"""
    #################################################################################################################################################################################################
    # 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 collections import OrderedDict
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, QuickGELUActivation
from ...modeling_outputs import (BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, MaskedLMOutput, ModelOutput, SequenceClassifierOutput)
from ...modeling_utils import PreTrainedModel, apply_chunking_to_forward
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_bridgetower import BridgeTowerConfig, BridgeTowerTextConfig, BridgeTowerVisionConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "BridgeTowerConfig"
_CHECKPOINT_FOR_DOC = "BridgeTower/bridgetower-base"
_TOKENIZER_FOR_DOC = "RobertaTokenizer"
BRIDGETOWER_START_DOCSTRING = r"""
    This model is 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 ([`BridgeTowerConfig`]): 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.
"""
BRIDGETOWER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See
            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
            IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `(0)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`BridgeTowerImageProcessor`]. See
            [`BridgeTowerImageProcessor.__call__`] for details.
        pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
            Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:
            - 1 for pixels that are real (i.e. *not masked*),
            - 0 for pixels that are padding (i.e. *masked*).
            `What are attention masks? <../glossary.html#attention-mask>`__
        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.
        image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):
            Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `pixel_values` into patch embeddings.
        image_token_type_idx (`int`, *optional*):
            - The token type ids for images.
        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 BridgeTowerModelOutput(ModelOutput):
    """Args:"""
    text_features: torch.FloatTensor = None
    image_features: torch.FloatTensor = None
    pooler_output: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BridgeTowerContrastiveOutput(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    text_embeds: Optional[Tuple[torch.FloatTensor]] = None
    image_embeds: Optional[Tuple[torch.FloatTensor]] = None
    cross_embeds: Optional[Tuple[torch.FloatTensor]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
class BridgeTowerResidualAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.attn = nn.MultiheadAttention(config.hidden_size, config.hidden_size // 64)
        self.ln_1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.mlp = nn.ModuleDict(OrderedDict([("c_fc", nn.Linear(config.hidden_size, config.hidden_size * 4)), ("gelu", QuickGELUActivation()), ("c_proj", nn.Linear(config.hidden_size * 4, config.hidden_size))]))
        self.ln_2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attn_mask = None
    def attention(self, hidden_state: torch.Tensor, attention_mask: torch.Tensor):
        if attention_mask is not None: attention_mask = attention_mask.to(dtype=torch.bool, device=hidden_state.device)
        self.attn_mask = (self.attn_mask.to(dtype=hidden_state.dtype, device=hidden_state.device) if self.attn_mask is not None else None)
        return self.attn(hidden_state, hidden_state, hidden_state, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=attention_mask)[0]
    def forward(self, hidden_state: torch.Tensor, attention_mask: torch.Tensor = None):
        residual_state = hidden_state + self.attention(self.ln_1(hidden_state), attention_mask)
        hidden_state = self.ln_2(residual_state)
        for _, layer in self.mlp.items(): hidden_state = layer(hidden_state)
        hidden_state = residual_state + hidden_state
        return hidden_state
class BridgeTowerTransformer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.num_hidden_layers = config.num_hidden_layers
        if config.remove_last_layer: self.resblocks = nn.ModuleList([BridgeTowerResidualAttention(config) for _ in range(self.num_hidden_layers - 1)])
        else: self.resblocks = nn.ModuleList([BridgeTowerResidualAttention(config) for _ in range(self.num_hidden_layers)])
        self.stop_gradient = config.stop_gradient
    def forward(self, hidden_state: torch.Tensor, attention_mask: Optional[torch.Tensor] = None):
        hidden_states = []
        for block in self.resblocks:
            hidden_state = block(hidden_state, attention_mask)
            if self.stop_gradient: hidden_states.append(hidden_state.detach())
            else: hidden_states.append(hidden_state)
        return hidden_states
class BridgeTowerVisionEmbeddings(nn.Module):
    def __init__(self, config: BridgeTowerVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size
        self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
        self.patch_embedding = nn.Conv2d(in_channels=config.num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False)
        self.num_patches = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches + 1
        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
        self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
        batch_size = pixel_values.shape[0]
        target_dtype = self.patch_embedding.weight.dtype
        patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
        embeddings = embeddings + self.position_embedding(self.position_ids)
        return embeddings
class BridgeTowerVisionTransformer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.embeddings = BridgeTowerVisionEmbeddings(config)
        self.ln_pre = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.transformer = BridgeTowerTransformer(config)
        self.ln_post = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.share_layernorm = config.share_layernorm
        if not config.share_layernorm: self.ln_separate = nn.ModuleList([nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) for _ in range(config.num_hidden_layers)])
    def forward(self, pixel_values: torch.Tensor, attention_mask):
        hidden_states = self.embeddings(pixel_values)
        hidden_states = self.ln_pre(hidden_states)
        hidden_states = hidden_states.permute(1, 0, 2)
        hidden_states = self.transformer(hidden_states, attention_mask)
        hidden_states = torch.stack(hidden_states, dim=0)
        hidden_states = hidden_states.permute(0, 2, 1, 3)
        if self.share_layernorm: hidden_states = self.ln_post(hidden_states)
        else:
            hidden_states_stack = []
            for hidden_states, ln in zip(hidden_states, self.ln_separate):
                hidden_states = ln(hidden_states)
                hidden_states_stack.append(hidden_states)
            hidden_states = torch.stack(hidden_states_stack, dim=0)
        return hidden_states
    def forward_pre(self, pixel_values: torch.Tensor):
        hidden_states = self.embeddings(pixel_values)
        hidden_states = self.ln_pre(hidden_states)
        hidden_states = hidden_states.permute(1, 0, 2)
        return hidden_states
    def forward_post(self, hidden_state: torch.Tensor):
        visual_output_post = hidden_state.permute(1, 0, 2)
        visual_output_post = self.ln_post(visual_output_post)
        return visual_output_post
class BridgeTowerLinkTower(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.link_tower_type = config.link_tower_type
        self.hidden_size = config.hidden_size
        if config.link_tower_type in ["add", "scaled_add", "interpolate"]:
            if config.link_tower_type == "scaled_add": self.scaled_factor = nn.Parameter(torch.tensor(1.0))
            elif config.link_tower_type == "interpolate": self.beta = nn.Parameter(torch.tensor(0.5))
            self.LayerNorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)
        else: raise NotImplementedError(f"link_tower_type {config.link_tower_type} is not implemented")
    def forward(self, hidden_states, cross_modal_hidden_states, attention_mask):
        if self.link_tower_type == "add": return self.LayerNorm(hidden_states + cross_modal_hidden_states)
        elif self.link_tower_type == "scaled_add": return self.LayerNorm(hidden_states * self.scaled_factor + cross_modal_hidden_states)
        elif self.link_tower_type == "interpolate": return self.LayerNorm(hidden_states * (1 - self.beta) + cross_modal_hidden_states * self.beta)
        else: raise NotImplementedError(f"link_tower_type {self.link_tower_type} is not implemented")
class BridgeTowerSelfOutput(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 BridgeTowerIntermediate(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 BridgeTowerOutput(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 BridgeTowerPooler(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 BridgeTowerSelfAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})")
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = position_embedding_type or getattr(config, "position_embedding_type", "absolute")
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
        self.is_decoder = config.is_decoder
    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(new_x_shape)
        return x.permute(0, 2, 1, 3)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor]:
        mixed_query_layer = self.query(hidden_states)
        is_cross_attention = encoder_hidden_states is not None
        if is_cross_attention and past_key_value is not None:
            key_layer = past_key_value[0]
            value_layer = past_key_value[1]
            attention_mask = encoder_attention_mask
        elif is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)
        use_cache = past_key_value is not None
        if self.is_decoder: past_key_value = (key_layer, value_layer)
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            query_length, key_length = query_layer.shape[2], key_layer.shape[2]
            if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
            distance = position_ids_l - position_ids_r
            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)
            if self.position_embedding_type == "relative_key":
                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None: attention_scores = attention_scores + attention_mask
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
        attention_probs = self.dropout(attention_probs)
        if head_mask is not None: attention_probs = attention_probs * head_mask
        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        if self.is_decoder: outputs = outputs + (past_key_value,)
        return outputs
BRIDGE_TOWER_SELF_ATTENTION_CLASSES = {"eager": BridgeTowerSelfAttention}
class BridgeTowerAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        self.self = BRIDGE_TOWER_SELF_ATTENTION_CLASSES[config._attn_implementation](config, position_embedding_type=position_embedding_type)
        self.output = BridgeTowerSelfOutput(config)
        self.pruned_heads = set()
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads)
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor]:
        self_outputs = self.self(hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class BridgeTowerBertCrossLayer(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 = BridgeTowerAttention(config)
        self.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        self.crossattention = BridgeTowerAttention(config)
        self.intermediate = BridgeTowerIntermediate(config)
        self.output = BridgeTowerOutput(config)
    def forward(self, hidden_states, encoder_hidden_states, attention_mask=None, head_mask=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False):
        self_attention_outputs = self.attention(hidden_states, attention_mask=attention_mask, head_mask=None, output_attentions=output_attentions, past_key_value=None)
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]
        cross_attention_outputs = self.crossattention(attention_output, 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 = cross_attention_outputs[0]
        outputs = outputs + cross_attention_outputs[1:-1]
        layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output)
        outputs = (layer_output,) + outputs
        return outputs
    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output
class BridgeTowerTextLayer(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 = BridgeTowerAttention(config)
        self.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
            self.crossattention = BridgeTowerAttention(config, position_embedding_type="absolute")
        self.intermediate = BridgeTowerIntermediate(config)
        self.output = BridgeTowerOutput(config)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None,
    past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor]:
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value)
        attention_output = self_attention_outputs[0]
        if self.is_decoder:
            outputs = self_attention_outputs[1:-1]
            present_key_value = self_attention_outputs[-1]
        else: outputs = self_attention_outputs[1:]
        cross_attn_present_key_value = None
        if self.is_decoder and encoder_hidden_states is not None:
            if not hasattr(self, "crossattention"): raise ValueError(f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`")
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            cross_attention_outputs = self.crossattention(attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions)
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[1:-1]
            cross_attn_present_key_value = cross_attention_outputs[-1]
            present_key_value = present_key_value + cross_attn_present_key_value
        layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output)
        outputs = (layer_output,) + outputs
        if self.is_decoder: outputs = outputs + (present_key_value,)
        return outputs
    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output
class BridgeTowerTextEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([BridgeTowerTextLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False,
    output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
                use_cache = False
        next_decoder_cache = () if use_cache else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_head_mask = head_mask[i] if head_mask is not None else None
            past_key_value = past_key_values[i] if past_key_values is not None else None
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
            else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
            hidden_states = layer_outputs[0]
            if use_cache: next_decoder_cache += (layer_outputs[-1],)
            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)
                if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions] if v is not None)
        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions)
class BridgeTowerTextEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False)
        self.register_buffer("token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False)
        self.padding_idx = config.pad_token_id
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx)
    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0):
        if position_ids is None:
            if input_ids is not None: position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
            else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
        if input_ids is not None: input_shape = input_ids.size()
        else: input_shape = inputs_embeds.size()[:-1]
        seq_length = input_shape[1]
        if token_type_ids is None:
            if hasattr(self, "token_type_ids"):
                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
        if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + token_type_embeddings
        if self.position_embedding_type == "absolute":
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
        input_shape = inputs_embeds.size()[:-1]
        sequence_length = input_shape[1]
        position_ids = torch.arange(self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device)
        return position_ids.unsqueeze(0).expand(input_shape)
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
    mask = input_ids.ne(padding_idx).int()
    incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
    return incremental_indices.long() + padding_idx
class BridgeTowerPreTrainedModel(PreTrainedModel):
    config_class = BridgeTowerConfig
    base_model_prefix = "bridgetower"
    supports_gradient_checkpointing = False
    _no_split_modules = ["BridgeTowerSelfAttention", "BridgeTowerResidualAttention"]
    _skip_keys_device_placement = "past_key_values"
    def _init_weights(self, module):
        if isinstance(module, BridgeTowerVisionModel):
            proj_std = (module.visual.transformer.hidden_size**-0.5) * ((2 * module.visual.transformer.num_hidden_layers) ** -0.5)
            attn_std = module.visual.transformer.hidden_size**-0.5
            fc_std = (2 * module.visual.transformer.hidden_size) ** -0.5
            for block in module.visual.transformer.resblocks:
                nn.init.normal_(block.attn.in_proj_weight, std=attn_std * self.config.initializer_factor)
                nn.init.normal_(block.attn.out_proj.weight, std=proj_std * self.config.initializer_factor)
                nn.init.normal_(block.mlp.c_fc.weight, std=fc_std * self.config.initializer_factor)
                nn.init.normal_(block.mlp.c_proj.weight, std=proj_std * self.config.initializer_factor)
            nn.init.normal_(module.visual.embeddings.class_embedding, std=attn_std * self.config.initializer_factor)
            nn.init.normal_(module.visual.embeddings.position_embedding.weight, std=attn_std * self.config.initializer_factor)
        elif isinstance(module, (nn.Linear, nn.Conv2d, nn.Embedding)): module.weight.data.normal_(mean=0.0, std=0.05 * self.config.initializer_factor)
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_()
class BridgeTowerVisionModel(BridgeTowerPreTrainedModel):
    config_class = BridgeTowerVisionConfig
    def __init__(self, config):
        super().__init__(config)
        self.visual = BridgeTowerVisionTransformer(config)
    @property
    def dtype(self): return self.visual.embeddings.patch_embedding.weight.dtype
    def forward(self, image, image_mask=None): return self.visual(image.type(self.dtype), image_mask)
class BridgeTowerTextModel(BridgeTowerPreTrainedModel):
    config_class = BridgeTowerTextConfig
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.embeddings = BridgeTowerTextEmbeddings(config)
        self.encoder = BridgeTowerTextEncoder(config)
        self.pooler = BridgeTowerPooler(config) if add_pooling_layer else None
        self.post_init()
    def get_input_embeddings(self): return self.embeddings.word_embeddings
    def set_input_embeddings(self, value): self.embeddings.word_embeddings = value
    def _prune_heads(self, heads_to_prune):
        for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads)
    def forward(self, input_ids: 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:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        batch_size, seq_length = input_shape
        device = input_ids.device if input_ids is not None else inputs_embeds.device
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
        if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
        if token_type_ids is None:
            if hasattr(self.embeddings, "token_type_ids"):
                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
        if self.config.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else: encoder_extended_attention_mask = None
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length)
        encoder_outputs = self.encoder(embedding_output, 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("The bare BridgeTower Model transformer outputting BridgeTowerModelOutput object without any specific head on top.", BRIDGETOWER_START_DOCSTRING)
class BridgeTowerModel(BridgeTowerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        vision_config = config.vision_config
        text_config = config.text_config
        if config.share_cross_modal_transformer_layers:
            self.cross_modal_text_transform = nn.Linear(text_config.hidden_size, config.hidden_size)
            self.cross_modal_image_transform = nn.Linear(vision_config.hidden_size, config.hidden_size)
        else:
            self.cross_modal_text_transform = nn.ModuleList([nn.Linear(text_config.hidden_size, config.hidden_size) for _ in range(config.num_hidden_layers)])
            self.cross_modal_image_transform = nn.ModuleList([nn.Linear(vision_config.hidden_size, config.hidden_size) for _ in range(config.num_hidden_layers)])
        self.token_type_embeddings = nn.Embedding(2, config.hidden_size)
        self.vision_model = BridgeTowerVisionModel(vision_config)
        self.text_model = BridgeTowerTextModel(text_config)
        if not vision_config.share_layernorm and config.init_layernorm_from_vision_encoder:
            for ln in self.vision_model.visual.cross_modal_ln_separate:
                ln.weight.data = self.vision_model.visual.ln_post.weight.data
                ln.bias.data = self.vision_model.visual.ln_post.bias.data
        self.cross_modal_image_layers = nn.ModuleList([BridgeTowerBertCrossLayer(text_config) for _ in range(config.num_hidden_layers)])
        self.cross_modal_text_layers = nn.ModuleList([BridgeTowerBertCrossLayer(text_config) for _ in range(config.num_hidden_layers)])
        self.cross_modal_image_pooler = BridgeTowerPooler(config)
        self.cross_modal_text_pooler = BridgeTowerPooler(config)
        self.cross_modal_text_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.cross_modal_image_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        if config.share_link_tower_layers:
            self.cross_modal_text_link_tower = BridgeTowerLinkTower(config)
            self.cross_modal_image_link_tower = BridgeTowerLinkTower(config)
        else:
            self.cross_modal_text_link_tower = nn.ModuleList([BridgeTowerLinkTower(config) for _ in range(config.num_hidden_layers - 1)])
            self.cross_modal_image_link_tower = nn.ModuleList([BridgeTowerLinkTower(config) for _ in range(config.num_hidden_layers - 1)])
        self.post_init()
    def get_input_embeddings(self): return self.text_model.get_input_embeddings()
    def set_input_embeddings(self, value): self.text_model.set_input_embeddings(value)
    @add_start_docstrings_to_model_forward(BRIDGETOWER_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    pixel_values: Optional[torch.FloatTensor] = None, pixel_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    image_embeds: Optional[torch.FloatTensor] = None, image_token_type_idx: Optional[int] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, labels: Optional[torch.LongTensor] = None) -> Union[Tuple[torch.Tensor], BridgeTowerModelOutput]:
        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)
        all_hidden_states_text = () if output_hidden_states else None
        all_hidden_states_image = () if output_hidden_states else None
        all_hidden_states_cross = () if output_hidden_states else None
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        if inputs_embeds is not None and input_ids is None: raise NotImplementedError("BridgeTowerModel does not use `inputs_embeds`.  Make sure to pass in `input_ids` instead.")
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        image_token_type_idx = image_token_type_idx if image_token_type_idx else 1
        input_shape = input_ids.size()
        text_embeds = self.text_model.embeddings(input_ids=input_ids)
        if output_hidden_states: all_hidden_states_text += (text_embeds,)
        if attention_mask is None: attention_mask = torch.ones(input_shape, dtype=torch.long, device=input_ids.device)
        extend_text_masks = self.text_model.get_extended_attention_mask(attention_mask, input_shape).to(input_ids.device)
        split_index = len(self.text_model.encoder.layer) - self.config.num_hidden_layers + 1
        for layer in self.text_model.encoder.layer[:split_index]:
            text_embeds = layer(text_embeds, extend_text_masks)[0]
            if output_hidden_states: all_hidden_states_text += (text_embeds,)
        if image_embeds is None: image_embeds = self.vision_model.visual.forward_pre(pixel_values.type(self.vision_model.dtype))
        else: image_embeds = image_embeds.permute(1, 0, 2)
        if output_hidden_states: all_hidden_states_image += (image_embeds,)
        for block in self.vision_model.visual.transformer.resblocks[:split_index]:
            image_embeds = block(image_embeds)
            if output_hidden_states: all_hidden_states_image += (image_embeds,)
        image_embeds_with_ln = self.vision_model.visual.forward_post(image_embeds.type(self.vision_model.dtype))
        cross_modal_text = self.cross_modal_text_transform(text_embeds)
        text_token_type_embeddings = self.token_type_embeddings(torch.zeros(1, dtype=torch.long, device=input_ids.device)).expand_as(cross_modal_text)
        cross_modal_text = self.cross_modal_text_layernorm(cross_modal_text + text_token_type_embeddings)
        image_embeds_with_ln = self.cross_modal_image_transform(image_embeds_with_ln)
        image_token_type_embeddings = self.token_type_embeddings(torch.full((1,), image_token_type_idx, dtype=torch.long, device=input_ids.device)).expand_as(image_embeds_with_ln)
        image_embeds_with_ln = image_embeds_with_ln + image_token_type_embeddings
        cross_modal_image = self.cross_modal_image_layernorm(image_embeds_with_ln)
        pixel_mask = torch.ones((cross_modal_image.size(0), cross_modal_image.size(1)), dtype=torch.long, device=input_ids.device)
        extend_image_masks = self.text_model.get_extended_attention_mask(pixel_mask, pixel_mask.size()).to(input_ids.device)
        layer_outputs_text = self.cross_modal_text_layers[0](cross_modal_text, cross_modal_image, attention_mask=extend_text_masks, encoder_attention_mask=extend_image_masks, output_attentions=output_attentions)
        cross_text_features = layer_outputs_text[0]
        layer_outputs_image = self.cross_modal_image_layers[0](cross_modal_image, cross_modal_text, attention_mask=extend_image_masks, encoder_attention_mask=extend_text_masks, output_attentions=output_attentions)
        cross_image_features = layer_outputs_image[0]
        if output_hidden_states: all_hidden_states_cross += ((cross_text_features, cross_image_features),)
        if output_attentions: all_self_attentions += ((layer_outputs_text[1], layer_outputs_image[1]),)
        link_layer_index = 0
        for i in range(split_index, len(self.text_model.encoder.layer)):
            text_embeds = self.text_model.encoder.layer[i](text_embeds, extend_text_masks)[0]
            image_embeds = self.vision_model.visual.transformer.resblocks[i](image_embeds).type(self.vision_model.dtype)
            image_embeds_with_ln = (self.cross_modal_image_transform(self.vision_model.visual.forward_post(image_embeds)) + image_token_type_embeddings)
            text_link_tower = self.cross_modal_text_link_tower[link_layer_index]
            image_link_tower = self.cross_modal_image_link_tower[link_layer_index]
            cross_text_features_ = text_link_tower(self.cross_modal_text_transform(text_embeds) + text_token_type_embeddings, cross_text_features, extend_text_masks)
            cross_image_features_ = image_link_tower(image_embeds_with_ln, cross_image_features, extend_image_masks)
            layer_outputs_text = self.cross_modal_text_layers[link_layer_index + 1](cross_text_features_, cross_image_features_, attention_mask=extend_text_masks, encoder_attention_mask=extend_image_masks, output_attentions=output_attentions)
            cross_text_features = layer_outputs_text[0]
            layer_outputs_image = self.cross_modal_image_layers[link_layer_index + 1](cross_image_features_, cross_text_features_, attention_mask=extend_image_masks, encoder_attention_mask=extend_text_masks, output_attentions=output_attentions)
            cross_image_features = layer_outputs_image[0]
            link_layer_index += 1
            if output_hidden_states:
                all_hidden_states_text += (text_embeds,)
                all_hidden_states_image += (image_embeds,)
                all_hidden_states_cross += ((cross_text_features, cross_image_features),)
            if output_attentions: all_self_attentions += ((layer_outputs_text[1], layer_outputs_image[1]),)
        text_features, image_features = cross_text_features, cross_image_features
        cls_features = self.get_cls_features(text_features, image_features)
        if output_hidden_states: all_hidden_states = (all_hidden_states_text, all_hidden_states_image, all_hidden_states_cross)
        if not return_dict: return tuple(v for v in [text_features, image_features, cls_features, all_hidden_states, all_self_attentions] if v is not None)
        return BridgeTowerModelOutput(text_features=text_features, image_features=image_features, pooler_output=cls_features, hidden_states=all_hidden_states, attentions=all_self_attentions)
    def get_cls_features(self, text_features, image_features):
        cls_features_text = self.cross_modal_text_pooler(text_features)
        cls_features_image = self.cross_modal_image_pooler(image_features)
        return torch.cat([cls_features_text, cls_features_image], dim=-1)
class BridgeTowerPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act]
        else: self.transform_act_fn = config.hidden_act
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states
class BridgeTowerMLMHead(nn.Module):
    def __init__(self, config, weight=None):
        super().__init__()
        self.config = config
        self.transform = BridgeTowerPredictionHeadTransform(config)
        self.decoder = nn.Linear(config.hidden_size, config.text_config.vocab_size, bias=False)
        self.bias = nn.Parameter(torch.zeros(config.text_config.vocab_size))
        if weight is not None: self.decoder.weight = weight
    def forward(self, x):
        mlm_score = self.transform(x)
        mlm_score = self.decoder(mlm_score) + self.bias
        return mlm_score
class BridgeTowerITMHead(nn.Module):
    def __init__(self, hidden_size):
        super().__init__()
        self.fc = nn.Linear(hidden_size, 2)
    def forward(self, x):
        itm_score = self.fc(x)
        return itm_score
@add_start_docstrings("BridgeTower Model with a language modeling head on top as done during pretraining.", BRIDGETOWER_START_DOCSTRING)
class BridgeTowerForMaskedLM(BridgeTowerPreTrainedModel):
    _tied_weights_keys = ["mlm_score.decoder.weight"]
    def __init__(self, config):
        super().__init__(config)
        self.bridgetower = BridgeTowerModel(config)
        self.mlm_score = BridgeTowerMLMHead(config)
        self.post_init()
    def get_output_embeddings(self): return self.mlm_score.decoder
    def set_output_embeddings(self, new_embeddings): self.mlm_score.decoder = new_embeddings
    @add_start_docstrings_to_model_forward(BRIDGETOWER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    pixel_values: Optional[torch.FloatTensor] = None, pixel_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    image_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    labels: Optional[torch.LongTensor] = None) -> Union[MaskedLMOutput, Tuple[torch.FloatTensor]]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.bridgetower(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, pixel_values=pixel_values, pixel_mask=pixel_mask, head_mask=head_mask,
        inputs_embeds=inputs_embeds, image_embeds=image_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        mlm_logits = self.mlm_score(outputs.text_features if return_dict else outputs[0])
        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            labels = labels.to(mlm_logits.device)
            masked_lm_loss = loss_fct(mlm_logits.view(-1, self.config.text_config.vocab_size), labels.view(-1))
        if not return_dict:
            output = tuple(mlm_logits)
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
        return MaskedLMOutput(loss=masked_lm_loss, logits=mlm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("BridgeTower Model transformer with a classifier head on top (a linear layer on top of the final hidden state of the [CLS] token) for image-to-text matching.", BRIDGETOWER_START_DOCSTRING)
class BridgeTowerForImageAndTextRetrieval(BridgeTowerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.bridgetower = BridgeTowerModel(config)
        self.itm_score = BridgeTowerITMHead(config.hidden_size * 2)
        self.post_init()
    @add_start_docstrings_to_model_forward(BRIDGETOWER_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    pixel_values: Optional[torch.FloatTensor] = None, pixel_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    image_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    labels: Optional[torch.LongTensor] = None) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.bridgetower(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, pixel_values=pixel_values, pixel_mask=pixel_mask, head_mask=head_mask,
        inputs_embeds=inputs_embeds, image_embeds=image_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooler_output = outputs.pooler_output if return_dict else outputs[2]
        logits = self.itm_score(pooler_output)
        itm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            labels = labels.to(logits.device)
            itm_loss = loss_fct(logits, labels)
        if not return_dict:
            output = tuple(logits)
            return ((itm_loss,) + output) if itm_loss is not None else output
        return SequenceClassifierOutput(loss=itm_loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
class BridgeTowerContrastiveHead(nn.Module):
    def __init__(self, hidden_size, embed_size):
        super().__init__()
        self.fc = nn.Linear(hidden_size, embed_size)
    def forward(self, x):
        x = self.fc(x)
        return x
@add_start_docstrings("BridgeTower Model with a image-text contrastive head on top computing image-text contrastive loss.", BRIDGETOWER_START_DOCSTRING)
class BridgeTowerForContrastiveLearning(BridgeTowerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.bridgetower = BridgeTowerModel(config)
        self.itc_text_head = BridgeTowerContrastiveHead(config.hidden_size, config.contrastive_hidden_size)
        self.itc_image_head = BridgeTowerContrastiveHead(config.hidden_size, config.contrastive_hidden_size)
        self.itc_cross_modal_head = BridgeTowerContrastiveHead(config.hidden_size * 2, config.contrastive_hidden_size)
        self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
        self.post_init()
    @add_start_docstrings_to_model_forward(BRIDGETOWER_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    pixel_values: Optional[torch.FloatTensor] = None, pixel_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    image_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = True, return_dict: Optional[bool] = None,
    return_loss: Optional[bool] = None) -> Union[BridgeTowerContrastiveOutput, Tuple[torch.FloatTensor]]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.bridgetower(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, pixel_values=pixel_values, pixel_mask=pixel_mask, head_mask=head_mask,
        inputs_embeds=inputs_embeds, image_embeds=image_embeds, output_attentions=output_attentions, output_hidden_states=True, return_dict=return_dict)
        pooler_output = outputs.pooler_output if return_dict else outputs[2]
        hidden_states_txt, hidden_states_img, hidden_states_cross_modal = (outputs.hidden_states if return_dict else outputs[3])
        text_embeds = hidden_states_txt[-1]
        image_embeds = hidden_states_img[-1]
        image_embeds_with_ln = self.bridgetower.vision_model.visual.forward_post(image_embeds)
        image_token_type_embeddings = self.bridgetower.token_type_embeddings(torch.full((1,), 1, dtype=torch.long, device=self.bridgetower.token_type_embeddings.weight.device)).expand_as(image_embeds_with_ln)
        image_embeds = self.bridgetower.cross_modal_image_transform(image_embeds_with_ln) + image_token_type_embeddings
        text_embeds = nn.functional.normalize(self.itc_text_head(text_embeds[:, 0, :]), dim=-1, p=2)
        image_embeds = nn.functional.normalize(self.itc_image_head(image_embeds[:, 0, :]), dim=-1, p=2).to(device=text_embeds.device)
        cross_embeds = nn.functional.normalize(self.itc_cross_modal_head(pooler_output), dim=-1, p=2).to(device=text_embeds.device)
        logits = torch.stack([text_embeds, image_embeds, cross_embeds], dim=-2)
        logit_scale = self.logit_scale.exp().to(device=text_embeds.device)
        logits_text_to_image = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
        logits_text_to_cross = torch.matmul(text_embeds, cross_embeds.t()) * logit_scale
        logits_image_to_cross = torch.matmul(image_embeds, cross_embeds.t()) * logit_scale
        itc_loss = None
        if return_loss:
            labels = torch.arange(len(logits), device=logits.device)
            text_to_image_loss = nn.functional.cross_entropy(logits_text_to_image, labels)
            text_to_cross_loss = nn.functional.cross_entropy(logits_text_to_cross, labels)
            image_to_cross_loss = nn.functional.cross_entropy(logits_image_to_cross, labels)
            itc_loss = (text_to_image_loss + text_to_cross_loss + image_to_cross_loss) / 3.0
        if not return_dict:
            output = (logits, text_embeds, image_embeds, cross_embeds) + outputs[3:]
            return ((itc_loss,) + output) if itc_loss is not None else output
        return BridgeTowerContrastiveOutput(loss=itc_loss, logits=logits, text_embeds=text_embeds, image_embeds=image_embeds, cross_embeds=cross_embeds, 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.                                                                 #
    #################################################################################################################################################################################################
"""
