"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_outputs import (BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, TokenClassifierOutput)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_bros import BrosConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "jinho8345/bros-base-uncased"
_CONFIG_FOR_DOC = "BrosConfig"
BROS_START_DOCSTRING = r"""
    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.
    Parameters:
        config ([`BrosConfig`]): 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.
"""
BROS_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`BrosProcessor`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        bbox ('torch.FloatTensor' of shape '(batch_size, num_boxes, 4)'):
            Bounding box coordinates for each token in the input sequence. Each bounding box is a list of four values
            (x1, y1, x2, y2), where (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner of the
            bounding box.
        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)
        bbox_first_token_mask (`torch.FloatTensor` of shape `(0)`, *optional*):
            Mask to indicate the first token of each bounding box. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
        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 [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@dataclass
class BrosSpadeOutput(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    initial_token_logits: torch.FloatTensor = None
    subsequent_token_logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
class BrosPositionalEmbedding1D(nn.Module):
    def __init__(self, config):
        super(BrosPositionalEmbedding1D, self).__init__()
        self.dim_bbox_sinusoid_emb_1d = config.dim_bbox_sinusoid_emb_1d
        inv_freq = 1 / (10000 ** (torch.arange(0.0, self.dim_bbox_sinusoid_emb_1d, 2.0) / self.dim_bbox_sinusoid_emb_1d))
        self.register_buffer("inv_freq", inv_freq)
    def forward(self, pos_seq: torch.Tensor) -> torch.Tensor:
        seq_size = pos_seq.size()
        b1, b2, b3 = seq_size
        sinusoid_inp = pos_seq.view(b1, b2, b3, 1) * self.inv_freq.view(1, 1, 1, self.dim_bbox_sinusoid_emb_1d // 2)
        pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=-1)
        return pos_emb
class BrosPositionalEmbedding2D(nn.Module):
    def __init__(self, config):
        super(BrosPositionalEmbedding2D, self).__init__()
        self.dim_bbox = config.dim_bbox
        self.x_pos_emb = BrosPositionalEmbedding1D(config)
        self.y_pos_emb = BrosPositionalEmbedding1D(config)
    def forward(self, bbox: torch.Tensor) -> torch.Tensor:
        stack = []
        for i in range(self.dim_bbox):
            if i % 2 == 0: stack.append(self.x_pos_emb(bbox[..., i]))
            else: stack.append(self.y_pos_emb(bbox[..., i]))
        bbox_pos_emb = torch.cat(stack, dim=-1)
        return bbox_pos_emb
class BrosBboxEmbeddings(nn.Module):
    def __init__(self, config):
        super(BrosBboxEmbeddings, self).__init__()
        self.bbox_sinusoid_emb = BrosPositionalEmbedding2D(config)
        self.bbox_projection = nn.Linear(config.dim_bbox_sinusoid_emb_2d, config.dim_bbox_projection, bias=False)
    def forward(self, bbox: torch.Tensor):
        bbox_t = bbox.transpose(0, 1)
        bbox_pos = bbox_t[None, :, :, :] - bbox_t[:, None, :, :]
        bbox_pos_emb = self.bbox_sinusoid_emb(bbox_pos)
        bbox_pos_emb = self.bbox_projection(bbox_pos_emb)
        return bbox_pos_emb
class BrosTextEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
        self.register_buffer("token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device), persistent=False)
    def forward(self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    past_key_values_length: int = 0) -> torch.Tensor:
        if input_ids is not None: input_shape = input_ids.size()
        else: input_shape = inputs_embeds.size()[:-1]
        seq_length = input_shape[1]
        if position_ids is None: position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
        if token_type_ids is None:
            if hasattr(self, "token_type_ids"):
                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
        if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + token_type_embeddings
        if self.position_embedding_type == "absolute":
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
class BrosSelfAttention(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.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
        self.is_decoder = config.is_decoder
    def transpose_for_scores(self, x: torch.Tensor):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)
    def forward(self, hidden_states: torch.Tensor, bbox_pos_emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None,
    encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
    output_attentions: Optional[torch.Tensor] = False) -> Tuple[torch.Tensor]:
        mixed_query_layer = self.query(hidden_states)
        is_cross_attention = encoder_hidden_states is not None
        if is_cross_attention and past_key_value is not None:
            key_layer = past_key_value[0]
            value_layer = past_key_value[1]
            attention_mask = encoder_attention_mask
        elif is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)
        if self.is_decoder: past_key_value = (key_layer, value_layer)
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            seq_length = hidden_states.size()[1]
            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
            distance = position_ids_l - position_ids_r
            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
            if self.position_embedding_type == "relative_key":
                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
        batch_size, n_head, seq_length, d_head = query_layer.shape
        bbox_pos_emb = bbox_pos_emb.view(seq_length, seq_length, batch_size, d_head)
        bbox_pos_emb = bbox_pos_emb.permute([2, 0, 1, 3])
        bbox_pos_scores = torch.einsum("bnid,bijd->bnij", (query_layer, bbox_pos_emb))
        attention_scores = attention_scores + bbox_pos_scores
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None: attention_scores = attention_scores + attention_mask
        attention_probs = nn.Softmax(dim=-1)(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,)
        if self.is_decoder: outputs = outputs + (past_key_value,)
        return outputs
class BrosSelfOutput(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 BrosAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.self = BrosSelfAttention(config)
        self.output = BrosSelfOutput(config)
        self.pruned_heads = set()
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads)
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    def forward(self, hidden_states: torch.Tensor, bbox_pos_emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None,
    encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
    output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor]:
        self_outputs = self.self(hidden_states=hidden_states, bbox_pos_emb=bbox_pos_emb, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states,
        encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions)
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class BrosIntermediate(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 BrosOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class BrosLayer(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 = BrosAttention(config)
        self.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder: raise Exception(f"{self} should be used as a decoder model if cross attention is added")
            self.crossattention = BrosAttention(config)
        self.intermediate = BrosIntermediate(config)
        self.output = BrosOutput(config)
    def forward(self, hidden_states: torch.Tensor, bbox_pos_emb: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None,
    encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
    output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor]:
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        self_attention_outputs = self.attention(hidden_states, bbox_pos_emb=bbox_pos_emb, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value)
        attention_output = self_attention_outputs[0]
        if self.is_decoder:
            outputs = self_attention_outputs[1:-1]
            present_key_value = self_attention_outputs[-1]
        else: outputs = self_attention_outputs[1:]
        cross_attn_present_key_value = None
        if self.is_decoder and encoder_hidden_states is not None:
            if hasattr(self, "crossattention"): raise Exception(f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`")
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            cross_attention_outputs = self.crossattention(attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions)
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[1:-1]
            cross_attn_present_key_value = cross_attention_outputs[-1]
            present_key_value = present_key_value + cross_attn_present_key_value
        layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output)
        outputs = (layer_output,) + outputs
        if self.is_decoder: outputs = outputs + (present_key_value,)
        return outputs
    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output
class BrosEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([BrosLayer(config) for _ in range(config.num_hidden_layers)])
    def forward(self, hidden_states: torch.Tensor, bbox_pos_emb: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None,
    encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
    use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
        next_decoder_cache = () if use_cache else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_head_mask = head_mask[i] if head_mask is not None else None
            past_key_value = past_key_values[i] if past_key_values is not None else None
            if getattr(self.config, "gradient_checkpointing", False) and self.training:
                if use_cache:
                    logger.warning("`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting `use_cache=False`...")
                    use_cache = False
                layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, bbox_pos_emb, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions)
            else:
                layer_outputs = layer_module(hidden_states=hidden_states, bbox_pos_emb=bbox_pos_emb, attention_mask=attention_mask, head_mask=layer_head_mask, encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions)
            hidden_states = layer_outputs[0]
            if use_cache: next_decoder_cache += (layer_outputs[-1],)
            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)
                if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions] if v is not None)
        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions)
class BrosPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output
class BrosRelationExtractor(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.n_relations = config.n_relations
        self.backbone_hidden_size = config.hidden_size
        self.head_hidden_size = config.hidden_size
        self.classifier_dropout_prob = config.classifier_dropout_prob
        self.drop = nn.Dropout(self.classifier_dropout_prob)
        self.query = nn.Linear(self.backbone_hidden_size, self.n_relations * self.head_hidden_size)
        self.key = nn.Linear(self.backbone_hidden_size, self.n_relations * self.head_hidden_size)
        self.dummy_node = nn.Parameter(torch.zeros(1, self.backbone_hidden_size))
    def forward(self, query_layer: torch.Tensor, key_layer: torch.Tensor):
        query_layer = self.query(self.drop(query_layer))
        dummy_vec = self.dummy_node.unsqueeze(0).repeat(1, key_layer.size(1), 1)
        key_layer = torch.cat([key_layer, dummy_vec], axis=0)
        key_layer = self.key(self.drop(key_layer))
        query_layer = query_layer.view(query_layer.size(0), query_layer.size(1), self.n_relations, self.head_hidden_size)
        key_layer = key_layer.view(key_layer.size(0), key_layer.size(1), self.n_relations, self.head_hidden_size)
        relation_score = torch.matmul(query_layer.permute(2, 1, 0, 3), key_layer.permute(2, 1, 3, 0))
        return relation_score
class BrosPreTrainedModel(PreTrainedModel):
    config_class = BrosConfig
    base_model_prefix = "bros"
    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
@add_start_docstrings("The bare Bros Model transformer outputting raw hidden-states without any specific head on top.", BROS_START_DOCSTRING)
class BrosModel(BrosPreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.embeddings = BrosTextEmbeddings(config)
        self.bbox_embeddings = BrosBboxEmbeddings(config)
        self.encoder = BrosEncoder(config)
        self.pooler = BrosPooler(config) if add_pooling_layer else None
        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)
    @add_start_docstrings_to_model_forward(BROS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, bbox: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None,
    encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache
        else: use_cache = False
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None: input_shape = input_ids.size()
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        if bbox is None: raise ValueError("You have to specify bbox")
        batch_size, seq_length = input_shape
        device = input_ids.device if input_ids is not None else inputs_embeds.device
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
        if attention_mask is None: attention_mask = torch.ones(input_shape, device=device)
        if token_type_ids is None:
            if hasattr(self.embeddings, "token_type_ids"):
                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
        if self.config.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else: encoder_extended_attention_mask = None
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length)
        if bbox.shape[-1] == 4: bbox = bbox[:, :, [0, 1, 2, 1, 2, 3, 0, 3]]
        scaled_bbox = bbox * self.config.bbox_scale
        bbox_position_embeddings = self.bbox_embeddings(scaled_bbox)
        encoder_outputs = self.encoder(embedding_output, bbox_pos_emb=bbox_position_embeddings, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states,
        encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
        if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:]
        return BaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions)
@add_start_docstrings("Bros Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.", BROS_START_DOCSTRING)
class BrosForTokenClassification(BrosPreTrainedModel):
    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.bros = BrosModel(config)
        classifier_dropout = (config.classifier_dropout if hasattr(config, "classifier_dropout") else config.hidden_dropout_prob)
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.init_weights()
    @add_start_docstrings_to_model_forward(BROS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, bbox: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, bbox_first_token_mask: Optional[torch.Tensor] = None,
    token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.bros(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)
        sequence_output = outputs[0]
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            if bbox_first_token_mask is not None:
                bbox_first_token_mask = bbox_first_token_mask.view(-1)
                loss = loss_fct(logits.view(-1, self.num_labels)[bbox_first_token_mask], labels.view(-1)[bbox_first_token_mask])
            else: loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""
    Bros Model with a token classification head on top (initial_token_layers and subsequent_token_layer on top of the
    hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. The initial_token_classifier is used to
    predict the first token of each entity, and the subsequent_token_classifier is used to predict the subsequent
    tokens within an entity. Compared to BrosForTokenClassification, this model is more robust to serialization errors
    since it predicts next token from one token.
    """, BROS_START_DOCSTRING)
class BrosSpadeEEForTokenClassification(BrosPreTrainedModel):
    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.num_labels = config.num_labels
        self.n_relations = config.n_relations
        self.backbone_hidden_size = config.hidden_size
        self.bros = BrosModel(config)
        classifier_dropout = (config.classifier_dropout if hasattr(config, "classifier_dropout") else config.hidden_dropout_prob)
        self.initial_token_classifier = nn.Sequential(nn.Dropout(classifier_dropout), nn.Linear(config.hidden_size, config.hidden_size),
        nn.Dropout(classifier_dropout), nn.Linear(config.hidden_size, config.num_labels))
        self.subsequent_token_classifier = BrosRelationExtractor(config)
        self.init_weights()
    @add_start_docstrings_to_model_forward(BROS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, bbox: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, bbox_first_token_mask: Optional[torch.Tensor] = None,
    token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    initial_token_labels: Optional[torch.Tensor] = None, subsequent_token_labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], BrosSpadeOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.bros(input_ids=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)
        last_hidden_states = outputs[0]
        last_hidden_states = last_hidden_states.transpose(0, 1).contiguous()
        initial_token_logits = self.initial_token_classifier(last_hidden_states).transpose(0, 1).contiguous()
        subsequent_token_logits = self.subsequent_token_classifier(last_hidden_states, last_hidden_states).squeeze(0)
        inv_attention_mask = 1 - attention_mask
        batch_size, max_seq_length = inv_attention_mask.shape
        device = inv_attention_mask.device
        invalid_token_mask = torch.cat([inv_attention_mask, torch.zeros([batch_size, 1]).to(device)], axis=1).bool()
        subsequent_token_logits = subsequent_token_logits.masked_fill(invalid_token_mask[:, None, :], torch.finfo(subsequent_token_logits.dtype).min)
        self_token_mask = torch.eye(max_seq_length, max_seq_length + 1).to(device).bool()
        subsequent_token_logits = subsequent_token_logits.masked_fill(self_token_mask[None, :, :], torch.finfo(subsequent_token_logits.dtype).min)
        subsequent_token_mask = attention_mask.view(-1).bool()
        loss = None
        if initial_token_labels is not None and subsequent_token_labels is not None:
            loss_fct = CrossEntropyLoss()
            initial_token_labels = initial_token_labels.view(-1)
            if bbox_first_token_mask is not None:
                bbox_first_token_mask = bbox_first_token_mask.view(-1)
                initial_token_loss = loss_fct(initial_token_logits.view(-1, self.num_labels)[bbox_first_token_mask], initial_token_labels[bbox_first_token_mask])
            else: initial_token_loss = loss_fct(initial_token_logits.view(-1, self.num_labels), initial_token_labels)
            subsequent_token_labels = subsequent_token_labels.view(-1)
            subsequent_token_loss = loss_fct(subsequent_token_logits.view(-1, max_seq_length + 1)[subsequent_token_mask], subsequent_token_labels[subsequent_token_mask])
            loss = initial_token_loss + subsequent_token_loss
        if not return_dict:
            output = (initial_token_logits, subsequent_token_logits) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return BrosSpadeOutput(loss=loss, initial_token_logits=initial_token_logits, subsequent_token_logits=subsequent_token_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("Bros Model with a token classification head on top (a entity_linker layer on top of the hidden-states output) e.g. for Entity-Linking. The entity_linker is used to predict intra-entity links (one entity to another entity).", BROS_START_DOCSTRING)
class BrosSpadeELForTokenClassification(BrosPreTrainedModel):
    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.num_labels = config.num_labels
        self.n_relations = config.n_relations
        self.backbone_hidden_size = config.hidden_size
        self.bros = BrosModel(config)
        (config.classifier_dropout if hasattr(config, "classifier_dropout") else config.hidden_dropout_prob)
        self.entity_linker = BrosRelationExtractor(config)
        self.init_weights()
    @add_start_docstrings_to_model_forward(BROS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, bbox: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, bbox_first_token_mask: Optional[torch.Tensor] = None,
    token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.bros(input_ids=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)
        last_hidden_states = outputs[0]
        last_hidden_states = last_hidden_states.transpose(0, 1).contiguous()
        logits = self.entity_linker(last_hidden_states, last_hidden_states).squeeze(0)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            batch_size, max_seq_length = attention_mask.shape
            device = attention_mask.device
            self_token_mask = torch.eye(max_seq_length, max_seq_length + 1).to(device).bool()
            mask = bbox_first_token_mask.view(-1)
            bbox_first_token_mask = torch.cat([~bbox_first_token_mask, torch.zeros([batch_size, 1], dtype=torch.bool).to(device)], axis=1)
            logits = logits.masked_fill(bbox_first_token_mask[:, None, :], torch.finfo(logits.dtype).min)
            logits = logits.masked_fill(self_token_mask[None, :, :], torch.finfo(logits.dtype).min)
            loss = loss_fct(logits.view(-1, max_seq_length + 1)[mask], labels.view(-1)[mask])
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
