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import copy |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class HybridCLIPConfig(PretrainedConfig): |
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r""" |
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:class:`HybridCLIPConfig` is the configuration class to store the configuration of a |
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:class:`~HybridCLIPModel`. It is used to instantiate HybridCLIPModel model according to the specified arguments, |
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defining the text model and vision model configs. |
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Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model |
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outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. |
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Args: |
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text_config_dict (:obj:`dict`): |
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Dictionary of configuration options that defines text model config. |
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vision_config_dict (:obj:`dict`): |
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Dictionary of configuration options that defines vison model config. |
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projection_dim (:obj:`int`, `optional`, defaults to 512): |
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Dimentionality of text and vision projection layers. |
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kwargs (`optional`): |
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Dictionary of keyword arguments. |
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Examples:: |
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>>> from transformers import BertConfig, CLIPConfig, HybridCLIPConfig, FlaxHybridCLIP |
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>>> # Initializing a BERT and CLIP configuration |
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>>> config_text = BertConfig() |
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>>> config_vision = CLIPConfig() |
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>>> config = HybridCLIPConfig.from_text_vision_configs(config_text, config_vision, projection_dim=512) |
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>>> # Initializing a BERT and CLIPVision model |
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>>> model = EncoderDecoderModel(config=config) |
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>>> # Accessing the model configuration |
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>>> config_text = model.config.text_config |
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>>> config_vision = model.config.vision_config |
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>>> # Saving the model, including its configuration |
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>>> model.save_pretrained('my-model') |
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>>> # loading model and config from pretrained folder |
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>>> encoder_decoder_config = HybridCLIPConfig.from_pretrained('my-model') |
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>>> model = FlaxHybridCLIP.from_pretrained('my-model', config=encoder_decoder_config) |
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""" |
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model_type = "hybrid-clip" |
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is_composition = True |
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def __init__(self, projection_dim=512, **kwargs): |
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super().__init__(**kwargs) |
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if "text_config" not in kwargs: |
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raise ValueError("`text_config` can not be `None`.") |
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if "vision_config" not in kwargs: |
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raise ValueError("`vision_config` can not be `None`.") |
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text_config = kwargs.pop("text_config") |
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vision_config = kwargs.pop("vision_config") |
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text_model_type = text_config.pop("model_type") |
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vision_model_type = vision_config.pop("model_type") |
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from transformers import AutoConfig |
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self.text_config = AutoConfig.for_model(text_model_type, **text_config) |
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if vision_model_type == "clip": |
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self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config).vision_config |
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else: |
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self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config) |
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self.projection_dim = projection_dim |
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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: PretrainedConfig, vision_config: PretrainedConfig, **kwargs): |
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r""" |
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Instantiate a :class:`HybridCLIPConfig` (or a derived class) from text model configuration and |
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vision model configuration. |
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Returns: |
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:class:`HybridCLIPConfig`: 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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def to_dict(self): |
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""" |
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Serializes this instance to a Python dictionary. Override the default |
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:meth:`~transformers.PretrainedConfig.to_dict`. |
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Returns: |
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:obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, |
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""" |
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output = copy.deepcopy(self.__dict__) |
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output["text_config"] = self.text_config.to_dict() |
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output["vision_config"] = self.vision_config.to_dict() |
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output["model_type"] = self.__class__.model_type |
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return output |
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