278 lines
13 KiB
Python
278 lines
13 KiB
Python
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/siglip2/modular_siglip2.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_siglip2.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2025 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class Siglip2TextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Siglip2TextModel`]. It is used to instantiate a
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Siglip2 text encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the text encoder of the Siglip2
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[google/siglip2-base-patch16-224](https://huggingface.co/google/siglip2-base-patch16-224) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the Siglip2 text model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`Siglip2Model`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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max_position_embeddings (`int`, *optional*, defaults to 64):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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pad_token_id (`int`, *optional*, defaults to 1):
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The id of the padding token in the vocabulary.
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bos_token_id (`int`, *optional*, defaults to 49406):
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The id of the beginning-of-sequence token in the vocabulary.
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eos_token_id (`int`, *optional*, defaults to 49407):
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The id of the end-of-sequence token in the vocabulary.
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projection_size (`int`, *optional*, defaults to `hidden_size`):
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The size of the projection head.
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Example:
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```python
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>>> from transformers import Siglip2TextConfig, Siglip2TextModel
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>>> # Initializing a Siglip2TextConfig with google/siglip2-base-patch16-224 style configuration
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>>> configuration = Siglip2TextConfig()
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>>> # Initializing a Siglip2TextModel (with random weights) from the google/siglip2-base-patch16-224 style configuration
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>>> model = Siglip2TextModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "siglip2_text_model"
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base_config_key = "text_config"
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def __init__(
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self,
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vocab_size=32000,
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hidden_size=768,
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intermediate_size=3072,
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num_hidden_layers=12,
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num_attention_heads=12,
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max_position_embeddings=64,
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hidden_act="gelu_pytorch_tanh",
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layer_norm_eps=1e-6,
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attention_dropout=0.0,
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# This differs from `CLIPTokenizer`'s default and from openai/siglip2
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# See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538
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pad_token_id=1,
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bos_token_id=49406,
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eos_token_id=49407,
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projection_size=None,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.attention_dropout = attention_dropout
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self.projection_size = projection_size if projection_size is not None else hidden_size
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class Siglip2VisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Siglip2VisionModel`]. It is used to instantiate a
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Siglip2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip2
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[google/siglip2-base-patch16-naflex](https://huggingface.co/google/siglip2-base-patch16-naflex) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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Number of channels in the input images.
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num_patches (`int`, *optional*, defaults to 256):
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The number of patches in the image with the size of (`patch_size`, `patch_size`).
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The image is resized to fill maximum of this number of patches, and to preserve
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the aspect ratio. In case the resulted number of patches is lower, the image is
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padded in "patch" dimension.
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patch_size (`int`, *optional*, defaults to 16):
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The size (resolution) of each patch.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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Example:
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```python
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>>> from transformers import Siglip2VisionConfig, Siglip2VisionModel
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>>> # Initializing a Siglip2VisionConfig with google/siglip2-base-patch16-naflex style configuration
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>>> configuration = Siglip2VisionConfig()
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>>> # Initializing a Siglip2VisionModel (with random weights) from the google/siglip2-base-patch16-naflex style configuration
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>>> model = Siglip2VisionModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "siglip2_vision_model"
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base_config_key = "vision_config"
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def __init__(
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self,
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hidden_size=768,
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intermediate_size=3072,
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num_hidden_layers=12,
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num_attention_heads=12,
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num_channels=3,
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num_patches=256,
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patch_size=16,
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hidden_act="gelu_pytorch_tanh",
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layer_norm_eps=1e-6,
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attention_dropout=0.0,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_channels = num_channels
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self.patch_size = patch_size
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self.attention_dropout = attention_dropout
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.num_patches = num_patches
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class Siglip2Config(PretrainedConfig):
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r"""
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[`Siglip2Config`] is the configuration class to store the configuration of a [`Siglip2Model`]. It is used to
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instantiate a Siglip2 model according to the specified arguments, defining the text model and vision model configs.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the Siglip2
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[google/siglip2-base-patch16-224](https://huggingface.co/google/siglip2-base-patch16-224) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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text_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`Siglip2TextConfig`].
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vision_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`Siglip2VisionConfig`].
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kwargs (*optional*):
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Dictionary of keyword arguments.
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Example:
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```python
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>>> from transformers import Siglip2Config, Siglip2Model
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>>> # Initializing a Siglip2Config with google/siglip2-base-patch16-224 style configuration
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>>> configuration = Siglip2Config()
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>>> # Initializing a Siglip2Model (with random weights) from the google/siglip2-base-patch16-224 style configuration
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>>> model = Siglip2Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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>>> # We can also initialize a Siglip2Config from a Siglip2TextConfig and a Siglip2VisionConfig
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>>> from transformers import Siglip2TextConfig, Siglip2VisionConfig
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>>> # Initializing a Siglip2Text and Siglip2Vision configuration
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>>> config_text = Siglip2TextConfig()
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>>> config_vision = Siglip2VisionConfig()
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>>> config = Siglip2Config.from_text_vision_configs(config_text, config_vision)
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```"""
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model_type = "siglip2"
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sub_configs = {"text_config": Siglip2TextConfig, "vision_config": Siglip2VisionConfig}
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def __init__(self, text_config=None, vision_config=None, **kwargs):
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super().__init__(**kwargs)
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if text_config is None:
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text_config = {}
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logger.info("`text_config` is `None`. Initializing the `Siglip2TextConfig` with default values.")
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if vision_config is None:
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vision_config = {}
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logger.info("`vision_config` is `None`. initializing the `Siglip2VisionConfig` with default values.")
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self.text_config = Siglip2TextConfig(**text_config)
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self.vision_config = Siglip2VisionConfig(**vision_config)
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self.initializer_factor = 1.0
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@classmethod
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def from_text_vision_configs(cls, text_config: Siglip2TextConfig, vision_config: Siglip2VisionConfig, **kwargs):
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r"""
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Instantiate a [`Siglip2Config`] (or a derived class) from siglip2 text model configuration and siglip2 vision
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model configuration.
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Returns:
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[`Siglip2Config`]: An instance of a configuration object
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"""
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return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
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__all__ = ["Siglip2Config", "Siglip2TextConfig", "Siglip2VisionConfig"]
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