from __future__ import annotations
"""
    #################################################################################################################################################################################################
    # 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 warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_utils import (TFCausalLanguageModelingLoss, TFModelInputType, TFMultipleChoiceLoss, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss,
TFSequenceSummary, TFSharedEmbeddings, TFTokenClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_xlnet import XLNetConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "xlnet/xlnet-base-cased"
_CONFIG_FOR_DOC = "XLNetConfig"
class TFXLNetRelativeAttention(keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        if config.d_model % config.n_head != 0: raise ValueError(f"The hidden size ({config.d_model}) is not a multiple of the number of attention heads ({config.n_head}")
        self.n_head = config.n_head
        self.d_head = config.d_head
        self.d_model = config.d_model
        self.scale = 1 / (config.d_head**0.5)
        self.initializer_range = config.initializer_range
        self.output_attentions = config.output_attentions
        self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
        self.dropout = keras.layers.Dropout(config.dropout)
        self.config = config
    def build(self, input_shape=None):
        initializer = get_initializer(self.initializer_range)
        self.q = self.add_weight(shape=(self.d_model, self.n_head, self.d_head), initializer=initializer, trainable=True, name="q")
        self.k = self.add_weight(shape=(self.d_model, self.n_head, self.d_head), initializer=initializer, trainable=True, name="k")
        self.v = self.add_weight(shape=(self.d_model, self.n_head, self.d_head), initializer=initializer, trainable=True, name="v")
        self.o = self.add_weight(shape=(self.d_model, self.n_head, self.d_head), initializer=initializer, trainable=True, name="o")
        self.r = self.add_weight(shape=(self.d_model, self.n_head, self.d_head), initializer=initializer, trainable=True, name="r")
        self.r_r_bias = self.add_weight(shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_r_bias")
        self.r_s_bias = self.add_weight(shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_s_bias")
        self.r_w_bias = self.add_weight(shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_w_bias")
        self.seg_embed = self.add_weight(shape=(2, self.n_head, self.d_head), initializer=initializer, trainable=True, name="seg_embed")
        if self.built: return
        self.built = True
        if getattr(self, "layer_norm", None) is not None:
            with tf.name_scope(self.layer_norm.name): self.layer_norm.build([None, None, self.config.d_model])
    def prune_heads(self, heads): raise NotImplementedError
    def rel_shift(self, x, klen=-1):
        x_size = shape_list(x)
        x = tf.reshape(x, (x_size[1], x_size[0], x_size[2], x_size[3]))
        x = x[1:, ...]
        x = tf.reshape(x, (x_size[0], x_size[1] - 1, x_size[2], x_size[3]))
        x = x[:, 0:klen, :, :]
        return x
    def rel_attn_core(self, q_head, k_head_h, v_head_h, k_head_r, seg_mat, attn_mask, head_mask, output_attentions, training=False):
        ac = tf.einsum("ibnd,jbnd->ijbn", q_head + self.r_w_bias, k_head_h)
        bd = tf.einsum("ibnd,jbnd->ijbn", q_head + self.r_r_bias, k_head_r)
        bd = self.rel_shift(bd, klen=shape_list(ac)[1])
        if seg_mat is None: ef = 0
        else:
            ef = tf.einsum("ibnd,snd->ibns", q_head + self.r_s_bias, self.seg_embed)
            ef = tf.einsum("ijbs,ibns->ijbn", seg_mat, ef)
        attn_score = (ac + bd + ef) * self.scale
        if attn_mask is not None:
            if attn_mask.dtype == tf.float16 or attn_mask.dtype == tf.bfloat16: attn_score = attn_score - 65500 * attn_mask
            else: attn_score = attn_score - 1e30 * attn_mask
        attn_prob = stable_softmax(attn_score, axis=1)
        attn_prob = self.dropout(attn_prob, training=training)
        if head_mask is not None: attn_prob = attn_prob * head_mask
        attn_vec = tf.einsum("ijbn,jbnd->ibnd", attn_prob, v_head_h)
        if output_attentions: return attn_vec, attn_prob
        return attn_vec
    def post_attention(self, h, attn_vec, residual=True, training=False):
        attn_out = tf.einsum("ibnd,hnd->ibh", attn_vec, self.o)
        attn_out = self.dropout(attn_out, training=training)
        if residual: attn_out = attn_out + h
        output = self.layer_norm(attn_out)
        return output
    def call(self, h, g, attn_mask_h, attn_mask_g, r, seg_mat, mems: np.ndarray | tf.Tensor | None = None, target_mapping: np.ndarray | tf.Tensor | None = None,
    head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = False, training: bool = False):
        if g is not None:
            if mems is not None and len(shape_list(mems)) > 1: cat = tf.concat([mems, h], axis=0)
            else: cat = h
            k_head_h = tf.einsum("ibh,hnd->ibnd", cat, self.k)
            v_head_h = tf.einsum("ibh,hnd->ibnd", cat, self.v)
            k_head_r = tf.einsum("ibh,hnd->ibnd", r, self.r)
            q_head_h = tf.einsum("ibh,hnd->ibnd", h, self.q)
            attn_vec_h = self.rel_attn_core(q_head_h, k_head_h, v_head_h, k_head_r, seg_mat, attn_mask_h, head_mask, output_attentions, training=training)
            if output_attentions: attn_vec_h, attn_prob_h = attn_vec_h
            output_h = self.post_attention(h, attn_vec_h, training=training)
            q_head_g = tf.einsum("ibh,hnd->ibnd", g, self.q)
            if target_mapping is not None:
                q_head_g = tf.einsum("mbnd,mlb->lbnd", q_head_g, target_mapping)
                attn_vec_g = self.rel_attn_core(q_head_g, k_head_h, v_head_h, k_head_r, seg_mat, attn_mask_g, head_mask, output_attentions, training=training)
                if output_attentions: attn_vec_g, attn_prob_g = attn_vec_g
                attn_vec_g = tf.einsum("lbnd,mlb->mbnd", attn_vec_g, target_mapping)
            else:
                attn_vec_g = self.rel_attn_core(q_head_g, k_head_h, v_head_h, k_head_r, seg_mat, attn_mask_g, head_mask, output_attentions, training=training)
                if output_attentions: attn_vec_g, attn_prob_g = attn_vec_g
            output_g = self.post_attention(g, attn_vec_g, training=training)
            if output_attentions: attn_prob = attn_prob_h, attn_prob_g
        else:
            if mems is not None and len(shape_list(mems)) > 1: cat = tf.concat([mems, h], axis=0)
            else: cat = h
            q_head_h = tf.einsum("ibh,hnd->ibnd", h, self.q)
            k_head_h = tf.einsum("ibh,hnd->ibnd", cat, self.k)
            v_head_h = tf.einsum("ibh,hnd->ibnd", cat, self.v)
            k_head_r = tf.einsum("ibh,hnd->ibnd", r, self.r)
            attn_vec = self.rel_attn_core(q_head_h, k_head_h, v_head_h, k_head_r, seg_mat, attn_mask_h, head_mask, output_attentions, training=training)
            if output_attentions: attn_vec, attn_prob = attn_vec
            output_h = self.post_attention(h, attn_vec, training=training)
            output_g = None
        outputs = (output_h, output_g)
        if output_attentions: outputs = outputs + (attn_prob,)
        return outputs
class TFXLNetFeedForward(keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
        self.layer_1 = keras.layers.Dense(config.d_inner, kernel_initializer=get_initializer(config.initializer_range), name="layer_1")
        self.layer_2 = keras.layers.Dense(config.d_model, kernel_initializer=get_initializer(config.initializer_range), name="layer_2")
        self.dropout = keras.layers.Dropout(config.dropout)
        if isinstance(config.ff_activation, str): self.activation_function = get_tf_activation(config.ff_activation)
        else: self.activation_function = config.ff_activation
        self.config = config
    def call(self, inp, training=False):
        output = inp
        output = self.layer_1(output)
        output = self.activation_function(output)
        output = self.dropout(output, training=training)
        output = self.layer_2(output)
        output = self.dropout(output, training=training)
        output = self.layer_norm(output + inp)
        return output
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "layer_norm", None) is not None:
            with tf.name_scope(self.layer_norm.name): self.layer_norm.build([None, None, self.config.d_model])
        if getattr(self, "layer_1", None) is not None:
            with tf.name_scope(self.layer_1.name): self.layer_1.build([None, None, self.config.d_model])
        if getattr(self, "layer_2", None) is not None:
            with tf.name_scope(self.layer_2.name): self.layer_2.build([None, None, self.config.d_inner])
class TFXLNetLayer(keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.rel_attn = TFXLNetRelativeAttention(config, name="rel_attn")
        self.ff = TFXLNetFeedForward(config, name="ff")
        self.dropout = keras.layers.Dropout(config.dropout)
    def call(self, output_h, output_g, non_tgt_mask, attn_mask, pos_emb, seg_mat, mems: np.ndarray | tf.Tensor | None = None, target_mapping: np.ndarray | tf.Tensor | None = None,
    head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = False, training: bool = False):
        outputs = self.rel_attn(output_h, output_g, non_tgt_mask, attn_mask, pos_emb, seg_mat, mems, target_mapping, head_mask, output_attentions, training=training)
        output_h, output_g = outputs[:2]
        if output_g is not None: output_g = self.ff(output_g, training=training)
        output_h = self.ff(output_h, training=training)
        outputs = (output_h, output_g) + outputs[2:]
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "rel_attn", None) is not None:
            with tf.name_scope(self.rel_attn.name): self.rel_attn.build(None)
        if getattr(self, "ff", None) is not None:
            with tf.name_scope(self.ff.name): self.ff.build(None)
class TFXLNetLMHead(keras.layers.Layer):
    def __init__(self, config, input_embeddings, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.input_embeddings = input_embeddings
    def build(self, input_shape):
        self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
        super().build(input_shape)
    def get_output_embeddings(self): return self.input_embeddings
    def set_output_embeddings(self, value):
        self.input_embeddings.weight = value
        self.input_embeddings.vocab_size = shape_list(value)[0]
    def get_bias(self): return {"bias": self.bias}
    def set_bias(self, value):
        self.bias = value["bias"]
        self.config.vocab_size = shape_list(value["bias"])[0]
    def call(self, hidden_states):
        hidden_states = self.input_embeddings(hidden_states, mode="linear")
        hidden_states = hidden_states + self.bias
        return hidden_states
@keras_serializable
class TFXLNetMainLayer(keras.layers.Layer):
    config_class = XLNetConfig
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.output_hidden_states = config.output_hidden_states
        self.output_attentions = config.output_attentions
        self.return_dict = config.return_dict
        self.mem_len = config.mem_len
        self.reuse_len = config.reuse_len
        self.d_model = config.d_model
        self.same_length = config.same_length
        self.attn_type = config.attn_type
        self.bi_data = config.bi_data
        self.clamp_len = config.clamp_len
        self.n_layer = config.n_layer
        self.use_bfloat16 = config.use_bfloat16
        self.initializer_range = config.initializer_range
        self.word_embedding = TFSharedEmbeddings(config.vocab_size, config.d_model, initializer_range=config.initializer_range, name="word_embedding")
        self.layer = [TFXLNetLayer(config, name=f"layer_._{i}") for i in range(config.n_layer)]
        self.dropout = keras.layers.Dropout(config.dropout)
        self.use_mems_eval = config.use_mems_eval
        self.use_mems_train = config.use_mems_train
    def get_input_embeddings(self): return self.word_embedding
    def set_input_embeddings(self, value):
        self.word_embedding.weight = value
        self.word_embedding.vocab_size = shape_list(value)[0]
    def build(self, input_shape=None):
        initializer = get_initializer(self.initializer_range)
        self.mask_emb = self.add_weight(shape=(1, 1, self.d_model), initializer=initializer, trainable=True, name="mask_emb")
        if self.built: return
        self.built = True
        if getattr(self, "word_embedding", None) is not None:
            with tf.name_scope(self.word_embedding.name): self.word_embedding.build(None)
        if getattr(self, "layer", None) is not None:
            for layer in self.layer:
                with tf.name_scope(layer.name): layer.build(None)
    def _prune_heads(self, heads_to_prune): raise NotImplementedError
    def create_mask(self, qlen, mlen):
        attn_mask = tf.ones([qlen, qlen])
        mask_u = tf.linalg.band_part(attn_mask, 0, -1)
        mask_dia = tf.linalg.band_part(attn_mask, 0, 0)
        attn_mask_pad = tf.zeros([qlen, mlen])
        ret = tf.concat([attn_mask_pad, mask_u - mask_dia], 1)
        if self.same_length:
            mask_l = tf.linalg.band_part(attn_mask, -1, 0)
            ret = tf.concat([ret[:, :qlen] + mask_l - mask_dia, ret[:, qlen:]], 1)
        return ret
    def cache_mem(self, curr_out, prev_mem):
        if self.reuse_len is not None and self.reuse_len > 0: curr_out = curr_out[: self.reuse_len]
        if self.mem_len is None or self.mem_len == 0: cutoff = 0
        else: cutoff = -self.mem_len
        if prev_mem is None: new_mem = curr_out[cutoff:]
        else: new_mem = tf.concat([prev_mem, curr_out], 0)[cutoff:]
        return tf.stop_gradient(new_mem)
    @staticmethod
    def positional_embedding(pos_seq, inv_freq, bsz=None):
        sinusoid_inp = tf.einsum("i,d->id", pos_seq, inv_freq)
        pos_emb = tf.concat([tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)], axis=-1)
        pos_emb = pos_emb[:, None, :]
        if bsz is not None: pos_emb = tf.tile(pos_emb, [1, bsz, 1])
        return pos_emb
    def relative_positional_encoding(self, qlen, klen, bsz=None):
        freq_seq = tf.range(0, self.d_model, 2.0)
        inv_freq = 1 / (10000 ** (freq_seq / self.d_model))
        if self.attn_type == "bi": beg, end = klen, -qlen
        elif self.attn_type == "uni": beg, end = klen, -1
        else: raise ValueError(f"Unknown `attn_type` {self.attn_type}.")
        if self.bi_data:
            fwd_pos_seq = tf.range(beg, end, -1.0)
            bwd_pos_seq = tf.range(-beg, -end, 1.0)
            if self.clamp_len > 0:
                fwd_pos_seq = tf.clip_by_value(fwd_pos_seq, -self.clamp_len, self.clamp_len)
                bwd_pos_seq = tf.clip_by_value(bwd_pos_seq, -self.clamp_len, self.clamp_len)
            if bsz is not None:
                if bsz % 2 != 0: raise ValueError(f"With bi_data, the batch size {bsz} should be divisible by 2")
                fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz // 2)
                bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq, bsz // 2)
            else:
                fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq)
                bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq)
            pos_emb = tf.concat([fwd_pos_emb, bwd_pos_emb], axis=1)
        else:
            fwd_pos_seq = tf.range(beg, end, -1.0)
            if self.clamp_len > 0: fwd_pos_seq = tf.clip_by_value(fwd_pos_seq, -self.clamp_len, self.clamp_len)
            pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz)
        return pos_emb
    @unpack_inputs
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, mems: np.ndarray | tf.Tensor | None = None,
    perm_mask: np.ndarray | tf.Tensor | None = None, target_mapping: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None,
    input_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None,
    use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False):
        if training and use_mems is None: use_mems = self.use_mems_train
        else: use_mems = self.use_mems_eval
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_ids = tf.transpose(input_ids, perm=(1, 0))
            qlen, bsz = shape_list(input_ids)[:2]
        elif inputs_embeds is not None:
            inputs_embeds = tf.transpose(inputs_embeds, perm=(1, 0, 2))
            qlen, bsz = shape_list(inputs_embeds)[:2]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        token_type_ids = tf.transpose(token_type_ids, perm=(1, 0)) if token_type_ids is not None else None
        input_mask = tf.transpose(input_mask, perm=(1, 0)) if input_mask is not None else None
        attention_mask = tf.transpose(attention_mask, perm=(1, 0)) if attention_mask is not None else None
        perm_mask = tf.transpose(perm_mask, perm=(1, 2, 0)) if perm_mask is not None else None
        target_mapping = tf.transpose(target_mapping, perm=(1, 2, 0)) if target_mapping is not None else None
        mlen = shape_list(mems[0])[0] if mems is not None and mems[0] is not None else 0
        klen = mlen + qlen
        if self.attn_type == "uni":
            attn_mask = self.create_mask(qlen, mlen)
            attn_mask = attn_mask[:, :, None, None]
        elif self.attn_type == "bi": attn_mask = None
        else: raise ValueError(f"Unsupported attention type: {self.attn_type}")
        assert input_mask is None or attention_mask is None, ("You can only use one of input_mask (uses 1 for padding) or attention_mask (uses 0 for padding, added for compatibility with BERT). Please choose one.")
        if input_mask is None and attention_mask is not None:
            one_cst = tf.constant(1.0)
            input_mask = 1.0 - tf.cast(attention_mask, dtype=one_cst.dtype)
        if input_mask is not None and perm_mask is not None: data_mask = input_mask[None] + perm_mask
        elif input_mask is not None and perm_mask is None: data_mask = input_mask[None]
        elif input_mask is None and perm_mask is not None: data_mask = perm_mask
        else: data_mask = None
        if data_mask is not None:
            if mlen > 0:
                mems_mask = tf.zeros([shape_list(data_mask)[0], mlen, bsz])
                data_mask = tf.concat([mems_mask, data_mask], axis=1)
            if attn_mask is None: attn_mask = data_mask[:, :, :, None]
            else: attn_mask += data_mask[:, :, :, None]
        if attn_mask is not None: attn_mask = tf.cast(attn_mask > 0, dtype=attn_mask.dtype)
        if attn_mask is not None:
            non_tgt_mask = -tf.eye(qlen)
            if mlen > 0: non_tgt_mask = tf.concat([tf.zeros([qlen, mlen]), non_tgt_mask], axis=-1)
            non_tgt_mask = tf.cast((attn_mask + non_tgt_mask[:, :, None, None]) > 0, dtype=non_tgt_mask.dtype)
        else: non_tgt_mask = None
        if inputs_embeds is not None: word_emb_k = inputs_embeds
        else:
            check_embeddings_within_bounds(input_ids, self.word_embedding.vocab_size)
            word_emb_k = self.word_embedding(input_ids)
        output_h = self.dropout(word_emb_k, training=training)
        if target_mapping is not None:
            word_emb_q = tf.tile(self.mask_emb, [shape_list(target_mapping)[0], bsz, 1])
            output_g = self.dropout(word_emb_q, training=training)
        else: output_g = None
        if token_type_ids is not None:
            if mlen > 0:
                mem_pad = tf.zeros([mlen, bsz], dtype=token_type_ids.dtype)
                cat_ids = tf.concat([mem_pad, token_type_ids], 0)
            else: cat_ids = token_type_ids
            seg_mat = tf.cast(tf.logical_not(tf.equal(token_type_ids[:, None], cat_ids[None, :])), dtype=token_type_ids.dtype)
            seg_mat = tf.one_hot(seg_mat, 2)
        else: seg_mat = None
        pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz)
        pos_emb = self.dropout(pos_emb, training=training)
        if head_mask is not None: raise NotImplementedError
        else: head_mask = [None] * self.n_layer
        new_mems = ()
        if mems is None: mems = [None] * len(self.layer)
        attentions = [] if output_attentions else None
        hidden_states = [] if output_hidden_states else None
        for i, layer_module in enumerate(self.layer):
            if use_mems: new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)
            if output_hidden_states: hidden_states.append((output_h, output_g) if output_g is not None else output_h)
            outputs = layer_module(output_h, output_g, non_tgt_mask, attn_mask, pos_emb, seg_mat, mems[i], target_mapping, head_mask[i], output_attentions, training=training)
            output_h, output_g = outputs[:2]
            if output_attentions: attentions.append(outputs[2])
        if output_hidden_states: hidden_states.append((output_h, output_g) if output_g is not None else output_h)
        output = self.dropout(output_g if output_g is not None else output_h, training=training)
        output = tf.transpose(output, perm=(1, 0, 2))
        if not use_mems: new_mems = None
        if output_hidden_states:
            if output_g is not None: hidden_states = tuple(tf.transpose(h, perm=(1, 0, 2)) for hs in hidden_states for h in hs)
            else: hidden_states = tuple(tf.transpose(hs, perm=(1, 0, 2)) for hs in hidden_states)
        if output_attentions:
            if target_mapping is not None: attentions = tuple(tuple(tf.transpose(attn_stream, perm=(2, 3, 0, 1)) for attn_stream in t) for t in attentions)
            else: attentions = tuple(tf.transpose(t, perm=(2, 3, 0, 1)) for t in attentions)
        if not return_dict: return tuple(v for v in [output, new_mems, hidden_states, attentions] if v is not None)
        return TFXLNetModelOutput(last_hidden_state=output, mems=new_mems, hidden_states=hidden_states, attentions=attentions)
class TFXLNetPreTrainedModel(TFPreTrainedModel):
    config_class = XLNetConfig
    base_model_prefix = "transformer"
@dataclass
class TFXLNetModelOutput(ModelOutput):
    """Args:"""
    last_hidden_state: tf.Tensor = None
    mems: List[tf.Tensor] | None = None
    hidden_states: Tuple[tf.Tensor, ...] | None = None
    attentions: Tuple[tf.Tensor, ...] | None = None
@dataclass
class TFXLNetLMHeadModelOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    logits: tf.Tensor = None
    mems: List[tf.Tensor] | None = None
    hidden_states: Tuple[tf.Tensor, ...] | None = None
    attentions: Tuple[tf.Tensor, ...] | None = None
@dataclass
class TFXLNetForSequenceClassificationOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    logits: tf.Tensor = None
    mems: List[tf.Tensor] | None = None
    hidden_states: Tuple[tf.Tensor, ...] | None = None
    attentions: Tuple[tf.Tensor, ...] | None = None
@dataclass
class TFXLNetForTokenClassificationOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    logits: tf.Tensor = None
    mems: List[tf.Tensor] | None = None
    hidden_states: Tuple[tf.Tensor, ...] | None = None
    attentions: Tuple[tf.Tensor, ...] | None = None
@dataclass
class TFXLNetForMultipleChoiceOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    logits: tf.Tensor = None
    mems: List[tf.Tensor] | None = None
    hidden_states: Tuple[tf.Tensor, ...] | None = None
    attentions: Tuple[tf.Tensor, ...] | None = None
@dataclass
class TFXLNetForQuestionAnsweringSimpleOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    start_logits: tf.Tensor = None
    end_logits: tf.Tensor = None
    mems: List[tf.Tensor] | None = None
    hidden_states: Tuple[tf.Tensor, ...] | None = None
    attentions: Tuple[tf.Tensor, ...] | None = None
XLNET_START_DOCSTRING = r"""
    This model inherits from [`TFPreTrainedModel`]. 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 [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.
    <Tip>
    TensorFlow models and layers in `transformers` accept two formats as input:
    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional argument.
    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
    positional argument:
    - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!
    </Tip>
    Parameters:
        config ([`XLNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
XLNET_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` 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 (`torch.FloatTensor` 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)
        mems (`List[torch.FloatTensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (see `mems` output below) . Can be used to speed up sequential
            decoding. The token ids which have their past given to this model should not be passed as `input_ids` as
            they have already been computed.
            `use_mems` has to be set to `True` to make use of `mems`.
        perm_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length)`, *optional*):
            Mask to indicate the attention pattern for each input token with values selected in `[0, 1]`:
            - if `perm_mask[k, i, j] = 0`, i attend to j in batch k;
            - if `perm_mask[k, i, j] = 1`, i does not attend to j in batch k.
            If not set, each token attends to all the others (full bidirectional attention). Only used during
            pretraining (to define factorization order) or for sequential decoding (generation).
        target_mapping (`torch.FloatTensor` of shape `(batch_size, num_predict, sequence_length)`, *optional*):
            Mask to indicate the output tokens to use. If `target_mapping[k, i, j] = 1`, the i-th predict in batch k is
            on the j-th token. Only used during pretraining for partial prediction or for sequential decoding
            (generation).
        token_type_ids (`torch.LongTensor` 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)
        input_mask (`torch.FloatTensor` of shape `{0}`, *optional*):
            Mask to avoid performing attention on padding token indices. Negative of `attention_mask`, i.e. with 0 for
            real tokens and 1 for padding which is kept for compatibility with the original code base.
            Mask values selected in `[0, 1]`:
            - 1 for tokens that are *masked*,
            - 0 for tokens that are *not masked*.
            You can only uses one of `input_mask` and `attention_mask`.
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        inputs_embeds (`torch.FloatTensor` of shape `(0, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        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.
"""
@add_start_docstrings("The bare XLNet Model transformer outputting raw hidden-states without any specific head on top.", XLNET_START_DOCSTRING)
class TFXLNetModel(TFXLNetPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFXLNetMainLayer(config, name="transformer")
    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, mems: np.ndarray | tf.Tensor | None = None,
    perm_mask: np.ndarray | tf.Tensor | None = None, target_mapping: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None,
    input_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None,
    use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    training: bool = False) -> Union[TFXLNetModelOutput, Tuple[tf.Tensor]]:
        outputs = self.transformer(input_ids=input_ids, attention_mask=attention_mask, mems=mems, perm_mask=perm_mask, target_mapping=target_mapping, token_type_ids=token_type_ids,
        input_mask=input_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_mems=use_mems, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        return_dict=return_dict, training=training)
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "transformer", None) is not None:
            with tf.name_scope(self.transformer.name): self.transformer.build(None)
@add_start_docstrings("XLNet Model with a language modeling head on top (linear layer with weights tied to the input embeddings).", XLNET_START_DOCSTRING)
class TFXLNetLMHeadModel(TFXLNetPreTrainedModel, TFCausalLanguageModelingLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFXLNetMainLayer(config, name="transformer")
        self.lm_loss = TFXLNetLMHead(config, self.transformer.word_embedding, name="lm_loss")
        self.supports_xla_generation = False
    def get_lm_head(self): return self.lm_loss
    def get_prefix_bias_name(self):
        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
        return self.name + "/" + self.lm_loss.name
    def prepare_inputs_for_generation(self, inputs, past_key_values=None, use_mems=None, **kwargs):
        effective_batch_size = inputs.shape[0]
        dummy_token = tf.zeros((effective_batch_size, 1), dtype=inputs.dtype)
        offset = 2
        if past_key_values: input_ids = tf.concat([inputs[:, -offset:], dummy_token], axis=1)
        else: input_ids = tf.concat([inputs, dummy_token], axis=1)
        sequence_length = input_ids.shape[1]
        perm_mask = tf.zeros((effective_batch_size, sequence_length, sequence_length - 1))
        perm_mask_seq_end = tf.ones((effective_batch_size, sequence_length, 1))
        perm_mask = tf.concat([perm_mask, perm_mask_seq_end], axis=-1)
        target_mapping = tf.zeros((effective_batch_size, 1, sequence_length - 1))
        target_mapping_seq_end = tf.ones((effective_batch_size, 1, 1))
        target_mapping = tf.concat([target_mapping, target_mapping_seq_end], axis=-1)
        inputs = {"input_ids": input_ids, "perm_mask": perm_mask, "target_mapping": target_mapping, "use_mems": use_mems}
        if past_key_values: inputs["mems"] = tuple(layer_past[:-offset, :, :] for layer_past in past_key_values)
        return inputs
    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, mems: np.ndarray | tf.Tensor | None = None,
    perm_mask: np.ndarray | tf.Tensor | None = None, target_mapping: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None,
    input_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None,
    use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    labels: np.ndarray | tf.Tensor | None = None, training: bool = False) -> Union[TFXLNetLMHeadModelOutput, Tuple[tf.Tensor]]:
        transformer_outputs = self.transformer(input_ids=input_ids, attention_mask=attention_mask, mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
        token_type_ids=token_type_ids, input_mask=input_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_mems=use_mems, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        hidden_state = transformer_outputs[0]
        logits = self.lm_loss(hidden_state, training=training)
        loss = None
        if labels is not None: loss = self.hf_compute_loss(labels, logits)
        if not return_dict:
            output = (logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TFXLNetLMHeadModelOutput(loss=loss, logits=logits, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "transformer", None) is not None:
            with tf.name_scope(self.transformer.name): self.transformer.build(None)
        if getattr(self, "lm_loss", None) is not None:
            with tf.name_scope(self.lm_loss.name): self.lm_loss.build(None)
@add_start_docstrings("XLNet Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", XLNET_START_DOCSTRING)
class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.transformer = TFXLNetMainLayer(config, name="transformer")
        self.sequence_summary = TFSequenceSummary(config, initializer_range=config.initializer_range, name="sequence_summary")
        self.logits_proj = keras.layers.Dense(config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="logits_proj")
        self.config = config
    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, mems: np.ndarray | tf.Tensor | None = None,
    perm_mask: np.ndarray | tf.Tensor | None = None, target_mapping: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None,
    input_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None,
    use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    labels: np.ndarray | tf.Tensor | None = None, training: bool = False) -> Union[TFXLNetForSequenceClassificationOutput, Tuple[tf.Tensor]]:
        transformer_outputs = self.transformer(input_ids=input_ids, attention_mask=attention_mask, mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
        token_type_ids=token_type_ids, input_mask=input_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_mems=use_mems, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        output = transformer_outputs[0]
        output = self.sequence_summary(output)
        logits = self.logits_proj(output)
        loss = None if labels is None else self.hf_compute_loss(labels, logits)
        if not return_dict:
            output = (logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TFXLNetForSequenceClassificationOutput(loss=loss, logits=logits, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "transformer", None) is not None:
            with tf.name_scope(self.transformer.name): self.transformer.build(None)
        if getattr(self, "sequence_summary", None) is not None:
            with tf.name_scope(self.sequence_summary.name): self.sequence_summary.build(None)
        if getattr(self, "logits_proj", None) is not None:
            with tf.name_scope(self.logits_proj.name): self.logits_proj.build([None, None, self.config.d_model])
@add_start_docstrings("XLNET 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.", XLNET_START_DOCSTRING)
class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFXLNetMainLayer(config, name="transformer")
        self.sequence_summary = TFSequenceSummary(config, initializer_range=config.initializer_range, name="sequence_summary")
        self.logits_proj = keras.layers.Dense(1, kernel_initializer=get_initializer(config.initializer_range), name="logits_proj")
        self.config = config
    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, input_mask: np.ndarray | tf.Tensor | None = None,
    attention_mask: np.ndarray | tf.Tensor | None = None, mems: np.ndarray | tf.Tensor | None = None, perm_mask: np.ndarray | tf.Tensor | None = None,
    target_mapping: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None,
    use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    labels: np.ndarray | tf.Tensor | None = None, training: bool = False) -> Union[TFXLNetForMultipleChoiceOutput, Tuple[tf.Tensor]]:
        if input_ids is not None:
            num_choices = shape_list(input_ids)[1]
            seq_length = shape_list(input_ids)[2]
        else:
            num_choices = shape_list(inputs_embeds)[1]
            seq_length = shape_list(inputs_embeds)[2]
        flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
        flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
        flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
        flat_input_mask = tf.reshape(input_mask, (-1, seq_length)) if input_mask is not None else None
        flat_inputs_embeds = (tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3])) if inputs_embeds is not None else None)
        transformer_outputs = self.transformer(flat_input_ids, flat_attention_mask, mems, perm_mask, target_mapping, flat_token_type_ids, flat_input_mask, head_mask,
        flat_inputs_embeds, use_mems, output_attentions, output_hidden_states, return_dict=return_dict, training=training)
        output = transformer_outputs[0]
        logits = self.sequence_summary(output)
        logits = self.logits_proj(logits)
        reshaped_logits = tf.reshape(logits, (-1, num_choices))
        loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits)
        if not return_dict:
            output = (reshaped_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TFXLNetForMultipleChoiceOutput(loss=loss, logits=reshaped_logits, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "transformer", None) is not None:
            with tf.name_scope(self.transformer.name): self.transformer.build(None)
        if getattr(self, "sequence_summary", None) is not None:
            with tf.name_scope(self.sequence_summary.name): self.sequence_summary.build(None)
        if getattr(self, "logits_proj", None) is not None:
            with tf.name_scope(self.logits_proj.name): self.logits_proj.build([None, None, self.config.d_model])
@add_start_docstrings("XLNet 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.", XLNET_START_DOCSTRING)
class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificationLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.transformer = TFXLNetMainLayer(config, name="transformer")
        self.classifier = keras.layers.Dense(config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier")
        self.config = config
    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, mems: np.ndarray | tf.Tensor | None = None,
    perm_mask: np.ndarray | tf.Tensor | None = None, target_mapping: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None,
    input_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None,
    use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    labels: np.ndarray | tf.Tensor | None = None, training: bool = False) -> Union[TFXLNetForTokenClassificationOutput, Tuple[tf.Tensor]]:
        transformer_outputs = self.transformer(input_ids=input_ids, attention_mask=attention_mask, mems=mems, perm_mask=perm_mask, target_mapping=target_mapping, token_type_ids=token_type_ids,
        input_mask=input_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_mems=use_mems, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        output = transformer_outputs[0]
        logits = self.classifier(output)
        loss = None if labels is None else self.hf_compute_loss(labels, logits)
        if not return_dict:
            output = (logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TFXLNetForTokenClassificationOutput(loss=loss, logits=logits, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "transformer", None) is not None:
            with tf.name_scope(self.transformer.name): self.transformer.build(None)
        if getattr(self, "classifier", None) is not None:
            with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size])
@add_start_docstrings("XLNet 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`).", XLNET_START_DOCSTRING)
class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnsweringLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFXLNetMainLayer(config, name="transformer")
        self.qa_outputs = keras.layers.Dense(config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs")
        self.config = config
    @unpack_inputs
    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, mems: np.ndarray | tf.Tensor | None = None,
    perm_mask: np.ndarray | tf.Tensor | None = None, target_mapping: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None,
    input_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None,
    use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    start_positions: np.ndarray | tf.Tensor | None = None, end_positions: np.ndarray | tf.Tensor | None = None,
    training: bool = False) -> Union[TFXLNetForQuestionAnsweringSimpleOutput, Tuple[tf.Tensor]]:
        transformer_outputs = self.transformer(input_ids=input_ids, attention_mask=attention_mask, mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
        token_type_ids=token_type_ids, input_mask=input_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_mems=use_mems, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        sequence_output = transformer_outputs[0]
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = tf.split(logits, 2, axis=-1)
        start_logits = tf.squeeze(start_logits, axis=-1)
        end_logits = tf.squeeze(end_logits, axis=-1)
        loss = None
        if start_positions is not None and end_positions is not None:
            labels = {"start_position": start_positions}
            labels["end_position"] = end_positions
            loss = self.hf_compute_loss(labels, (start_logits, end_logits))
        if not return_dict:
            output = (start_logits, end_logits) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TFXLNetForQuestionAnsweringSimpleOutput(loss=loss, start_logits=start_logits, end_logits=end_logits, mems=transformer_outputs.mems,
        hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "transformer", None) is not None:
            with tf.name_scope(self.transformer.name): self.transformer.build(None)
        if getattr(self, "qa_outputs", None) is not None:
            with tf.name_scope(self.qa_outputs.name): self.qa_outputs.build([None, None, self.config.hidden_size])
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
