S^(hKdZddlmZddlmZmZmZmZer ddlm Z ddl m Z ddl m Z ddlmZdd l mZej"eZGd d e ZGd d e ZGdde ZGddeZgdZy)zCLIP model configuration OrderedDict) TYPE_CHECKINGAnyMappingOptional)ProcessorMixin) TensorType)PretrainedConfig) OnnxConfig)loggingcJeZdZdZdZdZ dfd ZxZS)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): 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 t|d| ||d|||_||_||_||_||_||_||_| |_ ||_ | |_ | |_ | |_ y)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)selfrrrrrrr r"r!r%r#r$rrrkwargs __class__s i/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/clip/configuration_clip.pyrzCLIPTextConfig.__init__as~* sl\hslrs$&!2,!2#6 '>$,$!2"4!2)iir+ M quick_geluh㈵>{Gz??ii__name__ __module__ __qualname____doc__ model_typebase_config_keyr __classcell__r(s@r)rr!sP: x#J#O "%"3"3r*rcFeZdZdZdZdZ dfd ZxZS)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): 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 t|di|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_y)Nr)rrrrrrr num_channels patch_size image_sizer#r$r%r!r")r&rrrrrrCrErDr"r!r%r#r$r'r(s r)rzCLIPVisionConfig.__init__sz" "6"&!2,!2#6 ($$!2"4!2,$r*) ii r+r,r,r r/r0r1r2r3r5r=s@r)r?r?sH4 l%J%O%%r*r?cNeZdZdZdZeedZ dfd Ze dedefdZ xZ S) CLIPConfigaN [`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) ```cliprrAc |jdd}|jdd}t |di|||i}tdi|j }|j D]A\} } | |vs | || k7s| dvs| |vr d| d| d} nd| d} t j| C|j|||i}tdi|j } d | vr3| d j D cic]\} } t| | c} } | d <| j D]A\} } | |vs | || k7s| dvs| |vr d| d | d} nd | d} t j| C|j| |i}t jd |i}t jd tdi||_ tdi||_ ||_ ||_d|_ycc} } w)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.r3r)poprrrto_dictitemsloggerinfoupdater?strrrArlogit_scale_init_valuer$)r&rrArrYr'rMrN_text_config_dictkeyvaluemessage_vision_config_dictr(s r)rzCLIPConfig.__init__sp "::&8$?#ZZ( >,&<#"%K3s2GrrAc P|d|j|jd|S)z Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model configuration. Returns: [`CLIPConfig`]: An instance of a configuration object rKr)rS)clsrrAr's r)from_text_vision_configsz#CLIPConfig.from_text_vision_configsks,f{224MDYDYD[f_effr*)NNr+g/L F@) r6r7r8r9r:rr? sub_configsr classmethodrar<r=s@r)rIrIsP+ ZJ"0CSTK`fV&p g> gRb g gr*rIc eZdZedeeeeefffdZedeeeeefffdZede fdZ d dddeded e d deee ff fd Z edefd ZxZS)CLIPOnnxConfigreturnc @tddddfdddddd fd dddfgS) N input_idsbatchsequence)rr4 pixel_valuesrCheightwidth)rr4r attention_maskrr&s r)inputszCLIPOnnxConfig.inputsys@'j9:WHQX!YZ!w:#>?   r*c@tdddifdddifdddifdddifgS)Nlogits_per_imagerrilogits_per_text text_embeds image_embedsrrps r)outputszCLIPOnnxConfig.outputssD#a\2"QL1G -!W.    r*cy)Ng-C6?rrps r)atol_for_validationz"CLIPOnnxConfig.atol_for_validationsr* processorr batch_size seq_length frameworkr ct||j|||}t||j||}i||S)N)r{r|r})r{r})rgenerate_dummy_inputs tokenizerimage_processor)r&rzr{r|r}text_input_dictimage_input_dictr(s r)rz$CLIPOnnxConfig.generate_dummy_inputss` '7   J:Yb8 !78  % %* 9 7/6%566r*cy)Nrrps r)default_onnx_opsetz!CLIPOnnxConfig.default_onnx_opsetsr*)rN)r6r7r8propertyrrXintrqrwfloatryrrrrr<r=s@r)rerexs  WS#X%6 67   gc3h&7!78  U ,0 7# 7 7 7 L) 7 c  7Cr*re)rIrerr?N)r9 collectionsrtypingrrrrprocessing_utilsr utilsr configuration_utilsr onnxr r get_loggerr6rUrr?rIre__all__rr*r)rs#882#3   H %b3%b3JY%'Y%xSg!Sgl+Z+\ Qr*