sg7dZddlmZddlmZmZmZmZmZer ddl m Z ddl m Z ddl mZddlmZdd l mZej$eZGd d eZGd d eZGddeZGddeZy)zOWL-ViT model configuration OrderedDict) TYPE_CHECKINGAnyDictMappingOptional)ProcessorMixin) TensorType)PretrainedConfig) OnnxConfig)loggingcHeZdZdZdZdZ dfd ZxZS)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-05): 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 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). pad_token_id (`int`, *optional*, defaults to 0): The id of the padding token in the input sequences. bos_token_id (`int`, *optional*, defaults to 49406): The id of the beginning-of-sequence token in the input sequences. eos_token_id (`int`, *optional*, defaults to 49407): The id of the end-of-sequence token in the input sequences. 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 ```owlvit_text_model text_configc t|d| | |d|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ y)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)selfrrrrrr r!r"r#r$r%rrrkwargs __class__s b/var/www/html/venv/lib/python3.12/site-packages/transformers/models/owlvit/configuration_owlvit.pyrzOwlViTTextConfig.__init__`sv$ sl\hslrs$&!2!2#6 '>$$,!2!2"4)ii  quick_geluh㈵>{Gz??rii__name__ __module__ __qualname____doc__ model_typebase_config_keyr __classcell__r(s@r)rr!sK9 v%J#O "55r*rcDeZdZdZdZdZ dfd ZxZS)OwlViTVisionConfigah 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-05): 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 1.0): 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 ```owlvit_vision_model vision_configc  t|di| ||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ y)Nr)rrrrrr num_channels image_size patch_sizer!r"r#r$r%)r&rrrrrBrCrDr!r"r#r$r%r'r(s r)rzOwlViTVisionConfig.__init__sr "6"&!2!2#6 ($$$,!2!2"4r*) i r,r,r rE r/r0r1r2r3r4r<s@r)r>r>sE2 h'J%O55r*r>cVeZdZdZdZeedZ dfd Ze de de fdZ xZ S) 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 initial value of the *logit_scale* parameter. Default is used as per the original OWL-ViT implementation. return_dict (`bool`, *optional*, defaults to `True`): Whether or not the model should return a dictionary. If `False`, returns a tuple. kwargs (*optional*): Dictionary of keyword arguments. owlvit)rr@c t|di||i}tjd|i}tjdt di||_t di||_||_||_ ||_ d|_ y)NzKtext_config is None. Initializing the OwlViTTextConfig with default values.zOvision_config is None. initializing the OwlViTVisionConfig with default values.r3r) rrloggerinforrr>r@projection_dimlogit_scale_init_value return_dictr%)r&rr@rMrNrOr'r(s r)rzOwlViTConfig.__init__s "6"  K KKe f  M KKi j+:k:/@-@,&<#&"%r*rr@c @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 rr@) from_dict)clsrr@r' config_dicts r)from_text_vision_configsz%OwlViTConfig.from_text_vision_configss3 %0 M"'4 O$s}}[3F33r*)NNr+g/L F@T) r5r6r7r8r9rr> sub_configsr classmethodrrTr;r<s@r)rHrHsR2J"2EWXK% &6 44 4 4 4r*rHc eZdZedeeeeefffdZedeeeeefffdZede fdZ d dddeded e d deee ff fd Z edefd ZxZS)OwlViTOnnxConfigreturnc @tddddfdddddd fd dddfgS) N input_idsbatchsequence)r pixel_valuesrBheightwidth)rr^r attention_maskrr&s r)inputszOwlViTOnnxConfig.inputs"s@'j9:WHQX!YZ!w:#>?   r*c@tdddifdddifdddifdddifgS)Nlogits_per_imagerr\logits_per_text text_embeds image_embedsrrds r)outputszOwlViTOnnxConfig.outputs,sD#a\2"QL1G -!W.    r*cy)Ng-C6?rrds r)atol_for_validationz$OwlViTOnnxConfig.atol_for_validation7sr* processorr batch_size seq_length frameworkr ct||j|||}t||j||}i||S)N)rorprq)rorq)rgenerate_dummy_inputs tokenizerimage_processor)r&rnrorprqtext_input_dictimage_input_dictr(s r)rsz&OwlViTOnnxConfig.generate_dummy_inputs;s` '7   J:Yb8 !78  % %* 9 7/6%566r*cy)Nrrds r)default_onnx_opsetz#OwlViTOnnxConfig.default_onnx_opsetJsr*)r{N)r5r6r7propertyrstrintrerkfloatrmr rrsrzr;r<s@r)rXrX!s  WS#X%6 67   gc3h&7!78  U ,0 7# 7 7 7 L) 7 c  7Cr*rXN)r8 collectionsrtypingrrrrr processing_utilsr utilsr configuration_utilsr onnxrr get_loggerr5rKrr>rHrXrr*r)rsw"#>>2#3   H %]5']5@U5)U5pE4#E4P+z+r*