"""
    #################################################################################################################################################################################################
    # 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 collections
import math
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (BaseModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward
from ...utils import (add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, torch_int)
from .configuration_layoutlmv3 import LayoutLMv3Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "LayoutLMv3Config"
LAYOUTLMV3_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 ([`LayoutLMv3Config`]): 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.
"""
LAYOUTLMV3_MODEL_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        bbox (`torch.LongTensor` of shape `(0, 4)`, *optional*):
            Bounding boxes of each input sequence tokens. Selected in the range `[0,
            config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
            format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
            y1) represents the position of the lower right corner.
            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size,
            config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height /
            config.patch_size) * (width / config.patch_size))`.
        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*.
            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.
            [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.
            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.
            [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]`.
            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.
            [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.
"""
LAYOUTLMV3_DOWNSTREAM_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)
        bbox (`torch.LongTensor` of shape `(0, 4)`, *optional*):
            Bounding boxes of each input sequence tokens. Selected in the range `[0,
            config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
            format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
            y1) represents the position of the lower right corner.
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size,
            config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height /
            config.patch_size) * (width / config.patch_size))`.
        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.
"""
class LayoutLMv3PatchEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        image_size = (config.input_size if isinstance(config.input_size, collections.abc.Iterable) else (config.input_size, config.input_size))
        patch_size = (config.patch_size if isinstance(config.patch_size, collections.abc.Iterable) else (config.patch_size, config.patch_size))
        self.patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
        self.proj = nn.Conv2d(config.num_channels, config.hidden_size, kernel_size=patch_size, stride=patch_size)
    def forward(self, pixel_values, position_embedding=None):
        embeddings = self.proj(pixel_values)
        if position_embedding is not None:
            position_embedding = position_embedding.view(1, self.patch_shape[0], self.patch_shape[1], -1)
            position_embedding = position_embedding.permute(0, 3, 1, 2)
            patch_height, patch_width = embeddings.shape[2], embeddings.shape[3]
            position_embedding = F.interpolate(position_embedding, size=(patch_height, patch_width), mode="bicubic")
            embeddings = embeddings + position_embedding
        embeddings = embeddings.flatten(2).transpose(1, 2)
        return embeddings
class LayoutLMv3TextEmbeddings(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.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)
        self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
        self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
        self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
        self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
    def calculate_spatial_position_embeddings(self, bbox):
        try:
            left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
            upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
            right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
            lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
        except IndexError as e: raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e
        h_position_embeddings = self.h_position_embeddings(torch.clip(bbox[:, :, 3] - bbox[:, :, 1], 0, 1023))
        w_position_embeddings = self.w_position_embeddings(torch.clip(bbox[:, :, 2] - bbox[:, :, 0], 0, 1023))
        spatial_position_embeddings = torch.cat([left_position_embeddings, upper_position_embeddings, right_position_embeddings, lower_position_embeddings, h_position_embeddings, w_position_embeddings], dim=-1)
        return spatial_position_embeddings
    def create_position_ids_from_input_ids(self, input_ids, padding_idx):
        mask = input_ids.ne(padding_idx).int()
        incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask
        return incremental_indices.long() + 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, bbox=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
        if position_ids is None:
            if input_ids is not None: position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx).to(input_ids.device)
            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]
        if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
        if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + token_type_embeddings
        position_embeddings = self.position_embeddings(position_ids)
        embeddings += position_embeddings
        spatial_position_embeddings = self.calculate_spatial_position_embeddings(bbox)
        embeddings = embeddings + spatial_position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
class LayoutLMv3PreTrainedModel(PreTrainedModel):
    config_class = LayoutLMv3Config
    base_model_prefix = "layoutlmv3"
    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
class LayoutLMv3SelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})")
        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.has_relative_attention_bias = config.has_relative_attention_bias
        self.has_spatial_attention_bias = config.has_spatial_attention_bias
    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)
    def cogview_attention(self, attention_scores, alpha=32):
        scaled_attention_scores = attention_scores / alpha
        max_value = scaled_attention_scores.amax(dim=(-1)).unsqueeze(-1)
        new_attention_scores = (scaled_attention_scores - max_value) * alpha
        return nn.Softmax(dim=-1)(new_attention_scores)
    def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None):
        mixed_query_layer = self.query(hidden_states)
        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)
        attention_scores = torch.matmul(query_layer / math.sqrt(self.attention_head_size), key_layer.transpose(-1, -2))
        if self.has_relative_attention_bias and self.has_spatial_attention_bias: attention_scores += (rel_pos + rel_2d_pos) / math.sqrt(self.attention_head_size)
        elif self.has_relative_attention_bias: attention_scores += rel_pos / math.sqrt(self.attention_head_size)
        if attention_mask is not None: attention_scores = attention_scores + attention_mask
        attention_probs = self.cogview_attention(attention_scores)
        attention_probs = self.dropout(attention_probs)
        if head_mask is not None: attention_probs = attention_probs * head_mask
        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs
class LayoutLMv3SelfOutput(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 LayoutLMv3Attention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.self = LayoutLMv3SelfAttention(config)
        self.output = LayoutLMv3SelfOutput(config)
    def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None):
        self_outputs = self.self(hidden_states, attention_mask, head_mask, output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos)
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class LayoutLMv3Layer(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 = LayoutLMv3Attention(config)
        self.intermediate = LayoutLMv3Intermediate(config)
        self.output = LayoutLMv3Output(config)
    def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None):
        self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask, output_attentions=output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos)
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]
        layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output)
        outputs = (layer_output,) + outputs
        return outputs
    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output
class LayoutLMv3Encoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([LayoutLMv3Layer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False
        self.has_relative_attention_bias = config.has_relative_attention_bias
        self.has_spatial_attention_bias = config.has_spatial_attention_bias
        if self.has_relative_attention_bias:
            self.rel_pos_bins = config.rel_pos_bins
            self.max_rel_pos = config.max_rel_pos
            self.rel_pos_bias = nn.Linear(self.rel_pos_bins, config.num_attention_heads, bias=False)
        if self.has_spatial_attention_bias:
            self.max_rel_2d_pos = config.max_rel_2d_pos
            self.rel_2d_pos_bins = config.rel_2d_pos_bins
            self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False)
            self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False)
    def relative_position_bucket(self, relative_position, bidirectional=True, num_buckets=32, max_distance=128):
        ret = 0
        if bidirectional:
            num_buckets //= 2
            ret += (relative_position > 0).long() * num_buckets
            n = torch.abs(relative_position)
        else: n = torch.max(-relative_position, torch.zeros_like(relative_position))
        max_exact = num_buckets // 2
        is_small = n < max_exact
        val_if_large = max_exact + (torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)).to(torch.long)
        val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
        ret += torch.where(is_small, n, val_if_large)
        return ret
    def _cal_1d_pos_emb(self, position_ids):
        rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1)
        rel_pos = self.relative_position_bucket(rel_pos_mat, num_buckets=self.rel_pos_bins, max_distance=self.max_rel_pos)
        with torch.no_grad(): rel_pos = self.rel_pos_bias.weight.t()[rel_pos].permute(0, 3, 1, 2)
        rel_pos = rel_pos.contiguous()
        return rel_pos
    def _cal_2d_pos_emb(self, bbox):
        position_coord_x = bbox[:, :, 0]
        position_coord_y = bbox[:, :, 3]
        rel_pos_x_2d_mat = position_coord_x.unsqueeze(-2) - position_coord_x.unsqueeze(-1)
        rel_pos_y_2d_mat = position_coord_y.unsqueeze(-2) - position_coord_y.unsqueeze(-1)
        rel_pos_x = self.relative_position_bucket(rel_pos_x_2d_mat, num_buckets=self.rel_2d_pos_bins, max_distance=self.max_rel_2d_pos)
        rel_pos_y = self.relative_position_bucket(rel_pos_y_2d_mat, num_buckets=self.rel_2d_pos_bins, max_distance=self.max_rel_2d_pos)
        with torch.no_grad():
            rel_pos_x = self.rel_pos_x_bias.weight.t()[rel_pos_x].permute(0, 3, 1, 2)
            rel_pos_y = self.rel_pos_y_bias.weight.t()[rel_pos_y].permute(0, 3, 1, 2)
        rel_pos_x = rel_pos_x.contiguous()
        rel_pos_y = rel_pos_y.contiguous()
        rel_2d_pos = rel_pos_x + rel_pos_y
        return rel_2d_pos
    def forward(self, hidden_states, bbox=None, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, position_ids=None, patch_height=None, patch_width=None):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        rel_pos = self._cal_1d_pos_emb(position_ids) if self.has_relative_attention_bias else None
        rel_2d_pos = self._cal_2d_pos_emb(bbox) if self.has_spatial_attention_bias else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_head_mask = head_mask[i] if head_mask is not None else None
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, attention_mask, layer_head_mask, output_attentions, rel_pos, rel_2d_pos)
            else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos)
            hidden_states = layer_outputs[0]
            if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions)
class LayoutLMv3Intermediate(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 LayoutLMv3Output(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
@add_start_docstrings("The bare LayoutLMv3 Model transformer outputting raw hidden-states without any specific head on top.", LAYOUTLMV3_START_DOCSTRING)
class LayoutLMv3Model(LayoutLMv3PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        if config.text_embed: self.embeddings = LayoutLMv3TextEmbeddings(config)
        if config.visual_embed:
            self.patch_embed = LayoutLMv3PatchEmbeddings(config)
            size = int(config.input_size / config.patch_size)
            self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
            self.pos_embed = nn.Parameter(torch.zeros(1, size * size + 1, config.hidden_size))
            self.pos_drop = nn.Dropout(p=0.0)
            self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
            self.dropout = nn.Dropout(config.hidden_dropout_prob)
            if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias: self.init_visual_bbox(image_size=(size, size))
            self.norm = nn.LayerNorm(config.hidden_size, eps=1e-6)
        self.encoder = LayoutLMv3Encoder(config)
        self.init_weights()
    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)
    def init_visual_bbox(self, image_size=(14, 14), max_len=1000):
        visual_bbox_x = torch.div(torch.arange(0, max_len * (image_size[1] + 1), max_len), image_size[1], rounding_mode="trunc")
        visual_bbox_y = torch.div(torch.arange(0, max_len * (image_size[0] + 1), max_len), image_size[0], rounding_mode="trunc")
        visual_bbox = torch.stack([visual_bbox_x[:-1].repeat(image_size[0], 1), visual_bbox_y[:-1].repeat(image_size[1], 1).transpose(0, 1), visual_bbox_x[1:].repeat(image_size[0], 1),
        visual_bbox_y[1:].repeat(image_size[1], 1).transpose(0, 1)], dim=-1).view(-1, 4)
        cls_token_box = torch.tensor([[0 + 1, 0 + 1, max_len - 1, max_len - 1]])
        self.visual_bbox = torch.cat([cls_token_box, visual_bbox], dim=0)
    def calculate_visual_bbox(self, device, dtype, batch_size):
        visual_bbox = self.visual_bbox.repeat(batch_size, 1, 1)
        visual_bbox = visual_bbox.to(device).type(dtype)
        return visual_bbox
    def forward_image(self, pixel_values):
        embeddings = self.patch_embed(pixel_values)
        batch_size, seq_len, _ = embeddings.size()
        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        embeddings = torch.cat((cls_tokens, embeddings), dim=1)
        if self.pos_embed is not None: embeddings = embeddings + self.pos_embed
        embeddings = self.pos_drop(embeddings)
        embeddings = self.norm(embeddings)
        return embeddings
    @add_start_docstrings_to_model_forward(LAYOUTLMV3_MODEL_INPUTS_DOCSTRING.format("batch_size, token_sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, bbox: 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, pixel_values: Optional[torch.FloatTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, BaseModelOutput]:
        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:
            input_shape = input_ids.size()
            batch_size, seq_length = input_shape
            device = input_ids.device
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            batch_size, seq_length = input_shape
            device = inputs_embeds.device
        elif pixel_values is not None:
            batch_size = len(pixel_values)
            device = pixel_values.device
        else: raise ValueError("You have to specify either input_ids or inputs_embeds or pixel_values")
        if input_ids is not None or inputs_embeds is not None:
            if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device)
            if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
            if bbox is None: bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device)
            embedding_output = self.embeddings(input_ids=input_ids, bbox=bbox, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
        final_bbox = final_position_ids = None
        patch_height = patch_width = None
        if pixel_values is not None:
            patch_height, patch_width = (torch_int(pixel_values.shape[2] / self.config.patch_size), torch_int(pixel_values.shape[3] / self.config.patch_size))
            visual_embeddings = self.forward_image(pixel_values)
            visual_attention_mask = torch.ones((batch_size, visual_embeddings.shape[1]), dtype=torch.long, device=device)
            if attention_mask is not None: attention_mask = torch.cat([attention_mask, visual_attention_mask], dim=1)
            else: attention_mask = visual_attention_mask
            if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
                if self.config.has_spatial_attention_bias:
                    visual_bbox = self.calculate_visual_bbox(device, dtype=torch.long, batch_size=batch_size)
                    if bbox is not None: final_bbox = torch.cat([bbox, visual_bbox], dim=1)
                    else: final_bbox = visual_bbox
                visual_position_ids = torch.arange(0, visual_embeddings.shape[1], dtype=torch.long, device=device).repeat(batch_size, 1)
                if input_ids is not None or inputs_embeds is not None:
                    position_ids = torch.arange(0, input_shape[1], device=device).unsqueeze(0)
                    position_ids = position_ids.expand(input_shape)
                    final_position_ids = torch.cat([position_ids, visual_position_ids], dim=1)
                else: final_position_ids = visual_position_ids
            if input_ids is not None or inputs_embeds is not None: embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)
            else: embedding_output = visual_embeddings
            embedding_output = self.LayerNorm(embedding_output)
            embedding_output = self.dropout(embedding_output)
        elif self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
            if self.config.has_spatial_attention_bias: final_bbox = bbox
            if self.config.has_relative_attention_bias:
                position_ids = self.embeddings.position_ids[:, : input_shape[1]]
                position_ids = position_ids.expand_as(input_ids)
                final_position_ids = position_ids
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, None, device, dtype=embedding_output.dtype)
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        encoder_outputs = self.encoder(embedding_output, bbox=final_bbox, position_ids=final_position_ids, attention_mask=extended_attention_mask, head_mask=head_mask,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, patch_height=patch_height, patch_width=patch_width)
        sequence_output = encoder_outputs[0]
        if not return_dict: return (sequence_output,) + encoder_outputs[1:]
        return BaseModelOutput(last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
class LayoutLMv3ClassificationHead(nn.Module):
    def __init__(self, config, pool_feature=False):
        super().__init__()
        self.pool_feature = pool_feature
        if pool_feature: self.dense = nn.Linear(config.hidden_size * 3, config.hidden_size)
        else: 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, x):
        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("""
    LayoutLMv3 Model with a token classification head on top (a linear layer on top of the final hidden states) e.g.
    for sequence labeling (information extraction) tasks such as [FUNSD](https://guillaumejaume.github.io/FUNSD/),
    [SROIE](https://rrc.cvc.uab.es/?ch=13), [CORD](https://github.com/clovaai/cord) and
    [Kleister-NDA](https://github.com/applicaai/kleister-nda).
    """, LAYOUTLMV3_START_DOCSTRING)
class LayoutLMv3ForTokenClassification(LayoutLMv3PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.layoutlmv3 = LayoutLMv3Model(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        if config.num_labels < 10: self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        else: self.classifier = LayoutLMv3ClassificationHead(config, pool_feature=False)
        self.init_weights()
    @add_start_docstrings_to_model_forward(LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, bbox: 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,
    pixel_values: Optional[torch.LongTensor] = None) -> Union[Tuple, TokenClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.layoutlmv3(input_ids, bbox=bbox, 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, pixel_values=pixel_values)
        if input_ids is not None: input_shape = input_ids.size()
        else: input_shape = inputs_embeds.size()[:-1]
        seq_length = input_shape[1]
        sequence_output = outputs[0][:, :seq_length]
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""
    LayoutLMv3 Model with a span classification head on top for extractive question-answering tasks such as
    [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the text part of the hidden-states output to
    compute `span start logits` and `span end logits`).
    """, LAYOUTLMV3_START_DOCSTRING)
class LayoutLMv3ForQuestionAnswering(LayoutLMv3PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.layoutlmv3 = LayoutLMv3Model(config)
        self.qa_outputs = LayoutLMv3ClassificationHead(config, pool_feature=False)
        self.init_weights()
    @add_start_docstrings_to_model_forward(LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None,
    end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    bbox: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.LongTensor] = None) -> Union[Tuple, QuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.layoutlmv3(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, bbox=bbox, pixel_values=pixel_values)
        sequence_output = outputs[0]
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()
        total_loss = None
        if start_positions is not None and end_positions is not None:
            if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1)
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)
            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2
        if not return_dict:
            output = (start_logits, end_logits) + outputs[1:]
            return ((total_loss,) + output) if total_loss is not None else output
        return QuestionAnsweringModelOutput(loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""
    LayoutLMv3 Model with a sequence classification head on top (a linear layer on top of the final hidden state of the
    [CLS] token) e.g. for document image classification tasks such as the
    [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
    """, LAYOUTLMV3_START_DOCSTRING)
class LayoutLMv3ForSequenceClassification(LayoutLMv3PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config
        self.layoutlmv3 = LayoutLMv3Model(config)
        self.classifier = LayoutLMv3ClassificationHead(config, pool_feature=False)
        self.init_weights()
    @add_start_docstrings_to_model_forward(LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, bbox: Optional[torch.LongTensor] = None,
    pixel_values: Optional[torch.LongTensor] = None) -> Union[Tuple, SequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.layoutlmv3(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, bbox=bbox, pixel_values=pixel_values)
        sequence_output = outputs[0][:, 0, :]
        logits = self.classifier(sequence_output)
        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1: self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification"
                else: self.config.problem_type = "multi_label_classification"
            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze())
                else: loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
