fThKSrSSKJr SSKJrJrJrJr \(a SSKJ r SSK J r SSK J r SSKJr SS K Jr \R""\5r"S S \ 5r"S S \ 5r"SS\ 5r"SS\5r/SQrg)zCLIP model configuration OrderedDict) TYPE_CHECKINGAnyMappingOptional)ProcessorMixin) TensorType)PretrainedConfig) OnnxConfig)loggingcV^\rSrSrSrSrSrSU4SjjrSrU=r $)CLIPTextConfig!a This is the configuration class to store the configuration of a [`CLIPTextModel`]. It is used to instantiate a CLIP 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 text encoder of the CLIP [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-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 CLIP text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`CLIPModel`]. 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. projection_dim (`int`, *optional*, defaults to 512): Dimensionality of text and vision projection layers. 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 77): 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 1): Padding token id. bos_token_id (`int`, *optional*, defaults to 49406): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 49407): End of stream token id. Example: ```python >>> from transformers import CLIPTextConfig, CLIPTextModel >>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPTextConfig() >>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```clip_text_model text_configc >[TU]"SXUS.UD6 XlX lX0lX@lXPlX`lXplXl Xl Xl Xl Xl g)N) pad_token_id bos_token_id eos_token_id)super__init__ vocab_size hidden_sizeintermediate_sizeprojection_dimnum_hidden_layersnum_attention_headsmax_position_embeddingslayer_norm_eps hidden_actinitializer_rangeinitializer_factorattention_dropout)selfrrrrrr r!r#r"r&r$r%rrrkwargs __class__s c/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/clip/configuration_clip.pyrCLIPTextConfig.__init__asd* sl\hslrs$&!2,!2#6 '>$,$!2"4!2) r&r#rr%r$rr"r!r rrr)iir- M quick_geluh㈵>{Gz??ii __name__ __module__ __qualname____firstlineno____doc__ model_typebase_config_keyr__static_attributes__ __classcell__r)s@r*rr!sP: x#J#O "%"3"3r,rcR^\rSrSrSrSrSrSU4SjjrSrU=r $)CLIPVisionConfigaB This is the configuration class to store the configuration of a [`CLIPVisionModel`]. It is used to instantiate a CLIP vision 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 vision encoder of the CLIP [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-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. projection_dim (`int`, *optional*, defaults to 512): Dimensionality of text and vision projection layers. 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): The number of input channels. image_size (`int`, *optional*, defaults to 224): 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 CLIPVisionConfig, CLIPVisionModel >>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPVisionConfig() >>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```clip_vision_model vision_configc >[TU]"S0UD6 XlX lX0lX@lXPlX`lXlXpl Xl Xl Xl Xl Xlg)Nr)rrrrrrr num_channels patch_size image_sizer$r%r&r"r#)r'rrrrr rHrJrIr#r"r&r$r%r(r)s r*rCLIPVisionConfig.__init__s`" "6"&!2,!2#6 ($$!2"4!2,$r,) r&r#rrJr%r$rr"r rHrrIr) ii r-r.r.r r1r2r3r4r5r7rAs@r*rCrCsH4 l%J%O%%r,rCc\^\rSrSrSrSr\\S.rS U4Sjjr \ S\S\4Sj5r S r U=r $) CLIPConfiga [`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate a CLIP 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 CLIP [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-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 [`CLIPTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`CLIPVisionConfig`]. 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 CLIP implementation. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import CLIPConfig, CLIPModel >>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPConfig() >>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig >>> from transformers import CLIPTextConfig, CLIPVisionConfig >>> # Initializing a CLIPText and CLIPVision configuration >>> config_text = CLIPTextConfig() >>> config_vision = CLIPVisionConfig() >>> config = CLIPConfig.from_text_vision_configs(config_text, config_vision) ```cliprrFc >URSS5nURSS5n[T U]"S0UD6 UbUc0n[S0UD6R 5nUR 5HHupX;dM XU :wdMU S;dMX;a SU SU S3n OSU S3n [ RU 5 MJ URU5 UbUc0n[S0UD6R 5n S U ;a5U S R 5V V s0sHup[U 5U _M sn n U S 'U R 5HHupX;dM XU :wdMU S;dMX;a SU S U S3n OS U S3n [ RU 5 MJ URU 5 Uc0n[ RS 5 Uc0n[ RS 5 [S0UD6Ul [S0UD6Ul X0l X@lSUlgs sn n f)Ntext_config_dictvision_config_dict)transformers_version`zp` is found in both `text_config_dict` and `text_config` but with different values. The value `text_config_dict["z"]` will be used instead.zj`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config["z"]` will be overridden.id2labelzv` is found in both `vision_config_dict` and `vision_config` but with different values. The value `vision_config_dict["zp`vision_config_dict` is provided which will be used to initialize `CLIPVisionConfig`. The value `vision_config["zO`text_config` is `None`. Initializing the `CLIPTextConfig` with default values.zS`vision_config` is `None`. initializing the `CLIPVisionConfig` with default values.r5r)poprrrto_dictitemsloggerinfoupdaterCstrrrFrlogit_scale_init_valuer%)r'rrFrr`r(rTrU_text_config_dictkeyvaluemessage_vision_config_dictr)s r*rCLIPConfig.__init__sS "::&8$?#ZZ( >,&<#"%K3sG5rrFc PU"SUR5UR5S.UD6$)z Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model configuration. Returns: [`CLIPConfig`]: An instance of a configuration object rRr)rZ)clsrrFr(s r*from_text_vision_configs#CLIPConfig.from_text_vision_configsks,f{224MDYDYD[f_effr,)r%r`rrrF)NNr-g/L F@)r8r9r:r;r<r=rrC sub_configsr classmethodrir?r@rAs@r*rOrOsP+ ZJ"0CSTK`fV&p g> gRb g gr,rOc ^\rSrSr\S\\\\\444Sj5r\S\\\\\444Sj5r \S\ 4Sj5r SSSS\S \S \ S S\\\ 44 U4S jjjr\S\4S j5rSrU=r$)CLIPOnnxConfigixreturnc @[SSSS.4SSSSSS .4S SSS.4/5$) N input_idsbatchsequence)rr6 pixel_valuesrHheightwidth)rr6r attention_maskrr's r*inputsCLIPOnnxConfig.inputsys@'j9:WHQX!YZ!w:#>?   r,c@[SSS04SSS04SSS04SSS04/5$)Nlogits_per_imagerrrlogits_per_text text_embeds image_embedsrrys r*outputsCLIPOnnxConfig.outputssD#a\2"QL1G -!W.    r,cg)Ng-C6?rrys r*atol_for_validation"CLIPOnnxConfig.atol_for_validationsr, processorr batch_size seq_length frameworkr cr>[TU]URX#US9n[TU]URX$S9n0UEUE$)N)rrr)rr)rgenerate_dummy_inputs tokenizerimage_processor)r'rrrrtext_input_dictimage_input_dictr)s r*r$CLIPOnnxConfig.generate_dummy_inputss\ '7   JYb8 !78  % %*9 7/6%566r,cg)Nrrys r*default_onnx_opset!CLIPOnnxConfig.default_onnx_opsetsr,r)rN)r8r9r:r;propertyrr_intrzrfloatrrrrrr?r@rAs@r*rnrnxs  WS#X%6 67   gc3h&7!78  U ,0 7# 7 7 7 L) 7 c  7 7Cr,rn)rOrnrrCN)r< collectionsrtypingrrrrprocessing_utilsr utilsr configuration_utilsr onnxr r get_loggerr8r\rrCrOrn__all__rr,r*rs#882#3   H %b3%b3JY%'Y%xSg!Sgl+Z+\ Qr,