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Grounded-Segment-Anything / transformers_4_35_0 /models /align /modeling_align.py

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	# coding=utf-8
	# Copyright 2023 The Google Research Team Authors and The HuggingFace 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 ALIGN model."""

	import math
	from dataclasses import dataclass
	from typing import Any, Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from torch import nn

	from ...activations import ACT2FN
	from ...modeling_outputs import (
	BaseModelOutputWithNoAttention,
	BaseModelOutputWithPastAndCrossAttentions,
	BaseModelOutputWithPoolingAndCrossAttentions,
	BaseModelOutputWithPoolingAndNoAttention,
	)
	from ...modeling_utils import PreTrainedModel
	from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
	from ...utils import (
	ModelOutput,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	logging,
	replace_return_docstrings,
	)
	from .configuration_align import AlignConfig, AlignTextConfig, AlignVisionConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "kakaobrain/align-base"
	_CONFIG_FOR_DOC = "AlignConfig"


	ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"kakaobrain/align-base",
	# See all ALIGN models at https://huggingface.co/models?filter=align
	]


	ALIGN_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 ([`AlignConfig`]): 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.
	"""

	ALIGN_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)
	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)
	head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional):
	Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
	is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
	model's internal embedding lookup matrix.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""

	ALIGN_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 [`EfficientNetImageProcessor.__call__`] for details.
	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.
	"""

	ALIGN_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)
	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)
	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.
	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 [`EfficientNetImageProcessor.__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.
	"""


	@dataclass
	class AlignVisionModelOutput(ModelOutput):
	"""
	Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.

	Args:
	image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` optional returned when model is initialized with `with_projection=True`):
	The image embeddings obtained by applying the projection layer to the pooler_output.
	last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	Sequence of hidden-states at the output of the last layer of the model.
	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.
	"""

	image_embeds: Optional[torch.FloatTensor] = None
	last_hidden_state: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor]] = None


	@dataclass
	class AlignTextModelOutput(ModelOutput):
	"""
	Base class for text model's outputs that also contains a pooling of the last hidden states.

	Args:
	text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` optional returned when model is initialized with `with_projection=True`):
	The text embeddings obtained by applying the projection layer to the pooler_output.
	last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	Sequence of hidden-states at the output of the last layer of the model.
	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_embeds: Optional[torch.FloatTensor] = None
	last_hidden_state: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	attentions: Optional[Tuple[torch.FloatTensor]] = None


	@dataclass
	class AlignOutput(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 [`AlignTextModel`].
	image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
	The output of [`AlignVisionModel`].
	text_model_output(`BaseModelOutputWithPoolingAndCrossAttentions`):
	The output of the [`AlignTextModel`].
	vision_model_output(`BaseModelOutputWithPoolingAndNoAttention`):
	The output of the [`AlignVisionModel`].
	"""

	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: BaseModelOutputWithPoolingAndCrossAttentions = None
	vision_model_output: BaseModelOutputWithPoolingAndNoAttention = 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()
	)


	# 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), label_smoothing=0.1)


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


	# Copied from transformers.models.efficientnet.modeling_efficientnet.round_filters with EfficientNet->AlignVision
	def round_filters(config: AlignVisionConfig, num_channels: int):
	r"""
	Round number of filters based on depth multiplier.
	"""
	divisor = config.depth_divisor
	num_channels *= config.width_coefficient
	new_dim = max(divisor, int(num_channels + divisor / 2) // divisor * divisor)

	# Make sure that round down does not go down by more than 10%.
	if new_dim < 0.9 * num_channels:
	new_dim += divisor

	return int(new_dim)


	# Copied from transformers.models.efficientnet.modeling_efficientnet.correct_pad
	def correct_pad(kernel_size: Union[int, Tuple], adjust: bool = True):
	r"""
	Utility function to get the tuple padding value for the depthwise convolution.

	Args:
	kernel_size (`int` or `tuple`):
	Kernel size of the convolution layers.
	adjust (`bool`, optional, defaults to `True`):
	Adjusts padding value to apply to right and bottom sides of the input.
	"""
	if isinstance(kernel_size, int):
	kernel_size = (kernel_size, kernel_size)

	correct = (kernel_size[0] // 2, kernel_size[1] // 2)
	if adjust:
	return (correct[1] - 1, correct[1], correct[0] - 1, correct[0])
	else:
	return (correct[1], correct[1], correct[0], correct[0])


	# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetEmbeddings with EfficientNet->AlignVision
	class AlignVisionEmbeddings(nn.Module):
	r"""
	A module that corresponds to the stem module of the original work.
	"""

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

	self.out_dim = round_filters(config, 32)
	self.padding = nn.ZeroPad2d(padding=(0, 1, 0, 1))
	self.convolution = nn.Conv2d(
	config.num_channels, self.out_dim, kernel_size=3, stride=2, padding="valid", bias=False
	)
	self.batchnorm = nn.BatchNorm2d(self.out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum)
	self.activation = ACT2FN[config.hidden_act]

	def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
	features = self.padding(pixel_values)
	features = self.convolution(features)
	features = self.batchnorm(features)
	features = self.activation(features)

	return features


	# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseConv2d with EfficientNet->AlignVision
	class AlignVisionDepthwiseConv2d(nn.Conv2d):
	def __init__(
	self,
	in_channels,
	depth_multiplier=1,
	kernel_size=3,
	stride=1,
	padding=0,
	dilation=1,
	bias=True,
	padding_mode="zeros",
	):
	out_channels = in_channels * depth_multiplier
	super().__init__(
	in_channels=in_channels,
	out_channels=out_channels,
	kernel_size=kernel_size,
	stride=stride,
	padding=padding,
	dilation=dilation,
	groups=in_channels,
	bias=bias,
	padding_mode=padding_mode,
	)


	# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetExpansionLayer with EfficientNet->AlignVision
	class AlignVisionExpansionLayer(nn.Module):
	r"""
	This corresponds to the expansion phase of each block in the original implementation.
	"""

	def __init__(self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int):
	super().__init__()
	self.expand_conv = nn.Conv2d(
	in_channels=in_dim,
	out_channels=out_dim,
	kernel_size=1,
	padding="same",
	bias=False,
	)
	self.expand_bn = nn.BatchNorm2d(num_features=out_dim, eps=config.batch_norm_eps)
	self.expand_act = ACT2FN[config.hidden_act]

	def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
	# Expand phase
	hidden_states = self.expand_conv(hidden_states)
	hidden_states = self.expand_bn(hidden_states)
	hidden_states = self.expand_act(hidden_states)

	return hidden_states


	# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseLayer with with EfficientNet->AlignVision
	class AlignVisionDepthwiseLayer(nn.Module):
	r"""
	This corresponds to the depthwise convolution phase of each block in the original implementation.
	"""

	def __init__(
	self,
	config: AlignVisionConfig,
	in_dim: int,
	stride: int,
	kernel_size: int,
	adjust_padding: bool,
	):
	super().__init__()
	self.stride = stride
	conv_pad = "valid" if self.stride == 2 else "same"
	padding = correct_pad(kernel_size, adjust=adjust_padding)

	self.depthwise_conv_pad = nn.ZeroPad2d(padding=padding)
	self.depthwise_conv = AlignVisionDepthwiseConv2d(
	in_dim, kernel_size=kernel_size, stride=stride, padding=conv_pad, bias=False
	)
	self.depthwise_norm = nn.BatchNorm2d(
	num_features=in_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
	)
	self.depthwise_act = ACT2FN[config.hidden_act]

	def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
	# Depthwise convolution
	if self.stride == 2:
	hidden_states = self.depthwise_conv_pad(hidden_states)

	hidden_states = self.depthwise_conv(hidden_states)
	hidden_states = self.depthwise_norm(hidden_states)
	hidden_states = self.depthwise_act(hidden_states)

	return hidden_states


	# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetSqueezeExciteLayer with with EfficientNet->AlignVision
	class AlignVisionSqueezeExciteLayer(nn.Module):
	r"""
	This corresponds to the Squeeze and Excitement phase of each block in the original implementation.
	"""

	def __init__(self, config: AlignVisionConfig, in_dim: int, expand_dim: int, expand: bool = False):
	super().__init__()
	self.dim = expand_dim if expand else in_dim
	self.dim_se = max(1, int(in_dim * config.squeeze_expansion_ratio))

	self.squeeze = nn.AdaptiveAvgPool2d(output_size=1)
	self.reduce = nn.Conv2d(
	in_channels=self.dim,
	out_channels=self.dim_se,
	kernel_size=1,
	padding="same",
	)
	self.expand = nn.Conv2d(
	in_channels=self.dim_se,
	out_channels=self.dim,
	kernel_size=1,
	padding="same",
	)
	self.act_reduce = ACT2FN[config.hidden_act]
	self.act_expand = nn.Sigmoid()

	def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
	inputs = hidden_states
	hidden_states = self.squeeze(hidden_states)
	hidden_states = self.reduce(hidden_states)
	hidden_states = self.act_reduce(hidden_states)

	hidden_states = self.expand(hidden_states)
	hidden_states = self.act_expand(hidden_states)
	hidden_states = torch.mul(inputs, hidden_states)

	return hidden_states


	class AlignVisionFinalBlockLayer(nn.Module):
	r"""
	This corresponds to the final phase of each block in the original implementation.
	"""

	def __init__(
	self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int, drop_rate: float, id_skip: bool
	):
	super().__init__()
	self.apply_dropout = stride == 1 and not id_skip
	self.project_conv = nn.Conv2d(
	in_channels=in_dim,
	out_channels=out_dim,
	kernel_size=1,
	padding="same",
	bias=False,
	)
	self.project_bn = nn.BatchNorm2d(
	num_features=out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
	)
	self.dropout = nn.Dropout(p=drop_rate)

	def forward(self, embeddings: torch.FloatTensor, hidden_states: torch.FloatTensor) -> torch.Tensor:
	hidden_states = self.project_conv(hidden_states)
	hidden_states = self.project_bn(hidden_states)

	if self.apply_dropout:
	hidden_states = self.dropout(hidden_states)
	hidden_states = hidden_states + embeddings

	return hidden_states


	class AlignVisionBlock(nn.Module):
	r"""
	This corresponds to the block module of original the EfficientNet vision encoder implementation.

	Args:
	config ([`AlignVisionConfig`]):
	Model configuration class.
	in_dim (`int`):
	Number of input channels.
	out_dim (`int`):
	Number of output channels.
	stride (`int`):
	Stride size to be used in convolution layers.
	expand_ratio (`int`):
	Expand ratio to set the output dimensions for the expansion and squeeze-excite layers.
	kernel_size (`int`):
	Kernel size for the depthwise convolution layer.
	drop_rate (`float`):
	Dropout rate to be used in the final phase of each block.
	id_skip (`bool`):
	Whether to apply dropout and sum the final hidden states with the input embeddings during the final phase
	of each block. Set to `True` for the first block of each stage.
	adjust_padding (`bool`):
	Whether to apply padding to only right and bottom side of the input kernel before the depthwise convolution
	operation, set to `True` for inputs with odd input sizes.
	"""

	def __init__(
	self,
	config: AlignVisionConfig,
	in_dim: int,
	out_dim: int,
	stride: int,
	expand_ratio: int,
	kernel_size: int,
	drop_rate: float,
	id_skip: bool,
	adjust_padding: bool,
	):
	super().__init__()
	self.expand_ratio = expand_ratio
	self.expand = True if self.expand_ratio != 1 else False
	expand_in_dim = in_dim * expand_ratio

	if self.expand:
	self.expansion = AlignVisionExpansionLayer(
	config=config, in_dim=in_dim, out_dim=expand_in_dim, stride=stride
	)

	self.depthwise_conv = AlignVisionDepthwiseLayer(
	config=config,
	in_dim=expand_in_dim if self.expand else in_dim,
	stride=stride,
	kernel_size=kernel_size,
	adjust_padding=adjust_padding,
	)
	self.squeeze_excite = AlignVisionSqueezeExciteLayer(
	config=config, in_dim=in_dim, expand_dim=expand_in_dim, expand=self.expand
	)
	self.projection = AlignVisionFinalBlockLayer(
	config=config,
	in_dim=expand_in_dim if self.expand else in_dim,
	out_dim=out_dim,
	stride=stride,
	drop_rate=drop_rate,
	id_skip=id_skip,
	)

	def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
	embeddings = hidden_states
	# Expansion and depthwise convolution phase
	if self.expand_ratio != 1:
	hidden_states = self.expansion(hidden_states)
	hidden_states = self.depthwise_conv(hidden_states)

	# Squeeze and excite phase
	hidden_states = self.squeeze_excite(hidden_states)
	hidden_states = self.projection(embeddings, hidden_states)
	return hidden_states


	class AlignVisionEncoder(nn.Module):
	r"""
	Forward propogates the embeddings through each vision encoder (EfficientNet) block.

	Args:
	config ([`AlignVisionConfig`]):
	Model configuration class.
	"""

	def __init__(self, config: AlignVisionConfig):
	super().__init__()
	self.depth_coefficient = config.depth_coefficient

	def round_repeats(repeats):
	# Round number of block repeats based on depth multiplier.
	return int(math.ceil(self.depth_coefficient * repeats))

	num_base_blocks = len(config.in_channels)
	num_blocks = sum(round_repeats(n) for n in config.num_block_repeats)

	curr_block_num = 0
	blocks = []
	for i in range(num_base_blocks):
	in_dim = round_filters(config, config.in_channels[i])
	out_dim = round_filters(config, config.out_channels[i])
	stride = config.strides[i]
	kernel_size = config.kernel_sizes[i]
	expand_ratio = config.expand_ratios[i]

	for j in range(round_repeats(config.num_block_repeats[i])):
	id_skip = True if j == 0 else False
	stride = 1 if j > 0 else stride
	in_dim = out_dim if j > 0 else in_dim
	adjust_padding = False if curr_block_num in config.depthwise_padding else True
	drop_rate = config.drop_connect_rate * curr_block_num / num_blocks

	block = AlignVisionBlock(
	config=config,
	in_dim=in_dim,
	out_dim=out_dim,
	stride=stride,
	kernel_size=kernel_size,
	expand_ratio=expand_ratio,
	drop_rate=drop_rate,
	id_skip=id_skip,
	adjust_padding=adjust_padding,
	)
	blocks.append(block)
	curr_block_num += 1

	self.blocks = nn.ModuleList(blocks)

	def forward(
	self,
	hidden_states: torch.FloatTensor,
	output_hidden_states: Optional[bool] = False,
	return_dict: Optional[bool] = True,
	) -> BaseModelOutputWithPoolingAndNoAttention:
	all_hidden_states = (hidden_states,) if output_hidden_states else None

	for block in self.blocks:
	hidden_states = block(hidden_states)
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	if not return_dict:
	return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)

	return BaseModelOutputWithNoAttention(
	last_hidden_state=hidden_states,
	hidden_states=all_hidden_states,
	)


	# Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert->AlignText
	class AlignTextEmbeddings(nn.Module):
	"""Construct the embeddings from word, position and token_type embeddings."""

	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
	)

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	past_key_values_length: int = 0,
	) -> torch.Tensor:
	if input_ids is not None:
	input_shape = input_ids.size()
	else:
	input_shape = inputs_embeds.size()[:-1]

	seq_length = input_shape[1]

	if position_ids is None:
	position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]

	# 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


	# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->AlignText
	class AlignTextSelfAttention(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 AlignTextModel 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.BertSelfOutput with Bert->AlignText
	class AlignTextSelfOutput(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.BertAttention with Bert->AlignText
	class AlignTextAttention(nn.Module):
	def __init__(self, config, position_embedding_type=None):
	super().__init__()
	self.self = AlignTextSelfAttention(config, position_embedding_type=position_embedding_type)
	self.output = AlignTextSelfOutput(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.bert.modeling_bert.BertIntermediate with Bert->AlignText
	class AlignTextIntermediate(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->AlignText
	class AlignTextOutput(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.BertLayer with Bert->AlignText
	class AlignTextLayer(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 = AlignTextAttention(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 = AlignTextAttention(config, position_embedding_type="absolute")
	self.intermediate = AlignTextIntermediate(config)
	self.output = AlignTextOutput(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.bert.modeling_bert.BertEncoder with Bert->AlignText
	class AlignTextEncoder(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.layer = nn.ModuleList([AlignTextLayer(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.bert.modeling_bert.BertPooler with Bert -> AlignText
	class AlignTextPooler(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


	class AlignPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	config_class = AlignConfig
	base_model_prefix = "align"
	supports_gradient_checkpointing = True

	def _init_weights(self, module):
	"""Initialize the weights"""
	if isinstance(module, (nn.Linear, nn.Conv2d)):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, AlignModel):
	nn.init.xavier_uniform_(module.text_projection.weight)
	module.text_projection.bias.data.zero_()
	module.text_projection._is_hf_initialized = True
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()
	if isinstance(module, nn.LayerNorm):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, (AlignTextModel, AlignVisionModel)):
	module.gradient_checkpointing = value


	@add_start_docstrings(
	"""The text model from ALIGN without any head or projection on top.""",
	ALIGN_START_DOCSTRING,
	)
	class AlignTextModel(AlignPreTrainedModel):
	config_class = AlignTextConfig

	def __init__(self, config: AlignTextConfig, add_pooling_layer: bool = True):
	super().__init__(config)
	self.config = config

	self.embeddings = AlignTextEmbeddings(config)
	self.encoder = AlignTextEncoder(config)

	self.pooler = AlignTextPooler(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

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

	Examples:

	```python
	>>> from transformers import AutoTokenizer, AlignTextModel

	>>> model = AlignTextModel.from_pretrained("kakaobrain/align-base")
	>>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base")

	>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")

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

	if input_ids is not None and inputs_embeds is not None:
	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

	if attention_mask is None:
	attention_mask = torch.ones(((batch_size, seq_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)

	# 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,
	)
	encoder_outputs = self.encoder(
	embedding_output,
	attention_mask=extended_attention_mask,
	head_mask=head_mask,
	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,
	hidden_states=encoder_outputs.hidden_states,
	attentions=encoder_outputs.attentions,
	cross_attentions=encoder_outputs.cross_attentions,
	)


	@add_start_docstrings(
	"""The vision model from ALIGN without any head or projection on top.""",
	ALIGN_START_DOCSTRING,
	)
	class AlignVisionModel(AlignPreTrainedModel):
	config_class = AlignVisionConfig
	main_input_name = "pixel_values"

	def __init__(self, config: AlignVisionConfig):
	super().__init__(config)
	self.config = config
	self.embeddings = AlignVisionEmbeddings(config)
	self.encoder = AlignVisionEncoder(config)

	# Final pooling layer
	if config.pooling_type == "mean":
	self.pooler = nn.AvgPool2d(config.hidden_dim, ceil_mode=True)
	elif config.pooling_type == "max":
	self.pooler = nn.MaxPool2d(config.hidden_dim, ceil_mode=True)
	else:
	raise ValueError(f"config.pooling must be one of ['mean', 'max'] got {config.pooling}")

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

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

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

	Examples:

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

	>>> model = AlignVisionModel.from_pretrained("kakaobrain/align-base")
	>>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")

	>>> 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
	```"""
	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")

	embedding_output = self.embeddings(pixel_values)
	encoder_outputs = self.encoder(
	embedding_output,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	# Apply pooling
	last_hidden_state = encoder_outputs[0]
	pooled_output = self.pooler(last_hidden_state)
	# Reshape (batch_size, projection_dim, 1 , 1) -> (batch_size, projection_dim)
	pooled_output = pooled_output.reshape(pooled_output.shape[:2])

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

	return BaseModelOutputWithPoolingAndNoAttention(
	last_hidden_state=last_hidden_state,
	pooler_output=pooled_output,
	hidden_states=encoder_outputs.hidden_states,
	)


	@add_start_docstrings(ALIGN_START_DOCSTRING)
	class AlignModel(AlignPreTrainedModel):
	config_class = AlignConfig

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

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

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

	text_config = config.text_config
	vision_config = config.vision_config

	self.projection_dim = config.projection_dim
	self.text_embed_dim = text_config.hidden_size

	self.text_model = AlignTextModel(text_config)
	self.vision_model = AlignVisionModel(vision_config)

	self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim)
	self.temperature = nn.Parameter(torch.tensor(self.config.temperature_init_value))

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

	@add_start_docstrings_to_model_forward(ALIGN_TEXT_INPUTS_DOCSTRING)
	def get_text_features(
	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,
	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 [`AlignTextModel`].

	Examples:

	```python
	>>> from transformers import AutoTokenizer, AlignModel

	>>> model = AlignModel.from_pretrained("kakaobrain/align-base")
	>>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base")

	>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
	>>> text_features = model.get_text_features(**inputs)
	```"""
	# Use ALIGN 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,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	last_hidden_state = text_outputs[0][:, 0, :]
	text_features = self.text_projection(last_hidden_state)

	return text_features

	@add_start_docstrings_to_model_forward(ALIGN_VISION_INPUTS_DOCSTRING)
	def get_image_features(
	self,
	pixel_values: Optional[torch.FloatTensor] = 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 [`AlignVisionModel`].

	Examples:

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

	>>> model = AlignModel.from_pretrained("kakaobrain/align-base")
	>>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")

	>>> 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 ALIGN model's config for some fields (if specified) instead of those of vision & text components.
	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_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	image_features = vision_outputs[1] # pooled_output

	return image_features

	@add_start_docstrings_to_model_forward(ALIGN_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=AlignOutput, config_class=AlignConfig)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	pixel_values: Optional[torch.FloatTensor] = 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,
	return_loss: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, AlignOutput]:
	r"""
	Returns:

	Examples:

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

	>>> model = AlignModel.from_pretrained("kakaobrain/align-base")
	>>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")

	>>> 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 ALIGN 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_hidden_states=output_hidden_states,
	return_dict=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,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	image_embeds = vision_outputs[1]
	text_embeds = text_outputs[0][:, 0, :]
	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
	logits_per_text = torch.matmul(text_embeds, image_embeds.t()) / self.temperature
	logits_per_image = logits_per_text.t()

	loss = None
	if return_loss:
	loss = align_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 AlignOutput(
	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,
	)