"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import math
import random
from functools import partial
from typing import 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.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey
from ...modeling_flax_outputs import (FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions)
from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_xglm import XGLMConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/xglm-564M"
_CONFIG_FOR_DOC = "XGLMConfig"
XGLM_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 or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a Flax Linen
    [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
    regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
    Finally, this model supports inherent JAX features such as:
    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
    Parameters:
        config ([`XGLMConfig`]): 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`].
"""
XGLM_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *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)
        position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
def create_sinusoidal_positions(n_pos, dim, padding_idx=1):
    half_dim = dim // 2
    emb = math.log(10000) / (half_dim - 1)
    emb = np.exp(np.arange(half_dim) * -emb)
    emb = np.expand_dims(np.arange(n_pos), 1) * np.expand_dims(emb, 0)
    emb = np.concatenate([np.sin(emb), np.cos(emb)], 1)
    emb = np.reshape(emb, (n_pos, dim))
    if padding_idx is not None: emb[padding_idx, :] = 0
    return jnp.array(emb)
class FlaxXGLMAttention(nn.Module):
    config: XGLMConfig
    embed_dim: int
    num_heads: int
    dropout: float = 0.0
    causal: bool = False
    bias: bool = True
    dtype: jnp.dtype = jnp.float32
    def setup(self) -> None:
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim: raise ValueError(f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads}).")
        dense = partial(nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std))
        self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
        self.out_proj = dense()
        self.dropout_layer = nn.Dropout(rate=self.dropout)
        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.num_heads, self.head_dim))
    def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
    @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: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True) -> Tuple[jnp.ndarray]:
        is_cross_attention = key_value_states is not None
        batch_size = hidden_states.shape[0]
        query_states = self.q_proj(hidden_states)
        if is_cross_attention:
            key_states = self.k_proj(key_value_states)
            value_states = self.v_proj(key_value_states)
        else:
            key_states = self.k_proj(hidden_states)
            value_states = self.v_proj(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.dropout > 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.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None)
        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = self._merge_heads(attn_output)
        attn_output = self.out_proj(attn_output)
        return attn_output, attn_weights
class FlaxXGLMDecoderLayer(nn.Module):
    config: XGLMConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self) -> None:
        self.embed_dim = self.config.d_model
        self.self_attn = FlaxXGLMAttention(config=self.config, embed_dim=self.embed_dim, num_heads=self.config.attention_heads, dropout=self.config.attention_dropout, causal=True, dtype=self.dtype)
        self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
        self.activation_fn = ACT2FN[self.config.activation_function]
        self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
        if self.config.add_cross_attention:
            self.encoder_attn = FlaxXGLMAttention(config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads,
            dropout=self.config.attention_dropout, dtype=self.dtype)
            self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
        self.fc1 = nn.Dense(self.config.ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std))
        self.fc2 = nn.Dense(self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std))
        self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
    def __call__(self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None,
    init_cache: bool = False, output_attentions: bool = True, deterministic: bool = True) -> Tuple[jnp.ndarray]:
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, self_attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache)
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = residual + hidden_states
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)
            hidden_states, cross_attn_weights = self.encoder_attn(hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask)
            hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
            hidden_states = residual + hidden_states
        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = self.fc2(hidden_states)
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = residual + hidden_states
        outputs = (hidden_states,)
        if output_attentions: outputs += (self_attn_weights, cross_attn_weights)
        return outputs
class FlaxXGLMDecoderLayerCollection(nn.Module):
    config: XGLMConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.layers = [FlaxXGLMDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_layers)]
        self.layerdrop = self.config.layerdrop
    def __call__(self, hidden_states, attention_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None,
    deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        for decoder_layer in self.layers:
            if output_hidden_states: all_hidden_states += (hidden_states,)
            dropout_probability = random.uniform(0, 1)
            if not deterministic and (dropout_probability < self.layerdrop): layer_outputs = (None, None, None)
            else: layer_outputs = decoder_layer(hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache, output_attentions=output_attentions, deterministic=deterministic)
            hidden_states = layer_outputs[0]
            if output_attentions:
                all_self_attns += (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_self_attns, 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_self_attns, cross_attentions=all_cross_attentions)
class FlaxXGLMModule(nn.Module):
    config: XGLMConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
        embed_dim = self.config.d_model
        self.padding_idx = self.config.pad_token_id
        self.max_target_positions = self.config.max_position_embeddings
        self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0
        self.embed_tokens = nn.Embed(self.config.vocab_size, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std))
        self.offset = 2
        self.embed_positions = create_sinusoidal_positions(self.config.max_position_embeddings + self.offset, embed_dim)
        self.layers = FlaxXGLMDecoderLayerCollection(self.config, self.dtype)
        self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
    def __call__(self, input_ids, attention_mask, position_ids, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None,
    init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True):
        input_shape = input_ids.shape
        input_ids = input_ids.reshape(-1, input_shape[-1])
        inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
        position_ids = position_ids + self.offset
        positions = jnp.take(self.embed_positions, position_ids, axis=0)
        hidden_states = inputs_embeds + positions
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
        outputs = self.layers(hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, deterministic=deterministic, init_cache=init_cache,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        last_hidden_states = outputs[0]
        last_hidden_states = self.layer_norm(last_hidden_states)
        hidden_states = None
        if output_hidden_states:
            hidden_states = outputs[1]
            hidden_states = hidden_states[:-1] + (last_hidden_states,)
        if not return_dict:
            outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
            return tuple(v for v in outputs if v is not None)
        return FlaxBaseModelOutputWithPastAndCrossAttentions(last_hidden_state=last_hidden_states, hidden_states=hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions)
class FlaxXGLMPreTrainedModel(FlaxPreTrainedModel):
    config_class = XGLMConfig
    base_model_prefix: str = "model"
    module_class: nn.Module = None
    def __init__(self, config: XGLMConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs):
        module = self.module_class(config=config, dtype=dtype, **kwargs)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        input_ids = jnp.zeros(input_shape, dtype="i4")
        attention_mask = jnp.ones_like(input_ids)
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
        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.n_embd,))
            encoder_attention_mask = attention_mask
            module_init_outputs = self.module.init(rngs, input_ids, attention_mask, position_ids, encoder_hidden_states, encoder_attention_mask, return_dict=False)
        else: module_init_outputs = self.module.init(rngs, input_ids, attention_mask, position_ids, 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(XGLM_INPUTS_DOCSTRING)
    def __call__(self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None,
    encoder_attention_mask: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    train: bool = False, params: dict = None, past_key_values: dict = None, dropout_rng: PRNGKey = 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 encoder_hidden_states is not None and encoder_attention_mask is None:
            batch_size, sequence_length = encoder_hidden_states.shape[:2]
            encoder_attention_mask = jnp.ones((batch_size, sequence_length))
        if attention_mask is None: attention_mask = jnp.ones_like(input_ids)
        if position_ids is None:
            batch_size, sequence_length = input_ids.shape
            position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
        rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
        inputs = {"params": params or self.params}
        if past_key_values:
            inputs["cache"] = past_key_values
            mutable = ["cache"]
        else: mutable = False
        outputs = self.module.apply(inputs, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"),
        encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        return_dict=return_dict, deterministic=not train, 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:]
        return outputs
@add_start_docstrings("The bare XGLM Model transformer outputting raw hidden-states without any specific head on top.", XGLM_START_DOCSTRING)
class FlaxXGLMModel(FlaxXGLMPreTrainedModel): module_class = FlaxXGLMModule
append_call_sample_docstring(FlaxXGLMModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPastAndCrossAttentions, _CONFIG_FOR_DOC)
class FlaxXGLMForCausalLMModule(nn.Module):
    config: XGLMConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.model = FlaxXGLMModule(self.config, self.dtype)
        self.lm_head = nn.Dense(self.config.vocab_size, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std))
    def __call__(self, input_ids, attention_mask, position_ids, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None,
    init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True):
        outputs = self.model(input_ids, attention_mask, position_ids, encoder_hidden_states, encoder_attention_mask, deterministic=deterministic, init_cache=init_cache,
        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.model.variables["params"]["embed_tokens"]["embedding"]
            lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
        else: lm_logits = self.lm_head(hidden_states)
        if not return_dict: return (lm_logits,) + outputs[1:]
        return FlaxCausalLMOutputWithCrossAttentions(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions)
@add_start_docstrings("The XGLM Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).", XGLM_START_DOCSTRING)
class FlaxXGLMForCausalLM(FlaxXGLMPreTrainedModel):
    module_class = FlaxXGLMForCausalLMModule
    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(FlaxXGLMForCausalLM, _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.                                                                 #
    #################################################################################################################################################################################################
"""
