g&~dZddlZddlmZddlmZddlmZddlm Z ej e Z Gd d eZ dS) zTVP model configurationN)PretrainedConfig)logging) verify_backbone_config_arguments)CONFIG_MAPPINGc|eZdZdZdZ dfd ZedefdZdZ xZ S) TvpConfiga This is the configuration class to store the configuration of a [`TvpModel`]. It is used to instantiate an Tvp 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 Tvp [Intel/tvp-base](https://huggingface.co/Intel/tvp-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: backbone_config (`PretrainedConfig` or `dict`, *optional*): The configuration of the backbone model. backbone (`str`, *optional*): Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. use_pretrained_backbone (`bool`, *optional*, defaults to `False`): Whether to use pretrained weights for the backbone. use_timm_backbone (`bool`, *optional*, defaults to `False`): Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers library. backbone_kwargs (`dict`, *optional*): Keyword arguments to be passed to AutoBackbone when loading from a checkpoint e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. distance_loss_weight (`float`, *optional*, defaults to 1.0): The weight of distance loss. duration_loss_weight (`float`, *optional*, defaults to 0.1): The weight of duration loss. visual_prompter_type (`str`, *optional*, defaults to `"framepad"`): Visual prompt type. The type of padding. Framepad means padding on each frame. Should be one of "framepad" or "framedownpad" visual_prompter_apply (`str`, *optional*, defaults to `"replace"`): The way of applying visual prompt. Replace means use the value of prompt to change the original value in visual inputs. Should be one of "replace", or "add", or "remove". visual_prompt_size (`int`, *optional*, defaults to 96): The size of visual prompt. max_img_size (`int`, *optional*, defaults to 448): The maximum size of frame. num_frames (`int`, *optional*, defaults to 48): The number of frames extracted from a video. vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the Tvp text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`TvpModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers. 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. max_position_embeddings (`int`, *optional*, defaults to 512): 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). max_grid_col_position_embeddings (`int`, *optional*, defaults to 100): The largest number of horizontal patches from a video frame. max_grid_row_position_embeddings (`int`, *optional*, defaults to 100): The largest number of vertical patches from a video frame. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability of hidden layers. hidden_act (`str` or `function`, *optional*, defaults to `"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-12): The epsilon used by the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability of attention layers. tvpNF?皙?framepadreplace`0:w dgelu-q={Gz?c tjdi||4|2tdt ddg}nLt |t r7|d}t|}||}t|||||||_ ||_ ||_ ||_ ||_||_||_||_| |_| |_| |_| |_| |_||_||_||_||_||_||_||_||_||_||_ ||_!||_"dS)NzX`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.resnetstage4) out_features model_type)use_timm_backboneuse_pretrained_backbonebackbonebackbone_configbackbone_kwargs)#super__init__loggerinfor isinstancedictget from_dictrr$r#r"r!r%distance_loss_weightduration_loss_weightvisual_prompter_typevisual_prompter_applyvisual_prompt_size max_img_size num_frames vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsmax_position_embeddings max_grid_col_position_embeddings max_grid_row_position_embeddingslayer_norm_epshidden_dropout_prob hidden_actinitializer_rangeattention_probs_dropout_prob)selfr$r#r"r!r%r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r?r@r>rArBkwargsbackbone_model_type config_class __class__s e/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/tvp/configuration_tvp.pyr(zTvpConfig.__init__gs: ""6"""  "x'7 KKr s s s,X6XJOOOOO  . . F"1"5"5l"C"C )*=>L*44_EEO(/$;++      /  '>$!2.$8!$8!$8!%:""4($$&!2!2#6 '>$0P-0P-,#6 $!2,H)))r$c |dd|i|S)a+Instantiate a [`TvpConfig`] (or a derived class) from a pre-trained backbone model configuration. Args: backbone_config ([`PretrainedConfig`]): The backbone configuration. Returns: [`TvpConfig`]: An instance of a configuration object r$r&r&)clsr$rDs rHfrom_backbone_configzTvpConfig.from_backbone_configss==?=f===rIctj|j}|d|j|d<|jj|d<|S)z Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, r$Nr )copydeepcopy__dict__r$to_dictrGr )rCoutputs rHrQzTvpConfig.to_dictsOt}-- # $ 0(,(<(D(D(F(FF$ %#~8| rI)NNFFNr r rrrrrrrrrrrrrr rrrr ) __name__ __module__ __qualname____doc__r r( classmethodrrLrQ __classcell__)rGs@rHr r sFFPJ %  '' #),),%(5FIFIFIFIFIFIP >3C > > >[ >       rIr )rVrNconfiguration_utilsrutilsrutils.backbone_utilsrautor get_loggerrSr)r r&rIrHr^s 333333DDDDDD!!!!!!  H % %jjjjj jjjjjrI