tjFddlmZddlmZddlmZmZejeZ edeGddeZ ed eGd d eZ ed eGd d eZ gdZ dS))strict)PreTrainedConfig)auto_docstringloggingzgoogle/videoprism-base-f16r288) checkpointcneZdZUdZdZdZeeezeeefze d<dZ ee d<dZ eeeedfze d <d Z ee d <d Z ee d <dZee d<dZee d<dZee d<dZeeze d<dZeeze d<dZee d<dZee d<dZee d<dZdZee d<dZee d <d!Zee d"<d#Zee d$<dZee d%<d&S)'VideoPrismVisionConfiga num_frames (`int`, *optional*, defaults to 16): The number of frames in the input video. tubelet_size (`List[int]`, *optional*, defaults to `[1, 18, 18]`): The size of the tubelet patch. num_spatial_layers (`int`, *optional*, defaults to 12): Number of spatial transformer blocks. num_temporal_layers (`int`, *optional*, defaults to 4): Number of temporal transformer blocks. attn_logit_softcapping (`float`, *optional*, defaults to 50.0): Softcapping constant for attention logits. num_auxiliary_layers (`int`, *optional*, defaults to 2): Number of auxiliary layers. This is used in the VideoPrismVideoModel that is a part of VideoPrismClipModel. apply_l2norm (`bool`, *optional*, defaults to `True`): Whether to apply L2 normalization to the output. This is used in the VideoPrismVideoModel that is a part of VideoPrismClipModel. videoprism_vision_modeli  image_size num_frames)r. tubelet_sizer num_channels hidden_size num_attention_heads intermediate_size gelu_python hidden_acthidden_dropout_probattention_probs_dropout_prob{Gz?initializer_rangeư>layer_norm_epsTqkv_bias vision_confignum_spatial_layersnum_temporal_layersI@attn_logit_softcappingnum_auxiliary_layers apply_l2normN)__name__ __module__ __qualname____doc__ model_typer intlisttuple__annotations__rrrrrrrstrrfloatrrr!r"boolbase_config_keyr$r&r(r*r+u/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/videoprism/configuration_videoprism.pyr r s"+J47Jd3i%S/1777J0;L$s)eCHo-;;;L#K!!!!!s!!!#J###'****03 %#+333#u###!NE!!!Hd%O        $(E((( !#!!!L$r:r z"google/videoprism-lvt-base-f16r288c4eZdZUdZdZdZdZeed<dZ eed<dZ eed <d Z eed <d Z eed <d Z eed<dZeed<dZeed<dZedzed<dZedzed<dZeeezdzed<dZeezed<dZeed<dZeed<dZeed<dZeed<d Zeed!<dS)"VideoPrismTextConfiga  apply_l2norm (`bool`, *optional*, defaults to `True`): Whether to apply L2 normalization to the output of VideoPrismTextEncoder. attn_logit_softcapping (`float`, *optional*, defaults to 50.0): Softcapping constant for attention logits. videoprism_text_model text_configi} vocab_sizerrrrrnum_hidden_layersr@max_position_embeddingsrelurr r!rN pad_token_id bos_token_id eos_token_idrrTr+r"rrrr'r()r,r-r.r/r0r8r@r1r4rrrArrCrr5r!r6rErFrGr2rr+r7r"rrr(r9r:r;r=r=IsY)J#OJK!s!!!s!!!!#%S%%%J NE    L#*   #L#*###+/L#S /D(///03 %#+333L$Hd!$$$$#u###$(E(((((r:r=cfeZdZUdZdZeedZdZe e zdze d<dZ e e zdze d<fdZ xZS)VideoPrismConfiga Example: ```python >>> from transformers import VideoPrismClipModel, VideoPrismConfig >>> # Initializing a VideoPrismConfig with default values >>> configuration = VideoPrismConfig() >>> # Initializing a VideoPrismClipModel with the configuration >>> model = VideoPrismClipModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` videoprism)r?r#Nr?r#c |j.t|_tdn0t |jt rtdi|j|_|j.t|_tdn0t |jt rtdi|j|_tj di|dS)NzU`text_config` is `None`. Initializing the `VideoPrismTextConfig` with default values.zY`vision_config` is `None`. initializing the `VideoPrismVisionConfig` with default values.r9) r?r=loggerinfo isinstancedictr#r super __post_init__)selfkwargs __class__s r;rQzVideoPrismConfig.__post_init__s   #355D  KKo p p p p ($ / / H3GGd6FGGD    %!7!9!9D  KKs t t t t *D 1 1 N!7!M!M$:L!M!MD '''''''r:)r,r-r.r/r0r=r sub_configsr?rOrr4r#rQ __classcell__)rTs@r;rIrIjs"J"6I_``K26K((4/66648M4**T1888 ( ( ( ( ( ( ( ( (r:rI)r r=rIN)huggingface_hub.dataclassesrconfiguration_utilsrutilsrr get_loggerr,rLr r=rI__all__r9r:r;r\s],/.....333333,,,,,,,,  H % %;<<<%%%%%-%%=<%P?@@@)))))+))A@)>?@@@%(%(%(%(%('%(%(A@%(P Q P Pr: