302 lines
13 KiB
Python
302 lines
13 KiB
Python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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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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""" Siglip model configuration"""
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import os
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from typing import Union
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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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from ..deprecated._archive_maps import SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class SiglipTextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SiglipTextModel`]. It is used to instantiate a
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Siglip 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 Siglip
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[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-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 Siglip text model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`SiglipModel`].
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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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Example:
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```python
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>>> from transformers import SiglipTextConfig, SiglipTextModel
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>>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 style configuration
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>>> configuration = SiglipTextConfig()
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>>> # Initializing a SiglipTextModel (with random weights) from the google/siglip-base-patch16-224 style configuration
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>>> model = SiglipTextModel(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 = "siglip_text_model"
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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/siglip
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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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**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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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the text config dict if we are loading from SiglipConfig
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if config_dict.get("model_type") == "siglip":
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config_dict = config_dict["text_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class SiglipVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
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Siglip 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 Siglip
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[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-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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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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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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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 SiglipVisionConfig, SiglipVisionModel
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>>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration
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>>> configuration = SiglipVisionConfig()
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>>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration
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>>> model = SiglipVisionModel(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 = "siglip_vision_model"
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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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image_size=224,
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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.image_size = image_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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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the vision config dict if we are loading from SiglipConfig
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if config_dict.get("model_type") == "siglip":
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config_dict = config_dict["vision_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class SiglipConfig(PretrainedConfig):
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r"""
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[`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to
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instantiate a Siglip 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 Siglip
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[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-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 [`SiglipTextConfig`].
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vision_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`SiglipVisionConfig`].
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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 SiglipConfig, SiglipModel
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>>> # Initializing a SiglipConfig with google/siglip-base-patch16-224 style configuration
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>>> configuration = SiglipConfig()
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>>> # Initializing a SiglipModel (with random weights) from the google/siglip-base-patch16-224 style configuration
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>>> model = SiglipModel(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 SiglipConfig from a SiglipTextConfig and a SiglipVisionConfig
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>>> from transformers import SiglipTextConfig, SiglipVisionConfig
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>>> # Initializing a SiglipText and SiglipVision configuration
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>>> config_text = SiglipTextConfig()
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>>> config_vision = SiglipVisionConfig()
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>>> config = SiglipConfig.from_text_vision_configs(config_text, config_vision)
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```"""
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model_type = "siglip"
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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 `SiglipTextConfig` 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 `SiglipVisionConfig` with default values.")
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self.text_config = SiglipTextConfig(**text_config)
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self.vision_config = SiglipVisionConfig(**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: SiglipTextConfig, vision_config: SiglipVisionConfig, **kwargs):
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r"""
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Instantiate a [`SiglipConfig`] (or a derived class) from siglip text model configuration and siglip vision
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model configuration.
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Returns:
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[`SiglipConfig`]: 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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