ZjH)SrSSKrSSKJr SSKJr SSKJrJr \R"\ 5r \"SS9\"S S \555r \"SS9\"S S \555r \"SS9\"S S\555r/SQrg)zCLVP model configurationN)strict)PreTrainedConfig)auto_docstringloggingzsusnato/clvp_dev) checkpointcz\rSrSr%SrSrSS/rSr\\ S'Sr \\ S 'S r \\ S 'Sr \\ S 'S r \\ S'Sr\\ S'Sr\\ S'Sr\\ S'Sr\\-\ S'Sr\\-\ S'Sr\\ S'Sr\\ S'Sr\\ S'Sr\\ S'S r\S!-\ S"'S#r\\\-S!-\ S$'S!r\S!-\ S%'\S*S&\\R@-S'\4S(jj5r!S)r"g!)+ClvpEncoderConfigar use_rotary_embedding (`bool`, *optional*, defaults to `True`): Whether to use rotary_embedding or not. use_attention_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in Query, Key and Value layers during self attention. summary_type (`str`, *optional*, defaults to `"mean"`): What strategy to use to get pooler_output from the last_hidden_state. `"last"`, `"first"`, `"mean"` and `"cls_index"` are supported. Example: ```python >>> from transformers import ClvpEncoderConfig, ClvpEncoder >>> # Initializing a ClvpEncoderConfig with susnato/clvp_dev style configuration >>> encoder_configuration = ClvpEncoderConfig() >>> # Initializing a ClvpEncoder (with random weights) from the susnato/clvp_dev style configuration >>> model = ClvpEncoder(encoder_configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` clvp_encoder text_config speech_config vocab_size hidden_sizeiintermediate_sizeprojection_dimnum_hidden_layers num_attention_headsgelu hidden_acth㈵>layer_norm_eps皙?attention_dropoutdropoutTuse_rotary_embeddingFuse_attention_biasmean summary_type?initializer_factorN bos_token_idr eos_token_id pad_token_idpretrained_model_name_or_path config_typec dUR"U40UD6upCX R;a[SU35eURS5S:XaXBnSU;aM[ US5(a<USUR :wa)[ RSUSSUR S35 UR"U40UD6$)NzSWe 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)clsr*r+kwargs config_dicts |/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/clvp/configuration_clvp.pyfrom_pretrained!ClvpEncoderConfig.from_pretrainedKs"112OZSYZ  11 1efqers  ??< (F 2%2K ; &73 +E+E+VbJcgjguguJu NN0\1J0KKk>>""pr  }}[3F33)r )#__name__ __module__ __qualname____firstlineno____doc__r-r0rint__annotations__rrrrrrstrrfloatrrr boolr!r#r%r'r(listr) classmethodosPathLiker;__static_attributes__r>r=r:r r s 0 J$o6OJK!s!NCs!!J NE %(us{(GUS[!%$%$$L# ##"L#*"+,L#S /D(,#L#*#R_4,/"++,=4LO44r=r c\rSrSr%SrSrSrSr\\ S'Sr \\ S'S r \\ S 'S r \\ S 'S r \\ S'Sr\\ S'Sr\S-\ S'Sr\\ S'Sr\\ S'Sr\\-\ S'Sr\\-\ S'Sr\\-\ S'Sr\\ S'Sr\\ S'Sr\\ S 'S!r\\ S"'Sr\S-\ S#'S!r\\ S$'Sr\\-\ S%'S!r\\ S&'S'r \S-\ S('S)r!\\"\-S-\ S*'Sr#\S-\ S+'S,r$\\ S-'S!r%\\ S.'S/r&\\ S0'S1r'\"\\(\S24-\ S3'S4r)\\ S5'S6r*g)7ClvpDecoderConfigea 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 ri`max_position_embeddingsimax_text_tokensirrrNn_innernum_mel_attn_blocksgelu_newactivation_functionr resid_pdrop embd_pdroprrlayer_norm_epsilong{Gz?initializer_range cls_indexr#Tsummary_use_projsummary_activationsummary_proj_to_labelssummary_first_dropout use_cachei r'i r(r)P feature_sizer!r$r%)S-ri.decoder_fixing_codesFadd_cross_attentionr>)+r?r@rArBrCr-r0rrDrErSrTrrrrWrYr[rFr\rGr]rr^r_r#rarHrbrcrdrer'r(rIr)rgr!r%rktuplerlrMr>r=r:rOrOesz, \ J&OJ#&S&OSKs!!GS4Z  ))"K"!J !%(us{( $$#u##L##!d!%)d )#'D'),53;,It#L#*#+/L#S /D(/#L#*#L### ##8I$s)eCHo5I %%r=rOc^\rSrSr%SrSr\\\S.rSr \ \ -S-\ S'Sr \ \ -S-\ S'Sr\ \ -S-\ S'S r\\ S 'S r\\ S 'S r\\ S'U4SjrSrU=r$) 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 rrRNr rrRrrg/L F@logit_scale_init_valuer$r%c >URc%[5Ul[RS5 O9[ UR[ 5(a[S0URD6UlUR c%[5Ul[RS5 O9[ UR [ 5(a[S0UR D6UlURc%[5Ul[RS5 O9[ UR[ 5(a[S0URD6Ul[TU]("S0UD6 g)NzR`text_config` is `None`. initializing the `ClvpEncoderConfig` with default values.zT`speech_config` is `None`. initializing the `ClvpEncoderConfig` with default values.zS`image_config` is `None`. initializing the `ClvpDecoderConfig` with default values.r>) r r r4info isinstancedictrrRrOsuper __post_init__)selfr8 __class__s r:rwClvpConfig.__post_init__s    #02D  KKl m (($ / /0D43C3CDD     %!2!4D  KKn o **D 1 1!2!HT5G5G!HD     &"3"5D  KKm n ++T 2 2"3"Jd6I6I"JD  ''r=)rRrr )r?r@rArBrCr-r rO sub_configsr rurrErrRrrDrqrGr%rwrM __classcell__)rys@r:roros >J(*+K 37K((4/648M4**T1859ND++d29NC$*E* ##((r=ro)rorOr )rCrKhuggingface_hub.dataclassesrconfiguration_utilsrutilsrr get_loggerr?r4r rOro__all__r>r=r:rs .3,   H %-.E4(E4/E4P-.M&(M&/M&`-.A(!A(/A(H Cr=