+ jH)Rt^RIt^RIHt^RIHt^RIHtHt]P!] 4t ]!RR7]!RR]444t ]!RR7]!R R ]444t ]!RR7]!R R ]444t.R OtR#)zCLVP model configurationN)strict)PreTrainedConfig)auto_docstringloggingzsusnato/clvp_dev) checkpointca]tRt^toRtRtRR.tRtRtRt Rt ^t ^ t Rt R tR tR tR tR tR tRt^t^tRt]RV3RlRll4tV3RltRtVtR#)ClvpEncoderConfigar use_rotary_embedding (`bool`, *optional*, defaults to `True`): Whether to use rotary_embedding or not. use_attention_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in Query, Key and Value layers during self attention. summary_type (`str`, *optional*, defaults to `"mean"`): What strategy to use to get pooler_output from the last_hidden_state. `"last"`, `"first"`, `"mean"` and `"cls_index"` are supported. Example: ```python >>> from transformers import ClvpEncoderConfig, ClvpEncoder >>> # Initializing a ClvpEncoderConfig with susnato/clvp_dev style configuration >>> encoder_configuration = ClvpEncoderConfig() >>> # Initializing a ClvpEncoder (with random weights) from the susnato/clvp_dev style configuration >>> model = ClvpEncoder(encoder_configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` clvp_encoder text_config speech_configigeluh㈵>皙?TFmean?NcJ<V^8dQhRS[S[P,RS[/#)pretrained_model_name_or_path config_type)strosPathLike)format __classdict__s"l/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/clvp/configuration_clvp.py __annotate__ClvpEncoderConfig.__annotate__Ls&44,/"++,=4LO4c VP!V3/VBwrCW P9d\RV 24hVPR4R8Xd WB,pRV9dX\ VR4'dFVR,VP 8wd.\ PRVR, RVP R24VP!V3/VB#)zSWe can only load either 'text_config' or 'speech_config' but you are trying to load model_typeclvpzYou are using a model of type z to instantiate a model of type zN. This is not supported for all configurations of models and can yield errors.) get_config_dictbase_config_key ValueErrorgethasattrr!loggerwarning from_dict)clsrrkwargs config_dicts&&&, rfrom_pretrained!ClvpEncoderConfig.from_pretrainedKs"112OZSYZ  11 1efqers  ??< (F 2%2K ; &73 +E+E+VbJcgjguguJu NN0\1J0KKk>>""pr  }}[3F33rcP<V^8dQh/S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R &S[S[,;R &S[;R &S[;R &S[;R &S[;R&S[R,;R&S[S[S[,,R,;R&S[R,;R&#)r vocab_size hidden_sizeintermediate_sizeprojection_dimnum_hidden_layersnum_attention_heads hidden_actlayer_norm_epsattention_dropoutdropoutuse_rotary_embeddinguse_attention_bias summary_typeinitializer_factorN bos_token_id eos_token_id pad_token_id)intrfloatboollist)rrs"rrrs<=>?@!ABCDEF!GHIJ KLs{(MNS[OP%QR$STUV#WX*"YZS /D(,[\*#]r)r )__name__ __module__ __qualname____firstlineno____doc__r!r$r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rA classmethodr.__annotate_func____static_attributes____classdictcell__rs@rrrs 0 J$o6OJK!N!J N%(G!%$L #"L+,L#L444crrca]tRt^etoRtRtRtRtRtRt Rt ^t ^t Rt ^tR tR tR tR tR tR tR tRtRtRtR tRtRtRtRt^PtRtRt Rt!Rt"V3Rlt#Rt$Vt%R#)ClvpDecoderConfiga max_text_tokens (`int`, *optional*, defaults to 404): The maximum sequence length of text tokens that this model might ever be used with. Similar to `n_positions` in `GPT2Config`. n_inner (`int`, *optional*): Dimensionality of the inner feed-forward layers. `None` will set it to 4 times `hidden_size`. num_mel_attn_blocks (`int`, *optional*, defaults to 6): Denotes the number of self attention layers in [`ClvpConditioningEncoder`]. summary_type (`string`, *optional*, defaults to `"cls_index"`): Argument used when doing sequence summary. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`bool`, *optional*, defaults to `True`): Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_first_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio to be used after the projection and activation. feature_size (`int`, *optional*, defaults to 80): The feature dimension of the extracted mel features. This value is used in [`ClvpConditioningEncoder`]. use_attention_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in Query, Key and Value layers during self attention. decoder_fixing_codes (`list`, *optional*, defaults to `[83, 45, 45, 248]`): These values are used in the method `fix_speech_decoder_output` to fix decoder generated outputs. Example: ```python >>> from transformers import ClvpDecoderConfig, ClvpDecoder >>> # Initializing a ClvpDecoderConfig with susnato/clvp_dev style configuration >>> decoder_configuration = ClvpDecoderConfig() >>> # Initializing a ClvpDecoder (with random weights) from the susnato/clvp_dev style configuration >>> model = ClvpDecoder(decoder_configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` clvp_decoderdecoder_configi i`iiNgelu_newrrg{Gz? cls_indexTi i rFcD<V^8dQh/S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[R,;R&S[;R &S[;R &S[S[,;R &S[S[,;R &S[S[,;R &S[;R&S[;R&S[;R&S[;R&S[R,;R&S[;R&S[S[,;R&S[;R&S[R,;R&S[S[S[,,R,;R&S[R,;R&S[;R&S[;R&S[;R&S[S[,S[S[R3,,;R&S[;R&#)rr1max_position_embeddingsmax_text_tokensr2r5r6Nn_innernum_mel_attn_blocksactivation_function resid_pdrop embd_pdropr9layer_norm_epsiloninitializer_ranger=summary_use_projsummary_activationsummary_proj_to_labelssummary_first_dropout use_cacher?r@rA feature_sizer<r>.decoder_fixing_codesadd_cross_attention)rBrrCrDrEtuple)rrs"rrClvpDecoderConfig.__annotate__eshij!&klmnopqr!st4Zuv wx)yz"{| !}~s{(@$AB#CD#EF!GHd )IJ!'KL!3;,MNOP*#QRS /D(/ST*#UVWX#YZ#[\s)eCHo5I]^%_rrF)S-rl)&rGrHrIrJrKr!r$r1rXrYr2r5r6rZr[r\r]r^r9r_r`r=rarbrcrdrer?r@rArfr<r>rgrhrMrNrOrPs@rrRrRes, \ J&OJ#&OK!G )"K!J%( $##L!%)#'),I#L+/L#LL# #8I %_rrRcnaa]tRt^toRtRtR]R]R]/tRt Rt Rt Rt Rt R tV3R ltV3R ltR tVtV;t#) ClvpConfigaz speech_config (`dict`, *optional*): Dictionary of configuration options used to initialize CLVP speech encoder. decoder_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`ClvpDecoderConfig`]. Example: ```python >>> from transformers import ClvpConfig, ClvpModelForConditionalGeneration >>> # Initializing a ClvpConfig with susnato/clvp_dev style configuration >>> configuration = ClvpConfig() >>> # Initializing a ClvpModelForConditionalGeneration (with random weights) from the susnato/clvp_dev style configuration >>> model = ClvpModelForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a CLVPConfig from a CLVPTextConfig, CLVPSpeechConfig and a CLVPAutoRegressiveConfig >>> from transformers import ClvpEncoderConfig, ClvpDecoderConfig >>> # Initializing a CLVP text, CLVP speech and CLVP decoder configuration >>> config_text = ClvpEncoderConfig() >>> config_speech = ClvpEncoderConfig() >>> decoder_config = ClvpDecoderConfig() >>> config = ClvpConfig(config_text, config_speech, decoder_config) ```r"r r rTNr g/L F@rc <VPf&\4Vn\PR4M:\ VP\ 4'd\R/VPBVnVP f&\4Vn\PR4M:\ VP \ 4'd\R/VP BVnVPf&\4Vn\PR4M:\ VP\ 4'd\R/VPBVn\SV`(!R/VBR#)NzR`text_config` is `None`. initializing the `ClvpEncoderConfig` with default values.zT`speech_config` is `None`. initializing the `ClvpEncoderConfig` with default values.zS`image_config` is `None`. initializing the `ClvpDecoderConfig` with default values.rF) r rr(info isinstancedictr rTrRsuper __post_init__)selfr, __class__s&,rruClvpConfig.__post_init__s    #02D  KKl m (($ / /0D43C3CDD     %!2!4D  KKn o **D 1 1!2!HT5G5G!HD     &"3"5D  KKm n ++T 2 2"3"Jd6I6I"JD  ''rc<V^8dQh/S[S[,R,;R&S[S[,R,;R&S[S[,R,;R&S[;R&S[;R&S[;R&#)rNr r rTr4logit_scale_init_valuer>)rsrrBrC)rrs"rrClvpConfig.__annotate__sR((4/6ST**T18UV++d29WXYZ"*[\#]r)rTr r )rGrHrIrJrKr!rrR sub_configsr r rTr4rzr>rurMrNrO __classcell__)rwrs@@rrorosY >J(*+K 37K48M59NN$* #(arro)rorRr)rKrhuggingface_hub.dataclassesrconfiguration_utilsrutilsrr get_loggerrGr(rrRro__all__rFrrrs .3,   H %-.E4(E4/E4P-.M&(M&/M&`-.A(!A(/A(H Cr