+ jjlRt^RIHt^RIHt^RIHt]!RR7]!RR]444tR.tR#) zPerceiver model configuration)strict)PreTrainedConfig)auto_docstringzdeepmind/language-perceiver) checkpointca]tRt^toRtRtRtRtRt^t ^t ^t ^t Rt RtRt^t^tRtR tR tR tR tR tRt^8tRt^tRt^tRtRtRt V3Rlt!Rt"Vt#R#)PerceiverConfiga num_latents (`int`, *optional*, defaults to 256): The number of latents. d_latents (`int`, *optional*, defaults to 1280): Dimension of the latent embeddings. num_blocks (`int`, *optional*, defaults to 1): Number of blocks in the Transformer encoder. num_self_attends_per_block (`int`, *optional*, defaults to 26): The number of self-attention layers per block. num_self_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each self-attention layer in the Transformer encoder. num_cross_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each cross-attention layer in the Transformer encoder. qk_channels (`int`, *optional*): Dimension to project the queries + keys before applying attention in the cross-attention and self-attention layers of the encoder. Will default to preserving the dimension of the queries if not specified. v_channels (`int`, *optional*): Dimension to project the values before applying attention in the cross-attention and self-attention layers of the encoder. Will default to preserving the dimension of the queries if not specified. cross_attention_shape_for_attention (`str`, *optional*, defaults to `"kv"`): Dimension to use when downsampling the queries and keys in the cross-attention layer of the encoder. self_attention_widening_factor (`int`, *optional*, defaults to 1): Dimension of the feed-forward layer in the cross-attention layer of the Transformer encoder. cross_attention_widening_factor (`int`, *optional*, defaults to 1): Dimension of the feed-forward layer in the self-attention layers of the Transformer encoder. use_query_residual (`float`, *optional*, defaults to `True`): Whether to add a query residual in the cross-attention layer of the encoder. image_size (`int`, *optional*, defaults to 56): Size of the images after preprocessing, for [`PerceiverForImageClassificationLearned`]. train_size (`list[int]`, *optional*, defaults to `[368, 496]`): Training size of the images for the optical flow model. num_frames (`int`, *optional*, defaults to 16): Number of video frames used for the multimodal autoencoding model. audio_samples_per_frame (`int`, *optional*, defaults to 1920): Number of audio samples per frame for the multimodal autoencoding model. samples_per_patch (`int`, *optional*, defaults to 16): Number of audio samples per patch when preprocessing the audio for the multimodal autoencoding model. output_shape (`list[int]`, *optional*, defaults to `[1, 16, 224, 224]`): Shape of the output (batch_size, num_frames, height, width) for the video decoder queries of the multimodal autoencoding model. This excludes the channel dimension. output_num_channels (`int`, *optional*, defaults to 512): Number of output channels for each modalitiy decoder. Example: ```python >>> from transformers import PerceiverModel, PerceiverConfig >>> # Initializing a Perceiver deepmind/language-perceiver style configuration >>> configuration = PerceiverConfig() >>> # Initializing a model from the deepmind/language-perceiver style configuration >>> model = PerceiverModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` perceiveriiNkvgelug?g{Gz?g-q=Tiiiiic8<V^8dQh/S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[R,;R &S[R,;R &S[;R &S[;R &S[;R &S[;R&S[S[,;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[S[S[,,S[S[S[3,,;R&S[S[,S[S[R3,,;R&S[;R&S[;R&S[;R&S[S[,S[S[R3,,;R&S[;R&S[;R&#) num_latents d_latentsd_model num_blocksnum_self_attends_per_blocknum_self_attention_headsnum_cross_attention_headsN qk_channels v_channels#cross_attention_shape_for_attentionself_attention_widening_factorcross_attention_widening_factor hidden_actattention_probs_dropout_probinitializer_rangelayer_norm_epsuse_query_residual vocab_sizemax_position_embeddings image_size. train_size num_framesaudio_samples_per_framesamples_per_patch output_shapeoutput_num_channels_label_trainable_num_channels)intstrfloatboollisttuple)format __classdict__s"v/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/perceiver/configuration_perceiver.py __annotate__PerceiverConfig.__annotate__s~@ABCDEF!$(GH"%IJ #&KLt"MNd !OP*-3QR%(+ST&),UVWX#(#+3YZ#[\!]^#_`ab!'cdd3i%S/16efS E#s(O+8ghij!'klmns)eCHo-Aop"qr$'-s)ipi)r8)$__name__ __module__ __qualname____firstlineno____doc__ model_typerrrrrrrrrrrrrrrrrrr r!r"r#r$r%r&r'r(__annotate_func____static_attributes____classdictcell__)r0s@r1rrs8 tJKIGJ&($%%&"K!J/3'*+"+,#J03 #!N#J#'46J.8JJ#'0AL")-!sr4rN) r=huggingface_hub.dataclassesrconfiguration_utilsrutilsrr__all__r5r4r1rFsK$.3#89W.&W.:W.t  r4