a  d"B@sdZddlZddlZddlmZddlmZmZmZm Z m Z m Z er\ddl m Z ddlmZddlmZdd lmZdd lmZeeZd d d dZGdddeZGdddeZGdddeZGdddeZdS)z OWL-ViT model configurationN OrderedDict) TYPE_CHECKINGAnyDictMappingOptionalUnion)ProcessorMixin) TensorType)PretrainedConfig) OnnxConfig)loggingzJhttps://huggingface.co/google/owlvit-base-patch32/resolve/main/config.jsonzJhttps://huggingface.co/google/owlvit-base-patch16/resolve/main/config.jsonzKhttps://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json)zgoogle/owlvit-base-patch32zgoogle/owlvit-base-patch16zgoogle/owlvit-large-patch14csDeZdZdZdZdfdd Zeeee j fddddZ Z S)OwlViTTextConfiga This is the configuration class to store the configuration of an [`OwlViTTextModel`]. It is used to instantiate an OwlViT 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 OwlViT [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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 49408): Vocabulary size of the OWL-ViT text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`OwlViTTextModel`]. hidden_size (`int`, *optional*, defaults to 512): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 2048): 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 8): Number of attention heads for each attention layer in the Transformer encoder. max_position_embeddings (`int`, *optional*, defaults to 16): 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 `"quick_gelu"`): 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-5): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). 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 ```Zowlvit_text_model  quick_geluh㈵>{Gz??rc s`tjf| | |d|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ dS)N) pad_token_id bos_token_id eos_token_id) super__init__ vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsmax_position_embeddings hidden_actlayer_norm_epsattention_dropoutinitializer_rangeinitializer_factor)selfr#r$r%r&r'r(r)r*r+r,r-rrr kwargs __class__x/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/transformers/models/owlvit/configuration_owlvit.pyr"`szOwlViTTextConfig.__init__r pretrained_model_name_or_pathreturncKs||j|fi|\}}|ddkr,|d}d|vrjt|drj|d|jkrjtd|dd|jd|j|fi|S)N model_typeowlvit text_configYou are using a model of type to instantiate a model of type N. This is not supported for all configurations of models and can yield errors.get_config_dictgethasattrr7loggerwarning from_dictclsr5r/ config_dictr2r2r3from_pretraineds  z OwlViTTextConfig.from_pretrained)rrrrrrrrrrrrrr __name__ __module__ __qualname____doc__r7r" classmethodr strosPathLikerG __classcell__r2r2r0r3r)s&4 rc sDeZdZdZdZdfd d Zeeee j fddddZ Z S)OwlViTVisionConfigag This is the configuration class to store the configuration of an [`OwlViTVisionModel`]. It is used to instantiate an OWL-ViT image 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 OWL-ViT [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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 768): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): 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-5): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float``, *optional*, defaults to 1): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). 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 ```Zowlvit_vision_model rr rrrrrc  s^tjfi| ||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ dS)N)r!r"r$r%r&r' num_channels image_size patch_sizer)r*r+r,r-)r.r$r%r&r'rVrWrXr)r*r+r,r-r/r0r2r3r"szOwlViTVisionConfig.__init__r r4cKs||j|fi|\}}|ddkr,|d}d|vrjt|drj|d|jkrjtd|dd|jd|j|fi|S)Nr7r8 vision_configr:r;r<r=rDr2r2r3rGs  z"OwlViTVisionConfig.from_pretrained) rSrTrrr rSrUrrrrrrHr2r2r0r3rRs"4rRcsdeZdZdZdZdZdfdd Zeee e j fd d d d Z ee e d ddZddZZS) OwlViTConfiga  [`OwlViTConfig`] is the configuration class to store the configuration of an [`OwlViTModel`]. It is used to instantiate an OWL-ViT 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 OWL-ViT [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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 [`OwlViTTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`OwlViTVisionConfig`]. projection_dim (`int`, *optional*, defaults to 512): Dimensionality of text and vision projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The inital value of the *logit_scale* parameter. Default is used as per the original OWL-ViT implementation. kwargs (*optional*): Dictionary of keyword arguments. r8TNr/L F@c sztjfi||dur(i}td|dur>i}tdtfi||_tfi||_||_||_ ||_ d|_ dS)NzKtext_config is None. Initializing the OwlViTTextConfig with default values.zOvision_config is None. initializing the OwlViTVisionConfig with default values.r) r!r"rAinforr9rRrYprojection_dimlogit_scale_init_value return_dictr-)r.r9rYr]r^r_r/r0r2r3r"s   zOwlViTConfig.__init__r r4cKsf|j|fi|\}}d|vrTt|drT|d|jkrTtd|dd|jd|j|fi|S)Nr7r:r;r<)r>r@r7rArBrCrDr2r2r3rG.s  zOwlViTConfig.from_pretrained)r9rYcKs&i}||d<||d<|j|fi|S)z Instantiate a [`OwlViTConfig`] (or a derived class) from owlvit text model configuration and owlvit vision model configuration. Returns: [`OwlViTConfig`]: An instance of a configuration object r9rY)rC)rEr9rYr/rFr2r2r3from_text_vision_configs:s z%OwlViTConfig.from_text_vision_configscCs8t|j}|j|d<|j|d<|jj|d<|S)z Serializes this instance to a Python dictionary. 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