+ œ°³j­%ãó|€Rt^RIt^RIt^RIHt^RIHt^RIHt]!RR7]!RR]444t R.t R#) zSEW model configurationN)Ústrict)ÚPreTrainedConfig)Úauto_docstringzBAAI/seggpt-vit-large)Ú checkpointcóðaa€]tRt^toRtRt^ tRt^ t^ t Rt ^t Rt Rt RtRtRtRtRtRtR tR tRtRtRtRtR t^€t^tR tRt^ t^t Rt!^ t"^t#Rt$R t%R t&R t'^t(^t)^t*V3Rlt+Rt,]-R4t.V3Rlt/Rt0Vt1V;t2#)Ú SEWConfiga¢ squeeze_factor (`int`, *optional*, defaults to 2): Sequence length downsampling factor after the encoder and upsampling factor after the transformer. feat_proj_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for output of the feature encoder. final_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the final projection layer of [`SEWForCTC`]. feat_extract_norm (`str`, *optional*, defaults to `"group"`): The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D convolutional layers. feat_extract_activation (`str, `optional`, defaults to `"gelu"`): The non-linear activation function (function or string) in the 1D convolutional layers of the feature extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. conv_dim (`tuple[int]` or `list[int]`, *optional*, defaults to `(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)`): A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers. conv_stride (`tuple[int]` or `list[int]`, *optional*, defaults to `(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)`): A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. conv_kernel (`tuple[int]` or `list[int]`, *optional*, defaults to `(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The length of *conv_kernel* defines the number of convolutional layers and has to match the length of *conv_dim*. conv_bias (`bool`, *optional*, defaults to `False`): Whether the 1D convolutional layers have a bias. num_conv_pos_embeddings (`int`, *optional*, defaults to 128): Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional embeddings layer. num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): Number of groups of 1D convolutional positional embeddings layer. apply_spec_augment (`bool`, *optional*, defaults to `True`): Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://huggingface.co/papers/1904.08779). mask_time_prob (`float`, *optional*, defaults to 0.05): Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking procedure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector span to be masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. mask_time_length (`int`, *optional*, defaults to 10): Length of vector span along the time axis. mask_time_min_masks (`int`, *optional*, defaults to 2),: The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < mask_time_min_masks'' mask_feature_prob (`float`, *optional*, defaults to 0.0): Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The masking procedure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. mask_feature_length (`int`, *optional*, defaults to 10): Length of vector span along the feature axis. mask_feature_min_masks (`int`, *optional*, defaults to 0): The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks'' ctc_zero_infinity (`bool`, *optional*, defaults to `False`): Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance of [`SEWForCTC`]. use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an instance of [`Wav2Vec2ForSequenceClassification`]. classifier_proj_size (`int`, *optional*, defaults to 256): Dimensionality of the projection before token mean-pooling for classification. Example: ```python >>> from transformers import SEWConfig, SEWModel >>> # Initializing a SEW asapp/sew-tiny-100k style configuration >>> configuration = SEWConfig() >>> # Initializing a model (with random weights) from the asapp/sew-tiny-100k style configuration >>> model = SEWModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Úsewii Úgelugš™™™™™¹?gg{®Gáz”?gñh㈵øä>ÚgroupéFTgš™™™™™©?Úmeanc ó\<€\VP4Vn\SV`!R/VBR#)N©)ÚlenÚconv_dimÚnum_feat_extract_layersÚsuperÚ __post_init__)ÚselfÚkwargsÚ __class__s&,€Új/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/sew/configuration_sew.pyrÚSEWConfig.__post_init__™s$ø€Ü'*¨4¯=©=Ó'9ˆÔ$Ü ‰ÒÑ' Ô'óc  óz€\VP4VP8wgI\VP4VP8wg%\VP4VP8wdO\ R\VP4 R\VP4 R\VP4 R24hR#)zOPart of `@strict`-powered validation. Validates the architecture of the config.zºConfiguration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) = z`, `len(config.conv_stride) = z`, `len(config.conv_kernel) = z`.N)rÚ conv_striderÚ conv_kernelrÚ ValueError©rs&rÚvalidate_architectureÚSEWConfig.validate_architectures§€ô×!Ñ!Ó " d×&BÑ&BÔ BÜD×$Ñ$Ó%¨×)EÑ)EÔEÜD—M‘MÓ" d×&BÑ&BÔBäð2ä25°d·m±mÓ2DÐ1EðFܘ×)Ñ)Ó*Ð+Ð+IÌ#Èd×N^ÑN^ÓJ_ÐI`Ð`bðdóð ñCrcób€\P!\PVP^4#)é)Ú functoolsÚreduceÚoperatorÚmulrrs&rÚinputs_to_logits_ratioÚ SEWConfig.inputs_to_logits_ratio«s!€ä×Ò¤§ ¡ ¨d×.>Ñ.>ÀÓBÐBrcó<<€V^8„dQh/S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R&S[S[,;R &S[S[,;R &S[S[,;R &S[S[,;R &S[S[,;R &S[;R&S[;R&S[;R&S[;R&S[S[,S[S[R3,,;R&S[S[,S[S[R3,,;R&S[S[,S[S[R3,,;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R&S[;R&S[;R&S[S[,;R&S[;R&S[;R&S[;R &S[;R!&S[;R"&S[;R#&S[R$,;R%&S[R$,;R&&S[S[S[,,R$,;R'&#)(éÚ vocab_sizeÚ hidden_sizeÚnum_hidden_layersÚnum_attention_headsÚintermediate_sizeÚsqueeze_factorÚ hidden_actÚhidden_dropoutÚactivation_dropoutÚattention_dropoutÚfeat_proj_dropoutÚ final_dropoutÚ layerdropÚinitializer_rangeÚlayer_norm_epsÚfeat_extract_normÚfeat_extract_activation.rrrÚ conv_biasÚnum_conv_pos_embeddingsÚnum_conv_pos_embedding_groupsÚapply_spec_augmentÚmask_time_probÚmask_time_lengthÚmask_time_min_masksÚmask_feature_probÚmask_feature_lengthÚmask_feature_min_masksÚctc_loss_reductionÚctc_zero_infinityÚuse_weighted_layer_sumÚclassifier_proj_sizeNÚ pad_token_idÚ bos_token_idÚ eos_token_id)ÚintÚstrÚfloatÚlistÚtupleÚbool)ÚformatÚ __classdict__s"€rÚ __annotate__ÚSEWConfig.__annotate__s˜ø‡‚ñtÑñuñvÑñwñxÑñyñzÑ!ñ{ñ|Ñ!ñ}ñ~Ñññ@ÑñAñB™C•KÑ%ñCñD¡ Ñ)ñEñF™s•{Ñ(ñGñH™s•{Ñ(ñIñJ™3•;Ñ$ñKñL‘s{Ñ ñMñNÑ#ñOñPÑ ñQñRÑ$ñSñT!Ñ)ñUñV‘3i™%¡ S /Õ)ÑlñWñX‘c•™U¡3¨ 8_Õ,ÑVñYñZ‘c•™U¡3¨ 8_Õ,ÑWñ[ñ\Ññ]ñ^!Ñ&ñ_ñ`$'Ñ+ñañbÑ#ñcñd™C•KÑ&ñeñfÑñgñhÑ ñiñj™s•{Ñ(ñkñlÑ!ñmñn Ñ#ñoñpÑ$ñqñrÑ#ñsñt!Ñ(ñuñvÑ#ñwñx˜•*Ñ ñyñz˜•*Ñ ñ{ñ|™™S •/ DÕ(Ñ,ò}r)r) é@é€rXrXrXr r r r érYrYrY) ér*r"r*r"r*r"r*r"r*r"r*r") é ér"r\r"r\r"r\r"r*r"r*r")3Ú__name__Ú __module__Ú __qualname__Ú__firstlineno__Ú__doc__Ú model_typer+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;rrrr<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLrrÚpropertyr'Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú __classcell__)rrTs@@rrrs ù‡€ñS ðj€Jà€JØ€KØÐØ!ÐØ!ÐØ€NØ€JØ"%€NØ&)ÐØ%(ÐØ%(ÐØ!$€MØ €IØ#ÐØ €NØ$ÐØ#)ÐØ,l€HØ/V€KØ/W€KØ€IØ#&ÐØ)+Ð!Ø#ÐØ"&€NØÐØ ÐØ%(ÐØ!ÐØ"#ÐØ$ÐØ#ÐØ#(ÐØ #ÐØ €LØ €LØ+,€Lõ(ò ðñCóðC÷g…rr) rar#r%Úhuggingface_hub.dataclassesrÚconfiguration_utilsrÚutilsrrÚ__all__rrrÚrlsSðñãÛå.å3Ý#ñÐ2Ô3ØôRCÐ óRCóó4ðRCðj ˆ-‚r