"""
    #################################################################################################################################################################################################
    # 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 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
from ...file_utils import (add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings)
from ...modeling_outputs import (BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, MaskedLMOutput, QuestionAnsweringModelOutput,
SequenceClassifierOutput, TokenClassifierOutput)
from ...modeling_utils import (PreTrainedModel, apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer)
from ...utils import logging
from .configuration_markuplm import MarkupLMConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "microsoft/markuplm-base"
_CONFIG_FOR_DOC = "MarkupLMConfig"
class XPathEmbeddings(nn.Module):
    def __init__(self, config):
        super(XPathEmbeddings, self).__init__()
        self.max_depth = config.max_depth
        self.xpath_unitseq2_embeddings = nn.Linear(config.xpath_unit_hidden_size * self.max_depth, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.activation = nn.ReLU()
        self.xpath_unitseq2_inner = nn.Linear(config.xpath_unit_hidden_size * self.max_depth, 4 * config.hidden_size)
        self.inner2emb = nn.Linear(4 * config.hidden_size, config.hidden_size)
        self.xpath_tag_sub_embeddings = nn.ModuleList([nn.Embedding(config.max_xpath_tag_unit_embeddings, config.xpath_unit_hidden_size) for _ in range(self.max_depth)])
        self.xpath_subs_sub_embeddings = nn.ModuleList([nn.Embedding(config.max_xpath_subs_unit_embeddings, config.xpath_unit_hidden_size) for _ in range(self.max_depth)])
    def forward(self, xpath_tags_seq=None, xpath_subs_seq=None):
        xpath_tags_embeddings = []
        xpath_subs_embeddings = []
        for i in range(self.max_depth):
            xpath_tags_embeddings.append(self.xpath_tag_sub_embeddings[i](xpath_tags_seq[:, :, i]))
            xpath_subs_embeddings.append(self.xpath_subs_sub_embeddings[i](xpath_subs_seq[:, :, i]))
        xpath_tags_embeddings = torch.cat(xpath_tags_embeddings, dim=-1)
        xpath_subs_embeddings = torch.cat(xpath_subs_embeddings, dim=-1)
        xpath_embeddings = xpath_tags_embeddings + xpath_subs_embeddings
        xpath_embeddings = self.inner2emb(self.dropout(self.activation(self.xpath_unitseq2_inner(xpath_embeddings))))
        return xpath_embeddings
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
class MarkupLMEmbeddings(nn.Module):
    def __init__(self, config):
        super(MarkupLMEmbeddings, self).__init__()
        self.config = config
        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.max_depth = config.max_depth
        self.xpath_embeddings = XPathEmbeddings(config)
        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.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), 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 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)
    def forward(self, input_ids=None, xpath_tags_seq=None, xpath_subs_seq=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0):
        if input_ids is not None: input_shape = input_ids.size()
        else: input_shape = inputs_embeds.size()[:-1]
        device = input_ids.device if input_ids is not None else inputs_embeds.device
        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 token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
        if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids)
        if xpath_tags_seq is None: xpath_tags_seq = self.config.tag_pad_id * torch.ones(tuple(list(input_shape) + [self.max_depth]), dtype=torch.long, device=device)
        if xpath_subs_seq is None: xpath_subs_seq = self.config.subs_pad_id * torch.ones(tuple(list(input_shape) + [self.max_depth]), dtype=torch.long, device=device)
        words_embeddings = inputs_embeds
        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        xpath_embeddings = self.xpath_embeddings(xpath_tags_seq, xpath_subs_seq)
        embeddings = words_embeddings + position_embeddings + token_type_embeddings + xpath_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
class MarkupLMSelfOutput(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 MarkupLMIntermediate(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 MarkupLMOutput(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.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 MarkupLMPooler(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 MarkupLMPredictionHeadTransform(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
class MarkupLMLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = MarkupLMPredictionHeadTransform(config)
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        self.decoder.bias = self.bias
    def _tie_weights(self): self.decoder.bias = self.bias
    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
class MarkupLMOnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = MarkupLMLMPredictionHead(config)
    def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores
class MarkupLMSelfAttention(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)  # fp16 compatibility
            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
MARKUPLM_SELF_ATTENTION_CLASSES = {"eager": MarkupLMSelfAttention}
class MarkupLMAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        self.self = MARKUPLM_SELF_ATTENTION_CLASSES[config._attn_implementation](config, position_embedding_type=position_embedding_type)
        self.output = MarkupLMSelfOutput(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.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_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], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class MarkupLMLayer(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 = MarkupLMAttention(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 = MarkupLMAttention(config, position_embedding_type="absolute")
        self.intermediate = MarkupLMIntermediate(config)
        self.output = MarkupLMOutput(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.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
        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
        if self.is_decoder: outputs = outputs + (present_key_value,)
        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 MarkupLMEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([MarkupLMLayer(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.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]:
        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
        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
        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, attention_mask, layer_head_mask, encoder_hidden_states,
                encoder_attention_mask, past_key_value, output_attentions)
            else: layer_outputs = layer_module(hidden_states, 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 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 MarkupLMPreTrainedModel(PreTrainedModel):
    config_class = MarkupLMConfig
    base_model_prefix = "markuplm"
    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)
    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs): return super(MarkupLMPreTrainedModel, cls).from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
MARKUPLM_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 ([`MarkupLMConfig`]): 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.
"""
MARKUPLM_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)
        xpath_tags_seq (`torch.LongTensor` of shape `(0, config.max_depth)`, *optional*):
            Tag IDs for each token in the input sequence, padded up to config.max_depth.
        xpath_subs_seq (`torch.LongTensor` of shape `(0, config.max_depth)`, *optional*):
            Subscript IDs for each token in the input sequence, padded up to config.max_depth.
        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 MASKED tokens.
            [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 `(batch_size, sequence_length, 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*):
            If set to `True`, the attentions tensors of all attention layers are returned. See `attentions` under
            returned tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            If set to `True`, the hidden states of all layers are returned. See `hidden_states` under returned tensors
            for more detail.
        return_dict (`bool`, *optional*):
            If set to `True`, the model will return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings("The bare MarkupLM Model transformer outputting raw hidden-states without any specific head on top.", MARKUPLM_START_DOCSTRING)
class MarkupLMModel(MarkupLMPreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.embeddings = MarkupLMEmbeddings(config)
        self.encoder = MarkupLMEncoder(config)
        self.pooler = MarkupLMPooler(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(MARKUPLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, xpath_tags_seq: Optional[torch.LongTensor] = None, xpath_subs_seq: 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, 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 input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        device = input_ids.device if input_ids is not None else inputs_embeds.device
        if attention_mask is None: attention_mask = torch.ones(input_shape, device=device)
        if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
        if head_mask is not None:
            if head_mask.dim() == 1:
                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
                head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
            elif head_mask.dim() == 2: head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
            head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
        else: head_mask = [None] * self.config.num_hidden_layers
        embedding_output = self.embeddings(input_ids=input_ids, xpath_tags_seq=xpath_tags_seq, xpath_subs_seq=xpath_subs_seq, position_ids=position_ids,
        token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
        encoder_outputs = self.encoder(embedding_output, extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(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, hidden_states=encoder_outputs.hidden_states,
        attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions)
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, use_cache=True, **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, "use_cache": use_cache}
    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("""
    MarkupLM 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`).
    """, MARKUPLM_START_DOCSTRING)
class MarkupLMForQuestionAnswering(MarkupLMPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.markuplm = MarkupLMModel(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(MARKUPLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, xpath_tags_seq: Optional[torch.Tensor] = None, xpath_subs_seq: Optional[torch.Tensor] = None,
    attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None,
    head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None,
    end_positions: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.markuplm(input_ids, xpath_tags_seq=xpath_tags_seq, xpath_subs_seq=xpath_subs_seq, 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.clamp_(0, ignored_index)
            end_positions.clamp_(0, ignored_index)
            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2
        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output
        return QuestionAnsweringModelOutput(loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""MarkupLM Model with a `token_classification` head on top.""", MARKUPLM_START_DOCSTRING)
class MarkupLMForTokenClassification(MarkupLMPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.markuplm = MarkupLMModel(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(MARKUPLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, xpath_tags_seq: Optional[torch.Tensor] = None, xpath_subs_seq: Optional[torch.Tensor] = None,
    attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None,
    head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.markuplm(input_ids, xpath_tags_seq=xpath_tags_seq, xpath_subs_seq=xpath_subs_seq, attention_mask=attention_mask, token_type_ids=token_type_ids,
        position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        prediction_scores = self.classifier(sequence_output)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(prediction_scores.view(-1, self.config.num_labels), labels.view(-1))
        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TokenClassifierOutput(loss=loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("MarkupLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", MARKUPLM_START_DOCSTRING)
class MarkupLMForSequenceClassification(MarkupLMPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config
        self.markuplm = MarkupLMModel(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(MARKUPLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, xpath_tags_seq: Optional[torch.Tensor] = None, xpath_subs_seq: Optional[torch.Tensor] = None,
    attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None,
    head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.markuplm(input_ids, xpath_tags_seq=xpath_tags_seq, xpath_subs_seq=xpath_subs_seq, attention_mask=attention_mask, token_type_ids=token_type_ids,
        position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1: self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification"
                else: self.config.problem_type = "multi_label_classification"
            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze())
                else: loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
