# Copyright 2022 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. """OWL-ViT model configuration""" from huggingface_hub.dataclasses import strict from ...configuration_utils import PreTrainedConfig from ...utils import auto_docstring, logging logger = logging.get_logger(__name__) @auto_docstring(checkpoint="google/owlvit-base-patch16") @strict class OwlViTTextConfig(PreTrainedConfig): r""" Example: ```python >>> from transformers import OwlViTTextConfig, OwlViTTextModel >>> # Initializing a OwlViTTextModel with google/owlvit-base-patch32 style configuration >>> configuration = OwlViTTextConfig() >>> # Initializing a OwlViTTextConfig from the google/owlvit-base-patch32 style configuration >>> model = OwlViTTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "owlvit_text_model" base_config_key = "text_config" vocab_size: int = 49408 hidden_size: int = 512 intermediate_size: int = 2048 num_hidden_layers: int = 12 num_attention_heads: int = 8 max_position_embeddings: int = 16 hidden_act: str = "quick_gelu" layer_norm_eps: float = 1e-5 attention_dropout: float | int = 0.0 initializer_range: float = 0.02 initializer_factor: float = 1.0 pad_token_id: int | None = 0 bos_token_id: int | None = 49406 eos_token_id: int | list[int] | None = 49407 @auto_docstring(checkpoint="google/owlvit-base-patch16") @strict class OwlViTVisionConfig(PreTrainedConfig): r""" Example: ```python >>> from transformers import OwlViTVisionConfig, OwlViTVisionModel >>> # Initializing a OwlViTVisionModel with google/owlvit-base-patch32 style configuration >>> configuration = OwlViTVisionConfig() >>> # Initializing a OwlViTVisionModel model from the google/owlvit-base-patch32 style configuration >>> model = OwlViTVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "owlvit_vision_model" base_config_key = "vision_config" hidden_size: int = 768 intermediate_size: int = 3072 num_hidden_layers: int = 12 num_attention_heads: int = 12 num_channels: int = 3 image_size: int | list[int] | tuple[int, int] = 768 patch_size: int | list[int] | tuple[int, int] = 32 hidden_act: str = "quick_gelu" layer_norm_eps: float = 1e-5 attention_dropout: float | int = 0.0 initializer_range: float = 0.02 initializer_factor: float = 1.0 @auto_docstring(checkpoint="google/owlvit-base-patch16") @strict class OwlViTConfig(PreTrainedConfig): model_type = "owlvit" sub_configs = {"text_config": OwlViTTextConfig, "vision_config": OwlViTVisionConfig} text_config: dict | PreTrainedConfig | None = None vision_config: dict | PreTrainedConfig | None = None projection_dim: int = 512 logit_scale_init_value: float = 2.6592 return_dict: bool = True initializer_factor: float = 1.0 def __post_init__(self, **kwargs): if self.text_config is None: self.text_config = OwlViTTextConfig() logger.info("`text_config` is `None`. initializing the `OwlViTTextConfig` with default values.") elif isinstance(self.text_config, dict): self.text_config = OwlViTTextConfig(**self.text_config) if self.vision_config is None: self.vision_config = OwlViTVisionConfig() logger.info("`vision_config` is `None`. initializing the `OwlViTVisionConfig` with default values.") elif isinstance(self.vision_config, dict): self.vision_config = OwlViTVisionConfig(**self.vision_config) super().__post_init__(**kwargs) __all__ = ["OwlViTConfig", "OwlViTTextConfig", "OwlViTVisionConfig"]