"""
    #################################################################################################################################################################################################
    # 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 os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput,
QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_mobilebert import MobileBertConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/mobilebert-uncased"
_CONFIG_FOR_DOC = "MobileBertConfig"
_CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "mrm8488/mobilebert-finetuned-ner"
_TOKEN_CLASS_EXPECTED_OUTPUT = "['I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC']"
_TOKEN_CLASS_EXPECTED_LOSS = 0.03
_CHECKPOINT_FOR_QA = "csarron/mobilebert-uncased-squad-v2"
_QA_EXPECTED_OUTPUT = "'a nice puppet'"
_QA_EXPECTED_LOSS = 3.98
_QA_TARGET_START_INDEX = 12
_QA_TARGET_END_INDEX = 13
_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "lordtt13/emo-mobilebert"
_SEQ_CLASS_EXPECTED_OUTPUT = "'others'"
_SEQ_CLASS_EXPECTED_LOSS = "4.72"
def load_tf_weights_in_mobilebert(model, config, tf_checkpoint_path):
    try:
        import re
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error("Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.")
        raise
    tf_path = os.path.abspath(tf_checkpoint_path)
    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array)
    for name, array in zip(names, arrays):
        name = name.replace("ffn_layer", "ffn")
        name = name.replace("FakeLayerNorm", "LayerNorm")
        name = name.replace("extra_output_weights", "dense/kernel")
        name = name.replace("bert", "mobilebert")
        name = name.split("/")
        if any(n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] for n in name):
            logger.info(f"Skipping {'/'.join(name)}")
            continue
        pointer = model
        for m_name in name:
            if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name)
            else: scope_names = [m_name]
            if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight")
            elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias")
            elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight")
            elif scope_names[0] == "squad": pointer = getattr(pointer, "classifier")
            else:
                try: pointer = getattr(pointer, scope_names[0])
                except AttributeError:
                    logger.info(f"Skipping {'/'.join(name)}")
                    continue
            if len(scope_names) >= 2:
                num = int(scope_names[1])
                pointer = pointer[num]
        if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight")
        elif m_name == "kernel": array = np.transpose(array)
        try: assert (pointer.shape == array.shape), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
        except AssertionError as e:
            e.args += (pointer.shape, array.shape)
            raise
        logger.info(f"Initialize PyTorch weight {name}")
        pointer.data = torch.from_numpy(array)
    return model
class NoNorm(nn.Module):
    def __init__(self, feat_size, eps=None):
        super().__init__()
        self.bias = nn.Parameter(torch.zeros(feat_size))
        self.weight = nn.Parameter(torch.ones(feat_size))
    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
        return input_tensor * self.weight + self.bias
NORM2FN = {"layer_norm": nn.LayerNorm, "no_norm": NoNorm}
class MobileBertEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.trigram_input = config.trigram_input
        self.embedding_size = config.embedding_size
        self.hidden_size = config.hidden_size
        self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        embed_dim_multiplier = 3 if self.trigram_input else 1
        embedded_input_size = self.embedding_size * embed_dim_multiplier
        self.embedding_transformation = nn.Linear(embedded_input_size, config.hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None) -> torch.Tensor:
        if input_ids is not None: input_shape = input_ids.size()
        else: input_shape = inputs_embeds.size()[:-1]
        seq_length = input_shape[1]
        if position_ids is None: position_ids = self.position_ids[:, :seq_length]
        if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
        if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids)
        if self.trigram_input: inputs_embeds = torch.cat([nn.functional.pad(inputs_embeds[:, 1:], [0, 0, 0, 1, 0, 0], value=0.0), inputs_embeds, nn.functional.pad(inputs_embeds[:, :-1], [0, 0, 1, 0, 0, 0], value=0.0)], dim=2)
        if self.trigram_input or self.embedding_size != self.hidden_size: inputs_embeds = self.embedding_transformation(inputs_embeds)
        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
class MobileBertSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.true_hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = nn.Linear(config.true_hidden_size, self.all_head_size)
        self.key = nn.Linear(config.true_hidden_size, self.all_head_size)
        self.value = nn.Linear(config.true_hidden_size if config.use_bottleneck_attention else config.hidden_size, self.all_head_size)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(new_x_shape)
        return x.permute(0, 2, 1, 3)
    def forward(self, query_tensor: torch.Tensor, key_tensor: torch.Tensor, value_tensor: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None,
    head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None) -> Tuple[torch.Tensor]:
        mixed_query_layer = self.query(query_tensor)
        mixed_key_layer = self.key(key_tensor)
        mixed_value_layer = self.value(value_tensor)
        query_layer = self.transpose_for_scores(mixed_query_layer)
        key_layer = self.transpose_for_scores(mixed_key_layer)
        value_layer = self.transpose_for_scores(mixed_value_layer)
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None: attention_scores = attention_scores + attention_mask
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
        attention_probs = self.dropout(attention_probs)
        if head_mask is not None: attention_probs = attention_probs * head_mask
        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs
class MobileBertSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.use_bottleneck = config.use_bottleneck
        self.dense = nn.Linear(config.true_hidden_size, config.true_hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)
        if not self.use_bottleneck: self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
        layer_outputs = self.dense(hidden_states)
        if not self.use_bottleneck: layer_outputs = self.dropout(layer_outputs)
        layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
        return layer_outputs
class MobileBertAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.self = MobileBertSelfAttention(config)
        self.output = MobileBertSelfOutput(config)
        self.pruned_heads = set()
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads)
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    def forward(self, query_tensor: torch.Tensor, key_tensor: torch.Tensor, value_tensor: torch.Tensor, layer_input: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None,
    head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None) -> Tuple[torch.Tensor]:
        self_outputs = self.self(query_tensor, key_tensor, value_tensor, attention_mask, head_mask, output_attentions)
        attention_output = self.output(self_outputs[0], layer_input)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class MobileBertIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.true_hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else: self.intermediate_act_fn = config.hidden_act
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states
class OutputBottleneck(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.true_hidden_size, config.hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
        layer_outputs = self.dense(hidden_states)
        layer_outputs = self.dropout(layer_outputs)
        layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
        return layer_outputs
class MobileBertOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.use_bottleneck = config.use_bottleneck
        self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size)
        if not self.use_bottleneck: self.dropout = nn.Dropout(config.hidden_dropout_prob)
        else: self.bottleneck = OutputBottleneck(config)
    def forward(self, intermediate_states: torch.Tensor, residual_tensor_1: torch.Tensor, residual_tensor_2: torch.Tensor) -> torch.Tensor:
        layer_output = self.dense(intermediate_states)
        if not self.use_bottleneck:
            layer_output = self.dropout(layer_output)
            layer_output = self.LayerNorm(layer_output + residual_tensor_1)
        else:
            layer_output = self.LayerNorm(layer_output + residual_tensor_1)
            layer_output = self.bottleneck(layer_output, residual_tensor_2)
        return layer_output
class BottleneckLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intra_bottleneck_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.intra_bottleneck_size, eps=config.layer_norm_eps)
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        layer_input = self.dense(hidden_states)
        layer_input = self.LayerNorm(layer_input)
        return layer_input
class Bottleneck(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.key_query_shared_bottleneck = config.key_query_shared_bottleneck
        self.use_bottleneck_attention = config.use_bottleneck_attention
        self.input = BottleneckLayer(config)
        if self.key_query_shared_bottleneck: self.attention = BottleneckLayer(config)
    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
        bottlenecked_hidden_states = self.input(hidden_states)
        if self.use_bottleneck_attention: return (bottlenecked_hidden_states,) * 4
        elif self.key_query_shared_bottleneck:
            shared_attention_input = self.attention(hidden_states)
            return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states)
        else: return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states)
class FFNOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)
    def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
        layer_outputs = self.dense(hidden_states)
        layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
        return layer_outputs
class FFNLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.intermediate = MobileBertIntermediate(config)
        self.output = FFNOutput(config)
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        intermediate_output = self.intermediate(hidden_states)
        layer_outputs = self.output(intermediate_output, hidden_states)
        return layer_outputs
class MobileBertLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.use_bottleneck = config.use_bottleneck
        self.num_feedforward_networks = config.num_feedforward_networks
        self.attention = MobileBertAttention(config)
        self.intermediate = MobileBertIntermediate(config)
        self.output = MobileBertOutput(config)
        if self.use_bottleneck: self.bottleneck = Bottleneck(config)
        if config.num_feedforward_networks > 1: self.ffn = nn.ModuleList([FFNLayer(config) for _ in range(config.num_feedforward_networks - 1)])
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None) -> Tuple[torch.Tensor]:
        if self.use_bottleneck: query_tensor, key_tensor, value_tensor, layer_input = self.bottleneck(hidden_states)
        else: query_tensor, key_tensor, value_tensor, layer_input = [hidden_states] * 4
        self_attention_outputs = self.attention(query_tensor, key_tensor, value_tensor, layer_input, attention_mask, head_mask, output_attentions=output_attentions)
        attention_output = self_attention_outputs[0]
        s = (attention_output,)
        outputs = self_attention_outputs[1:]
        if self.num_feedforward_networks != 1:
            for i, ffn_module in enumerate(self.ffn):
                attention_output = ffn_module(attention_output)
                s += (attention_output,)
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output, hidden_states)
        outputs = ((layer_output,) + outputs + (torch.tensor(1000), query_tensor, key_tensor, value_tensor, layer_input, attention_output, intermediate_output) + s)
        return outputs
class MobileBertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.layer = nn.ModuleList([MobileBertLayer(config) for _ in range(config.num_hidden_layers)])
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False,
    output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True) -> Union[Tuple, BaseModelOutput]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_outputs = layer_module(hidden_states, attention_mask, head_mask[i], output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions: all_attentions = all_attentions + (layer_outputs[1],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions)
class MobileBertPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.do_activate = config.classifier_activation
        if self.do_activate: self.dense = nn.Linear(config.hidden_size, config.hidden_size)
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        first_token_tensor = hidden_states[:, 0]
        if not self.do_activate: return first_token_tensor
        else:
            pooled_output = self.dense(first_token_tensor)
            pooled_output = torch.tanh(pooled_output)
            return pooled_output
class MobileBertPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act]
        else: self.transform_act_fn = config.hidden_act
        self.LayerNorm = NORM2FN["layer_norm"](config.hidden_size, eps=config.layer_norm_eps)
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states
class MobileBertLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = MobileBertPredictionHeadTransform(config)
        self.dense = nn.Linear(config.vocab_size, config.hidden_size - config.embedding_size, bias=False)
        self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=False)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        self.decoder.bias = self.bias
    def _tie_weights(self) -> None:
        self.decoder.bias = self.bias
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.transform(hidden_states)
        hidden_states = hidden_states.matmul(torch.cat([self.decoder.weight.t(), self.dense.weight], dim=0))
        hidden_states += self.decoder.bias
        return hidden_states
class MobileBertOnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = MobileBertLMPredictionHead(config)
    def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores
class MobileBertPreTrainingHeads(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = MobileBertLMPredictionHead(config)
        self.seq_relationship = nn.Linear(config.hidden_size, 2)
    def forward(self, sequence_output: torch.Tensor, pooled_output: torch.Tensor) -> Tuple[torch.Tensor]:
        prediction_scores = self.predictions(sequence_output)
        seq_relationship_score = self.seq_relationship(pooled_output)
        return prediction_scores, seq_relationship_score
class MobileBertPreTrainedModel(PreTrainedModel):
    config_class = MobileBertConfig
    load_tf_weights = load_tf_weights_in_mobilebert
    base_model_prefix = "mobilebert"
    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, (nn.LayerNorm, NoNorm)):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
@dataclass
class MobileBertForPreTrainingOutput(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    prediction_logits: torch.FloatTensor = None
    seq_relationship_logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
MOBILEBERT_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. 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 PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.
    Parameters:
        config ([`MobileBertConfig`]): 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.
"""
MOBILEBERT_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)
        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)
        position_ids (`torch.LongTensor` 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]`.
            [What are position IDs?](../glossary#position-ids)
        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 MobileBert Model transformer outputting raw hidden-states without any specific head on top.", MOBILEBERT_START_DOCSTRING)
class MobileBertModel(MobileBertPreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.embeddings = MobileBertEmbeddings(config)
        self.encoder = MobileBertEncoder(config)
        self.pooler = MobileBertPooler(config) if add_pooling_layer else None
        self.post_init()
    def get_input_embeddings(self): return self.embeddings.word_embeddings
    def set_input_embeddings(self, value): self.embeddings.word_embeddings = value
    def _prune_heads(self, heads_to_prune):
        for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads)
    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_hidden_states: Optional[bool] = None,
    output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, BaseModelOutputWithPooling]:
        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.use_return_dict
        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:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        device = input_ids.device if input_ids is not None else inputs_embeds.device
        if attention_mask is None: attention_mask = torch.ones(input_shape, device=device)
        if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
        encoder_outputs = self.encoder(embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
        if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:]
        return BaseModelOutputWithPooling(last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
@add_start_docstrings("MobileBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next sentence prediction (classification)` head.", MOBILEBERT_START_DOCSTRING)
class MobileBertForPreTraining(MobileBertPreTrainedModel):
    _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
    def __init__(self, config):
        super().__init__(config)
        self.mobilebert = MobileBertModel(config)
        self.cls = MobileBertPreTrainingHeads(config)
        self.post_init()
    def get_output_embeddings(self): return self.cls.predictions.decoder
    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings
        self.cls.predictions.bias = new_embeddings.bias
    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
        self.cls.predictions.dense = self._get_resized_lm_head(self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True)
        return super().resize_token_embeddings(new_num_tokens=new_num_tokens)
    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    labels: Optional[torch.LongTensor] = None, next_sentence_label: Optional[torch.LongTensor] = None, output_attentions: Optional[torch.FloatTensor] = None,
    output_hidden_states: Optional[torch.FloatTensor] = None, return_dict: Optional[torch.FloatTensor] = None) -> Union[Tuple, MobileBertForPreTrainingOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.mobilebert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output, pooled_output = outputs[:2]
        prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
        total_loss = None
        if labels is not None and next_sentence_label is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
            total_loss = masked_lm_loss + next_sentence_loss
        if not return_dict:
            output = (prediction_scores, seq_relationship_score) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output
        return MobileBertForPreTrainingOutput(loss=total_loss, prediction_logits=prediction_scores, seq_relationship_logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""MobileBert Model with a `language modeling` head on top.""", MOBILEBERT_START_DOCSTRING)
class MobileBertForMaskedLM(MobileBertPreTrainedModel):
    _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
    def __init__(self, config):
        super().__init__(config)
        self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
        self.cls = MobileBertOnlyMLMHead(config)
        self.config = config
        self.post_init()
    def get_output_embeddings(self): return self.cls.predictions.decoder
    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings
        self.cls.predictions.bias = new_embeddings.bias
    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
        self.cls.predictions.dense = self._get_resized_lm_head(self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True)
        return super().resize_token_embeddings(new_num_tokens=new_num_tokens)
    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, MaskedLMOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.mobilebert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        prediction_scores = self.cls(sequence_output)
        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
        return MaskedLMOutput(loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
class MobileBertOnlyNSPHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.seq_relationship = nn.Linear(config.hidden_size, 2)
    def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
        seq_relationship_score = self.seq_relationship(pooled_output)
        return seq_relationship_score
@add_start_docstrings("MobileBert Model with a `next sentence prediction (classification)` head on top.", MOBILEBERT_START_DOCSTRING)
class MobileBertForNextSentencePrediction(MobileBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.mobilebert = MobileBertModel(config)
        self.cls = MobileBertOnlyNSPHead(config)
        self.post_init()
    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs) -> Union[Tuple, NextSentencePredictorOutput]:
        if "next_sentence_label" in kwargs:
            warnings.warn("The `next_sentence_label` argument is deprecated and will be removed in a future version, use `labels` instead.", FutureWarning)
            labels = kwargs.pop("next_sentence_label")
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.mobilebert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs[1]
        seq_relationship_score = self.cls(pooled_output)
        next_sentence_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), labels.view(-1))
        if not return_dict:
            output = (seq_relationship_score,) + outputs[2:]
            return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
        return NextSentencePredictorOutput(loss=next_sentence_loss, logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", MOBILEBERT_START_DOCSTRING)
class MobileBertForSequenceClassification(MobileBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config
        self.mobilebert = MobileBertModel(config)
        classifier_dropout = (config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob)
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.mobilebert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1: self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification"
                else: self.config.problem_type = "multi_label_classification"
            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze())
                else: loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""
    MobileBert 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`).
    """, MOBILEBERT_START_DOCSTRING)
class MobileBertForQuestionAnswering(MobileBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None,
    end_positions: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.mobilebert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()
        total_loss = None
        if start_positions is not None and end_positions is not None:
            if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1)
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)
            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2
        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output
        return QuestionAnsweringModelOutput(loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""
    MobileBert 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.
    """, MOBILEBERT_START_DOCSTRING)
class MobileBertForMultipleChoice(MobileBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.mobilebert = MobileBertModel(config)
        classifier_dropout = (config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob)
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, 1)
        self.post_init()
    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
        inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None)
        outputs = self.mobilebert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)
        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return MultipleChoiceModelOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("MobileBert 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.", MOBILEBERT_START_DOCSTRING)
class MobileBertForTokenClassification(MobileBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
        classifier_dropout = (config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob)
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.mobilebert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
