"""
    #################################################################################################################################################################################################
    # 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
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN, gelu
from ...generation import GenerationMixin
from ...modeling_outputs import (BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions,
MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_xmod import XmodConfig
logger = logging.get_logger(__name__)
class XmodEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        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)
        self.padding_idx = config.pad_token_id
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx)
    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0):
        if position_ids is None:
            if input_ids is not None: position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
            else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
        if input_ids is not None: input_shape = input_ids.size()
        else: input_shape = inputs_embeds.size()[:-1]
        seq_length = input_shape[1]
        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
    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
        input_shape = inputs_embeds.size()[:-1]
        sequence_length = input_shape[1]
        position_ids = torch.arange(self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device)
        return position_ids.unsqueeze(0).expand(input_shape)
class XmodSelfAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        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.attention_head_size = int(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.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = position_embedding_type or 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)
        self.is_decoder = config.is_decoder
    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 forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor]:
        mixed_query_layer = self.query(hidden_states)
        is_cross_attention = encoder_hidden_states is not None
        if is_cross_attention and past_key_value is not None:
            key_layer = past_key_value[0]
            value_layer = past_key_value[1]
            attention_mask = encoder_attention_mask
        elif is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)
        use_cache = past_key_value is not None
        if self.is_decoder: past_key_value = (key_layer, value_layer)
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            query_length, key_length = query_layer.shape[2], key_layer.shape[2]
            if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            position_ids_r = torch.arange(key_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_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None: attention_scores = attention_scores + attention_mask
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
        attention_probs = self.dropout(attention_probs)
        if head_mask is not None: attention_probs = attention_probs * head_mask
        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        if self.is_decoder: outputs = outputs + (past_key_value,)
        return outputs
class XmodSelfOutput(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 = hidden_states + input_tensor
        return hidden_states
class XmodAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        self.self = XmodSelfAttention(config, position_embedding_type=position_embedding_type)
        self.output = XmodSelfOutput(config)
        self.pruned_heads = set()
        self.pre_norm = config.pre_norm
    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.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor]:
        residual = hidden_states
        if self.pre_norm: hidden_states = self.output.LayerNorm(hidden_states)
        self_outputs = self.self(hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
        attention_output = self.output(self_outputs[0], residual)
        if not self.pre_norm: attention_output = self.output.LayerNorm(attention_output)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class XmodIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else: self.intermediate_act_fn = config.hidden_act
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states
class XmodAdapter(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.bottleneck_size = config.hidden_size // config.adapter_reduction_factor
        self.dense1 = nn.Linear(config.hidden_size, self.bottleneck_size)
        self.dense2 = nn.Linear(self.bottleneck_size, config.hidden_size)
        if isinstance(config.hidden_act, str): self.adapter_act_fn = ACT2FN[config.hidden_act]
        else: self.adapter_act_fn = config.hidden_act
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense1(hidden_states)
        hidden_states = self.adapter_act_fn(hidden_states)
        hidden_states = self.dense2(hidden_states)
        return hidden_states
class XmodOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.ln_before_adapter = config.ln_before_adapter
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        if config.adapter_layer_norm: self.adapter_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        else: self.adapter_layer_norm = None
        self.adapter_reuse_layer_norm = config.adapter_reuse_layer_norm
        self.adapter_modules = nn.ModuleDict({})
        for language in config.languages: self.adapter_modules[str(language)] = XmodAdapter(config)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor, lang_ids: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = hidden_states + input_tensor
        hidden_states = self.lang_adapter(lang_ids, hidden_states)
        return hidden_states
    def lang_adapter(self, lang_ids: torch.Tensor, hidden_states: torch.Tensor):
        lang_ids, lang_lengths = torch.unique_consecutive(lang_ids, return_counts=True)
        if not self.ln_before_adapter: residual = hidden_states
        if self.adapter_layer_norm is not None: hidden_states = self.adapter_layer_norm(hidden_states)
        elif self.adapter_reuse_layer_norm: hidden_states = self.LayerNorm(hidden_states)
        if self.ln_before_adapter: residual = hidden_states
        split_hidden_states = torch.split(hidden_states, lang_lengths.tolist(), 0)
        lang_wise_outputs = []
        for i, (lang_id, split_hidden_state) in enumerate(zip(lang_ids, split_hidden_states)):
            lang = list(self.adapter_modules.keys())[int(lang_id.item())]
            lang_wise_outputs.append(self.adapter_modules[lang](split_hidden_state))
        hidden_states = torch.cat(lang_wise_outputs, 0)
        hidden_states = self.dropout(hidden_states)
        hidden_states += residual
        return hidden_states
class XmodLayer(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 = XmodAttention(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 ValueError(f"{self} should be used as a decoder model if cross attention is added")
            self.crossattention = XmodAttention(config, position_embedding_type="absolute")
        self.intermediate = XmodIntermediate(config)
        self.output = XmodOutput(config)
        self.pre_norm = config.pre_norm
    def forward(self, hidden_states: torch.Tensor, lang_ids: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None,
    encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
    output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor]:
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value)
        attention_output = self_attention_outputs[0]
        if self.is_decoder:
            outputs = self_attention_outputs[1:-1]
            present_key_value = self_attention_outputs[-1]
        else: outputs = self_attention_outputs[1:]
        cross_attn_present_key_value = None
        if self.is_decoder and encoder_hidden_states is not None:
            if not hasattr(self, "crossattention"): raise ValueError(f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`")
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            cross_attention_outputs = self.crossattention(attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions)
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[1:-1]
            cross_attn_present_key_value = cross_attention_outputs[-1]
            present_key_value = present_key_value + cross_attn_present_key_value
        residual = attention_output
        if self.pre_norm: attention_output = self.output.LayerNorm(attention_output)
        intermediate_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output)
        layer_output = self.output(intermediate_output, residual, lang_ids)
        if not self.pre_norm: layer_output = self.output.LayerNorm(layer_output)
        outputs = (layer_output,) + outputs
        if self.is_decoder: outputs = outputs + (present_key_value,)
        return outputs
    def feed_forward_chunk(self, attention_output): return self.intermediate(attention_output)
class XmodEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([XmodLayer(config) for _ in range(config.num_hidden_layers)])
        self.is_pre_norm = config.pre_norm
        if self.is_pre_norm: self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.gradient_checkpointing = False
    def forward(self, hidden_states: torch.Tensor, lang_ids: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None,
    encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
    use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False,
    return_dict: Optional[bool] = True) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
                use_cache = False
        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
        next_decoder_cache = () if use_cache 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
            past_key_value = past_key_values[i] if past_key_values is not None else None
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, lang_ids, attention_mask, layer_head_mask,
            encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
            else: layer_outputs = layer_module(hidden_states, lang_ids, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
            hidden_states = layer_outputs[0]
            if use_cache: next_decoder_cache += (layer_outputs[-1],)
            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 self.is_pre_norm: hidden_states = self.LayerNorm(hidden_states)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions] if v is not None)
        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states,
        attentions=all_self_attentions, cross_attentions=all_cross_attentions)
class XmodPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output
class XmodPreTrainedModel(PreTrainedModel):
    config_class = XmodConfig
    base_model_prefix = "roberta"
    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)
    def set_default_language(self, language: str):
        if language not in self.config.languages: raise ValueError(f"{self} does not have an adapter for {language}. Supported languages: {list(self.config.languages)}")
        self.config.default_language = language
    def freeze_embeddings_and_language_adapters(self):
        logger.info("Freezing embeddings")
        for parameter in self.roberta.embeddings.parameters(): parameter.requires_grad = False
        logger.info("Freezing adapters")
        for layer in self.roberta.encoder.layer:
            if layer.output.adapter_layer_norm is not None:
                for parameter in layer.output.adapter_layer_norm.parameters(): parameter.requires_grad = False
            for parameter in layer.output.adapter_modules.parameters():
                parameter.requires_grad = False
XMOD_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.
    Parameters:
        config ([`XmodConfig`]): 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.
"""
XMOD_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)
        lang_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            Indices of the language adapters that should be activated for each sample, respectively. Default: the index
            that corresponds to `self.config.default_language`.
        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 X-MOD Model transformer outputting raw hidden-states without any specific head on top.", XMOD_START_DOCSTRING)
class XmodModel(XmodPreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.embeddings = XmodEmbeddings(config)
        self.encoder = XmodEncoder(config)
        self.pooler = XmodPooler(config) if add_pooling_layer else None
        self.post_init()
    def get_input_embeddings(self): return self.embeddings.word_embeddings
    def set_input_embeddings(self, value): self.embeddings.word_embeddings = value
    def _prune_heads(self, heads_to_prune):
        for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads)
    @add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, lang_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None,
    token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None,
    use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
        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 self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache
        else: use_cache = False
        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
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
        if lang_ids is None:
            if self.config.default_language is None: raise ValueError("Input language unknown. Please call `XmodPreTrainedModel.set_default_language()`")
            adapter_languages = list(self.encoder.layer[0].output.adapter_modules.keys())
            default_lang_id = adapter_languages.index(self.config.default_language)
            lang_ids = default_lang_id * torch.ones(batch_size, device=device)
        if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), 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: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
        if self.config.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else: encoder_extended_attention_mask = None
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length)
        encoder_outputs = self.encoder(embedding_output, lang_ids=lang_ids, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states,
        encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
        if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:]
        return BaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values,
        hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions)
@add_start_docstrings("X-MOD Model with a `language modeling` head on top for CLM fine-tuning.", XMOD_START_DOCSTRING)
class XmodForCausalLM(XmodPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
    def __init__(self, config):
        super().__init__(config)
        if not config.is_decoder: logger.warning("If you want to use `XmodLMHeadModel` as a standalone, add `is_decoder=True.`")
        self.roberta = XmodModel(config, add_pooling_layer=False)
        self.lm_head = XmodLMHead(config)
        self.post_init()
    def get_output_embeddings(self): return self.lm_head.decoder
    def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings
    @add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, lang_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, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None,
    labels: Optional[torch.LongTensor] = None, past_key_values: Tuple[Tuple[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None: use_cache = False
        outputs = self.roberta(input_ids, lang_ids=lang_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        prediction_scores = self.lm_head(sequence_output)
        lm_loss = None
        if labels is not None:
            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
            labels = labels[:, 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((lm_loss,) + output) if lm_loss is not None else output
        return CausalLMOutputWithCrossAttentions(loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states,
        attentions=outputs.attentions, cross_attentions=outputs.cross_attentions)
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
        input_shape = input_ids.shape
        if attention_mask is None: attention_mask = input_ids.new_ones(input_shape)
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]
            if input_ids.shape[1] > past_length: remove_prefix_length = past_length
            else: remove_prefix_length = input_ids.shape[1] - 1
            input_ids = input_ids[:, remove_prefix_length:]
        return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}
    def _reorder_cache(self, past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values: reordered_past += (tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),)
        return reordered_past
@add_start_docstrings("X-MOD Model with a `language modeling` head on top.", XMOD_START_DOCSTRING)
class XmodForMaskedLM(XmodPreTrainedModel):
    _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
    def __init__(self, config):
        super().__init__(config)
        if config.is_decoder: logger.warning("If you want to use `XmodForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention.")
        self.roberta = XmodModel(config, add_pooling_layer=False)
        self.lm_head = XmodLMHead(config)
        self.post_init()
    def get_output_embeddings(self): return self.lm_head.decoder
    def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings
    @add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, lang_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, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: 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[torch.Tensor], MaskedLMOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.roberta(input_ids, lang_ids=lang_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        prediction_scores = self.lm_head(sequence_output)
        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
        return MaskedLMOutput(loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
class XmodLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        self.decoder.bias = self.bias
    def forward(self, features, **kwargs):
        x = self.dense(features)
        x = gelu(x)
        x = self.layer_norm(x)
        x = self.decoder(x)
        return x
    def _tie_weights(self):
        if self.decoder.bias.device.type == "meta": self.decoder.bias = self.bias
        else: self.bias = self.decoder.bias
@add_start_docstrings("X-MOD Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", XMOD_START_DOCSTRING)
class XmodForSequenceClassification(XmodPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config
        self.roberta = XmodModel(config, add_pooling_layer=False)
        self.classifier = XmodClassificationHead(config)
        self.post_init()
    @add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, lang_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[torch.Tensor], SequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.roberta(input_ids, lang_ids=lang_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[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("X-MOD 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.", XMOD_START_DOCSTRING)
class XmodForMultipleChoice(XmodPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.roberta = XmodModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, 1)
        self.post_init()
    @add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, lang_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    attention_mask: Optional[torch.FloatTensor] = None, labels: 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[torch.Tensor], MultipleChoiceModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
        flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        flat_lang_ids = lang_ids.repeat(input_ids.size(0) * input_ids.size(1)) if lang_ids is not None else None
        flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        flat_inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None)
        outputs = self.roberta(flat_input_ids, lang_ids=flat_lang_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask,
        head_mask=head_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)
        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return MultipleChoiceModelOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("X-MOD 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.", XMOD_START_DOCSTRING)
class XmodForTokenClassification(XmodPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.roberta = XmodModel(config, add_pooling_layer=False)
        classifier_dropout = (config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob)
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, lang_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[torch.Tensor], TokenClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.roberta(input_ids, lang_ids=lang_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)
class XmodClassificationHead(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)
    def forward(self, features, **kwargs):
        x = features[:, 0, :]
        x = self.dropout(x)
        x = self.dense(x)
        x = torch.tanh(x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x
@add_start_docstrings("X-MOD 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`).", XMOD_START_DOCSTRING)
class XmodForQuestionAnswering(XmodPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.roberta = XmodModel(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(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, lang_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[torch.Tensor], QuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.roberta(input_ids, lang_ids=lang_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()
        total_loss = None
        if start_positions is not None and end_positions is not None:
            if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1)
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)
            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2
        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output
        return QuestionAnsweringModelOutput(loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
    mask = input_ids.ne(padding_idx).int()
    incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
    return incremental_indices.long() + padding_idx
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
