"""
    #################################################################################################################################################################################################
    # 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
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask_for_sdpa
from ...modeling_outputs import (BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import (apply_chunking_to_forward, find_pruneable_heads_and_indices, is_torch_greater_or_equal_than_2_2, 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_albert import AlbertConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "albert/albert-base-v2"
_CONFIG_FOR_DOC = "AlbertConfig"
def load_tf_weights_in_albert(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): print(name)
    for name, array in zip(names, arrays):
        original_name = name
        name = name.replace("module/", "")
        name = name.replace("ffn_1", "ffn")
        name = name.replace("bert/", "albert/")
        name = name.replace("attention_1", "attention")
        name = name.replace("transform/", "")
        name = name.replace("LayerNorm_1", "full_layer_layer_norm")
        name = name.replace("LayerNorm", "attention/LayerNorm")
        name = name.replace("transformer/", "")
        name = name.replace("intermediate/dense/", "")
        name = name.replace("ffn/intermediate/output/dense/", "ffn_output/")
        name = name.replace("/output/", "/")
        name = name.replace("/self/", "/")
        name = name.replace("pooler/dense", "pooler")
        name = name.replace("cls/predictions", "predictions")
        name = name.replace("predictions/attention", "predictions")
        name = name.replace("embeddings/attention", "embeddings")
        name = name.replace("inner_group_", "albert_layers/")
        name = name.replace("group_", "albert_layer_groups/")
        if len(name.split("/")) == 1 and ("output_bias" in name or "output_weights" in name): name = "classifier/" + name
        if "seq_relationship" in name:
            name = name.replace("seq_relationship/output_", "sop_classifier/classifier/")
            name = name.replace("weights", "weight")
        name = name.split("/")
        if ("adam_m" in name or "adam_v" in name or "AdamWeightDecayOptimizer" in name or "AdamWeightDecayOptimizer_1" in name or "global_step" 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:
            if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
        except ValueError as e:
            e.args += (pointer.shape, array.shape)
            raise
        print(f"Initialize PyTorch weight {name} from {original_name}")
        pointer.data = torch.from_numpy(array)
    return model
class AlbertEmbeddings(nn.Module):
    def __init__(self, config: AlbertConfig):
        super().__init__()
        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.embedding_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
        self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        self.register_buffer("token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), 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, past_key_values_length: int = 0) -> 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[:, past_key_values_length : seq_length + past_key_values_length]
        if token_type_ids is None:
            if hasattr(self, "token_type_ids"):
                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else: 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)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + token_type_embeddings
        if self.position_embedding_type == "absolute":
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
class AlbertAttention(nn.Module):
    def __init__(self, config: AlbertConfig):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads}")
        self.num_attention_heads = config.num_attention_heads
        self.hidden_size = config.hidden_size
        self.attention_head_size = config.hidden_size // config.num_attention_heads
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)
        self.attention_dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.output_dropout = nn.Dropout(config.hidden_dropout_prob)
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.pruned_heads = set()
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
        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 prune_heads(self, heads: List[int]) -> None:
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.num_attention_heads, self.attention_head_size, self.pruned_heads)
        self.query = prune_linear_layer(self.query, index)
        self.key = prune_linear_layer(self.key, index)
        self.value = prune_linear_layer(self.value, index)
        self.dense = prune_linear_layer(self.dense, index, dim=1)
        self.num_attention_heads = self.num_attention_heads - len(heads)
        self.all_head_size = self.attention_head_size * self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: bool = False) -> Union[Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
        mixed_query_layer = self.query(hidden_states)
        mixed_key_layer = self.key(hidden_states)
        mixed_value_layer = self.value(hidden_states)
        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
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            seq_length = hidden_states.size()[1]
            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
            distance = position_ids_l - position_ids_r
            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)
            if self.position_embedding_type == "relative_key":
                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
        attention_probs = self.attention_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.transpose(2, 1).flatten(2)
        projected_context_layer = self.dense(context_layer)
        projected_context_layer_dropout = self.output_dropout(projected_context_layer)
        layernormed_context_layer = self.LayerNorm(hidden_states + projected_context_layer_dropout)
        return (layernormed_context_layer, attention_probs) if output_attentions else (layernormed_context_layer,)
class AlbertSdpaAttention(AlbertAttention):
    def __init__(self, config):
        super().__init__(config)
        self.dropout_prob = config.attention_probs_dropout_prob
        self.require_contiguous_qkv = not is_torch_greater_or_equal_than_2_2
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: bool = False) -> Union[Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
        if self.position_embedding_type != "absolute" or output_attentions or head_mask is not None:
            logger.warning("AlbertSdpaAttention is used but `torch.nn.functional.scaled_dot_product_attention` does not support non-absolute `position_embedding_type` or `output_attentions=True` or `head_mask`. Falling back to the eager attention implementation, but specifying the eager implementation will be required from sapiens_transformers version v5.0.0 onwards. This warning can be removed using the argument "+'`attn_implementation="eager"` when loading the model.')
            return super().forward(hidden_states, attention_mask, head_mask, output_attentions)
        batch_size, seq_len, _ = hidden_states.size()
        query_layer = self.transpose_for_scores(self.query(hidden_states))
        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        if self.require_contiguous_qkv and query_layer.device.type == "cuda" and attention_mask is not None:
            query_layer = query_layer.contiguous()
            key_layer = key_layer.contiguous()
            value_layer = value_layer.contiguous()
        attention_output = torch.nn.functional.scaled_dot_product_attention(query=query_layer, key=key_layer, value=value_layer, attn_mask=attention_mask, dropout_p=self.dropout_prob if self.training else 0.0, is_causal=False)
        attention_output = attention_output.transpose(1, 2)
        attention_output = attention_output.reshape(batch_size, seq_len, self.all_head_size)
        projected_context_layer = self.dense(attention_output)
        projected_context_layer_dropout = self.output_dropout(projected_context_layer)
        layernormed_context_layer = self.LayerNorm(hidden_states + projected_context_layer_dropout)
        return (layernormed_context_layer,)
ALBERT_ATTENTION_CLASSES = {"eager": AlbertAttention, "sdpa": AlbertSdpaAttention}
class AlbertLayer(nn.Module):
    def __init__(self, config: AlbertConfig):
        super().__init__()
        self.config = config
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attention = ALBERT_ATTENTION_CLASSES[config._attn_implementation](config)
        self.ffn = nn.Linear(config.hidden_size, config.intermediate_size)
        self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size)
        self.activation = ACT2FN[config.hidden_act]
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: bool = False, output_hidden_states: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
        attention_output = self.attention(hidden_states, attention_mask, head_mask, output_attentions)
        ffn_output = apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output[0])
        hidden_states = self.full_layer_layer_norm(ffn_output + attention_output[0])
        return (hidden_states,) + attention_output[1:]
    def ff_chunk(self, attention_output: torch.Tensor) -> torch.Tensor:
        ffn_output = self.ffn(attention_output)
        ffn_output = self.activation(ffn_output)
        ffn_output = self.ffn_output(ffn_output)
        return ffn_output
class AlbertLayerGroup(nn.Module):
    def __init__(self, config: AlbertConfig):
        super().__init__()
        self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)])
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: bool = False, output_hidden_states: bool = False) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
        layer_hidden_states = ()
        layer_attentions = ()
        for layer_index, albert_layer in enumerate(self.albert_layers):
            layer_output = albert_layer(hidden_states, attention_mask, head_mask[layer_index], output_attentions)
            hidden_states = layer_output[0]
            if output_attentions: layer_attentions = layer_attentions + (layer_output[1],)
            if output_hidden_states: layer_hidden_states = layer_hidden_states + (hidden_states,)
        outputs = (hidden_states,)
        if output_hidden_states: outputs = outputs + (layer_hidden_states,)
        if output_attentions: outputs = outputs + (layer_attentions,)
        return outputs
class AlbertTransformer(nn.Module):
    def __init__(self, config: AlbertConfig):
        super().__init__()
        self.config = config
        self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)
        self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)])
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True) -> Union[BaseModelOutput, Tuple]:
        hidden_states = self.embedding_hidden_mapping_in(hidden_states)
        all_hidden_states = (hidden_states,) if output_hidden_states else None
        all_attentions = () if output_attentions else None
        head_mask = [None] * self.config.num_hidden_layers if head_mask is None else head_mask
        for i in range(self.config.num_hidden_layers):
            layers_per_group = int(self.config.num_hidden_layers / self.config.num_hidden_groups)
            group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups))
            layer_group_output = self.albert_layer_groups[group_idx](hidden_states, attention_mask, head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group], output_attentions, output_hidden_states)
            hidden_states = layer_group_output[0]
            if output_attentions: all_attentions = all_attentions + layer_group_output[-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 AlbertPreTrainedModel(PreTrainedModel):
    config_class = AlbertConfig
    load_tf_weights = load_tf_weights_in_albert
    base_model_prefix = "albert"
    _supports_sdpa = True
    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):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
@dataclass
class AlbertForPreTrainingOutput(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    prediction_logits: torch.FloatTensor = None
    sop_logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
ALBERT_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.
    Args:
        config ([`AlbertConfig`]): 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.
"""
ALBERT_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.__call__`] and
            [`PreTrainedTokenizer.encode`] 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 ALBERT Model transformer outputting raw hidden-states without any specific head on top.", ALBERT_START_DOCSTRING)
class AlbertModel(AlbertPreTrainedModel):
    config_class = AlbertConfig
    base_model_prefix = "albert"
    def __init__(self, config: AlbertConfig, add_pooling_layer: bool = True):
        super().__init__(config)
        self.config = config
        self.embeddings = AlbertEmbeddings(config)
        self.encoder = AlbertTransformer(config)
        if add_pooling_layer:
            self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
            self.pooler_activation = nn.Tanh()
        else:
            self.pooler = None
            self.pooler_activation = None
        self.attn_implementation = config._attn_implementation
        self.position_embedding_type = config.position_embedding_type
        self.post_init()
    def get_input_embeddings(self) -> nn.Embedding: return self.embeddings.word_embeddings
    def set_input_embeddings(self, value: nn.Embedding) -> None: self.embeddings.word_embeddings = value
    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
        for layer, heads in heads_to_prune.items():
            group_idx = int(layer / self.config.inner_group_num)
            inner_group_idx = int(layer - group_idx * self.config.inner_group_num)
            self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads)
    @add_start_docstrings_to_model_forward(ALBERT_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_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[BaseModelOutputWithPooling, Tuple]:
        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")
        batch_size, seq_length = input_shape
        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:
            if hasattr(self.embeddings, "token_type_ids"):
                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
        embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
        use_sdpa_attention_mask = (self.attn_implementation == "sdpa" and self.position_embedding_type == "absolute" and head_mask is None and not output_attentions)
        if use_sdpa_attention_mask: extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, embedding_output.dtype, tgt_len=seq_length)
        else:
            extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
            extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
            extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        encoder_outputs = self.encoder(embedding_output, 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_activation(self.pooler(sequence_output[:, 0])) 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("""
    Albert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
    `sentence order prediction (classification)` head.
    """, ALBERT_START_DOCSTRING)
class AlbertForPreTraining(AlbertPreTrainedModel):
    _tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
    def __init__(self, config: AlbertConfig):
        super().__init__(config)
        self.albert = AlbertModel(config)
        self.predictions = AlbertMLMHead(config)
        self.sop_classifier = AlbertSOPHead(config)
        self.post_init()
    def get_output_embeddings(self) -> nn.Linear: return self.predictions.decoder
    def set_output_embeddings(self, new_embeddings: nn.Linear) -> None: self.predictions.decoder = new_embeddings
    def get_input_embeddings(self) -> nn.Embedding: return self.albert.embeddings.word_embeddings
    @add_start_docstrings_to_model_forward(ALBERT_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,
    sentence_order_label: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[AlbertForPreTrainingOutput, Tuple]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.albert(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 = self.predictions(sequence_output)
        sop_scores = self.sop_classifier(pooled_output)
        total_loss = None
        if labels is not None and sentence_order_label is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
            sentence_order_loss = loss_fct(sop_scores.view(-1, 2), sentence_order_label.view(-1))
            total_loss = masked_lm_loss + sentence_order_loss
        if not return_dict:
            output = (prediction_scores, sop_scores) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output
        return AlbertForPreTrainingOutput(loss=total_loss, prediction_logits=prediction_scores, sop_logits=sop_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
class AlbertMLMHead(nn.Module):
    def __init__(self, config: AlbertConfig):
        super().__init__()
        self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        self.dense = nn.Linear(config.hidden_size, config.embedding_size)
        self.decoder = nn.Linear(config.embedding_size, config.vocab_size)
        self.activation = ACT2FN[config.hidden_act]
        self.decoder.bias = self.bias
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        hidden_states = self.decoder(hidden_states)
        prediction_scores = hidden_states
        return prediction_scores
    def _tie_weights(self) -> None:
        if self.decoder.bias.device.type == "meta": self.decoder.bias = self.bias
        else: self.bias = self.decoder.bias
class AlbertSOPHead(nn.Module):
    def __init__(self, config: AlbertConfig):
        super().__init__()
        self.dropout = nn.Dropout(config.classifier_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
    def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
        dropout_pooled_output = self.dropout(pooled_output)
        logits = self.classifier(dropout_pooled_output)
        return logits
@add_start_docstrings("Albert Model with a `language modeling` head on top.", ALBERT_START_DOCSTRING)
class AlbertForMaskedLM(AlbertPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
    def __init__(self, config):
        super().__init__(config)
        self.albert = AlbertModel(config, add_pooling_layer=False)
        self.predictions = AlbertMLMHead(config)
        self.post_init()
    def get_output_embeddings(self) -> nn.Linear: return self.predictions.decoder
    def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
        self.predictions.decoder = new_embeddings
        self.predictions.bias = new_embeddings.bias
    def get_input_embeddings(self) -> nn.Embedding: return self.albert.embeddings.word_embeddings
    @add_start_docstrings_to_model_forward(ALBERT_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[MaskedLMOutput, Tuple]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.albert(input_ids=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_outputs = outputs[0]
        prediction_scores = self.predictions(sequence_outputs)
        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)
@add_start_docstrings("""
    Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
    output) e.g. for GLUE tasks.
    """, ALBERT_START_DOCSTRING)
class AlbertForSequenceClassification(AlbertPreTrainedModel):
    def __init__(self, config: AlbertConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config
        self.albert = AlbertModel(config)
        self.dropout = nn.Dropout(config.classifier_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(ALBERT_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[SequenceClassifierOutput, Tuple]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.albert(input_ids=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("""
    Albert 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.
    """, ALBERT_START_DOCSTRING)
class AlbertForTokenClassification(AlbertPreTrainedModel):
    def __init__(self, config: AlbertConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.albert = AlbertModel(config, add_pooling_layer=False)
        classifier_dropout_prob = (config.classifier_dropout_prob if config.classifier_dropout_prob is not None else config.hidden_dropout_prob)
        self.dropout = nn.Dropout(classifier_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(ALBERT_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[TokenClassifierOutput, Tuple]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.albert(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)
@add_start_docstrings("""
    Albert 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`).
    """, ALBERT_START_DOCSTRING)
class AlbertForQuestionAnswering(AlbertPreTrainedModel):
    def __init__(self, config: AlbertConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.albert = AlbertModel(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(ALBERT_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, start_positions: Optional[torch.LongTensor] = None,
    end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[AlbertForPreTrainingOutput, Tuple]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.albert(input_ids=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: torch.Tensor = 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("""
    Albert 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.
    """, ALBERT_START_DOCSTRING)
class AlbertForMultipleChoice(AlbertPreTrainedModel):
    def __init__(self, config: AlbertConfig):
        super().__init__(config)
        self.albert = AlbertModel(config)
        self.dropout = nn.Dropout(config.classifier_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, 1)
        self.post_init()
    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, 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[AlbertForPreTrainingOutput, Tuple]:
        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.albert(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: torch.Tensor = 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)
__all__ = ["load_tf_weights_in_albert", "AlbertPreTrainedModel", "AlbertModel", "AlbertForPreTraining", "AlbertForMaskedLM", "AlbertForSequenceClassification", "AlbertForTokenClassification", "AlbertForQuestionAnswering", "AlbertForMultipleChoice"]
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
