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Grounded-Segment-Anything / transformers_4_35_0 /models /altclip /modeling_altclip.py

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	# coding=utf-8
	# Copyright 2022 The BAAI Teams Authors and The 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 AltCLIP model."""
	import math
	from dataclasses import dataclass
	from typing import Any, List, Optional, Tuple, Union

	import torch
	import torch.nn as nn
	import torch.utils.checkpoint

	from ...activations import ACT2FN
	from ...modeling_outputs import (
	BaseModelOutput,
	BaseModelOutputWithPastAndCrossAttentions,
	BaseModelOutputWithPooling,
	BaseModelOutputWithPoolingAndCrossAttentions,
	BaseModelOutputWithPoolingAndProjection,
	)
	from ...modeling_utils import PreTrainedModel
	from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
	from ...utils import ModelOutput, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
	from .configuration_altclip import AltCLIPConfig, AltCLIPTextConfig, AltCLIPVisionConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "BAAI/AltCLIP"
	_CONFIG_FOR_DOC = "AltCLIPConfig"

	ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"BAAI/AltCLIP",
	# See all AltCLIP models at https://huggingface.co/models?filter=altclip
	]


	ALTCLIP_START_DOCSTRING = r"""
	This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
	library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
	etc.)

	This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
	and behavior.

	Parameters:
	config ([`CLIPConfig`]): 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.
	"""

	ALTCLIP_TEXT_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
	config.max_position_embeddings - 1]`.

	[What are position IDs?](../glossary#position-ids)
	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.
	"""

	ALTCLIP_VISION_INPUTS_DOCSTRING = r"""
	Args:
	pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
	Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
	[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
	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.
	"""

	ALTCLIP_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
	config.max_position_embeddings - 1]`.

	[What are position IDs?](../glossary#position-ids)
	pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
	Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
	[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
	return_loss (`bool`, optional):
	Whether or not to return the contrastive loss.
	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.
	"""


	# contrastive loss function, adapted from
	# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
	def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
	return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))


	def clip_loss(similarity: torch.Tensor) -> torch.Tensor:
	caption_loss = contrastive_loss(similarity)
	image_loss = contrastive_loss(similarity.t())
	return (caption_loss + image_loss) / 2.0


	@dataclass
	# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->AltCLIP
	class AltCLIPOutput(ModelOutput):
	"""
	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `return_loss` is `True`):
	Contrastive loss for image-text similarity.
	logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
	The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
	similarity scores.
	logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
	The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
	similarity scores.
	text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
	The text embeddings obtained by applying the projection layer to the pooled output of [`AltCLIPTextModel`].
	image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
	The image embeddings obtained by applying the projection layer to the pooled output of
	[`AltCLIPVisionModel`].
	text_model_output(`BaseModelOutputWithPooling`):
	The output of the [`AltCLIPTextModel`].
	vision_model_output(`BaseModelOutputWithPooling`):
	The output of the [`AltCLIPVisionModel`].
	"""

	loss: Optional[torch.FloatTensor] = None
	logits_per_image: torch.FloatTensor = None
	logits_per_text: torch.FloatTensor = None
	text_embeds: torch.FloatTensor = None
	image_embeds: torch.FloatTensor = None
	text_model_output: BaseModelOutputWithPooling = None
	vision_model_output: BaseModelOutputWithPooling = None

	def to_tuple(self) -> Tuple[Any]:
	return tuple(
	self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
	for k in self.keys()
	)


	# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->AltRoberta
	class AltRobertaEmbeddings(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.RobertaSelfAttention with Roberta->AltRoberta
	class AltRobertaSelfAttention(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 AltRobertaModel 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.roberta.modeling_roberta.RobertaSelfOutput
	class AltRobertaSelfOutput(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.roberta.modeling_roberta.RobertaAttention with Roberta->AltRoberta
	class AltRobertaAttention(nn.Module):
	def __init__(self, config, position_embedding_type=None):
	super().__init__()
	self.self = AltRobertaSelfAttention(config, position_embedding_type=position_embedding_type)
	self.output = AltRobertaSelfOutput(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


	# Copied from transformers.models.roberta.modeling_roberta.RobertaIntermediate with Roberta->AltRoberta
	class AltRobertaIntermediate(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.roberta.modeling_roberta.RobertaOutput
	class AltRobertaOutput(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.roberta.modeling_roberta.RobertaLayer with Roberta->AltRoberta
	class AltRobertaLayer(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 = AltRobertaAttention(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 = AltRobertaAttention(config, position_embedding_type="absolute")
	self.intermediate = AltRobertaIntermediate(config)
	self.output = AltRobertaOutput(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->AltRoberta
	class AltRobertaEncoder(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.layer = nn.ModuleList([AltRobertaLayer(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.RobertaPooler
	class AltRobertaPooler(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.clip.modeling_clip.CLIPAttention with CLIP->AltCLIP
	class AltCLIPAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	def __init__(self, config):
	super().__init__()
	self.config = config
	self.embed_dim = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.embed_dim // self.num_heads
	if self.head_dim * self.num_heads != self.embed_dim:
	raise ValueError(
	f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
	f" {self.num_heads})."
	)
	self.scale = self.head_dim**-0.5
	self.dropout = config.attention_dropout

	self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
	self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
	self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
	self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)

	def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
	return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	causal_attention_mask: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""Input shape: Batch x Time x Channel"""

	bsz, tgt_len, embed_dim = hidden_states.size()

	# get query proj
	query_states = self.q_proj(hidden_states) * self.scale
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

	proj_shape = (bsz * self.num_heads, -1, self.head_dim)
	query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
	key_states = key_states.view(*proj_shape)
	value_states = value_states.view(*proj_shape)

	src_len = key_states.size(1)
	attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

	if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
	raise ValueError(
	f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
	f" {attn_weights.size()}"
	)

	# apply the causal_attention_mask first
	if causal_attention_mask is not None:
	if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
	raise ValueError(
	f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
	f" {causal_attention_mask.size()}"
	)
	attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
	attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

	if attention_mask is not None:
	if attention_mask.size() != (bsz, 1, tgt_len, src_len):
	raise ValueError(
	f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
	)
	attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
	attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

	attn_weights = nn.functional.softmax(attn_weights, dim=-1)

	if output_attentions:
	# this operation is a bit akward, but it's required to
	# make sure that attn_weights keeps its gradient.
	# In order to do so, attn_weights have to reshaped
	# twice and have to be reused in the following
	attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
	attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
	else:
	attn_weights_reshaped = None

	attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

	attn_output = torch.bmm(attn_probs, value_states)

	if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
	raise ValueError(
	f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
	f" {attn_output.size()}"
	)

	attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
	attn_output = attn_output.transpose(1, 2)
	attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)

	attn_output = self.out_proj(attn_output)

	return attn_output, attn_weights_reshaped


	# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->AltCLIP
	class AltCLIPMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.activation_fn = ACT2FN[config.hidden_act]
	self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
	self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.fc1(hidden_states)
	hidden_states = self.activation_fn(hidden_states)
	hidden_states = self.fc2(hidden_states)
	return hidden_states


	# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->AltCLIP
	class AltCLIPEncoderLayer(nn.Module):
	def __init__(self, config: AltCLIPConfig):
	super().__init__()
	self.embed_dim = config.hidden_size
	self.self_attn = AltCLIPAttention(config)
	self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
	self.mlp = AltCLIPMLP(config)
	self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: torch.Tensor,
	causal_attention_mask: torch.Tensor,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.FloatTensor]:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
	attention_mask (`torch.FloatTensor`): attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	`(config.encoder_attention_heads,)`.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	"""
	residual = hidden_states

	hidden_states = self.layer_norm1(hidden_states)
	hidden_states, attn_weights = self.self_attn(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	causal_attention_mask=causal_attention_mask,
	output_attentions=output_attentions,
	)
	hidden_states = residual + hidden_states

	residual = hidden_states
	hidden_states = self.layer_norm2(hidden_states)
	hidden_states = self.mlp(hidden_states)
	hidden_states = residual + hidden_states

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (attn_weights,)

	return outputs


	# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->AltCLIP
	class AltCLIPEncoder(nn.Module):
	"""
	Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
	[`AltCLIPEncoderLayer`].

	Args:
	config: AltCLIPConfig
	"""

	def __init__(self, config: AltCLIPConfig):
	super().__init__()
	self.config = config
	self.layers = nn.ModuleList([AltCLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)])
	self.gradient_checkpointing = False

	def forward(
	self,
	inputs_embeds,
	attention_mask: Optional[torch.Tensor] = None,
	causal_attention_mask: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutput]:
	r"""
	Args:
	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	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.
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Causal mask for the text model. 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)
	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.
	"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	encoder_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None

	hidden_states = inputs_embeds
	for idx, encoder_layer in enumerate(self.layers):
	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)
	if self.gradient_checkpointing and self.training:

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

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(encoder_layer),
	hidden_states,
	attention_mask,
	causal_attention_mask,
	)
	else:
	layer_outputs = encoder_layer(
	hidden_states,
	attention_mask,
	causal_attention_mask,
	output_attentions=output_attentions,
	)

	hidden_states = layer_outputs[0]

	if output_attentions:
	all_attentions = all_attentions + (layer_outputs[1],)

	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)

	if not return_dict:
	return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
	return BaseModelOutput(
	last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
	)


	# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->AltCLIP
	class AltCLIPVisionEmbeddings(nn.Module):
	def __init__(self, config: AltCLIPVisionConfig):
	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 AltCLIPPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	config_class = AltCLIPConfig
	base_model_prefix = "altclip"
	supports_gradient_checkpointing = True

	def _init_weights(self, module):
	"""Initialize the weights"""
	factor = self.config.initializer_factor
	if isinstance(module, AltCLIPVisionEmbeddings):
	factor = self.config.initializer_factor
	nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim*-0.5 factor)
	nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
	nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
	elif isinstance(module, AltCLIPAttention):
	factor = self.config.initializer_factor
	in_proj_std = (module.embed_dim*-0.5) ((2 * module.config.num_hidden_layers) ** -0.5) * factor
	out_proj_std = (module.embed_dim*-0.5) factor
	nn.init.normal_(module.q_proj.weight, std=in_proj_std)
	nn.init.normal_(module.k_proj.weight, std=in_proj_std)
	nn.init.normal_(module.v_proj.weight, std=in_proj_std)
	nn.init.normal_(module.out_proj.weight, std=out_proj_std)
	elif isinstance(module, AltCLIPMLP):
	factor = self.config.initializer_factor
	in_proj_std = (
	(module.config.hidden_size*-0.5) ((2 * module.config.num_hidden_layers) ** -0.5) * factor
	)
	fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
	nn.init.normal_(module.fc1.weight, std=fc_std)
	nn.init.normal_(module.fc2.weight, std=in_proj_std)
	elif isinstance(module, AltCLIPModel):
	nn.init.normal_(
	module.text_projection.weight,
	std=module.text_embed_dim*-0.5 self.config.initializer_factor,
	)
	module.text_projection._is_hf_initialized = True
	nn.init.normal_(
	module.visual_projection.weight,
	std=module.vision_embed_dim*-0.5 self.config.initializer_factor,
	)
	module.visual_projection._is_hf_initialized = True
	elif isinstance(module, nn.LayerNorm):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)
	elif isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_factor)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_factor)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, AltCLIPEncoder):
	module.gradient_checkpointing = value
	if isinstance(module, AltRobertaEncoder):
	module.gradient_checkpointing = value


	# Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer with CLIPVisionTransformer->AltCLIPVisionTransformer,CLIPVisionConfig->AltCLIPVisionConfig,CLIPVisionEmbeddings->AltCLIPVisionEmbeddings,CLIPEncoder->AltCLIPEncoder,CLIP_VISION_INPUTS_DOCSTRING->ALTCLIP_VISION_INPUTS_DOCSTRING
	class AltCLIPVisionTransformer(nn.Module):
	def __init__(self, config: AltCLIPVisionConfig):
	super().__init__()
	self.config = config
	embed_dim = config.hidden_size

	self.embeddings = AltCLIPVisionEmbeddings(config)
	self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
	self.encoder = AltCLIPEncoder(config)
	self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)

	@add_start_docstrings_to_model_forward(ALTCLIP_VISION_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=AltCLIPVisionConfig)
	def forward(
	self,
	pixel_values: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPooling]:
	r"""
	Returns:

	"""
	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 pixel_values is None:
	raise ValueError("You have to specify pixel_values")

	hidden_states = self.embeddings(pixel_values)
	hidden_states = self.pre_layrnorm(hidden_states)

	encoder_outputs = self.encoder(
	inputs_embeds=hidden_states,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	last_hidden_state = encoder_outputs[0]
	pooled_output = last_hidden_state[:, 0, :]
	pooled_output = self.post_layernorm(pooled_output)

	if not return_dict:
	return (last_hidden_state, pooled_output) + encoder_outputs[1:]

	return BaseModelOutputWithPooling(
	last_hidden_state=last_hidden_state,
	pooler_output=pooled_output,
	hidden_states=encoder_outputs.hidden_states,
	attentions=encoder_outputs.attentions,
	)


	class AltCLIPVisionModel(AltCLIPPreTrainedModel):
	config_class = AltCLIPVisionConfig
	main_input_name = "pixel_values"

	def __init__(self, config: AltCLIPVisionConfig):
	super().__init__(config)
	self.vision_model = AltCLIPVisionTransformer(config)
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self) -> nn.Module:
	return self.vision_model.embeddings.patch_embedding

	@add_start_docstrings_to_model_forward(ALTCLIP_VISION_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=AltCLIPVisionConfig)
	def forward(
	self,
	pixel_values: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPooling]:
	r"""
	Returns:

	Examples:

	```python
	>>> from PIL import Image
	>>> import requests
	>>> from transformers import AutoProcessor, AltCLIPVisionModel

	>>> model = AltCLIPVisionModel.from_pretrained("BAAI/AltCLIP")
	>>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")

	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> image = Image.open(requests.get(url, stream=True).raw)

	>>> inputs = processor(images=image, return_tensors="pt")

	>>> outputs = model(**inputs)
	>>> last_hidden_state = outputs.last_hidden_state
	>>> pooled_output = outputs.pooler_output # pooled CLS states
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	return self.vision_model(
	pixel_values=pixel_values,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)


	class AltRobertaModel(AltCLIPPreTrainedModel):
	"""

	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 = AltCLIPTextConfig

	# Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->AltRoberta
	def __init__(self, config, add_pooling_layer=True):
	super().__init__(config)
	self.config = config

	self.embeddings = AltRobertaEmbeddings(config)
	self.encoder = AltRobertaEncoder(config)

	self.pooler = AltRobertaPooler(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.bert.modeling_bert.BertModel.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,
	)


	class AltCLIPTextModel(AltCLIPPreTrainedModel):
	config_class = AltCLIPTextConfig

	def __init__(self, config):
	super().__init__(config)
	self.roberta = AltRobertaModel(config, add_pooling_layer=False)
	self.transformation = nn.Linear(config.hidden_size, config.project_dim)
	self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.post_init()

	def get_input_embeddings(self) -> nn.Module:
	return self.roberta.embeddings.word_embeddings

	def set_input_embeddings(self, value: nn.Embedding) -> None:
	self.roberta.embeddings.word_embeddings = value

	def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
	return super().resize_token_embeddings(new_num_tokens)

	@add_start_docstrings_to_model_forward(ALTCLIP_TEXT_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndProjection, config_class=AltCLIPTextConfig)
	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,
	output_attentions: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPoolingAndProjection]:
	r"""
	Returns:

	Examples:

	```python
	>>> from transformers import AutoProcessor, AltCLIPTextModel

	>>> model = AltCLIPTextModel.from_pretrained("BAAI/AltCLIP")
	>>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")

	>>> texts = ["it's a cat", "it's a dog"]

	>>> inputs = processor(text=texts, padding=True, return_tensors="pt")

	>>> outputs = model(**inputs)
	>>> last_hidden_state = outputs.last_hidden_state
	>>> pooled_output = outputs.pooler_output # pooled CLS states
	```"""

	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.roberta(
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	# last module outputs
	sequence_output = outputs[0]

	# project every module
	sequence_output = self.pre_LN(sequence_output)

	# pooler
	projection_state = self.transformation(sequence_output)
	pooler_output = projection_state[:, 0]

	if not return_dict:
	return (projection_state, pooler_output) + outputs[2:4]

	return BaseModelOutputWithPoolingAndProjection(
	last_hidden_state=projection_state,
	pooler_output=pooler_output,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	class AltCLIPModel(AltCLIPPreTrainedModel):
	config_class = AltCLIPConfig

	def __init__(self, config: AltCLIPConfig):
	super().__init__(config)

	if not isinstance(config.vision_config, AltCLIPVisionConfig):
	raise ValueError(
	"config.vision_config is expected to be of type AltCLIPVisionConfig but is of type"
	f" {type(config.vision_config)}."
	)
	if not isinstance(config.text_config, AltCLIPTextConfig):
	raise ValueError(
	"config.text_config is expected to be of type AltCLIPTextConfig but is of type"
	f" {type(config.text_config)}."
	)

	text_config = config.text_config
	vision_config = config.vision_config

	self.projection_dim = config.projection_dim
	self.text_embed_dim = text_config.project_dim
	self.vision_embed_dim = vision_config.hidden_size

	self.text_model = AltCLIPTextModel(text_config)
	self.vision_model = AltCLIPVisionTransformer(vision_config)

	self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
	self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)
	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(ALTCLIP_TEXT_INPUTS_DOCSTRING)
	def get_text_features(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	token_type_ids=None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> torch.FloatTensor:
	r"""
	Returns:
	text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
	applying the projection layer to the pooled output of [`AltCLIPTextModel`].

	Examples:

	```python
	>>> from transformers import AutoProcessor, AltCLIPModel

	>>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
	>>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
	>>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
	>>> text_features = model.get_text_features(**inputs)
	```"""
	# Use AltCLIP model's config for some fields (if specified) instead of those of vision & text components.
	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

	text_outputs = self.text_model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	token_type_ids=token_type_ids,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	pooled_output = text_outputs[1]
	text_features = self.text_projection(pooled_output)

	return text_features

	@add_start_docstrings_to_model_forward(ALTCLIP_VISION_INPUTS_DOCSTRING)
	def get_image_features(
	self,
	pixel_values: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> torch.FloatTensor:
	r"""
	Returns:
	image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
	applying the projection layer to the pooled output of [`AltCLIPVisionModel`].

	Examples:

	```python
	>>> from PIL import Image
	>>> import requests
	>>> from transformers import AutoProcessor, AltCLIPModel

	>>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
	>>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> image = Image.open(requests.get(url, stream=True).raw)
	>>> inputs = processor(images=image, return_tensors="pt")
	>>> image_features = model.get_image_features(**inputs)
	```"""
	# Use AltCLIP model's config for some fields (if specified) instead of those of vision & text components.
	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

	vision_outputs = self.vision_model(
	pixel_values=pixel_values,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	pooled_output = vision_outputs[1] # pooled_output
	image_features = self.visual_projection(pooled_output)

	return image_features

	@add_start_docstrings_to_model_forward(ALTCLIP_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=AltCLIPOutput, config_class=AltCLIPConfig)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	pixel_values: Optional[torch.FloatTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	return_loss: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, AltCLIPOutput]:
	r"""
	Returns:

	Examples:

	```python
	>>> from PIL import Image
	>>> import requests
	>>> from transformers import AutoProcessor, AltCLIPModel

	>>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
	>>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> image = Image.open(requests.get(url, stream=True).raw)
	>>> inputs = processor(
	... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
	... )
	>>> outputs = model(**inputs)
	>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
	>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
	```"""
	# Use AltCLIP model's config for some fields (if specified) instead of those of vision & text components.
	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

	text_outputs = self.text_model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	vision_outputs = self.vision_model(
	pixel_values=pixel_values,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	image_embeds = vision_outputs[1]
	image_embeds = self.visual_projection(image_embeds)

	text_embeds = text_outputs[1]
	text_embeds = self.text_projection(text_embeds)

	# normalized features
	image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
	text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)

	# cosine similarity as logits
	logit_scale = self.logit_scale.exp()
	logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
	logits_per_image = logits_per_text.T

	loss = None
	if return_loss:
	loss = clip_loss(logits_per_text)

	if not return_dict:
	output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
	return ((loss,) + output) if loss is not None else output

	return AltCLIPOutput(
	loss=loss,
	logits_per_image=logits_per_image,
	logits_per_text=logits_per_text,
	text_embeds=text_embeds,
	image_embeds=image_embeds,
	text_model_output=text_outputs,
	vision_model_output=vision_outputs,
	)


	# 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