[VLM] Clean up Phi-4-MM ViT implementation (#14812)

Signed-off-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>

requirements/test.in

@@ -8,6 +8,7 @@ pytest-shard
 
 # testing utils
 awscli
+backoff # required for phi4mm test
 decord # required for video tests
 einops # required for MPT, qwen-vl and Mamba
 httpx

requirements/test.txt

@@ -33,6 +33,8 @@ audioread==3.0.1
     # via librosa
 awscli==1.35.23
     # via -r requirements/test.in
+backoff==2.2.1
+    # via -r requirements/test.in
 bitsandbytes==0.45.3
     # via -r requirements/test.in
 black==24.10.0

tests/models/decoder_only/vision_language/test_phi4mm.py

+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import re
+from typing import Optional
+
+import pytest
+from huggingface_hub import snapshot_download
+from transformers import AutoTokenizer
+
+from vllm.lora.request import LoRARequest
+from vllm.multimodal.image import rescale_image_size
+from vllm.platforms import current_platform
+from vllm.sequence import SampleLogprobs
+
+from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
+from ....utils import large_gpu_test
+from ...utils import check_logprobs_close
+
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
+    "stop_sign":
+    "<|user|>\n<|image_1|>\nWhat's the content of the image?<|end|>\n<|assistant|>\n",  # noqa: E501
+    "cherry_blossom":
+    "<|user|>\n<|image_1|>\nPlease infer the season with reason in details.<|end|>\n<|assistant|>\n",  # noqa: E501
+})
+HF_MULTIIMAGE_IMAGE_PROMPT = "<|user|>\n<|image_1|>\n<|image_2|>\nDescribe these images.<|end|>\n<|assistant|>\n"  # noqa: E501
+
+model_path = snapshot_download("microsoft/Phi-4-multimodal-instruct")
+# Since the vision-lora and speech-lora co-exist with the base model,
+# we have to manually specify the path of the lora weights.
+vision_lora_path = os.path.join(model_path, "vision-lora")
+models = [model_path]
+
+
+def vllm_to_hf_output(vllm_output: tuple[list[int], str,
+                                         Optional[SampleLogprobs]],
+                      model: str):
+    """Sanitize vllm output to be comparable with hf output."""
+    _, output_str, out_logprobs = vllm_output
+
+    output_str_without_image = re.sub(r"(<\|image_\d+\|>)+", "", output_str)
+    assert output_str_without_image[0] == " "
+    output_str_without_image = output_str_without_image[1:]
+
+    hf_output_str = output_str_without_image + "<|end|><|endoftext|>"
+
+    tokenizer = AutoTokenizer.from_pretrained(model)
+    hf_output_ids = tokenizer.encode(output_str_without_image)
+    assert hf_output_ids[0] == 1
+    hf_output_ids = hf_output_ids[1:]
+
+    return hf_output_ids, hf_output_str, out_logprobs
+
+
+target_dtype = "half"
+
+# ROCm Triton FA can run into shared memory issues with these models,
+# use other backends in the meantime
+# FIXME (mattwong, gshtrasb, hongxiayan)
+if current_platform.is_rocm():
+    os.environ["VLLM_USE_TRITON_FLASH_ATTN"] = "0"
+
+
+def run_test(
+    hf_runner: type[HfRunner],
+    vllm_runner: type[VllmRunner],
+    inputs: list[tuple[list[str], PromptImageInput]],
+    model: str,
+    *,
+    max_model_len: int,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+    mm_limit: int,
+    tensor_parallel_size: int,
+    distributed_executor_backend: Optional[str] = None,
+):
+    """Inference result should be the same between hf and vllm.
+
+    All the image fixtures for the test are from IMAGE_ASSETS.
+    For huggingface runner, we provide the PIL images as input.
+    For vllm runner, we provide MultiModalDataDict objects
+    and corresponding MultiModalConfig as input.
+    Note, the text input is also adjusted to abide by vllm contract.
+    The text output is sanitized to be able to compare with hf.
+    """
+    # NOTE: take care of the order. run vLLM first, and then run HF.
+    # vLLM needs a fresh new process without cuda initialization.
+    # if we run HF first, the cuda initialization will be done and it
+    # will hurt multiprocessing backend with fork method (the default method).
+    # max_model_len should be greater than image_feature_size
+    with vllm_runner(
+            model,
+            task="generate",
+            max_model_len=max_model_len,
+            max_num_seqs=2,
+            dtype=dtype,
+            limit_mm_per_prompt={"image": mm_limit},
+            tensor_parallel_size=tensor_parallel_size,
+            distributed_executor_backend=distributed_executor_backend,
+            enable_lora=True,
+            max_lora_rank=320,
+            lora_extra_vocab_size=0,
+            gpu_memory_utilization=0.8,  # set to 0.8 to avoid OOM in CI
+            enforce_eager=True,
+    ) as vllm_model:
+        lora_request = LoRARequest("vision", 1, vision_lora_path)
+        vllm_model.model.llm_engine.add_lora(lora_request=lora_request)
+        vllm_outputs_per_case = [
+            vllm_model.generate_greedy_logprobs(prompts,
+                                                max_tokens,
+                                                num_logprobs=num_logprobs,
+                                                images=images)
+            for prompts, images in inputs
+        ]
+
+    # use eager mode for hf runner, since phi3_v didn't work with flash_attn
+    hf_model_kwargs = {"_attn_implementation": "eager"}
+    with hf_runner(model, dtype=dtype,
+                   model_kwargs=hf_model_kwargs) as hf_model:
+        eos_token_id = hf_model.processor.tokenizer.eos_token_id
+        hf_outputs_per_case = [
+            hf_model.generate_greedy_logprobs_limit(prompts,
+                                                    max_tokens,
+                                                    num_logprobs=num_logprobs,
+                                                    images=images,
+                                                    eos_token_id=eos_token_id,
+                                                    num_logits_to_keep=0)
+            for prompts, images in inputs
+        ]
+
+    for hf_outputs, vllm_outputs in zip(hf_outputs_per_case,
+                                        vllm_outputs_per_case):
+        check_logprobs_close(
+            outputs_0_lst=hf_outputs,
+            outputs_1_lst=vllm_outputs,
+            name_0="hf",
+            name_1="vllm",
+        )
+
+
+# Since we use _attn_implementation="eager" for hf_runner, there is more
+# significant numerical difference. The basic `logprobs=5` fails to pass.
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize(
+    "size_factors",
+    [
+        # No image
+        [],
+        # Single-scale
+        [1.0],
+        # Single-scale, batched
+        [1.0, 1.0, 1.0],
+        # Multi-scale
+        [0.7, 0.75, 1.0],
+    ],
+)
+@pytest.mark.parametrize("dtype", [target_dtype])
+@pytest.mark.parametrize("max_model_len", [4096])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [10])
+def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
+                dtype: str, max_model_len: int, max_tokens: int,
+                num_logprobs: int) -> None:
+    images = [asset.pil_image for asset in image_assets]
+
+    inputs_per_image = [(
+        [prompt for _ in size_factors],
+        [rescale_image_size(image, factor) for factor in size_factors],
+    ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
+
+    run_test(
+        hf_runner,
+        vllm_runner,
+        inputs_per_image,
+        model,
+        dtype=dtype,
+        max_model_len=max_model_len,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        mm_limit=1,
+        tensor_parallel_size=1,
+    )
+
+
+@large_gpu_test(min_gb=48)
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize(
+    "size_factors",
+    [
+        # No image
+        # [],
+        # Single-scale
+        [1.0],
+        # Single-scale, batched
+        [1.0, 1.0, 1.0],
+        # Multi-scale
+        [0.25, 0.5, 1.0],
+    ],
+)
+@pytest.mark.parametrize("dtype", [target_dtype])
+@pytest.mark.parametrize("max_model_len", [10000])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [10])
+@pytest.mark.xfail(
+    reason="Phi-4-MM multi-image inference is divergent with hf model.")
+def test_multi_images_models(hf_runner, vllm_runner, image_assets, model,
+                             size_factors, dtype: str, max_model_len: int,
+                             max_tokens: int, num_logprobs: int) -> None:
+    images = [asset.pil_image for asset in image_assets]
+
+    inputs_per_case = [
+        ([HF_MULTIIMAGE_IMAGE_PROMPT for _ in size_factors],
+         [[rescale_image_size(image, factor) for image in images]
+          for factor in size_factors])
+    ]
+
+    run_test(
+        hf_runner,
+        vllm_runner,
+        inputs_per_case,
+        model,
+        dtype=dtype,
+        max_model_len=max_model_len,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        mm_limit=2,
+        tensor_parallel_size=1,
+    )

vllm/model_executor/models/aria.py

@@ -60,7 +60,7 @@ class AriaVisionTransformer(Idefics3VisionTransformer, SupportsQuant):
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
     ) -> None:
-        super().__init__(config, quant_config, prefix)
+        super().__init__(config, quant_config=quant_config, prefix=prefix)
         # Unlike Idefics3VisionTransformer which uses LayerNorm after the
         # final layer, Aria omits this normalization, so we replace it with an
         # Identity layer
@@ -512,7 +512,7 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
         self.config = config
         self.vision_tower = AriaVisionTransformer(
             config.vision_config,
-            quant_config,
+            quant_config=quant_config,
             prefix=f"{prefix}.vision_tower",
         )
         self.multi_modal_projector = AriaProjector(config)

vllm/model_executor/models/idefics2_vision_model.py

@@ -113,7 +113,7 @@ class Idefics2VisionAttention(nn.Module):
 
     def __init__(
         self,
-        config: Idefics2Config,
+        config: Idefics2VisionConfig,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
     ) -> None:
@@ -164,7 +164,7 @@ class Idefics2VisionMLP(nn.Module):
 
     def __init__(
         self,
-        config: Idefics2Config,
+        config: Idefics2VisionConfig,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
     ) -> None:
@@ -249,16 +249,24 @@ class Idefics2Encoder(nn.Module):
         self,
         config: Idefics2Config,
         quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
         prefix: str = "",
     ) -> None:
         super().__init__()
 
         self.config = config
+
+        if num_hidden_layers_override is None:
+            num_hidden_layers = config.num_hidden_layers
+        else:
+            num_hidden_layers = num_hidden_layers_override
+
         self.layers = nn.ModuleList([
             Idefics2EncoderLayer(config,
                                  quant_config=quant_config,
                                  prefix=f"{prefix}.layers.{layer_idx}")
-            for layer_idx in range(config.num_hidden_layers)
+            for layer_idx in range(num_hidden_layers)
         ])
 
     def forward(
@@ -287,6 +295,9 @@ class Idefics2VisionTransformer(nn.Module):
         self,
         config: Idefics2VisionConfig,
         quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        require_post_norm: bool = True,
         prefix: str = "",
     ) -> None:
         super().__init__()
@@ -294,11 +305,24 @@ class Idefics2VisionTransformer(nn.Module):
         embed_dim = config.hidden_size
         self.config = config
         self.embeddings = Idefics2VisionEmbeddings(config)
-        self.encoder = Idefics2Encoder(config,
+        self.encoder = Idefics2Encoder(
+            config,
             quant_config=quant_config,
+            num_hidden_layers_override=num_hidden_layers_override,
             prefix=f"{prefix}.encoder")
-        self.post_layernorm = nn.LayerNorm(embed_dim,
-                                           eps=config.layer_norm_eps)
+
+        num_hidden_layers = config.num_hidden_layers
+        if len(self.encoder.layers) > config.num_hidden_layers:
+            raise ValueError(
+                f"The original encoder only has {num_hidden_layers} "
+                f"layers, but you requested {len(self.encoder.layers)} layers."
+            )
+
+        self.require_post_norm = require_post_norm
+        self.post_layernorm = nn.LayerNorm(
+            embed_dim,
+            eps=config.layer_norm_eps,
+        ) if require_post_norm else nn.Identity()
 
     def get_input_embeddings(self):
         return self.embeddings
@@ -328,7 +352,24 @@ class Idefics2VisionTransformer(nn.Module):
         ]
         params_dict = dict(self.named_parameters())
         loaded_params: Set[str] = set()
+        layer_count = len(self.encoder.layers)
+
         for name, loaded_weight in weights:
+            # skip pooling header
+            if name.startswith("head."):
+                continue
+
+            # post_layernorm is optional
+            if (name.startswith("post_layernorm.")
+                    and not self.require_post_norm):
+                continue
+
+            # omit layers when num_hidden_layers_override is set
+            if name.startswith("encoder.layers."):
+                layer_idx = int(name.split(".")[2])
+                if layer_idx >= layer_count:
+                    continue
+
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:
                     continue

vllm/model_executor/models/phi4mm.py

@@ -11,7 +11,7 @@ import torch
 import torch.nn as nn
 import torchvision.transforms as T
 from PIL import Image
-from transformers import PretrainedConfig
+from transformers import PretrainedConfig, SiglipVisionConfig
 from transformers.utils import logging
 
 from vllm.config import VllmConfig
@@ -32,10 +32,10 @@ from vllm.multimodal.inputs import MultiModalInputs, NestedTensors
 from vllm.sequence import IntermediateTensors, SequenceData
 from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
 
+from .idefics2_vision_model import Idefics2VisionTransformer
 from .interfaces import SupportsLoRA, SupportsMultiModal
 from .phi4mm_audio import AudioEmbedding
 from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
-from .vision_siglip_navit import get_siglip_vision_model
 
 # <|endoftext10|> (see vocab.json in hf model)
 _IMAGE_PLACEHOLDER_TOKEN_ID = 200010
@@ -339,6 +339,33 @@ def preprocess(images, dynamic_hd_size, vit_resolution, vit_patch_size):
     return data
 
 
+def get_navit_vision_model(layer_idx: int = -1, **kwargs):
+    vision_config = {
+        "hidden_size": 1152,
+        "image_size": 448,
+        "intermediate_size": 4304,
+        "model_type": "siglip_vision_model",
+        "num_attention_heads": 16,
+        "num_hidden_layers": 27,
+        "patch_size": 14,
+    }
+
+    model_config = SiglipVisionConfig(**vision_config, **kwargs)
+    if layer_idx < 0:
+        num_hidden_layers = model_config.num_hidden_layers \
+            + layer_idx + 1
+    else:
+        num_hidden_layers = layer_idx + 1
+
+    vision_model = Idefics2VisionTransformer(
+        config=model_config,
+        require_post_norm=False,
+        num_hidden_layers_override=num_hidden_layers,
+    )
+
+    return vision_model
+
+
 class Phi4MMImageEncoder(nn.Module):
     """Image embedding."""
 
@@ -362,8 +389,7 @@ class Phi4MMImageEncoder(nn.Module):
             self.layer_idx = -2
             self.type_feature = 'patch'
 
-        self.img_processor = get_siglip_vision_model(
-            _flash_attn_2_enabled=True)
+        self.img_processor = get_navit_vision_model(layer_idx=self.layer_idx)
 
         pe_weight = self.img_processor.embeddings.position_embedding.weight
         L, D = pe_weight.size()
@@ -430,16 +456,11 @@ class Phi4MMImageEncoder(nn.Module):
     def get_img_features(self,
                          img_embeds: torch.FloatTensor,
                          attention_mask=None) -> torch.FloatTensor:
-        LAYER_IDX = self.layer_idx
-        TYPE_FEATURE = self.type_feature
 
-        img_processor_output = self.img_processor(
-            img_embeds,
-            output_hidden_states=True,
+        img_feature = self.img_processor(img_embeds,
                                          patch_attention_mask=attention_mask)
-        img_feature = img_processor_output.hidden_states[LAYER_IDX]
 
-        if TYPE_FEATURE == "patch":
+        if self.type_feature == "patch":
             patch_feature = img_feature
 
             use_token_compression = self.image_token_compression is not None

vllm/model_executor/models/vision_siglip_navit.py

-# SPDX-License-Identifier: Apache-2.0
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Siglip model configuration"""
-
-import math
-import os
-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.configuration_utils import PretrainedConfig
-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, logging,
-                                replace_return_docstrings)
-
-from vllm.platforms import _Backend
-
-from .vision import get_vit_attn_backend
-
-logger = logging.get_logger(__name__)
-
-SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "google/siglip-base-patch16-224":
-    "https://huggingface.co/google/siglip-base-patch16-224/"\
-        "resolve/main/config.json",
-}
-
-
-class SiglipTextConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a 
-    [`SiglipTextModel`]. It is used to instantiate a Siglip text encoder
-    according to the specified arguments, defining the model architecture. 
-    Instantiating a configuration with the defaults will yield a similar 
-    configuration to that of the text encoder of the Siglip [google/
-    siglip-base-patch16-224](https://huggingface.co/google/siglip-base
-    -patch16-224) architecture.
-    Configuration objects inherit from [`PretrainedConfig`] and can be used
-    to control the model outputs. Read the documentation from 
-    [`PretrainedConfig`] for more information.
-    Args:
-        vocab_size (`int`, *optional*, defaults to 32000):
-            Vocabulary size of the Siglip text model. Defines the number of 
-            different tokens that can be represented by the `inputs_ids` 
-            passed when calling [`SiglipModel`].
-        hidden_size (`int`, *optional*, defaults to 768):
-            Dimensionality of the encoder layers and the pooler layer.
-        intermediate_size (`int`, *optional*, defaults to 3072):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer 
-            in the Transformer encoder.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of attention heads for each attention layer in the 
-            Transformer encoder.
-        max_position_embeddings (`int`, *optional*, defaults to 64):
-            The maximum sequence length that this model might ever be used 
-            with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        hidden_act (`str` or `function`, *optional*, defaults to 
-            `"gelu_pytorch_tanh"`):
-            The non-linear activation function (function or string) in the
-            encoder and pooler. If string, `"gelu"`,
-            `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the layer normalization layers.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        pad_token_id (`int`, *optional*, defaults to 1):
-            The id of the padding token in the vocabulary.
-        bos_token_id (`int`, *optional*, defaults to 49406):
-            The id of the beginning-of-sequence token in the vocabulary.
-        eos_token_id (`int`, *optional*, defaults to 49407):
-            The id of the end-of-sequence token in the vocabulary.
-    Example:
-    ```python
-    >>> from transformers import SiglipTextConfig, SiglipTextModel
-    >>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 
-        style configuration
-    >>> configuration = SiglipTextConfig()
-    >>> # Initializing a SiglipTextModel (with random weights) from the 
-        google/siglip-base-patch16-224 style configuration
-    >>> model = SiglipTextModel(configuration)
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "siglip_text_model"
-
-    def __init__(
-        self,
-        vocab_size=32000,
-        hidden_size=768,
-        intermediate_size=3072,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        max_position_embeddings=64,
-        hidden_act="gelu_pytorch_tanh",
-        layer_norm_eps=1e-6,
-        attention_dropout=0.0,
-        # This differs from `CLIPTokenizer`'s default and from openai/siglip
-        # See https://github.com/huggingface/transformers/pull/24773#
-        # issuecomment-1632287538
-        pad_token_id=1,
-        bos_token_id=49406,
-        eos_token_id=49407,
-        _flash_attn_2_enabled=True,
-        **kwargs,
-    ):
-        super().__init__(pad_token_id=pad_token_id,
-                         bos_token_id=bos_token_id,
-                         eos_token_id=eos_token_id,
-                         **kwargs)
-
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.max_position_embeddings = max_position_embeddings
-        self.layer_norm_eps = layer_norm_eps
-        self.hidden_act = hidden_act
-        self.attention_dropout = attention_dropout
-        self._flash_attn_2_enabled = _flash_attn_2_enabled
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str,
-                                                                  os.PathLike],
-                        **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(
-            pretrained_model_name_or_path, **kwargs)
-
-        # get the text config dict if we are loading from SiglipConfig
-        if config_dict.get("model_type") == "siglip":
-            config_dict = config_dict["text_config"]
-
-        if "model_type" in config_dict and hasattr(
-                cls,
-                "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                "You are using a model of type %s to instantiate a model of "
-                "type %s. This is not supported for all configurations of "
-                "models and can yield errors.", config_dict['model_type'],
-                cls.model_type)
-
-        return cls.from_dict(config_dict, **kwargs)
-
-
-class SiglipVisionConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a 
-    [`SiglipVisionModel`]. It is used to instantiate a
-    Siglip vision encoder according to the specified arguments, defining the 
-    model architecture. Instantiating a configuration with the defaults will
-    yield a similar configuration to that of the vision encoder of the Siglip
-    [google/siglip-base-patch16-224](https://huggingface.co/google/
-    siglip-base-patch16-224) architecture.
-    Configuration objects inherit from [`PretrainedConfig`] and can be used 
-    to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-    Args:
-        hidden_size (`int`, *optional*, defaults to 768):
-            Dimensionality of the encoder layers and the pooler layer.
-        intermediate_size (`int`, *optional*, defaults to 3072):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer
-            in the Transformer encoder.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of attention heads for each attention layer in the
-            Transformer encoder.
-        num_channels (`int`, *optional*, defaults to 3):
-            Number of channels in the input images.
-        image_size (`int`, *optional*, defaults to 224):
-            The size (resolution) of each image.
-        patch_size (`int`, *optional*, defaults to 16):
-            The size (resolution) of each patch.
-        hidden_act (`str` or `function`, *optional*, defaults to 
-            `"gelu_pytorch_tanh"`):
-            The non-linear activation function (function or string) in the 
-            encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and
-            `"gelu_new"` ``"quick_gelu"` are supported.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the layer normalization layers.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-    Example:
-    ```python
-    >>> from transformers import SiglipVisionConfig, SiglipVisionModel
-    >>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224
-        style configuration
-    >>> configuration = SiglipVisionConfig()
-    >>> # Initializing a SiglipVisionModel (with random weights) from the 
-        google/siglip-base-patch16-224 style configuration
-    >>> model = SiglipVisionModel(configuration)
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "siglip_vision_model"
-
-    def __init__(
-        self,
-        hidden_size=768,
-        intermediate_size=3072,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        num_channels=3,
-        image_size=224,
-        patch_size=16,
-        hidden_act="gelu_pytorch_tanh",
-        layer_norm_eps=1e-6,
-        attention_dropout=0.0,
-        _flash_attn_2_enabled=True,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.num_channels = num_channels
-        self.patch_size = patch_size
-        self.image_size = image_size
-        self.attention_dropout = attention_dropout
-        self.layer_norm_eps = layer_norm_eps
-        self.hidden_act = hidden_act
-        self._flash_attn_2_enabled = _flash_attn_2_enabled
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str,
-                                                                  os.PathLike],
-                        **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(
-            pretrained_model_name_or_path, **kwargs)
-
-        # get the vision config dict if we are loading from SiglipConfig
-        if config_dict.get("model_type") == "siglip":
-            config_dict = config_dict["vision_config"]
-
-        if "model_type" in config_dict and hasattr(
-                cls,
-                "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                "You are using a model of type %s to "
-                "instantiate a model of type %s. This is not"
-                " supported for all configurations of models and can yield"
-                " errors.", config_dict['model_type'], cls.model_type)
-
-        return cls.from_dict(config_dict, **kwargs)
-
-
-class SiglipConfig(PretrainedConfig):
-    r"""
-    [`SiglipConfig`] is the configuration class to store the configuration of a
-    [`SiglipModel`]. It is used to instantiate a Siglip model according to the 
-    specified arguments, defining the text model and vision model configs.
-    Instantiating a configuration with the defaults will yield a similar
-    configuration to that of the Siglip [google/siglip-base-patch16-224](
-    https://huggingface.co/google/siglip-base-patch16-224) architecture.
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to
-    control the model outputs. Read the documentation from 
-    [`PretrainedConfig`] for more information.
-    Args:
-        text_config (`dict`, *optional*):
-            Dictionary of configuration options used to initialize 
-            [`SiglipTextConfig`].
-        vision_config (`dict`, *optional*):
-            Dictionary of configuration options used to initialize 
-            [`SiglipVisionConfig`].
-        kwargs (*optional*):
-            Dictionary of keyword arguments.
-    Example:
-    ```python
-    >>> from transformers import SiglipConfig, SiglipModel
-    >>> # Initializing a SiglipConfig with google/siglip-base-patch16-224 
-        style configuration
-    >>> configuration = SiglipConfig()
-    >>> # Initializing a SiglipModel (with random weights) from the 
-        google/siglip-base-patch16-224 style configuration
-    >>> model = SiglipModel(configuration)
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    >>> # We can also initialize a SiglipConfig from a SiglipTextConfig 
-        and a SiglipVisionConfig
-    >>> from transformers import SiglipTextConfig, SiglipVisionConfig
-    >>> # Initializing a SiglipText and SiglipVision configuration
-    >>> config_text = SiglipTextConfig()
-    >>> config_vision = SiglipVisionConfig()
-    >>> config = SiglipConfig.from_text_vision_configs(config_text, 
-        config_vision)
-    ```"""
-
-    model_type = "siglip"
-
-    def __init__(self, text_config=None, vision_config=None, **kwargs):
-        super().__init__(**kwargs)
-
-        if text_config is None:
-            text_config = {}
-            logger.info(
-                "`text_config` is `None`. Initializing the `SiglipTextConfig`"
-                " with default values.")
-
-        if vision_config is None:
-            vision_config = {}
-            logger.info("`vision_config` is `None`. initializing the "
-                        "`SiglipVisionConfig` with default values.")
-
-        self.text_config = SiglipTextConfig(**text_config)
-        self.vision_config = SiglipVisionConfig(**vision_config)
-
-        self.initializer_factor = 1.0
-
-    @classmethod
-    def from_text_vision_configs(cls, text_config: SiglipTextConfig,
-                                 vision_config: SiglipVisionConfig, **kwargs):
-        r"""
-        Instantiate a [`SiglipConfig`] (or a derived class) from siglip text 
-        model configuration and siglip vision
-        model configuration.
-        Returns:
-            [`SiglipConfig`]: An instance of a configuration object
-        """
-
-        return cls(text_config=text_config.to_dict(),
-                   vision_config=vision_config.to_dict(),
-                   **kwargs)
-
-
-# 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."""
-
-_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
-]
-
-
-# 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)  # noqa
-    u = norm_cdf((b - mean) / std)  # noqa
-
-    # 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 subsequently 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
-            model.
-            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
-        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.linspace(0, 1 - 1 / nb_patches_h,
-                                                 nb_patches_h)
-            fractional_coords_w = torch.linspace(0, 1 - 1 / nb_patches_w,
-                                                 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`:"
-                f" {self.embed_dim} and `num_heads`: {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 "
-                f"{(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 "
-                                 f"{(batch_size, 1, q_len, k_v_seq_len)}, "
-                                 f"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 "
-                f"{(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)
-
-        # 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 "
-                f"back the input in {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)
-        """
-        from flash_attn import flash_attn_func, flash_attn_varlen_func
-        from flash_attn.bert_padding import pad_input  # noqa
-
-        # 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):
-        from flash_attn.bert_padding import index_first_axis, unpad_input
-        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 "
-                             f"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,
-        )
-
-
-def get_siglip_vision_model(_flash_attn_2_enabled=True, **kwargs):
-    siglip_vision_config = {
-        "hidden_size": 1152,
-        "image_size": 448,
-        "intermediate_size": 4304,
-        "model_type": "siglip_vision_model",
-        "num_attention_heads": 16,
-        "num_hidden_layers": 27,
-        "patch_size": 14,
-    }
-
-    # Detect attention implementation.
-    attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
-    if attn_backend != _Backend.FLASH_ATTN:
-        _flash_attn_2_enabled = False
-
-    model_config = SiglipVisionConfig(
-        **siglip_vision_config,
-        _flash_attn_2_enabled=_flash_attn_2_enabled,
-        **kwargs)
-
-    vision_model = SiglipVisionModel(model_config).vision_model
-
-    return vision_model