siglip-so400m-14-980-flash-attn2-navit / modeling_siglip.py

fix discrepancy of speed in the case of full attention mask

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	# coding=utf-8
	# Copyright 2024 Google AI 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 Siglip model."""


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

	import numpy as np
	import torch
	import torch.nn.functional as F
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn.init import _calculate_fan_in_and_fan_out

	from transformers.activations import ACT2FN
	from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
	from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import (
	ModelOutput,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	is_flash_attn_2_available,
	logging,
	replace_return_docstrings,
	)
	from .configuration_siglip import SiglipConfig, SiglipTextConfig, SiglipVisionConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224"

	SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"google/siglip-base-patch16-224",
	# See all SigLIP models at https://huggingface.co/models?filter=siglip
	]

	if is_flash_attn_2_available():
	from flash_attn import flash_attn_func, flash_attn_varlen_func
	from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa


	# Copied from transformers.models.llama.modeling_llama._get_unpad_data
	def _get_unpad_data(attention_mask):
	seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
	indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
	max_seqlen_in_batch = seqlens_in_batch.max().item()
	cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
	return (
	indices,
	cu_seqlens,
	max_seqlen_in_batch,
	)


	def _trunc_normal_(tensor, mean, std, a, b):
	# Cut & paste from PyTorch official master until it's in a few official releases - RW
	# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
	def norm_cdf(x):
	# Computes standard normal cumulative distribution function
	return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0

	if (mean < a - 2 * std) or (mean > b + 2 * std):
	warnings.warn(
	"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
	"The distribution of values may be incorrect.",
	stacklevel=2,
	)

	# Values are generated by using a truncated uniform distribution and
	# then using the inverse CDF for the normal distribution.
	# Get upper and lower cdf values
	l = norm_cdf((a - mean) / std)
	u = norm_cdf((b - mean) / std)

	# Uniformly fill tensor with values from [l, u], then translate to
	# [2l-1, 2u-1].
	tensor.uniform_(2 * l - 1, 2 * u - 1)

	# Use inverse cdf transform for normal distribution to get truncated
	# standard normal
	if tensor.dtype in [torch.float16, torch.bfloat16]:
	# The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu
	og_dtype = tensor.dtype
	tensor = tensor.to(torch.float32)
	tensor.erfinv_()
	tensor = tensor.to(og_dtype)
	else:
	tensor.erfinv_()

	# Transform to proper mean, std
	tensor.mul_(std * math.sqrt(2.0))
	tensor.add_(mean)

	# Clamp to ensure it's in the proper range
	if tensor.dtype == torch.float16:
	# The `clamp_` op is not (yet?) defined in float16+cpu
	tensor = tensor.to(torch.float32)
	tensor.clamp_(min=a, max=b)
	tensor = tensor.to(torch.float16)
	else:
	tensor.clamp_(min=a, max=b)


	def trunc_normal_tf_(
	tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
	) -> torch.Tensor:
	"""Fills the input Tensor with values drawn from a truncated
	normal distribution. The values are effectively drawn from the
	normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
	with values outside :math:`[a, b]` redrawn until they are within
	the bounds. The method used for generating the random values works
	best when :math:`a \\leq \text{mean} \\leq b`.

	NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
	bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
	and the result is subsquently scaled and shifted by the mean and std args.

	Args:
	tensor: an n-dimensional `torch.Tensor`
	mean: the mean of the normal distribution
	std: the standard deviation of the normal distribution
	a: the minimum cutoff value
	b: the maximum cutoff value
	"""
	with torch.no_grad():
	_trunc_normal_(tensor, 0, 1.0, a, b)
	tensor.mul_(std).add_(mean)


	def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
	fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
	if mode == "fan_in":
	denom = fan_in
	elif mode == "fan_out":
	denom = fan_out
	elif mode == "fan_avg":
	denom = (fan_in + fan_out) / 2

	variance = scale / denom

	if distribution == "truncated_normal":
	# constant is stddev of standard normal truncated to (-2, 2)
	trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
	elif distribution == "normal":
	with torch.no_grad():
	tensor.normal_(std=math.sqrt(variance))
	elif distribution == "uniform":
	bound = math.sqrt(3 * variance)
	with torch.no_grad():
	tensor.uniform_(-bound, bound)
	else:
	raise ValueError(f"invalid distribution {distribution}")


	def lecun_normal_(tensor):
	variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")


	def default_flax_embed_init(tensor):
	variance_scaling_(tensor, mode="fan_in", distribution="normal")


	@dataclass
	# Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Siglip
	class SiglipVisionModelOutput(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.
	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.
	"""

	image_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
	# Copied from transformers.models.clip.modeling_clip.CLIPTextModelOutput with CLIP->Siglip
	class SiglipTextModelOutput(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
	# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->Siglip
	class SiglipOutput(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 [`SiglipTextModel`].
	image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
	The image embeddings obtained by applying the projection layer to the pooled output of [`SiglipVisionModel`].
	text_model_output(`BaseModelOutputWithPooling`):
	The output of the [`SiglipTextModel`].
	vision_model_output(`BaseModelOutputWithPooling`):
	The output of the [`SiglipVisionModel`].
	"""

	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()
	)


	class SiglipVisionEmbeddings(nn.Module):
	def __init__(self, config: SiglipVisionConfig):
	super().__init__()
	self.config = config
	self.embed_dim = config.hidden_size
	self.image_size = config.image_size
	self.patch_size = config.patch_size

	self.patch_embedding = nn.Conv2d(
	in_channels=config.num_channels,
	out_channels=self.embed_dim,
	kernel_size=self.patch_size,
	stride=self.patch_size,
	padding="valid",
	)

	self.num_patches_per_side = self.image_size // self.patch_size
	self.num_patches = self.num_patches_per_side**2
	self.num_positions = self.num_patches
	self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)

	def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor) -> torch.Tensor:
	batch_size = pixel_values.size(0)

	patch_embeds = self.patch_embedding(pixel_values)
	embeddings = patch_embeds.flatten(2).transpose(1, 2)

	max_im_h, max_im_w = pixel_values.size(2), pixel_values.size(3)
	max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size
	boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side)
	position_ids = torch.full(
	size=(
	batch_size,
	max_nb_patches_h * max_nb_patches_w,
	),
	fill_value=0,
	)

	for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
	nb_patches_h = p_attn_mask[:, 0].sum()
	nb_patches_w = p_attn_mask[0].sum()

	fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
	fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)

	bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True)
	bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)

	pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten()
	position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids

	position_ids = position_ids.to(self.position_embedding.weight.device)

	embeddings = embeddings + self.position_embedding(position_ids)
	return embeddings


	# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->Siglip
	class SiglipTextEmbeddings(nn.Module):
	def __init__(self, config: SiglipTextConfig):
	super().__init__()
	embed_dim = config.hidden_size

	self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
	self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)

	# position_ids (1, len position emb) is contiguous in memory and exported when serialized
	self.register_buffer(
	"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
	)

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	) -> torch.Tensor:
	seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]

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

	if inputs_embeds is None:
	inputs_embeds = self.token_embedding(input_ids)

	position_embeddings = self.position_embedding(position_ids)
	embeddings = inputs_embeds + position_embeddings

	return embeddings


	class SiglipAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	# Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
	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 forward(
	self,
	hidden_states: torch.Tensor,
	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"""

	batch_size, q_len, _ = hidden_states.size()

	query_states = self.q_proj(hidden_states)
	key_states = self.k_proj(hidden_states)
	value_states = self.v_proj(hidden_states)

	query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)

	k_v_seq_len = key_states.shape[-2]
	attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale

	if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len):
	raise ValueError(
	f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is"
	f" {attn_weights.size()}"
	)

	if attention_mask is not None:
	if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len):
	raise ValueError(
	f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}"
	)
	attn_weights = attn_weights + attention_mask

	# upcast attention to fp32
	attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
	attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
	attn_output = torch.matmul(attn_weights, value_states)

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

	attn_output = attn_output.transpose(1, 2).contiguous()
	attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)

	attn_output = self.out_proj(attn_output)

	return attn_output, attn_weights


	class SiglipFlashAttention2(SiglipAttention):
	"""
	Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
	untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
	flash attention and deal with padding tokens in case the input contains any of them.
	"""

	def __init__(self, args, *kwargs):
	super().__init__(args, *kwargs)
	self.is_causal = False # Hack to make sure we don't use a causal mask

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	**kwargs,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	output_attentions = False

	bsz, q_len, _ = hidden_states.size()

	query_states = self.q_proj(hidden_states)
	key_states = self.k_proj(hidden_states)
	value_states = self.v_proj(hidden_states)

	# Flash attention requires the input to have the shape
	# batch_size x seq_length x head_dim x hidden_dim
	# therefore we just need to keep the original shape
	query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)

	kv_seq_len = key_states.shape[-2]
	if past_key_value is not None:
	kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
	# cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
	# query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

	# if past_key_value is not None:
	# cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
	# key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

	# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
	# to be able to avoid many of these transpose/reshape/view.
	query_states = query_states.transpose(1, 2)
	key_states = key_states.transpose(1, 2)
	value_states = value_states.transpose(1, 2)

	dropout_rate = self.dropout if self.training else 0.0

	# In PEFT, usually we cast the layer norms in float32 for training stability reasons
	# therefore the input hidden states gets silently casted in float32. Hence, we need
	# cast them back in the correct dtype just to be sure everything works as expected.
	# This might slowdown training & inference so it is recommended to not cast the LayerNorms
	# in fp32. (LlamaRMSNorm handles it correctly)

	input_dtype = query_states.dtype
	if input_dtype == torch.float32:
	if torch.is_autocast_enabled():
	target_dtype = torch.get_autocast_gpu_dtype()
	# Handle the case where the model is quantized
	elif hasattr(self.config, "_pre_quantization_dtype"):
	target_dtype = self.config._pre_quantization_dtype
	else:
	target_dtype = self.q_proj.weight.dtype

	logger.warning_once(
	"The input hidden states seems to be silently casted in float32, this might be related to the fact"
	" you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
	f" {target_dtype}."
	)

	query_states = query_states.to(target_dtype)
	key_states = key_states.to(target_dtype)
	value_states = value_states.to(target_dtype)

	attn_output = self._flash_attention_forward(
	query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
	)

	attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous()
	attn_output = self.out_proj(attn_output)

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights

	def _flash_attention_forward(
	self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
	):
	"""
	Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
	first unpad the input, then computes the attention scores and pad the final attention scores.

	Args:
	query_states (`torch.Tensor`):
	Input query states to be passed to Flash Attention API
	key_states (`torch.Tensor`):
	Input key states to be passed to Flash Attention API
	value_states (`torch.Tensor`):
	Input value states to be passed to Flash Attention API
	attention_mask (`torch.Tensor`):
	The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
	position of padding tokens and 1 for the position of non-padding tokens.
	dropout (`int`, optional):
	Attention dropout
	softmax_scale (`float`, optional):
	The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
	"""

	# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
	causal = self.is_causal and query_length != 1

	# Contains at least one padding token in the sequence
	if attention_mask is not None:
	batch_size = query_states.shape[0]
	query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
	query_states, key_states, value_states, attention_mask, query_length
	)

	cu_seqlens_q, cu_seqlens_k = cu_seq_lens
	max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

	attn_output_unpad = flash_attn_varlen_func(
	query_states,
	key_states,
	value_states,
	cu_seqlens_q=cu_seqlens_q,
	cu_seqlens_k=cu_seqlens_k,
	max_seqlen_q=max_seqlen_in_batch_q,
	max_seqlen_k=max_seqlen_in_batch_k,
	dropout_p=dropout,
	softmax_scale=softmax_scale,
	causal=causal,
	)

	attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
	else:
	attn_output = flash_attn_func(
	query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
	)

	return attn_output

	def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
	indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
	batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

	key_layer = index_first_axis(
	key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
	)
	value_layer = index_first_axis(
	value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
	)
	if query_length == kv_seq_len:
	query_layer = index_first_axis(
	query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
	)
	cu_seqlens_q = cu_seqlens_k
	max_seqlen_in_batch_q = max_seqlen_in_batch_k
	indices_q = indices_k
	elif query_length == 1:
	max_seqlen_in_batch_q = 1
	cu_seqlens_q = torch.arange(
	batch_size + 1, dtype=torch.int32, device=query_layer.device
	) # There is a memcpy here, that is very bad.
	indices_q = cu_seqlens_q[:-1]
	query_layer = query_layer.squeeze(1)
	else:
	# The -q_len: slice assumes left padding.
	attention_mask = attention_mask[:, -query_length:]
	query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

	return (
	query_layer,
	key_layer,
	value_layer,
	indices_q,
	(cu_seqlens_q, cu_seqlens_k),
	(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
	)


	# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip
	class SiglipMLP(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->Siglip
	class SiglipEncoderLayer(nn.Module):
	def __init__(self, config: SiglipConfig):
	super().__init__()
	self.embed_dim = config.hidden_size
	self.self_attn = (
	SiglipAttention(config)
	if not getattr(config, "_flash_attn_2_enabled", False)
	else SiglipFlashAttention2(config)
	)
	self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
	self.mlp = SiglipMLP(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,
	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 shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
	output_attentions (`bool`, optional, defaults to `False`):
	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,
	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


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

	config_class = SiglipConfig
	base_model_prefix = "siglip"
	supports_gradient_checkpointing = True

	def _init_weights(self, module):
	"""Initialize the weights"""

	if isinstance(module, SiglipVisionEmbeddings):
	width = (
	self.config.vision_config.hidden_size
	if isinstance(self.config, SiglipConfig)
	else self.config.hidden_size
	)
	nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
	elif isinstance(module, nn.Embedding):
	default_flax_embed_init(module.weight)
	elif isinstance(module, SiglipAttention):
	nn.init.normal_(module.q_proj.weight)
	nn.init.normal_(module.k_proj.weight)
	nn.init.normal_(module.v_proj.weight)
	nn.init.normal_(module.out_proj.weight)
	nn.init.zeros_(module.q_proj.bias)
	nn.init.zeros_(module.k_proj.bias)
	nn.init.zeros_(module.v_proj.bias)
	nn.init.zeros_(module.out_proj.bias)
	elif isinstance(module, SiglipMLP):
	nn.init.normal_(module.fc1.weight)
	nn.init.normal_(module.fc2.weight)
	nn.init.normal_(module.fc1.bias, std=1e-6)
	nn.init.normal_(module.fc2.bias, std=1e-6)
	elif isinstance(module, SiglipMultiheadAttentionPoolingHead):
	nn.init.normal_(module.probe.data)
	nn.init.normal_(module.attention.in_proj_weight.data)
	nn.init.zeros_(module.attention.in_proj_bias.data)
	elif isinstance(module, SiglipModel):
	logit_scale_init = torch.tensor(0.0)
	module.logit_scale.data.fill_(logit_scale_init)
	module.logit_bias.data.zero_()
	elif isinstance(module, (nn.Linear, nn.Conv2d)):
	lecun_normal_(module.weight)
	if module.bias is not None:
	nn.init.zeros_(module.bias)
	elif isinstance(module, nn.LayerNorm):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)


	SIGLIP_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 ([`SiglipConfig`]): 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.
	"""

	SIGLIP_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.
	"""

	SIGLIP_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.
	"""

	SIGLIP_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.
	"""


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

	Args:
	config: SiglipConfig
	"""

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

	# Ignore copy
	def forward(
	self,
	inputs_embeds,
	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)
	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 encoder_layer in self.layers:
	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)
	if self.gradient_checkpointing and self.training:
	layer_outputs = self._gradient_checkpointing_func(
	encoder_layer.__call__,
	hidden_states,
	attention_mask,
	output_attentions,
	)
	else:
	layer_outputs = encoder_layer(
	hidden_states,
	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
	)


	class SiglipTextTransformer(nn.Module):
	def __init__(self, config: SiglipTextConfig):
	super().__init__()
	self.config = config
	embed_dim = config.hidden_size
	self.embeddings = SiglipTextEmbeddings(config)
	self.encoder = SiglipEncoder(config)
	self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)

	self.head = nn.Linear(embed_dim, embed_dim)

	@add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipTextConfig)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = 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 input_ids is None:
	raise ValueError("You have to specify input_ids")

	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])

	hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)

	# note: SigLIP's text model does not use a causal mask, unlike the original CLIP model.
	# expand attention_mask
	if attention_mask is not None:
	# [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
	attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)

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

	last_hidden_state = encoder_outputs[0]
	last_hidden_state = self.final_layer_norm(last_hidden_state)

	# Assuming "sticky" EOS tokenization, last token is always EOS.
	pooled_output = last_hidden_state[:, -1, :]
	pooled_output = self.head(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,
	)


	@add_start_docstrings(
	"""The text model from SigLIP without any head or projection on top.""",
	SIGLIP_START_DOCSTRING,
	)
	class SiglipTextModel(SiglipPreTrainedModel):
	config_class = SiglipTextConfig

	_no_split_modules = ["SiglipTextEmbeddings", "SiglipEncoderLayer"]

	def __init__(self, config: SiglipTextConfig):
	super().__init__(config)
	self.text_model = SiglipTextTransformer(config)
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self) -> nn.Module:
	return self.text_model.embeddings.token_embedding

	def set_input_embeddings(self, value):
	self.text_model.embeddings.token_embedding = value

	@add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipTextConfig)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = 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 transformers import AutoTokenizer, SiglipTextModel

	>>> model = SiglipTextModel.from_pretrained("google/siglip-base-patch16-224")
	>>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224")

	>>> # important: make sure to set padding="max_length" as that's how the model was trained
	>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt")

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

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


	class SiglipVisionTransformer(nn.Module):
	def __init__(self, config: SiglipVisionConfig):
	super().__init__()
	self.config = config
	embed_dim = config.hidden_size

	self.embeddings = SiglipVisionEmbeddings(config)
	self.encoder = SiglipEncoder(config)
	self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
	self.head = SiglipMultiheadAttentionPoolingHead(config)

	@add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig)
	def forward(
	self,
	pixel_values,
	patch_attention_mask: Optional[torch.BoolTensor] = 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

	batch_size = pixel_values.size(0)
	if patch_attention_mask is None:
	patch_attention_mask = torch.ones(
	size=(
	batch_size,
	pixel_values.size(2) // self.config.patch_size,
	pixel_values.size(3) // self.config.patch_size,
	),
	dtype=torch.bool,
	device=pixel_values.device,
	)

	hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask)

	patch_attention_mask = patch_attention_mask.view(batch_size, -1)
	# The call to `_upad_input` in `_flash_attention_forward` is expensive
	# So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence),
	# avoiding passing the attention_mask, which is equivalent to attending to the full sequence
	if not torch.any(~patch_attention_mask):
	attention_mask=None
	else:
	attention_mask = (
	_prepare_4d_attention_mask(patch_attention_mask, hidden_states.dtype)
	if not self.config._flash_attn_2_enabled
	else patch_attention_mask
	)

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

	last_hidden_state = encoder_outputs[0]
	last_hidden_state = self.post_layernorm(last_hidden_state)

	pooled_output = self.head(
	hidden_state=last_hidden_state,
	attention_mask=patch_attention_mask,
	)

	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 SiglipMultiheadAttentionPoolingHead(nn.Module):
	"""Multihead Attention Pooling."""

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

	self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
	self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True)
	self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.mlp = SiglipMLP(config)

	def forward(self, hidden_state, attention_mask):
	batch_size = hidden_state.shape[0]
	probe = self.probe.repeat(batch_size, 1, 1)

	hidden_state = self.attention(
	query=probe, key=hidden_state, value=hidden_state, key_padding_mask=~attention_mask
	)[0]

	residual = hidden_state
	hidden_state = self.layernorm(hidden_state)
	hidden_state = residual + self.mlp(hidden_state)

	return hidden_state[:, 0]


	@add_start_docstrings(
	"""The vision model from SigLIP without any head or projection on top.""",
	SIGLIP_START_DOCSTRING,
	)
	class SiglipVisionModel(SiglipPreTrainedModel):
	config_class = SiglipVisionConfig
	main_input_name = "pixel_values"

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

	self.vision_model = SiglipVisionTransformer(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(SIGLIP_VISION_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig)
	def forward(
	self,
	pixel_values,
	patch_attention_mask: Optional[torch.BoolTensor] = 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, SiglipVisionModel

	>>> model = SiglipVisionModel.from_pretrained("google/siglip-base-patch16-224")
	>>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")

	>>> 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 features
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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


	@add_start_docstrings(SIGLIP_START_DOCSTRING)
	class SiglipModel(SiglipPreTrainedModel):
	config_class = SiglipConfig

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

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

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

	text_config = config.text_config
	vision_config = config.vision_config

	self.text_model = SiglipTextTransformer(text_config)
	self.vision_model = SiglipVisionTransformer(vision_config)

	self.logit_scale = nn.Parameter(torch.randn(1))
	self.logit_bias = nn.Parameter(torch.randn(1))

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

	@add_start_docstrings_to_model_forward(SIGLIP_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,
	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 [`SiglipTextModel`].

	Examples:

	```python
	>>> from transformers import AutoTokenizer, AutoModel
	>>> import torch

	>>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
	>>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224")

	>>> # important: make sure to set padding="max_length" as that's how the model was trained
	>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt")
	>>> with torch.no_grad():
	... text_features = model.get_text_features(**inputs)
	```"""
	# Use SigLIP 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,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	pooled_output = text_outputs[1]

	return pooled_output

	@add_start_docstrings_to_model_forward(SIGLIP_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 [`SiglipVisionModel`].

	Examples:

	```python
	>>> from PIL import Image
	>>> import requests
	>>> from transformers import AutoProcessor, AutoModel
	>>> import torch

	>>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
	>>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")

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

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

	>>> with torch.no_grad():
	... image_features = model.get_image_features(**inputs)
	```"""
	# Use SiglipModel'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]

	return pooled_output

	@add_start_docstrings_to_model_forward(SIGLIP_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=SiglipOutput, config_class=SiglipConfig)
	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,
	return_loss: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, SiglipOutput]:
	r"""
	Returns:

	Examples:

	```python
	>>> from PIL import Image
	>>> import requests
	>>> from transformers import AutoProcessor, AutoModel
	>>> import torch

	>>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
	>>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")

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

	>>> texts = ["a photo of 2 cats", "a photo of 2 dogs"]
	>>> # important: we pass `padding=max_length` since the model was trained with this
	>>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")

	>>> with torch.no_grad():
	... outputs = model(**inputs)

	>>> logits_per_image = outputs.logits_per_image
	>>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
	>>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
	31.9% that image 0 is 'a photo of 2 cats'
	```"""
	# Use SigLIP 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,
	)

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

	image_embeds = vision_outputs[1]
	text_embeds = text_outputs[1]

	# 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.logit_scale.exp() + self.logit_bias
	logits_per_image = logits_per_text.t()

	loss = None
	if return_loss:
	raise NotImplementedError("SigLIP loss to be implemented")

	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 SiglipOutput(
	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,
	)