"""
    #################################################################################################################################################################################################
    # 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 logging
import math
import random
from abc import ABC, abstractmethod
from copy import deepcopy
from dataclasses import dataclass
from typing import Any, Dict, Optional, Sequence, Tuple, Union
import torch
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss
from sapiens_transformers import UdopConfig
from sapiens_transformers.modeling_outputs import (Seq2SeqLMOutput, Seq2SeqModelOutput)
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings)
logger = logging.getLogger(__name__)
_CONFIG_FOR_DOC = "UdopConfig"
UDOP_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.
    Args:
        config ([`UdopConfig`]): 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.
"""
UDOP_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. UDOP is a model with relative position embeddings so
            you should be able to pad the inputs on both the right and the left. Indices can be obtained using
            [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *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)
        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))`.
        visual_bbox (`torch.LongTensor` of shape `(batch_size, patch_sequence_length, 4)`, *optional*):
            Bounding boxes of each patch in the image. If not provided, bounding boxes are created in the model.
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using
            [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
            [What are decoder input IDs?](../glossary#decoder-input-ids) T5 uses the `pad_token_id` as the starting
            token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last
            `decoder_input_ids` have to be input (see `past_key_values`). To know more on how to prepare
            `decoder_input_ids` for pretraining take a look at [T5 Training](./t5#training).
        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0,
            1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
            1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
                Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
                `[0, 1]`:
                - 1 indicates the head is *not masked*,
                - 0 indicates the head is *masked*.
        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
            the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        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.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
            input (see `past_key_values`). This is useful if you want more control over how to convert
            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If
            `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of
            `inputs_embeds`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        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.
"""
UDOP_ENCODER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
            should be able to pad the inputs on both the right and the left.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for detail.
            To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *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)
        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))`.
        visual_bbox (`torch.LongTensor` of shape `(batch_size, patch_sequence_length, 4)`, *optional*):
            Bounding boxes of each patch in the image. If not provided, bounding boxes are created in the model.
        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*):
            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.
"""
@dataclass
class BaseModelOutputWithAttentionMask(ModelOutput):
    """Args:"""
    last_hidden_state: torch.FloatTensor = None
    attention_mask: torch.FloatTensor = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
def get_visual_bbox(image_size=224, patch_size=16):
    image_feature_pool_shape = [image_size // patch_size, image_size // patch_size]
    visual_bbox_x = torch.arange(0, 1.0 * (image_feature_pool_shape[1] + 1), 1.0)
    visual_bbox_x /= image_feature_pool_shape[1]
    visual_bbox_y = torch.arange(0, 1.0 * (image_feature_pool_shape[0] + 1), 1.0)
    visual_bbox_y /= image_feature_pool_shape[0]
    visual_bbox_input = torch.stack([visual_bbox_x[:-1].repeat(image_feature_pool_shape[0], 1), visual_bbox_y[:-1].repeat(image_feature_pool_shape[1], 1).transpose(0, 1),
    visual_bbox_x[1:].repeat(image_feature_pool_shape[0], 1), visual_bbox_y[1:].repeat(image_feature_pool_shape[1], 1).transpose(0, 1)], dim=-1)
    visual_bbox_input = visual_bbox_input.view(-1, 4)
    return visual_bbox_input
def pad_sequence(seq, target_len, pad_value=0):
    if isinstance(seq, torch.Tensor): n = seq.shape[0]
    else:
        n = len(seq)
        seq = torch.tensor(seq)
    m = target_len - n
    if m > 0:
        ret = torch.stack([pad_value] * m).to(seq)
        seq = torch.cat([seq, ret], dim=0)
    return seq[:target_len]
def combine_image_text_embeddings(image_embeddings, inputs_embeds, bbox, visual_bbox, attention_mask=None, num_patches=14, max_len=0, image_size=224, patch_size=16):
    sequence_length = num_patches
    ocr_points_x = torch.clip(torch.floor((bbox[:, :, 0] + bbox[:, :, 2]) / 2.0 * sequence_length).long(), 0, sequence_length - 1)
    ocr_points_y = (torch.clip(torch.floor((bbox[:, :, 1] + bbox[:, :, 3]) / 2.0 * sequence_length).long(), 0, sequence_length - 1) * sequence_length)
    ocr_points = ocr_points_x + ocr_points_y
    bbox = bbox.to(torch.float64)
    target_seg = (bbox.mean(-1) == 0.0) | (bbox.mean(-1) == 1.0)
    repeated_vision_embeds = torch.gather(image_embeddings, 1, ocr_points.unsqueeze(-1).repeat(1, 1, image_embeddings.size(-1)))
    repeated_vision_embeds[target_seg] = 0.0
    inputs_embeds += repeated_vision_embeds
    patch_inds = torch.full_like(image_embeddings[:, :, 0], True).bool()
    ind = torch.cat([torch.arange(len(ocr_points))[:, None].repeat(1, ocr_points.size(-1))[:, :, None].to(ocr_points), ocr_points[:, :, None]], dim=-1)
    ind = ind.flatten(0, 1)
    rows, cols = zip(*ind)
    patch_inds[rows, cols] = False
    input_vision_patches = [image_embeddings[i][patch_inds[i]] for i in range(len(patch_inds))]
    if visual_bbox is None:
        visual_bbox = get_visual_bbox(image_size=image_size, patch_size=patch_size)
        visual_bbox = visual_bbox.unsqueeze(0).repeat(image_embeddings.size(0), 1, 1)
        visual_bbox = visual_bbox.to(image_embeddings.device)
    visual_bbox = [visual_bbox[i][patch_inds[i]] for i in range(len(patch_inds))]
    if attention_mask is not None: visual_attention_mask = [torch.tensor([1] * len(item)).to(attention_mask) for item in visual_bbox]
    if max_len == 0: max_len = image_embeddings.size(1)
    else: max_len = max_len - inputs_embeds.size(1)
    inputs_vision_patches = torch.stack([pad_sequence(item, max_len, torch.zeros_like(image_embeddings[0, 0])) for item in input_vision_patches])
    visual_bbox = torch.stack([pad_sequence(item, max_len, torch.zeros_like(bbox[0, 0])) for item in visual_bbox])
    if attention_mask is not None: visual_attention_mask = torch.stack([pad_sequence(item, max_len, torch.zeros_like(attention_mask[0, 0])) for item in visual_attention_mask])
    inputs_embeds = torch.cat([inputs_embeds, inputs_vision_patches], 1)
    bbox = torch.cat([bbox, visual_bbox], 1)
    if attention_mask is not None: attention_mask = torch.cat([attention_mask, visual_attention_mask], 1)
    return inputs_embeds, bbox, attention_mask
class UdopPatchEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size
        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches
        self.proj = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
    def forward(self, pixel_values):
        batch_size, num_channels, height, width = pixel_values.shape
        if height != self.image_size[0] or width != self.image_size[1]: raise ValueError(f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]}).")
        embeddings = self.proj(pixel_values)
        embeddings = embeddings.flatten(2).transpose(1, 2)
        return embeddings
class UdopPreTrainedModel(PreTrainedModel):
    config_class = UdopConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    _keep_in_fp32_modules = ["wo"]
    def _init_weights(self, module):
        factor = self.config.initializer_factor
        if isinstance(module, UdopLayerNorm): module.weight.data.fill_(factor * 1.0)
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=factor)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.Conv2d):
            module.weight.data = nn.init.trunc_normal_(module.weight.data.to(torch.float32), mean=0.0, std=factor).to(module.weight.dtype)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, RelativePositionBiasBase):
            factor = self.config.initializer_factor
            d_model = self.config.d_model
            module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
        elif isinstance(module, UdopModel): module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
        elif isinstance(module, UdopForConditionalGeneration):
            if hasattr(module, "lm_head") and not self.config.tie_word_embeddings: module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0)
        elif isinstance(module, UdopDenseActDense):
            module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.wi, "bias") and module.wi.bias is not None: module.wi.bias.data.zero_()
            module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
            if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_()
        elif isinstance(module, UdopDenseGatedActDense):
            module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None: module.wi_0.bias.data.zero_()
            module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None: module.wi_1.bias.data.zero_()
            module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
            if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_()
        elif isinstance(module, UdopAttention):
            d_model = self.config.d_model
            key_value_proj_dim = self.config.d_kv
            n_heads = self.config.num_heads
            module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
            module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
            module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
            module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
            if module.has_relative_attention_bias: module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
    def _shift_right(self, input_ids):
        decoder_start_token_id = self.config.decoder_start_token_id
        pad_token_id = self.config.pad_token_id
        assert decoder_start_token_id is not None, ("self.model.config.decoder_start_token_id has to be defined. In Udop it is usually set to the pad_token_id. See Udop docs for more information")
        shifted_input_ids = input_ids.new_zeros(input_ids.shape)
        shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
        shifted_input_ids[..., 0] = decoder_start_token_id
        assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
        shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
        assert torch.all(shifted_input_ids >= 0).item(), "Verify that `shifted_input_ids` has only positive values"
        return shifted_input_ids
class UdopLayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps
    def forward(self, hidden_states):
        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype)
        return self.weight * hidden_states
class UdopDenseActDense(nn.Module):
    def __init__(self, config: UdopConfig):
        super().__init__()
        self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = ACT2FN[config.dense_act_fn]
    def forward(self, hidden_states):
        hidden_states = self.wi(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.dropout(hidden_states)
        if (isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8): hidden_states = hidden_states.to(self.wo.weight.dtype)
        hidden_states = self.wo(hidden_states)
        return hidden_states
class UdopDenseGatedActDense(nn.Module):
    def __init__(self, config: UdopConfig):
        super().__init__()
        self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = ACT2FN[config.dense_act_fn]
    def forward(self, hidden_states):
        hidden_gelu = self.act(self.wi_0(hidden_states))
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = hidden_gelu * hidden_linear
        hidden_states = self.dropout(hidden_states)
        if (isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8): hidden_states = hidden_states.to(self.wo.weight.dtype)
        hidden_states = self.wo(hidden_states)
        return hidden_states
class UdopLayerFF(nn.Module):
    def __init__(self, config: UdopConfig):
        super().__init__()
        if config.is_gated_act: self.DenseReluDense = UdopDenseGatedActDense(config)
        else: self.DenseReluDense = UdopDenseActDense(config)
        self.layer_norm = UdopLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, hidden_states):
        forwarded_states = self.layer_norm(hidden_states)
        forwarded_states = self.DenseReluDense(forwarded_states)
        hidden_states = hidden_states + self.dropout(forwarded_states)
        return hidden_states
class UdopAttention(nn.Module):
    def __init__(self, config: UdopConfig, has_relative_attention_bias=False):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.has_relative_attention_bias = has_relative_attention_bias
        self.relative_attention_num_buckets = config.relative_attention_num_buckets
        self.relative_attention_max_distance = config.relative_attention_max_distance
        self.d_model = config.d_model
        self.key_value_proj_dim = config.d_kv
        self.n_heads = config.num_heads
        self.dropout = config.dropout_rate
        self.inner_dim = self.n_heads * self.key_value_proj_dim
        self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
        if self.has_relative_attention_bias: self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
        self.pruned_heads = set()
        self.gradient_checkpointing = False
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads)
        self.q = prune_linear_layer(self.q, index)
        self.k = prune_linear_layer(self.k, index)
        self.v = prune_linear_layer(self.v, index)
        self.o = prune_linear_layer(self.o, index, dim=1)
        self.n_heads = self.n_heads - len(heads)
        self.inner_dim = self.key_value_proj_dim * self.n_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    @staticmethod
    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
        relative_buckets = 0
        if bidirectional:
            num_buckets //= 2
            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
            relative_position = torch.abs(relative_position)
        else: relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
        max_exact = num_buckets // 2
        is_small = relative_position < max_exact
        relative_position_if_large = max_exact + (torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)).to(torch.long)
        relative_position_if_large = torch.min(relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1))
        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
        return relative_buckets
    def compute_bias(self, query_length, key_length, device=None):
        if device is None: device = self.relative_attention_bias.weight.device
        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
        relative_position = memory_position - context_position
        relative_position_bucket = self._relative_position_bucket(relative_position, bidirectional=(not self.is_decoder), num_buckets=self.relative_attention_num_buckets,
        max_distance=self.relative_attention_max_distance)
        values = self.relative_attention_bias(relative_position_bucket)
        values = values.permute([2, 0, 1]).unsqueeze(0)
        return values
    def forward(self, hidden_states, mask=None, key_value_states=None, position_bias=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, output_attentions=False):
        batch_size, seq_length = hidden_states.shape[:2]
        real_seq_length = seq_length
        if past_key_value is not None:
            if len(past_key_value) != 2: raise ValueError(f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states")
            real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length
        key_length = real_seq_length if key_value_states is None else key_value_states.shape[1]
        def shape(states): return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
        def unshape(states): return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
        def project(hidden_states, proj_layer, key_value_states, past_key_value):
            if key_value_states is None: hidden_states = shape(proj_layer(hidden_states))
            elif past_key_value is None: hidden_states = shape(proj_layer(key_value_states))
            if past_key_value is not None:
                if key_value_states is None: hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
                elif past_key_value.shape[2] != key_value_states.shape[1]: hidden_states = shape(proj_layer(key_value_states))
                else: hidden_states = past_key_value
            return hidden_states
        query_states = shape(self.q(hidden_states))
        key_states = project(hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None)
        value_states = project(hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None)
        scores = torch.matmul(query_states, key_states.transpose(3, 2))
        if position_bias is None:
            if not self.has_relative_attention_bias:
                position_bias = torch.zeros((1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype)
                if self.gradient_checkpointing and self.training: position_bias.requires_grad = True
            else: position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device)
            if past_key_value is not None: position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
            if mask is not None: position_bias = position_bias + mask
        if self.pruned_heads:
            mask = torch.ones(position_bias.shape[1])
            mask[list(self.pruned_heads)] = 0
            position_bias_masked = position_bias[:, mask.bool()]
        else: position_bias_masked = position_bias
        scores += position_bias_masked
        attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
        if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask
        attn_output = unshape(torch.matmul(attn_weights, value_states))
        attn_output = self.o(attn_output)
        present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None
        outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
        if output_attentions: outputs = outputs + (attn_weights,)
        return outputs
class UdopLayerSelfAttention(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.SelfAttention = UdopAttention(config, has_relative_attention_bias=has_relative_attention_bias)
        self.layer_norm = UdopLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, hidden_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.SelfAttention(normed_hidden_states, mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value,
        use_cache=use_cache, output_attentions=output_attentions)
        hidden_states = hidden_states + self.dropout(attention_output[0])
        outputs = (hidden_states,) + attention_output[1:]
        return outputs
class UdopLayerCrossAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.EncDecAttention = UdopAttention(config, has_relative_attention_bias=False)
        self.layer_norm = UdopLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, hidden_states, key_value_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, query_length=None, output_attentions=False):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.EncDecAttention(normed_hidden_states, mask=attention_mask, key_value_states=key_value_states, position_bias=position_bias, layer_head_mask=layer_head_mask,
        past_key_value=past_key_value, use_cache=use_cache, query_length=query_length, output_attentions=output_attentions)
        layer_output = hidden_states + self.dropout(attention_output[0])
        outputs = (layer_output,) + attention_output[1:]
        return outputs
class UdopBlock(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.layer = nn.ModuleList()
        self.layer.append(UdopLayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias))
        if self.is_decoder: self.layer.append(UdopLayerCrossAttention(config))
        self.layer.append(UdopLayerFF(config))
    def forward(self, hidden_states, attention_mask=None, position_bias=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None,
    layer_head_mask=None, cross_attn_layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, return_dict=True):
        if past_key_value is not None:
            if not self.is_decoder: logger.warning("`past_key_values` is passed to the encoder. Please make sure this is intended.")
            expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
            if len(past_key_value) != expected_num_past_key_values: raise ValueError(f"There should be {expected_num_past_key_values} past states. {'2 (key / value) for cross attention.' if expected_num_past_key_values == 4 else ''} Got {len(past_key_value)} past key / value states")
            self_attn_past_key_value = past_key_value[:2]
            cross_attn_past_key_value = past_key_value[2:]
        else: self_attn_past_key_value, cross_attn_past_key_value = None, None
        self_attention_outputs = self.layer[0](hidden_states, attention_mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=self_attn_past_key_value,
        use_cache=use_cache, output_attentions=output_attentions)
        hidden_states, present_key_value_state = self_attention_outputs[:2]
        attention_outputs = self_attention_outputs[2:]
        if hidden_states.dtype == torch.float16:
            clamp_value = torch.where(torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max)
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
        do_cross_attention = self.is_decoder and encoder_hidden_states is not None
        if do_cross_attention:
            if present_key_value_state is not None: query_length = present_key_value_state[0].shape[2]
            else: query_length = None
            cross_attention_outputs = self.layer[1](hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, position_bias=encoder_decoder_position_bias,
            layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, query_length=query_length, use_cache=use_cache, output_attentions=output_attentions)
            hidden_states = cross_attention_outputs[0]
            if hidden_states.dtype == torch.float16:
                clamp_value = torch.where(torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max)
                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
            if present_key_value_state is not None: present_key_value_state = present_key_value_state + cross_attention_outputs[1]
            attention_outputs = attention_outputs + cross_attention_outputs[2:]
        hidden_states = self.layer[-1](hidden_states)
        if hidden_states.dtype == torch.float16:
            clamp_value = torch.where(torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max)
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
        outputs = (hidden_states,)
        if use_cache: outputs = outputs + (present_key_value_state,) + attention_outputs
        else: outputs = outputs + attention_outputs
        return outputs
class UdopCellEmbeddings(nn.Module):
    def __init__(self, max_2d_position_embeddings=501, hidden_size=1024):
        super(UdopCellEmbeddings, self).__init__()
        self.max_2d_position_embeddings = max_2d_position_embeddings
        self.x_position_embeddings = nn.Embedding(max_2d_position_embeddings, hidden_size)
        self.y_position_embeddings = nn.Embedding(max_2d_position_embeddings, hidden_size)
    def forward(self, bbox):
        bbox = torch.clip(bbox, 0.0, 1.0)
        bbox = (bbox * (self.max_2d_position_embeddings - 1)).long()
        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])
        embeddings = (left_position_embeddings + upper_position_embeddings + right_position_embeddings + lower_position_embeddings)
        return embeddings
get_relative_position_bucket = UdopAttention._relative_position_bucket
AUGMENTATION_RANGE = (0.80, 1.25)
class RelativePositionBiasBase(nn.Module, ABC):
    def __init__(self, num_heads=None, relative_attention_num_buckets=32, bidirectional=True, scaling_factor=1, max_distance=128, level="tokens", augmentation=False, prefix_bucket=False, expand=False):
        super(RelativePositionBiasBase, self).__init__()
        self.prefix_bucket = prefix_bucket
        self.augmentation = augmentation
        self.level = level
        self.max_distance = max_distance
        self.scaling_factor = scaling_factor
        self.bidirectional = bidirectional
        self.num_heads = num_heads
        self.expand = expand
        self.relative_attention_num_buckets = relative_attention_num_buckets
        extra_head = 2 if prefix_bucket and not self.expand else 0
        self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets + extra_head, self.num_heads)
    @abstractmethod
    def prepare_input(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor: pass
    def get_bucket(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
        relative_position = self.prepare_input(attention_mask, bbox)
        rp_bucket: Tensor = get_relative_position_bucket(relative_position, bidirectional=self.bidirectional, num_buckets=self.relative_attention_num_buckets, max_distance=self.max_distance)
        return rp_bucket
    def get_relative_position(self, positions):
        context_position = positions[:, :, None]
        memory_position = positions[:, None, :]
        relative_position = memory_position - context_position
        if self.augmentation and self.training: relative_position *= random.uniform(*AUGMENTATION_RANGE)
        relative_position *= self.scaling_factor
        return relative_position.to(torch.long)
    def forward(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
        if self.expand and self.prefix_bucket:
            new_bias = nn.Embedding(self.relative_attention_num_buckets + 2, self.num_heads)
            new_bias.weight.data[: self.relative_attention_num_buckets] = self.relative_attention_bias.weight.data
            new_bias.weight.data[self.relative_attention_num_buckets :] = 0.1
            self.relative_attention_bias = new_bias
            self.expand = False
        rp_bucket = self.get_bucket(attention_mask, bbox)
        if self.prefix_bucket:
            if rp_bucket.size(0) == 1 and attention_mask.size(0) > 1: rp_bucket = rp_bucket.repeat(attention_mask.size(0), 1, 1)
            is_prefix = bbox[:, :, 1] < 0
            num_prefix = is_prefix.sum(-1)
            for idx, num_prefix_row in enumerate(num_prefix.cpu().numpy()):
                rp_bucket[idx, :num_prefix_row, num_prefix_row:] = self.relative_attention_num_buckets
                rp_bucket[idx, num_prefix_row:, :num_prefix_row] = self.relative_attention_num_buckets + 1
        values: Tensor = self.relative_attention_bias(rp_bucket)
        if values.dim() != 4: raise ValueError("Wrong dimension of values tensor")
        values = values.permute([0, 3, 1, 2])
        return values
class RelativePositionBias1D(RelativePositionBiasBase):
    def __init__(self, scaling_factor=1, max_distance=128, **kwargs): super().__init__(scaling_factor=scaling_factor, max_distance=max_distance, **kwargs)
    def prepare_input(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
        if self.scaling_factor != 1: raise ValueError("No need to scale 1d features")
        relative_position = self.get_relative_position(torch.arange(attention_mask.size(1), dtype=torch.long, device=attention_mask.device)[None, :])
        return relative_position
class RelativePositionBiasHorizontal(RelativePositionBiasBase):
    def __init__(self, scaling_factor=100, max_distance=100, **kwargs): super().__init__(scaling_factor=scaling_factor, max_distance=max_distance, **kwargs)
    def prepare_input(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
        if not self.scaling_factor > 1.0: raise ValueError("Need to scale the values of bboxes, as there are in small (0,1) range")
        if bbox is None: raise ValueError("Bbox is required for horizontal relative position bias")
        horizontal_position: Tensor = bbox[:, :, [0, 2]].mean(dim=-1)
        return self.get_relative_position(horizontal_position)
class RelativePositionBiasVertical(RelativePositionBiasBase):
    def __init__(self, scaling_factor=100, max_distance=100, **kwargs): super().__init__(scaling_factor=scaling_factor, max_distance=max_distance, **kwargs)
    def prepare_input(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
        if not self.scaling_factor > 1.0: raise ValueError("Need to scale the values of bboxes, as there are in small (0,1) range")
        if bbox is None: raise ValueError("Bbox is required for vertical relative position bias")
        vertical_position: Tensor = bbox[:, :, [1, 3]].mean(dim=-1)
        return self.get_relative_position(vertical_position)
class RelativePositionBiasAggregated(nn.Module):
    def __init__(self, modules: Sequence[RelativePositionBiasBase]):
        super().__init__()
        self.biases = nn.ModuleList(modules)
    def forward(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Union[float, Tensor]:
        output = 0.0
        for bias in self.biases: output = bias(attention_mask, bbox) + output
        return output
BIAS_CLASSES = {"1d": RelativePositionBias1D, "horizontal": RelativePositionBiasHorizontal, "vertical": RelativePositionBiasVertical}
def create_relative_bias(config: UdopConfig) -> Sequence[RelativePositionBiasBase]:
    bias_list = []
    if hasattr(config, "relative_bias_args"):
        for bias_kwargs_org in config.relative_bias_args:
            bias_kwargs = deepcopy(bias_kwargs_org)
            bias_type = bias_kwargs.pop("type")
            model_num_heads = config.num_heads if hasattr(config, "num_heads") else config.num_attention_heads
            if "num_heads" in bias_kwargs:
                if bias_kwargs["num_heads"] != model_num_heads: raise ValueError("Number of heads must match num of heads in the model")
            else: bias_kwargs["num_heads"] = model_num_heads
            bias_list.append(BIAS_CLASSES[bias_type](**bias_kwargs))
    return bias_list
class UdopStack(UdopPreTrainedModel):
    def __init__(self, config, embed_tokens=None, embed_patches=None):
        super().__init__(config)
        self.embed_tokens = embed_tokens
        self.embed_patches = embed_patches
        self.is_decoder = config.is_decoder
        self._max_length = config.max_length
        self.num_layers = config.num_layers
        self.block = nn.ModuleList([UdopBlock(config, has_relative_attention_bias=bool(i == 0)) for i in range(self.num_layers)])
        self.final_layer_norm = UdopLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
        if not self.is_decoder: self.cell_2d_embedding = UdopCellEmbeddings(config.max_2d_position_embeddings, config.hidden_size)
        self.relative_bias = self._get_relative_bias(config)
    def _tie_weights(self):
        for bias in self.relative_bias.biases:
            if isinstance(bias, RelativePositionBias1D): self._tie_or_clone_weights(bias.relative_attention_bias, self.block[0].layer[0].SelfAttention.relative_attention_bias)
    @staticmethod
    def _get_relative_bias(config: UdopConfig) -> RelativePositionBiasAggregated:
        relative_bias_list = create_relative_bias(config)
        return RelativePositionBiasAggregated(relative_bias_list)
    def get_input_embeddings(self): return self.embed_tokens
    def get_output_embeddings(self): return self.embed_tokens
    def set_input_embeddings(self, new_embeddings): self.embed_tokens = new_embeddings
    def forward(self, input_ids=None, attention_mask=None, bbox=None, encoder_hidden_states=None, encoder_attention_mask=None, inputs_embeds=None, pixel_values=None,
    visual_bbox=None, image_embeddings=None, position_bias=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, use_cache=None, output_attentions=None,
    output_hidden_states=None, return_dict=None):
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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:
            err_msg_prefix = "decoder_" if self.is_decoder else ""
            raise ValueError(f"You cannot specify both {err_msg_prefix}inputs and {err_msg_prefix}inputs_embeds at the same time")
        elif input_ids is not None and torch.numel(input_ids) > 0:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is None and input_ids is not None and torch.numel(input_ids) == 0:
            input_ids = torch.full((4, 1024), self.config.pad_token_id, device=input_ids.device, dtype=input_ids.dtype)
            attention_mask = torch.zeros((4, 1024), device=input_ids.device, dtype=input_ids.dtype)
            bbox = torch.zeros((4, 1024, 4), device=input_ids.device, dtype=input_ids.dtype)
            input_shape = input_ids.size()
            position_bias = torch.zeros_like(self.get_extended_attention_mask(attention_mask, input_shape))
            logger.warning("Empty batch")
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else:
            err_msg_prefix = "decoder_" if self.is_decoder else ""
            raise ValueError(f"You have to specify either {err_msg_prefix}inputs or {err_msg_prefix}inputs_embeds")
        if inputs_embeds is None:
            if self.embed_tokens is None: raise ValueError("You have to intialize the model with valid token embeddings")
            inputs_embeds = self.embed_tokens(input_ids)
        if pixel_values is not None: image_embeddings = self.embed_patches(pixel_values)
        if image_embeddings is not None:
            num_patches = self.config.image_size // self.config.patch_size
            inputs_embeds, bbox, attention_mask = combine_image_text_embeddings(image_embeddings, inputs_embeds, bbox, visual_bbox, attention_mask,
            num_patches, 0, self.config.image_size, self.config.patch_size)
            input_shape = inputs_embeds.size()[:-1]
        if not self.is_decoder and bbox is not None: inputs_embeds += self.cell_2d_embedding(bbox)
        batch_size, seq_length = input_shape
        mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length
        if use_cache is True: assert self.is_decoder, "`use_cache` can only be set to `True` if {} is used as a decoder".format(self)
        if attention_mask is None: attention_mask = torch.ones(batch_size, mask_seq_length).to(inputs_embeds.device)
        if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
            encoder_seq_length = encoder_hidden_states.shape[1]
            encoder_attention_mask = torch.ones(batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long)
        if past_key_values is None: past_key_values = [None] * len(self.block)
        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
        if self.is_decoder and encoder_attention_mask is not None: 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.num_layers)
        present_key_value_states = () if use_cache else None
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and self.is_decoder) else None
        if self.is_decoder: position_bias = None
        else:
            position_bias = self.relative_bias(attention_mask=attention_mask, bbox=bbox)
            position_bias = position_bias + extended_attention_mask
        encoder_decoder_position_bias = None
        hidden_states = inputs_embeds
        hidden_states = self.dropout(hidden_states)
        for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_outputs = layer_module(hidden_states, attention_mask=extended_attention_mask, position_bias=position_bias, encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask, encoder_decoder_position_bias=encoder_decoder_position_bias, layer_head_mask=head_mask[i],
            past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions)
            if use_cache is False: layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
            hidden_states, present_key_value_state = layer_outputs[:2]
            position_bias = layer_outputs[2]
            if self.is_decoder and encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
            if use_cache: present_key_value_states = present_key_value_states + (present_key_value_state,)
            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[2],)
                if self.is_decoder: all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.dropout(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, attention_mask, present_key_value_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None)
        return BaseModelOutputWithAttentionMask(last_hidden_state=hidden_states, attention_mask=attention_mask, past_key_values=present_key_value_states, hidden_states=all_hidden_states,
        attentions=all_attentions, cross_attentions=all_cross_attentions)
@add_start_docstrings("The bare UDOP encoder-decoder Transformer outputting raw hidden-states without any specific head on top.", UDOP_START_DOCSTRING)
class UdopModel(UdopPreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "encoder.embed_patches.proj.weight", "encoder.embed_patches.proj.bias",
    "encoder.relative_bias.biases.0.relative_attention_bias.weight", "decoder.relative_bias.biases.0.relative_attention_bias.weight"]
    def __init__(self, config):
        super(UdopModel, self).__init__(config)
        self.shared = nn.Embedding(config.vocab_size, config.d_model)
        self.patch_embed = UdopPatchEmbeddings(config)
        encoder_config = deepcopy(config)
        encoder_config.is_decoder = False
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = UdopStack(encoder_config, self.shared, self.patch_embed)
        decoder_config = deepcopy(config)
        decoder_config.is_decoder = True
        decoder_config.is_encoder_decoder = False
        decoder_config.num_layers = config.num_decoder_layers
        self.decoder = UdopStack(decoder_config, self.shared)
        self.post_init()
    def get_input_embeddings(self): return self.shared
    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.set_input_embeddings(new_embeddings)
        self.decoder.set_input_embeddings(new_embeddings)
    def get_encoder(self): return self.encoder
    def get_decoder(self): return self.decoder
    @add_start_docstrings_to_model_forward(UDOP_INPUTS_DOCSTRING)
    def forward(self, input_ids: Tensor = None, attention_mask: Tensor = None, bbox: Dict[str, Any] = None, pixel_values: Optional[Tensor] = None, visual_bbox: Dict[str, Any] = None,
    decoder_input_ids: Optional[Tensor] = None, decoder_attention_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, encoder_outputs: Optional[Tensor] = None,
    past_key_values: Optional[Tensor] = None, head_mask: Optional[Tensor] = None, decoder_inputs_embeds: Optional[Tensor] = None, decoder_head_mask: Optional[Tensor] = None,
    cross_attn_head_mask: Optional[Tensor] = None, use_cache=True, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Tuple[Tensor, ...]:
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if encoder_outputs is None: encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, bbox=bbox, pixel_values=pixel_values,
        visual_bbox=visual_bbox, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = encoder_outputs[0]
        encoder_attention_mask = encoder_outputs.attention_mask if return_dict else encoder_outputs[1]
        decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values,
        encoder_hidden_states=hidden_states, encoder_attention_mask=encoder_attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        if not return_dict:
            decoder_outputs = tuple(value for idx, value in enumerate(decoder_outputs) if idx != 1)
            encoder_outputs = tuple(value for idx, value in enumerate(encoder_outputs) if idx != 1)
            return decoder_outputs + encoder_outputs
        return Seq2SeqModelOutput(last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states,
        decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state,
        encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions)
@add_start_docstrings("The UDOP encoder-decoder Transformer with a language modeling head on top, enabling to generate text given document images and an optional prompt. This class is based on [`T5ForConditionalGeneration`], extended to deal with images and layout (2D) data.", UDOP_START_DOCSTRING)
class UdopForConditionalGeneration(UdopPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "encoder.embed_patches.proj.weight", "encoder.embed_patches.proj.bias",
    "encoder.relative_bias.biases.0.relative_attention_bias.weight", "decoder.relative_bias.biases.0.relative_attention_bias.weight", "lm_head.weight"]
    def __init__(self, config):
        super(UdopForConditionalGeneration, self).__init__(config)
        self.shared = nn.Embedding(config.vocab_size, config.d_model)
        self.patch_embed = UdopPatchEmbeddings(config)
        encoder_config = deepcopy(config)
        encoder_config.is_decoder = False
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = UdopStack(encoder_config, self.shared, self.patch_embed)
        decoder_config = deepcopy(config)
        decoder_config.is_decoder = True
        decoder_config.is_encoder_decoder = False
        decoder_config.num_layers = config.num_decoder_layers
        self.decoder = UdopStack(decoder_config, self.shared)
        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
        self.post_init()
    def get_input_embeddings(self): return self.shared
    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.set_input_embeddings(new_embeddings)
        self.decoder.set_input_embeddings(new_embeddings)
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    def get_output_embeddings(self): return self.lm_head
    def get_encoder(self): return self.encoder
    def get_decoder(self): return self.decoder
    @add_start_docstrings_to_model_forward(UDOP_INPUTS_DOCSTRING)
    def forward(self, input_ids: Tensor = None, attention_mask: Tensor = None, bbox: Dict[str, Any] = None, pixel_values: Optional[Tensor] = None, visual_bbox: Dict[str, Any] = None,
    decoder_input_ids: Optional[Tensor] = None, decoder_attention_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, encoder_outputs: Optional[Tensor] = None,
    past_key_values: Optional[Tensor] = None, head_mask: Optional[Tensor] = None, decoder_inputs_embeds: Optional[Tensor] = None, decoder_head_mask: Optional[Tensor] = None,
    cross_attn_head_mask: Optional[Tensor] = None, use_cache=True, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, labels: Optional[Tensor] = None) -> Tuple[Tensor, ...]:
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if decoder_input_ids is None and labels is not None: decoder_input_ids = self._shift_right(labels)
        if encoder_outputs is None: encoder_outputs = self.encoder(input_ids=input_ids, bbox=bbox, visual_bbox=visual_bbox, pixel_values=pixel_values,
        attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = encoder_outputs[0]
        encoder_attention_mask = encoder_outputs.attention_mask if return_dict else encoder_outputs[1]
        decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values,
        encoder_hidden_states=hidden_states, encoder_attention_mask=encoder_attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = decoder_outputs[0]
        if self.config.tie_word_embeddings: sequence_output = sequence_output * (self.config.d_model**-0.5)
        lm_logits = self.lm_head(sequence_output)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=-100)
            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
        if not return_dict:
            output = (lm_logits,) + decoder_outputs[2:] + (encoder_outputs[0],) + encoder_outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return Seq2SeqLMOutput(loss=loss, logits=lm_logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states,
        decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state,
        encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions)
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None,
    use_cache=None, encoder_outputs=None, **kwargs):
        if past_key_values is not None: input_ids = input_ids[:, -1:]
        return {"decoder_input_ids": input_ids, "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "attention_mask": attention_mask, "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, "bbox": kwargs.get("bbox", None),
        "pixel_values": kwargs.get("pixel_values", None), "visual_bbox": kwargs.get("visual_bbox", None)}
    def _reorder_cache(self, past_key_values, beam_idx):
        if past_key_values is None:
            logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
            return past_key_values
        reordered_decoder_past = ()
        for layer_past_states in past_key_values:
            reordered_layer_past_states = ()
            for layer_past_state in layer_past_states: reordered_layer_past_states = reordered_layer_past_states + (layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),)
            if reordered_layer_past_states[0].shape != layer_past_states[0].shape: raise ValueError(f"reordered_layer_past_states[0] shape {reordered_layer_past_states[0].shape} and layer_past_states[0] shape {layer_past_states[0].shape} mismatched")
            if len(reordered_layer_past_states) != len(layer_past_states): raise ValueError(f"length of reordered_layer_past_states {len(reordered_layer_past_states)} and length of layer_past_states {len(layer_past_states)} mismatched")
            reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
        return reordered_decoder_past
@add_start_docstrings("The bare UDOP Model transformer outputting encoder's raw hidden-states without any specific head on top.", UDOP_START_DOCSTRING)
class UdopEncoderModel(UdopPreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "encoder.embed_patches.proj.weight", "encoder.embed_patches.proj.bias", "encoder.relative_bias.biases.0.relative_attention_bias.weight"]
    def __init__(self, config: UdopConfig):
        super().__init__(config)
        self.shared = nn.Embedding(config.vocab_size, config.d_model)
        self.patch_embed = UdopPatchEmbeddings(config)
        encoder_config = deepcopy(config)
        encoder_config.is_decoder = False
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = UdopStack(encoder_config, self.shared, self.patch_embed)
        self.post_init()
    def get_input_embeddings(self): return self.shared
    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.set_input_embeddings(new_embeddings)
    def get_encoder(self): return self.encoder
    def _prune_heads(self, heads_to_prune):
        for layer, heads in heads_to_prune.items(): self.encoder.block[layer].layer[0].SelfAttention.prune_heads(heads)
    @add_start_docstrings_to_model_forward(UDOP_ENCODER_INPUTS_DOCSTRING)
    def forward(self, input_ids: Tensor = None, bbox: Dict[str, Any] = None, attention_mask: Tensor = None, pixel_values: Optional[Tensor] = None, visual_bbox: Dict[str, Any] = None,
    head_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.FloatTensor], BaseModelOutputWithAttentionMask]:
        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
        encoder_outputs = self.encoder(input_ids=input_ids, bbox=bbox, visual_bbox=visual_bbox, pixel_values=pixel_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds,
        head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        return encoder_outputs
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
