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Grounded-Segment-Anything / transformers_4_35_0 /models /bridgetower /modeling_bridgetower.py

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	# coding=utf-8
	# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""PyTorch BridgeTower Model"""

	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_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"BridgeTower/bridgetower-base",
	"BridgeTower/bridgetower-base-itm-mlm"
	# See all bridgetower models at https://huggingface.co/BridgeTower
	]


	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):
	"""
	Output type of [`BridgeTowerModel`].

	Args:
	text_features (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_size)`):
	Sequence of hidden-states at the text output of the last layer of the model.
	image_features (`torch.FloatTensor` of shape `(batch_size, image_sequence_length, hidden_size)`):
	Sequence of hidden-states at the image output of the last layer of the model.
	pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size x 2)`):
	Concatenation of last layer hidden-state of the first token of the text and image sequence (classification
	token), respectively, after further processing through layers used for auxiliary pretraining tasks.
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of
	the model at the output of each layer plus the optional initial embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.

	Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.
	"""

	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):
	"""
	Output type of ['BridgeTowerForContrastiveLearning']

	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `return_loss` is `True`:
	Image-text contrastive loss.
	logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
	Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
	text_embeds (`torch.FloatTensor)`, optional, returned when model is initialized with `with_projection=True`):
	The text embeddings obtained by applying the projection layer to the pooler_output.
	image_embeds (`torch.FloatTensor)`, optional, returned when model is initialized with `with_projection=True`):
	The image embeddings obtained by applying the projection layer to the pooler_output.
	cross_embeds (`torch.FloatTensor)`, optional, returned when model is initialized with `with_projection=True`):
	The text-image cross-modal embeddings obtained by applying the projection layer to the pooler_output.
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of
	the model at the output of each layer plus the optional initial embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.
	"""

	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


	# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->BridgeTower
	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)) # shape = [*, width, grid, grid]
	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)
	# NLD -> LND
	hidden_states = hidden_states.permute(1, 0, 2)

	hidden_states = self.transformer(hidden_states, attention_mask)
	# shape = [num_hidden_layers, hidden_size, , grid * 2]
	hidden_states = torch.stack(hidden_states, dim=0)
	# shape = [num_hidden_layers, , hidden_size, grid * 2]
	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)
	# shape = [num_hidden_layers, , hidden_size, grid * 2]
	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)
	# NLD -> LND
	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")


	# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->BridgeTower
	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


	# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->BridgeTower
	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


	# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->BridgeTower
	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


	# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->BridgeTower
	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:
	# We "pool" the model by simply taking the hidden state corresponding
	# to the first token.
	first_token_tensor = hidden_states[:, 0]
	pooled_output = self.dense(first_token_tensor)
	pooled_output = self.activation(pooled_output)
	return pooled_output


	# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfAttention with Roberta->BridgeTower
	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 "
	f"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)

	# If this is instantiated as a cross-attention module, the keys
	# and values come from an encoder; the attention mask needs to be
	# such that the encoder's padding tokens are not attended to.
	is_cross_attention = encoder_hidden_states is not None

	if is_cross_attention and past_key_value is not None:
	# reuse k,v, cross_attentions
	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:
	# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
	# Further calls to cross_attention layer can then reuse all cross-attention
	# key/value_states (first "if" case)
	# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
	# all previous decoder key/value_states. Further calls to uni-directional self-attention
	# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
	# if encoder bi-directional self-attention `past_key_value` is always `None`
	past_key_value = (key_layer, value_layer)

	# Take the dot product between "query" and "key" to get the raw attention scores.
	attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

	if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
	query_length, key_length = query_layer.shape[2], key_layer.shape[2]
	if use_cache:
	position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
	-1, 1
	)
	else:
	position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
	position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
	distance = position_ids_l - position_ids_r

	positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
	positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility

	if self.position_embedding_type == "relative_key":
	relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores
	elif self.position_embedding_type == "relative_key_query":
	relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key

	attention_scores = attention_scores / math.sqrt(self.attention_head_size)
	if attention_mask is not None:
	# Apply the attention mask is (precomputed for all layers in BridgeTowerModel forward() function)
	attention_scores = attention_scores + attention_mask

	# Normalize the attention scores to probabilities.
	attention_probs = nn.functional.softmax(attention_scores, dim=-1)

	# This is actually dropping out entire tokens to attend to, which might
	# seem a bit unusual, but is taken from the original Transformer paper.
	attention_probs = self.dropout(attention_probs)

	# Mask heads if we want to
	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


	# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->BridgeTower
	class BridgeTowerAttention(nn.Module):
	def __init__(self, config, position_embedding_type=None):
	super().__init__()
	self.self = BridgeTowerSelfAttention(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
	)

	# Prune linear layers
	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)

	# Update hyper params and store pruned heads
	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:] # add attentions if we output them
	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,
	):
	# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
	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]

	# if decoder, the last output is tuple of self-attn cache
	# add self attentions if we output attention weights
	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]
	# add cross attentions if we output attention weights
	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]:
	# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
	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 decoder, the last output is tuple of self-attn cache
	if self.is_decoder:
	outputs = self_attention_outputs[1:-1]
	present_key_value = self_attention_outputs[-1]
	else:
	outputs = self_attention_outputs[1:] # add self attentions if we output attention weights

	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 cached key/values tuple is at positions 3,4 of past_key_value tuple
	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] # add cross attentions if we output attention weights

	# add cross-attn cache to positions 3,4 of present_key_value tuple
	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 decoder, return the attn key/values as the last output
	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


	# Copied from transformers.models.roberta.modeling_roberta.RobertaEncoder with Roberta->BridgeTowerText
	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:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, past_key_value, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(layer_module),
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	)
	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,
	)


	# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->BridgeTowerText
	class BridgeTowerTextEmbeddings(nn.Module):
	"""
	Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
	"""

	# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
	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 is not snake-cased to stick with TensorFlow model variable name and be able to load
	# any TensorFlow checkpoint file
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	# position_ids (1, len position emb) is contiguous in memory and exported when serialized
	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
	)

	# End copy
	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:
	# Create the position ids from the input token ids. Any padded tokens remain padded.
	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]

	# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
	# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
	# issue #5664
	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):
	"""
	We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.

	Args:
	inputs_embeds: torch.Tensor

	Returns: torch.Tensor
	"""
	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)


	# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
	def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
	"""
	Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
	are ignored. This is modified from fairseq's `utils.make_positions`.

	Args:
	x: torch.Tensor x:

	Returns: torch.Tensor
	"""
	# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
	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):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	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):
	"""

	The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
	cross-attention is added between the self-attention layers, following the architecture described in *Attention is
	all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
	Kaiser and Illia Polosukhin.

	To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
	to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
	`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.

	.. _Attention is all you need: https://arxiv.org/abs/1706.03762

	"""

	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

	# Initialize weights and apply final processing
	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):
	"""
	Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
	class PreTrainedModel
	"""
	for layer, heads in heads_to_prune.items():
	self.encoder.layer[layer].attention.prune_heads(heads)

	# Copied from transformers.models.roberta.modeling_roberta.RobertaModel.forward
	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]:
	r"""
	encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
	the model is configured as a decoder.
	encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
	the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
	Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
	don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
	`decoder_input_ids` of shape `(batch_size, sequence_length)`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).
	"""
	output_attentions = 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_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)

	# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
	# ourselves in which case we just need to make it broadcastable to all heads.
	extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)

	# If a 2D or 3D attention mask is provided for the cross-attention
	# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
	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

	# Prepare head mask if needed
	# 1.0 in head_mask indicate we keep the head
	# attention_probs has shape bsz x n_heads x N x N
	# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
	# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
	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)]
	)

	# Class token => Linear => Tanh
	self.cross_modal_image_pooler = BridgeTowerPooler(config)
	self.cross_modal_text_pooler = BridgeTowerPooler(config)

	# Initialize BridgeTower Components
	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)
	@replace_return_docstrings(output_type=BridgeTowerModelOutput, config_class=_CONFIG_FOR_DOC)
	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]:
	r"""
	output_hidden_states (`bool`, optional):
	If set to `True`, hidden states are returned as a list containing the hidden states of text, image, and
	cross-modal components respectively. i.e. `(hidden_states_text, hidden_states_image,
	hidden_states_cross_modal)` where each element is a list of the hidden states of the corresponding
	modality. `hidden_states_txt/img` are a list of tensors corresponding to unimodal hidden states and
	`hidden_states_cross_modal` is a list of tuples containing `cross_modal_text_hidden_states` and
	`cross_modal_image_hidden_states` of each brdige layer.
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels are currently not supported.
	Returns:

	Examples:

	```python
	>>> from transformers import BridgeTowerProcessor, BridgeTowerModel
	>>> from PIL import Image
	>>> import requests

	>>> # prepare image and text
	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> image = Image.open(requests.get(url, stream=True).raw)
	>>> text = "hello world"
	>>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base")
	>>> model = BridgeTowerModel.from_pretrained("BridgeTower/bridgetower-base")

	>>> inputs = processor(image, text, return_tensors="pt")
	>>> outputs = model(**inputs)
	>>> outputs.keys()
	odict_keys(['text_features', 'image_features', 'pooler_output'])
	```"""
	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

	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
	)

	# The split_index determines how many layers of the uni-modal encoder are applied before the cross-modal encoder
	split_index = len(self.text_model.encoder.layer) - self.config.num_hidden_layers + 1

	# Run the first 'split_index' layers of the textual encoder
	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:
	# Permute as BridgeTowerResidualAttention has batch_first=True
	image_embeds = image_embeds.permute(1, 0, 2)

	if output_hidden_states:
	all_hidden_states_image += (image_embeds,)

	# Run the first 'split_index' layers of the visual encoder
	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))

	# first layer is a special case because we don't have the output from the cross-encoder yet
	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

	# Each of the top 6 layers of the visual and textual encoders ([split_index:]) is connected to each layer of
	# the cross-modal encoder via bridge layers, which brings bottom-up alignment and fusion to the cross-modal encoder.
	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]

	# Bridge layers for textual and visual encoders
	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)

	# Cross-modal encoder via bridge layers of textual and visual encoders
	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]),)

	# Concatenate the cls token of the text and image features to get the final represtation
	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)


	# Copied from transformers.models.vilt.modeling_vilt.ViltPredictionHeadTransform with Vilt->BridgeTower
	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)

	# Initialize weights and apply final processing
	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"))
	@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
	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]]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
	config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
	loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
	Returns:

	Examples:

	```python
	>>> from transformers import BridgeTowerProcessor, BridgeTowerForMaskedLM
	>>> from PIL import Image
	>>> import requests

	>>> url = "http://images.cocodataset.org/val2017/000000360943.jpg"
	>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
	>>> text = "a <mask> looking out of the window"

	>>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
	>>> model = BridgeTowerForMaskedLM.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")

	>>> # prepare inputs
	>>> encoding = processor(image, text, return_tensors="pt")

	>>> # forward pass
	>>> outputs = model(**encoding)

	>>> results = processor.decode(outputs.logits.argmax(dim=-1).squeeze(0).tolist())

	>>> print(results)
	.a cat looking out of the window.
	```"""
	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() # -100 index = padding token

	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)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(BRIDGETOWER_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
	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]]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size, 1)`, optional):
	Labels for computing the image-text matching loss. 0 means the pairs don't match and 1 means they match.
	The pairs with 0 will be skipped for calculation.
	Returns:

	Examples:

	```python
	>>> from transformers import BridgeTowerProcessor, BridgeTowerForImageAndTextRetrieval
	>>> import requests
	>>> from PIL import Image

	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> image = Image.open(requests.get(url, stream=True).raw)
	>>> texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"]

	>>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
	>>> model = BridgeTowerForImageAndTextRetrieval.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")

	>>> # forward pass
	>>> scores = dict()
	>>> for text in texts:
	... # prepare inputs
	... encoding = processor(image, text, return_tensors="pt")
	... outputs = model(**encoding)
	... scores[text] = outputs.logits[0, 1].item()
	```"""
	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))
	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(BRIDGETOWER_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=BridgeTowerContrastiveOutput, config_class=_CONFIG_FOR_DOC)
	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]]:
	r"""
	return_loss (`bool`, optional):
	Whether or not to return the contrastive loss.
	Returns:

	Examples:

	```python
	>>> from transformers import BridgeTowerProcessor, BridgeTowerForContrastiveLearning
	>>> import requests
	>>> from PIL import Image
	>>> import torch

	>>> image_urls = [
	... "https://farm4.staticflickr.com/3395/3428278415_81c3e27f15_z.jpg",
	... "http://images.cocodataset.org/val2017/000000039769.jpg",
	... ]
	>>> texts = ["two dogs in a car", "two cats sleeping on a couch"]
	>>> images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls]

	>>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc")
	>>> model = BridgeTowerForContrastiveLearning.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc")

	>>> inputs = processor(images, texts, padding=True, return_tensors="pt")
	>>> loss = model(**inputs, return_loss=True).loss

	>>> inputs = processor(images, texts[::-1], padding=True, return_tensors="pt")
	>>> loss_swapped = model(**inputs, return_loss=True).loss

	>>> print("Loss", round(loss.item(), 4))
	Loss 0.0019

	>>> print("Loss with swapped images", round(loss_swapped.item(), 4))
	Loss with swapped images 2.126
	```"""
	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

	# normalized features
	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,
	)