"""
    #################################################################################################################################################################################################
    # 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 operator import attrgetter
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN, get_activation
from ...modeling_outputs import (BaseModelOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput)
from ...modeling_utils import PreTrainedModel, SequenceSummary
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_convbert import ConvBertConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "YituTech/conv-bert-base"
_CONFIG_FOR_DOC = "ConvBertConfig"
def load_tf_weights_in_convbert(model, config, tf_checkpoint_path):
    try: 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)
    tf_data = {}
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_path, name)
        tf_data[name] = array
    param_mapping = {'embeddings.word_embeddings.weight': 'electra/embeddings/word_embeddings', 'embeddings.position_embeddings.weight': 'electra/embeddings/position_embeddings', 'embeddings.token_type_embeddings.weight': 'electra/embeddings/token_type_embeddings', 'embeddings.LayerNorm.weight': 'electra/embeddings/LayerNorm/gamma', 'embeddings.LayerNorm.bias': 'electra/embeddings/LayerNorm/beta', 'embeddings_project.weight': 'electra/embeddings_project/kernel', 'embeddings_project.bias': 'electra/embeddings_project/bias'}
    if config.num_groups > 1: group_dense_name = "g_dense"
    else: group_dense_name = "dense"
    for j in range(config.num_hidden_layers):
        param_mapping[f"encoder.layer.{j}.attention.self.query.weight"] = (f"electra/encoder/layer_{j}/attention/self/query/kernel")
        param_mapping[f"encoder.layer.{j}.attention.self.query.bias"] = (f"electra/encoder/layer_{j}/attention/self/query/bias")
        param_mapping[f"encoder.layer.{j}.attention.self.key.weight"] = (f"electra/encoder/layer_{j}/attention/self/key/kernel")
        param_mapping[f"encoder.layer.{j}.attention.self.key.bias"] = (f"electra/encoder/layer_{j}/attention/self/key/bias")
        param_mapping[f"encoder.layer.{j}.attention.self.value.weight"] = (f"electra/encoder/layer_{j}/attention/self/value/kernel")
        param_mapping[f"encoder.layer.{j}.attention.self.value.bias"] = (f"electra/encoder/layer_{j}/attention/self/value/bias")
        param_mapping[f"encoder.layer.{j}.attention.self.key_conv_attn_layer.depthwise.weight"] = (f"electra/encoder/layer_{j}/attention/self/conv_attn_key/depthwise_kernel")
        param_mapping[f"encoder.layer.{j}.attention.self.key_conv_attn_layer.pointwise.weight"] = (f"electra/encoder/layer_{j}/attention/self/conv_attn_key/pointwise_kernel")
        param_mapping[f"encoder.layer.{j}.attention.self.key_conv_attn_layer.bias"] = (f"electra/encoder/layer_{j}/attention/self/conv_attn_key/bias")
        param_mapping[f"encoder.layer.{j}.attention.self.conv_kernel_layer.weight"] = (f"electra/encoder/layer_{j}/attention/self/conv_attn_kernel/kernel")
        param_mapping[f"encoder.layer.{j}.attention.self.conv_kernel_layer.bias"] = (f"electra/encoder/layer_{j}/attention/self/conv_attn_kernel/bias")
        param_mapping[f"encoder.layer.{j}.attention.self.conv_out_layer.weight"] = (f"electra/encoder/layer_{j}/attention/self/conv_attn_point/kernel")
        param_mapping[f"encoder.layer.{j}.attention.self.conv_out_layer.bias"] = (f"electra/encoder/layer_{j}/attention/self/conv_attn_point/bias")
        param_mapping[f"encoder.layer.{j}.attention.output.dense.weight"] = (f"electra/encoder/layer_{j}/attention/output/dense/kernel")
        param_mapping[f"encoder.layer.{j}.attention.output.LayerNorm.weight"] = (f"electra/encoder/layer_{j}/attention/output/LayerNorm/gamma")
        param_mapping[f"encoder.layer.{j}.attention.output.dense.bias"] = (f"electra/encoder/layer_{j}/attention/output/dense/bias")
        param_mapping[f"encoder.layer.{j}.attention.output.LayerNorm.bias"] = (f"electra/encoder/layer_{j}/attention/output/LayerNorm/beta")
        param_mapping[f"encoder.layer.{j}.intermediate.dense.weight"] = (f"electra/encoder/layer_{j}/intermediate/{group_dense_name}/kernel")
        param_mapping[f"encoder.layer.{j}.intermediate.dense.bias"] = (f"electra/encoder/layer_{j}/intermediate/{group_dense_name}/bias")
        param_mapping[f"encoder.layer.{j}.output.dense.weight"] = (f"electra/encoder/layer_{j}/output/{group_dense_name}/kernel")
        param_mapping[f"encoder.layer.{j}.output.dense.bias"] = (f"electra/encoder/layer_{j}/output/{group_dense_name}/bias")
        param_mapping[f"encoder.layer.{j}.output.LayerNorm.weight"] = (f"electra/encoder/layer_{j}/output/LayerNorm/gamma")
        param_mapping[f"encoder.layer.{j}.output.LayerNorm.bias"] = f"electra/encoder/layer_{j}/output/LayerNorm/beta"
    for param in model.named_parameters():
        param_name = param[0]
        retriever = attrgetter(param_name)
        result = retriever(model)
        tf_name = param_mapping[param_name]
        value = torch.from_numpy(tf_data[tf_name])
        logger.info(f"TF: {tf_name}, PT: {param_name} ")
        if tf_name.endswith("/kernel"):
            if not tf_name.endswith("/intermediate/g_dense/kernel"):
                if not tf_name.endswith("/output/g_dense/kernel"): value = value.T
        if tf_name.endswith("/depthwise_kernel"): value = value.permute(1, 2, 0)
        if tf_name.endswith("/pointwise_kernel"): value = value.permute(2, 1, 0)
        if tf_name.endswith("/conv_attn_key/bias"): value = value.unsqueeze(-1)
        result.data = value
    return model
class ConvBertEmbeddings(nn.Module):
    def __init__(self, config):
        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.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) -> torch.LongTensor:
        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:
            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)
        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 ConvBertPreTrainedModel(PreTrainedModel):
    config_class = ConvBertConfig
    load_tf_weights = load_tf_weights_in_convbert
    base_model_prefix = "convbert"
    supports_gradient_checkpointing = 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)
class SeparableConv1D(nn.Module):
    def __init__(self, config, input_filters, output_filters, kernel_size, **kwargs):
        super().__init__()
        self.depthwise = nn.Conv1d(input_filters, input_filters, kernel_size=kernel_size, groups=input_filters, padding=kernel_size // 2, bias=False)
        self.pointwise = nn.Conv1d(input_filters, output_filters, kernel_size=1, bias=False)
        self.bias = nn.Parameter(torch.zeros(output_filters, 1))
        self.depthwise.weight.data.normal_(mean=0.0, std=config.initializer_range)
        self.pointwise.weight.data.normal_(mean=0.0, std=config.initializer_range)
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        x = self.depthwise(hidden_states)
        x = self.pointwise(x)
        x += self.bias
        return x
class ConvBertSelfAttention(nn.Module):
    def __init__(self, config):
        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})")
        new_num_attention_heads = config.num_attention_heads // config.head_ratio
        if new_num_attention_heads < 1:
            self.head_ratio = config.num_attention_heads
            self.num_attention_heads = 1
        else:
            self.num_attention_heads = new_num_attention_heads
            self.head_ratio = config.head_ratio
        self.conv_kernel_size = config.conv_kernel_size
        if config.hidden_size % self.num_attention_heads != 0: raise ValueError("hidden_size should be divisible by num_attention_heads")
        self.attention_head_size = (config.hidden_size // self.num_attention_heads) // 2
        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.key_conv_attn_layer = SeparableConv1D(config, config.hidden_size, self.all_head_size, self.conv_kernel_size)
        self.conv_kernel_layer = nn.Linear(self.all_head_size, self.num_attention_heads * self.conv_kernel_size)
        self.conv_out_layer = nn.Linear(config.hidden_size, self.all_head_size)
        self.unfold = nn.Unfold(kernel_size=[self.conv_kernel_size, 1], padding=[int((self.conv_kernel_size - 1) / 2), 0])
        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, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
        mixed_query_layer = self.query(hidden_states)
        batch_size = hidden_states.size(0)
        if encoder_hidden_states is not None:
            mixed_key_layer = self.key(encoder_hidden_states)
            mixed_value_layer = self.value(encoder_hidden_states)
        else:
            mixed_key_layer = self.key(hidden_states)
            mixed_value_layer = self.value(hidden_states)
        mixed_key_conv_attn_layer = self.key_conv_attn_layer(hidden_states.transpose(1, 2))
        mixed_key_conv_attn_layer = mixed_key_conv_attn_layer.transpose(1, 2)
        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)
        conv_attn_layer = torch.multiply(mixed_key_conv_attn_layer, mixed_query_layer)
        conv_kernel_layer = self.conv_kernel_layer(conv_attn_layer)
        conv_kernel_layer = torch.reshape(conv_kernel_layer, [-1, self.conv_kernel_size, 1])
        conv_kernel_layer = torch.softmax(conv_kernel_layer, dim=1)
        conv_out_layer = self.conv_out_layer(hidden_states)
        conv_out_layer = torch.reshape(conv_out_layer, [batch_size, -1, self.all_head_size])
        conv_out_layer = conv_out_layer.transpose(1, 2).contiguous().unsqueeze(-1)
        conv_out_layer = nn.functional.unfold(conv_out_layer, kernel_size=[self.conv_kernel_size, 1], dilation=1, padding=[(self.conv_kernel_size - 1) // 2, 0], stride=1)
        conv_out_layer = conv_out_layer.transpose(1, 2).reshape(batch_size, -1, self.all_head_size, self.conv_kernel_size)
        conv_out_layer = torch.reshape(conv_out_layer, [-1, self.attention_head_size, self.conv_kernel_size])
        conv_out_layer = torch.matmul(conv_out_layer, conv_kernel_layer)
        conv_out_layer = torch.reshape(conv_out_layer, [-1, self.all_head_size])
        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()
        conv_out = torch.reshape(conv_out_layer, [batch_size, -1, self.num_attention_heads, self.attention_head_size])
        context_layer = torch.cat([context_layer, conv_out], 2)
        new_context_layer_shape = context_layer.size()[:-2] + (self.num_attention_heads * self.attention_head_size * 2)
        context_layer = context_layer.view(*new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs
class ConvBertSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class ConvBertAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.self = ConvBertSelfAttention(config)
        self.output = ConvBertSelfOutput(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, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor, Optional[torch.FloatTensor]]:
        self_outputs = self.self(hidden_states, attention_mask, head_mask, encoder_hidden_states, output_attentions)
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class GroupedLinearLayer(nn.Module):
    def __init__(self, input_size, output_size, num_groups):
        super().__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.num_groups = num_groups
        self.group_in_dim = self.input_size // self.num_groups
        self.group_out_dim = self.output_size // self.num_groups
        self.weight = nn.Parameter(torch.empty(self.num_groups, self.group_in_dim, self.group_out_dim))
        self.bias = nn.Parameter(torch.empty(output_size))
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        batch_size = list(hidden_states.size())[0]
        x = torch.reshape(hidden_states, [-1, self.num_groups, self.group_in_dim])
        x = x.permute(1, 0, 2)
        x = torch.matmul(x, self.weight)
        x = x.permute(1, 0, 2)
        x = torch.reshape(x, [batch_size, -1, self.output_size])
        x = x + self.bias
        return x
class ConvBertIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        if config.num_groups == 1: self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        else: self.dense = GroupedLinearLayer(input_size=config.hidden_size, output_size=config.intermediate_size, num_groups=config.num_groups)
        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 ConvBertOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        if config.num_groups == 1: self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        else: self.dense = GroupedLinearLayer(input_size=config.intermediate_size, output_size=config.hidden_size, num_groups=config.num_groups)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class ConvBertLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = ConvBertAttention(config)
        self.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder: raise TypeError(f"{self} should be used as a decoder model if cross attention is added")
            self.crossattention = ConvBertAttention(config)
        self.intermediate = ConvBertIntermediate(config)
        self.output = ConvBertOutput(config)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None,
    encoder_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor, Optional[torch.FloatTensor]]:
        self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask, output_attentions=output_attentions)
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]
        if self.is_decoder and encoder_hidden_states is not None:
            if not hasattr(self, "crossattention"): raise AttributeError(f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`")
            cross_attention_outputs = self.crossattention(attention_output, encoder_attention_mask, head_mask, encoder_hidden_states, output_attentions)
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[1:]
        layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output)
        outputs = (layer_output,) + outputs
        return outputs
    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output
class ConvBertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([ConvBertLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None,
    encoder_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False,
    return_dict: Optional[bool] = True) -> Union[Tuple, BaseModelOutputWithCrossAttentions]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_head_mask = head_mask[i] if head_mask is not None else None
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions)
            else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)
                if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
        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_self_attentions, all_cross_attentions] if v is not None)
        return BaseModelOutputWithCrossAttentions(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions)
class ConvBertPredictionHeadTransform(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 = nn.LayerNorm(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
CONVBERT_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
    Parameters:
        config ([`ConvBertConfig`]): 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.
"""
CONVBERT_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 ConvBERT Model transformer outputting raw hidden-states without any specific head on top.", CONVBERT_START_DOCSTRING)
class ConvBertModel(ConvBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.embeddings = ConvBertEmbeddings(config)
        if config.embedding_size != config.hidden_size: self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size)
        self.encoder = ConvBertEncoder(config)
        self.config = config
        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(CONVBERT_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[Tuple, BaseModelOutputWithCrossAttentions]:
        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)
        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
        if hasattr(self, "embeddings_project"): hidden_states = self.embeddings_project(hidden_states)
        hidden_states = self.encoder(hidden_states, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        return hidden_states
class ConvBertGeneratorPredictions(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.activation = get_activation("gelu")
        self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
        self.dense = nn.Linear(config.hidden_size, config.embedding_size)
    def forward(self, generator_hidden_states: torch.FloatTensor) -> torch.FloatTensor:
        hidden_states = self.dense(generator_hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states
@add_start_docstrings("ConvBERT Model with a `language modeling` head on top.", CONVBERT_START_DOCSTRING)
class ConvBertForMaskedLM(ConvBertPreTrainedModel):
    _tied_weights_keys = ["generator.lm_head.weight"]
    def __init__(self, config):
        super().__init__(config)
        self.convbert = ConvBertModel(config)
        self.generator_predictions = ConvBertGeneratorPredictions(config)
        self.generator_lm_head = nn.Linear(config.embedding_size, config.vocab_size)
        self.post_init()
    def get_output_embeddings(self): return self.generator_lm_head
    def set_output_embeddings(self, word_embeddings): self.generator_lm_head = word_embeddings
    @add_start_docstrings_to_model_forward(CONVBERT_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
        generator_hidden_states = self.convbert(input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)
        generator_sequence_output = generator_hidden_states[0]
        prediction_scores = self.generator_predictions(generator_sequence_output)
        prediction_scores = self.generator_lm_head(prediction_scores)
        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
        if not return_dict:
            output = (prediction_scores,) + generator_hidden_states[1:]
            return ((loss,) + output) if loss is not None else output
        return MaskedLMOutput(loss=loss, logits=prediction_scores, hidden_states=generator_hidden_states.hidden_states, attentions=generator_hidden_states.attentions)
class ConvBertClassificationHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        classifier_dropout = (config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob)
        self.dropout = nn.Dropout(classifier_dropout)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
        self.config = config
    def forward(self, hidden_states: torch.Tensor, **kwargs) -> torch.Tensor:
        x = hidden_states[:, 0, :]
        x = self.dropout(x)
        x = self.dense(x)
        x = ACT2FN[self.config.hidden_act](x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x
@add_start_docstrings("""
    ConvBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """, CONVBERT_START_DOCSTRING)
class ConvBertForSequenceClassification(ConvBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config
        self.convbert = ConvBertModel(config)
        self.classifier = ConvBertClassificationHead(config)
        self.post_init()
    @add_start_docstrings_to_model_forward(CONVBERT_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, SequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.convbert(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.classifier(sequence_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[1:]
            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("""
    ConvBERT 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.
    """, CONVBERT_START_DOCSTRING)
class ConvBertForMultipleChoice(ConvBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.convbert = ConvBertModel(config)
        self.sequence_summary = SequenceSummary(config)
        self.classifier = nn.Linear(config.hidden_size, 1)
        self.post_init()
    @add_start_docstrings_to_model_forward(CONVBERT_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[Tuple, 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.convbert(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]
        pooled_output = self.sequence_summary(sequence_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[1:]
            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("""
    ConvBERT 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.
    """, CONVBERT_START_DOCSTRING)
class ConvBertForTokenClassification(ConvBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.convbert = ConvBertModel(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(CONVBERT_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, TokenClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.convbert(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[1:]
            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("""
    ConvBERT 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`).
    """, CONVBERT_START_DOCSTRING)
class ConvBertForQuestionAnswering(ConvBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.convbert = ConvBertModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(CONVBERT_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[Tuple, QuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.convbert(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[1:]
            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)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
