U 4AfQ@sndZddlZddlmZddlmZddlmZee Z GdddeZ Gd d d eZ Gd d d eZ dS) zCLIPSeg model configurationN)Union)PretrainedConfig)loggingcsDeZdZdZdZdfdd Zeeee j fddddZ Z S)CLIPSegTextConfiga This is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to instantiate an CLIPSeg model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the CLIPSeg [CIDAS/clipseg-rd64](https://huggingface.co/CIDAS/clipseg-rd64) 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 CLIPSeg text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`CLIPSegModel`]. 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 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 CLIPSegTextConfig, CLIPSegTextModel >>> # Initializing a CLIPSegTextConfig with CIDAS/clipseg-rd64 style configuration >>> configuration = CLIPSegTextConfig() >>> # Initializing a CLIPSegTextModel (with random weights) from the CIDAS/clipseg-rd64 style configuration >>> model = CLIPSegTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zclipseg_text_model M quick_geluh㈵>{Gz??c 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_embeddingslayer_norm_eps hidden_actinitializer_rangeinitializer_factorattention_dropout)selfrrrrrrr!r r$r"r#rrrkwargs __class__U/tmp/pip-unpacked-wheel-zw5xktn0/transformers/models/clipseg/configuration_clipseg.pyrXszCLIPSegTextConfig.__init__rpretrained_model_name_or_pathreturncKs~|||j|f|\}}|ddkr2|d}d|krpt|drp|d|jkrptd|dd|jd|j|f|S)N model_typeclipseg 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.Z_set_token_in_kwargsZget_config_dictgethasattrr.loggerwarning from_dictclsr,r&Z config_dictr)r)r*from_pretrainedxs  z!CLIPSegTextConfig.from_pretrained)rrr r r r r rrrrrrr __name__ __module__ __qualname____doc__r.r classmethodrstrosPathLiker< __classcell__r)r)r'r*rs&: rc sDeZdZdZdZdfdd Zeeee j fddddZ Z S)CLIPSegVisionConfigaG This is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to instantiate an CLIPSeg model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the CLIPSeg [CIDAS/clipseg-rd64](https://huggingface.co/CIDAS/clipseg-rd64) 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): 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 CLIPSegVisionConfig, CLIPSegVisionModel >>> # Initializing a CLIPSegVisionConfig with CIDAS/clipseg-rd64 style configuration >>> configuration = CLIPSegVisionConfig() >>> # Initializing a CLIPSegVisionModel (with random weights) from the CIDAS/clipseg-rd64 style configuration >>> model = CLIPSegVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zclipseg_vision_model r r r rrrrc  sZtjf| ||_||_||_||_||_||_||_| |_ | |_ | |_ | |_ ||_ dS)N)rrrrrr num_channels patch_size image_sizer"r#r$r r!)r%rrrrrLrNrMr!r r$r"r#r&r'r)r*rszCLIPSegVisionConfig.__init__rr+cKs~|||j|f|\}}|ddkr2|d}d|krpt|drp|d|jkrptd|dd|jd|j|f|S)Nr.r/ vision_configr1r2r3r4r:r)r)r*r<s  z#CLIPSegVisionConfig.from_pretrained) rHrIr r rrJrKr rrrrr=r)r)r'r*rGs"4rGc sXeZdZdZdZdddddddgd d d d d ddf fdd ZeeedddZ Z S) CLIPSegConfiga [`CLIPSegConfig`] is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to instantiate a CLIPSeg 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 CLIPSeg [CIDAS/clipseg-rd64](https://huggingface.co/CIDAS/clipseg-rd64) 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 [`CLIPSegTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`CLIPSegVisionConfig`]. 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 CLIPSeg implementation. extract_layers (`List[int]`, *optional*, defaults to `[3, 6, 9]`): Layers to extract when forwarding the query image through the frozen visual backbone of CLIP. reduce_dim (`int`, *optional*, defaults to 64): Dimensionality to reduce the CLIP vision embedding. decoder_num_attention_heads (`int`, *optional*, defaults to 4): Number of attention heads in the decoder of CLIPSeg. decoder_attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. decoder_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. decoder_intermediate_size (`int`, *optional*, defaults to 2048): Dimensionality of the "intermediate" (i.e., feed-forward) layers in the Transformer decoder. conditional_layer (`int`, *optional*, defaults to 0): The layer to use of the Transformer encoder whose activations will be combined with the condition embeddings using FiLM (Feature-wise Linear Modulation). If 0, the last layer is used. use_complex_transposed_convolution (`bool`, *optional*, defaults to `False`): Whether to use a more complex transposed convolution in the decoder, enabling more fine-grained segmentation. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import CLIPSegConfig, CLIPSegModel >>> # Initializing a CLIPSegConfig with CIDAS/clipseg-rd64 style configuration >>> configuration = CLIPSegConfig() >>> # Initializing a CLIPSegModel (with random weights) from the CIDAS/clipseg-rd64 style configuration >>> model = CLIPSegModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a CLIPSegConfig from a CLIPSegTextConfig and a CLIPSegVisionConfig >>> # Initializing a CLIPSegText and CLIPSegVision configuration >>> config_text = CLIPSegTextConfig() >>> config_vision = CLIPSegVisionConfig() >>> config = CLIPSegConfig.from_text_vision_configs(config_text, config_vision) ```r/Nrg/L F@r @rr r rFc  s| dd}| dd}tjf| |dk r|dkr:i}tf|}|D]V\}}||krP|||krP|dkrP||krd|d|d}n d|d}t|qP|||dk rl|dkri}t f|}d |krd d |d D|d <|D]`\}}||kr|||kr|dkr||krHd|d |d}n d |d}t|q|||dkri}td|dkri}tdtf||_ t f||_ ||_ ||_ ||_||_||_||_| |_| |_| |_d|_| |_dS)Ntext_config_dictvision_config_dict)Ztransformers_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.zm`text_config_dict` is provided which will be used to initialize `CLIPSegTextConfig`. The value `text_config["z"]` will be overridden.Zid2labelcSsi|]\}}t||qSr))rC).0keyvaluer)r)r* tsz*CLIPSegConfig.__init__..zv` is found in both `vision_config_dict` and `vision_config` but with different values. The value `vision_config_dict["zs`vision_config_dict` is provided which will be used to initialize `CLIPSegVisionConfig`. The value `vision_config["zR`text_config` is `None`. Initializing the `CLIPSegTextConfig` with default values.zV`vision_config` is `None`. initializing the `CLIPSegVisionConfig` with default values.r)poprrrto_dictitemsr7infoupdaterGr0rOprojection_dimlogit_scale_init_valueextract_layers reduce_dimdecoder_num_attention_headsdecoder_attention_dropoutdecoder_hidden_actdecoder_intermediate_sizeconditional_layerr#"use_complex_transposed_convolution)r%r0rOrarbrcrdrerfrgrhrirjr&rUrVZ_text_config_dictrYrZmessageZ_vision_config_dictr'r)r*r6sh       "         zCLIPSegConfig.__init__r0rOcKs|f||d|S)z Instantiate a [`CLIPSegConfig`] (or a derived class) from clipseg text model configuration and clipseg vision model configuration. Returns: [`CLIPSegConfig`]: An instance of a configuration object rl)r])r;r0rOr&r)r)r*from_text_vision_configss z&CLIPSegConfig.from_text_vision_configs) r>r?r@rAr.rrBrrGrmrFr)r)r'r*rPs"?mrP)rArDtypingrZconfiguration_utilsrutilsrZ get_loggerr>r7rrGrPr)r)r)r*s    pi