`i!OdZddlZddlmZddlmZddlmZeje Z GddeZ Gd d eZ Gd d eZ gd ZdS)zCLVP model configurationN)Union)PretrainedConfig)loggingceZdZdZdZddgZ dfd Ze ddee e j fde fdZ xZ S)ClvpEncoderConfigak This is the configuration class to store the configuration of a [`ClvpEncoder`]. It is used to instantiate a CLVP text or CLVP speech encoder according to the specified arguments. Instantiating a configuration with the defaults will yield a similar configuration to that of the encoder of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 256): Vocabulary size of the CLVP Encoder model. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 1536): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. projection_dim (`int`, *optional*, defaults to 768): Dimensionality of the projection vector. num_hidden_layers (`int`, *optional*, defaults to 20): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the feed-forward layers in [`ClvpEncoderMLP`]. 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. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization testing). bos_token_id (`int`, *optional*, defaults to 255): Beginning of sequence token id. eos_token_id (`int`, *optional*, defaults to 0): End of sequence token id. 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 geluh㈵>皙?TFmean?rc ||_||_||_||_||_||_||_||_||_| |_ | |_ | |_ | |_ | |_ ||_||_t!jd||d|dSN) bos_token_id eos_token_id) vocab_size hidden_sizeintermediate_sizeprojection_dimnum_hidden_layersnum_attention_headslayer_norm_eps hidden_actinitializer_factorattention_dropoutdropoutuse_rotary_embeddinguse_attention_bias summary_typerrsuper__init__)selfrrrrr r!r#r"r%r&r'r(r)r$rrkwargs __class__s /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/clvp/configuration_clvp.pyr+zClvpEncoderConfig.__init__\s(%&!2,!2#6 ,$"4!2 $8!"4(((XlXXQWXXXXXpretrained_model_name_or_path config_typec |||j|fi|\}}||jvrtd||ddkr||}d|vrMt |dr=|d|jkr,td|dd|jd|j |fi|S)NzSWe can only load either 'text_config' or 'speech_config' but you are trying to load model_typeclvpzYou are using a model of type z to instantiate a model of type zN. This is not supported for all configurations of models and can yield errors.) _set_token_in_kwargsget_config_dictbase_config_key ValueErrorgethasattrr4loggerwarning from_dict)clsr1r2r- config_dicts r/from_pretrainedz!ClvpEncoderConfig.from_pretraineds   (((1c12OZZSYZZ V c1 1 1sfqss  ??< ( (F 2 2%k2K ; & &73 +E+E &+VbJcgjguJuJu NNr\1Jrr>rrr    s}[33F333r0)r r rr rrrrrrTFrrrr)r )__name__ __module__ __qualname____doc__r4r8r+ classmethodrstrosPathLikerA __classcell__r.s@r/rrs; ; z J$o6O! #%Y%Y%Y%Y%Y%YNXe44,1#r{2B,C4RU444[44444r0rcdeZdZdZdZdZdddddd d d d d d d ddddd dd ddddddgdffd ZxZS)ClvpDecoderConfigaN This is the configuration class to store the configuration of a [`ClvpDecoder`]. It is used to instantiate a CLVP Decoder Model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Decoder part of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. The architecture is similar to GPT2. Args: vocab_size (`int`, *optional*, defaults to 8194): Vocabulary size of the model. max_position_embeddings (`int`, *optional*, defaults to 608): The maximum sequence length of mel tokens that this model might ever be used with. Similar to `n_positions` in `GPT2Config`. 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`. hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the embeddings and hidden states. num_hidden_layers (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. 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`]. activation_function (`str`, *optional*, defaults to `"gelu_new"`): Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon to use in the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. 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. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). bos_token_id (`int`, *optional*, defaults to 8192): Beginning of sequence token id, used at the start of the generation. eos_token_id (`int`, *optional*, defaults to 8193): End of sequence token id, used in the method [`ClvpModelForConditionalGeneration.fix_speech_decoder_output()`] to correct decoder outputs. 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. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization testing). 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 Pr)S-rWc ||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_||_||_||_||_||_||_||_||_||_||_t5jd||d|dSr)rmax_position_embeddingsmax_text_tokensrr r!n_innernum_mel_attn_blocksactivation_function resid_pdrop embd_pdropr%layer_norm_epsiloninitializer_ranger)summary_use_projsummary_activationsummary_first_dropoutsummary_proj_to_labels use_cache feature_sizer(r$decoder_fixing_codesrrr*r+)r,rrZr[rr r!r\r]r^r_r`r%rarbr)rcrdrfrergrrrhr(r$rir-r.s r/r+zClvpDecoderConfig.__init__s<%'>$.&!2#6  #6 #6 &$!2"4!2( 0"4%:"&<#"("4"4$8!((XlXXQWXXXXXr0)rBrCrDrEr4r8r+rJrKs@r/rMrMsZ Z x J&O #& #!...7:Y:Y:Y:Y:Y:Y:Y:Y:Y:Yr0rMcdeZdZdZdZeeedZ d fd Ze d ed ed efd Z xZ S) ClvpConfigaQ [`ClvpConfig`] is the configuration class to store the configuration of a [`ClvpModelForConditionalGeneration`]. It is used to instantiate a CLVP model according to the specified arguments, defining the text model, speech model and decoder model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`dict`, *optional*): Dictionary of configuration options used to initialize the CLVP text encoder. 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`]. projection_dim (`int`, *optional*, defaults to 768): Dimensionality of text and speech projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The initial value of the *logit_scale* parameter. Default is used as per the original CLVP implementation. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization testing). kwargs (*optional*): Dictionary of keyword arguments. 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.from_sub_model_configs(config_text, config_speech, decoder_config) ```r5r r rONr /L F@rc ~tjdi||i}td|i}td|i}tdt di||_t di||_tdi||_||_ ||_ ||_ dS)NzR`text_config` is `None`. Initializing the `ClvpEncoderConfig` with default values.zT`speech_config` is `None`. initializing the `ClvpEncoderConfig` with default values.zU`decoder_config` is `None`. initializing the `ClvpDecoderConfig` with default values.r) r*r+r<inforr r rMrOrlogit_scale_init_valuer$) r,r r rOrrpr$r-r.s r/r+zClvpConfig.__init__ws ""6"""  K KKl m m m  M KKn o o o  !N KKo p p p,;;{;;.????/AA.AA,&<#"4r0r r rOc |d|||d|S)a Instantiate a [`ClvpConfig`] (or a derived class) from CLVP text model configuration, CLVP speech model configuration and CLVP decoder model configuration. Args: text_config (`ClvpEncoderConfig`): Text model configuration of type [`ClvpEncoderConfig`]. speech_config (`ClvpEncoderConfig`): Speech model configuration of type [`ClvpEncoderConfig`]. decoder_config (`ClvpDecoderConfig`): Decoder model configuration of type [`ClvpDecoderConfig`]. Returns: [`ClvpConfig`]: An instance of a configuration object rlr)to_dict)r?r r rOr-s r/from_sub_model_configsz!ClvpConfig.from_sub_model_configssY0s #++--'//11)1133      r0)NNNr rmr) rBrCrDrEr4rrM sub_configsr+rFrsrJrKs@r/rkrk<s1 1 fJ(*+K%555555@ & ) *    [     r0rk)rkrMr)rErHtypingrconfiguration_utilsrutilsr get_loggerrBr<rrMrk__all__rr0r/rzs 333333  H % %A4A4A4A4A4(A4A4A4HZYZYZYZYZY(ZYZYZYzx x x x x !x x x v C B Br0