o hR@sdZddlZddlmZddlmZmZmZmZm Z er(ddl m Z ddl m Z ddlmZdd lmZdd l mZeeZd d iZGd ddeZGdddeZGdddeZGdddeZdS)z CLIP model configurationN OrderedDict) TYPE_CHECKINGAnyMappingOptionalUnion)ProcessorMixin) TensorType)PretrainedConfig) OnnxConfig)loggingzopenai/clip-vit-base-patch32zLhttps://huggingface.co/openai/clip-vit-base-patch32/resolve/main/config.jsoncsdeZdZdZdZ          dfdd Zedeee j fddfddZ Z S)CLIPTextConfiga 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): Dimentionality 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 M quick_geluh㈵>{Gz??c sftjd| ||d|||_||_||_||_||_||_||_| |_ ||_ | |_ | |_ | |_ dS)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)selfr%r&r'r(r)r*r+r-r,r0r.r/rr r!kwargs __class__r"a/var/www/html/ai/venv/lib/python3.10/site-packages/transformers/models/clip/configuration_clip.pyr$fs zCLIPTextConfig.__init__pretrained_model_name_or_pathreturnr cK|||j|fi|\}}|ddkr|d}d|vr:t|dr:|d|jkr:td|dd|jd|j|fi|S)N model_typeclip text_configYou are using a model of type to instantiate a model of type N. This is not supported for all configurations of models and can yield errors._set_token_in_kwargsget_config_dictgethasattrr9loggerwarning from_dictclsr6r2 config_dictr"r"r5from_pretrained   zCLIPTextConfig.from_pretrained)rrrrrrrrrrrrrrr __name__ __module__ __qualname____doc__r9r$ classmethodrstrosPathLikerJ __classcell__r"r"r3r5r's*<$&rcs`eZdZdZdZ        dfdd Zedeee j fddfddZ Z S)CLIPVisionConfiga 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): Dimentionality 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 rrr rrrrrc sdtjdi|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_dS)Nr")r#r$r&r'r(r)r* num_channels patch_size image_sizer.r/r0r,r-)r1r&r'r(r)r*r\r^r]r-r,r0r.r/r2r3r"r5r$s zCLIPVisionConfig.__init__r6r7r cKr8)Nr9r: vision_configr<r=r>r?rGr"r"r5rJrKz CLIPVisionConfig.from_pretrained) rXrYrrrr rZr[rrrrrrLr"r"r3r5rVs&6!&rVcs>eZdZdZdZ d fdd Zeded efd d Z Z S) CLIPConfigaL [`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): Dimentionality of text and vision projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The inital value of the *logit_scale* paramter. 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) ```r:Nr/L F@c  s|dd}|dd}tjdi||dur]|duri}tdi|}|D]+\} } | |vrW| || krW| dvrW| |vrLd| d| d} nd| d} t| q,|||dur|durgi}t di|} d | vrd d | d D| d <| D]+\} } | |vr| || kr| dvr| |vrd| d | d} nd | d} t| q|| |duri}t d|duri}t dtdi||_ t di||_ ||_ ||_d|_dS)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 overriden.id2labelcSsi|] \}}t||qSr")rR).0keyvaluer"r"r5 ksz'CLIPConfig.__init__..zv` 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.rr")popr#r$rto_dictitemsrDrEupdaterVinfor;r_r(logit_scale_init_valuer/) r1r;r_r(rpr2rbrc_text_config_dictrhrimessage_vision_config_dictr3r"r5r$:sl           zCLIPConfig.__init__r;r_cKs|d||d|S)z Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model configuration. Returns: [`CLIPConfig`]: An instance of a configuration object )r;r_Nr")rl)rHr;r_r2r"r"r5from_text_vision_configss z#CLIPConfig.from_text_vision_configs)NNrra) rMrNrOrPr9r$rQrrVrtrUr"r"r3r5r` s-Xr`c seZdZedeeeeefffddZedeeeeefffddZede fddZ   dd d d ed ede ddeee ff fdd Z edefddZZS)CLIPOnnxConfigr7cCs0tddddfdddddd fd dddfgS) N input_idsbatchsequence)rr pixel_valuesr\heightwidth)rrr attention_maskrr1r"r"r5inputss   zCLIPOnnxConfig.inputscCs0tdddifdddifdddifdddifgS)Nlogits_per_imagerrwlogits_per_text text_embeds image_embedsrr~r"r"r5outputss    zCLIPOnnxConfig.outputscCdS)Ng-C6?r"r~r"r"r5atol_for_validationz"CLIPOnnxConfig.atol_for_validationN processorr batch_size seq_length frameworkr cs6tj|j|||d}tj|j||d}i||S)N)rrr)rr)r#generate_dummy_inputs tokenizerimage_processor)r1rrrrtext_input_dictimage_input_dictr3r"r5rs  z$CLIPOnnxConfig.generate_dummy_inputscCr)Nr"r~r"r"r5default_onnx_opsetrz!CLIPOnnxConfig.default_onnx_opset)rrN)rMrNrOpropertyrrRintrrfloatrrrrrrUr"r"r3r5rus.    ru)rPrS collectionsrtypingrrrrrprocessing_utilsr utilsr configuration_utilsr onnxr r get_loggerrMrD"CLIP_PRETRAINED_CONFIG_ARCHIVE_MAPrrVr`rur"r"r"r5s$       vm