_values["cache"])
                return outputs
            elif past_key_values is not None and not return_dict:
                outputs, past_key_values = outputs
                outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
        else: outputs = self.module.apply(inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"),
        position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), deterministic=not train, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict, rngs=rngs)
        return outputs
class FlaxRobertaPreLayerNormModule(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    add_pooling_layer: bool = True
    gradient_checkpointing: bool = False
    def setup(self):
        self.embeddings = FlaxRobertaPreLayerNormEmbeddings(self.config, dtype=self.dtype)
        self.encoder = FlaxRobertaPreLayerNormEncoder(self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.pooler = FlaxRobertaPreLayerNormPooler(self.config, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, head_mask: Optional[jnp.ndarray] = None,
    encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True,
    output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        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, attention_mask, deterministic=deterministic)
        outputs = self.encoder(hidden_states, attention_mask, head_mask=head_mask, deterministic=deterministic, encoder_hidden_states=encoder_hidden_states,
        encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        hidden_states = self.LayerNorm(hidden_states)
        pooled = self.pooler(hidden_states) if self.add_pooling_layer else None
        if not return_dict:
            if pooled is None: return (hidden_states,) + outputs[1:]
            return (hidden_states, pooled) + outputs[1:]
        return FlaxBaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=hidden_states, pooler_output=pooled, hidden_states=outputs.hidden_states,
        attentions=outputs.attentions, cross_attentions=outputs.cross_attentions)
@add_start_docstrings("The bare RoBERTa-PreLayerNorm Model transformer outputting raw hidden-states without any specific head on top.", ROBERTA_PRELAYERNORM_START_DOCSTRING)
class FlaxRobertaPreLayerNormModel(FlaxRobertaPreLayerNormPreTrainedModel):
    module_class = FlaxRobertaPreLayerNormModule
append_call_sample_docstring(FlaxRobertaPreLayerNormModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)
class FlaxRobertaPreLayerNormForMaskedLMModule(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False
    def setup(self):
        self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
        self.lm_head = FlaxRobertaPreLayerNormLMHead(config=self.config, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False,
    output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.roberta_prelayernorm(input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        if self.config.tie_word_embeddings: shared_embedding = self.roberta_prelayernorm.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else: shared_embedding = None
        logits = self.lm_head(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("RoBERTa-PreLayerNorm Model with a `language modeling` head on top.", ROBERTA_PRELAYERNORM_START_DOCSTRING)
class FlaxRobertaPreLayerNormForMaskedLM(FlaxRobertaPreLayerNormPreTrainedModel): module_class = FlaxRobertaPreLayerNormForMaskedLMModule
append_call_sample_docstring(FlaxRobertaPreLayerNormForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC, mask="<mask>")
class FlaxRobertaPreLayerNormForSequenceClassificationModule(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False
    def setup(self):
        self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing)
        self.classifier = FlaxRobertaPreLayerNormClassificationHead(config=self.config, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False,
    output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.roberta_prelayernorm(input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        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("RobertaPreLayerNorm Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", ROBERTA_PRELAYERNORM_START_DOCSTRING)
class FlaxRobertaPreLayerNormForSequenceClassification(FlaxRobertaPreLayerNormPreTrainedModel): module_class = FlaxRobertaPreLayerNormForSequenceClassificationModule
append_call_sample_docstring(FlaxRobertaPreLayerNormForSequenceClassification, _CHECKPOINT_FOR_DOC, FlaxSequenceClassifierOutput, _CONFIG_FOR_DOC)
class FlaxRobertaPreLayerNormForMultipleChoiceModule(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False
    def setup(self):
        self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
        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, 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]) 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.roberta_prelayernorm(input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        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("RobertaPreLayerNorm 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.", ROBERTA_PRELAYERNORM_START_DOCSTRING)
class FlaxRobertaPreLayerNormForMultipleChoice(FlaxRobertaPreLayerNormPreTrainedModel): module_class = FlaxRobertaPreLayerNormForMultipleChoiceModule
overwrite_call_docstring(FlaxRobertaPreLayerNormForMultipleChoice, ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
append_call_sample_docstring(FlaxRobertaPreLayerNormForMultipleChoice, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC)
class FlaxRobertaPreLayerNormForTokenClassificationModule(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False
    def setup(self):
        self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing)
        classifier_dropout = (self.config.classifier_dropout if self.config.classifier_dropout is not None else self.config.hidden_dropout_prob)
        self.dropout = nn.Dropout(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, head_mask, deterministic: bool = True, output_attentions: bool = False,
    output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.roberta_prelayernorm(input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        logits = self.classifier(hidden_states)
        if not return_dict: return (logits,) + outputs[1:]
        return FlaxTokenClassifierOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("RobertaPreLayerNorm 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.", ROBERTA_PRELAYERNORM_START_DOCSTRING)
class FlaxRobertaPreLayerNormForTokenClassification(FlaxRobertaPreLayerNormPreTrainedModel): module_class = FlaxRobertaPreLayerNormForTokenClassificationModule
append_call_sample_docstring(FlaxRobertaPreLayerNormForTokenClassification, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC)
class FlaxRobertaPreLayerNormForQuestionAnsweringModule(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False
    def setup(self):
        self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing)
        self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False,
    output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.roberta_prelayernorm(input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        logits = self.qa_outputs(hidden_states)
        start_logits, end_logits = jnp.split(logits, 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("RobertaPreLayerNorm 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`).", ROBERTA_PRELAYERNORM_START_DOCSTRING)
class FlaxRobertaPreLayerNormForQuestionAnswering(FlaxRobertaPreLayerNormPreTrainedModel): module_class = FlaxRobertaPreLayerNormForQuestionAnsweringModule
append_call_sample_docstring(FlaxRobertaPreLayerNormForQuestionAnswering, _CHECKPOINT_FOR_DOC, FlaxQuestionAnsweringModelOutput, _CONFIG_FOR_DOC)
class FlaxRobertaPreLayerNormForCausalLMModule(nn.Module):
    config: RobertaPreLayerNormConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False
    def setup(self):
        self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
        self.lm_head = FlaxRobertaPreLayerNormLMHead(config=self.config, dtype=self.dtype)
    def __call__(self, input_ids, attention_mask, position_ids, token_type_ids: Optional[jnp.ndarray] = None, head_mask: Optional[jnp.ndarray] = None,
    encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True,
    output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        outputs = self.roberta_prelayernorm(input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states=encoder_hidden_states,
        encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        if self.config.tie_word_embeddings: shared_embedding = self.roberta_prelayernorm.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else: shared_embedding = None
        logits = self.lm_head(hidden_states, shared_embedding=shared_embedding)
        if not return_dict: return (logits,) + outputs[1:]
        return FlaxCausalLMOutputWithCrossAttentions(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions)
@add_start_docstrings("RobertaPreLayerNorm Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for autoregressive tasks.", ROBERTA_PRELAYERNORM_START_DOCSTRING)
class FlaxRobertaPreLayerNormForCausalLM(FlaxRobertaPreLayerNormPreTrainedModel):
    module_class = FlaxRobertaPreLayerNormForCausalLMModule
    def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
        batch_size, seq_length = input_ids.shape
        past_key_values = self.init_cache(batch_size, max_length)
        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
        if attention_mask is not None:
            position_ids = attention_mask.cumsum(axis=-1) - 1
            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
        else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
        return {"past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids}
    def update_inputs_for_generation(self, model_outputs, model_kwargs):
        model_kwargs["past_key_values"] = model_outputs.past_key_values
        model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
        return model_kwargs
append_call_sample_docstring(FlaxRobertaPreLayerNormForCausalLM, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutputWithCrossAttentions, _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.                                                                 #
    #################################################################################################################################################################################################
"""
