o ZhcO@sdZddlZddlmZmZer ddlmZddlmZe e Z GdddeZ Gd d d eZ Gd d d eZgd ZdS)zCLVP model configurationN) TYPE_CHECKINGUnion)PretrainedConfig)loggingcsveZdZdZdZddgZ          dfdd Ze ddee e j fde ddfddZ Z 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 ```Z clvp_encoder text_config speech_config geluh㈵>皙?TFmean?rc s|||_||_||_||_||_||_||_||_||_| |_ | |_ | |_ | |_ | |_ ||_||_tjd||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__)selfrrrrrrr rr"r#r$r%r&r!rrkwargs __class__rZ/var/www/auris/lib/python3.10/site-packages/transformers/models/clvp/configuration_clvp.pyr(`s"zClvpEncoderConfig.__init__pretrained_model_name_or_path config_typereturnrcKs|||j|fi|\}}||jvrtd||ddkr'||}d|vrFt|drF|d|jkrFtd|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.) Z_set_token_in_kwargsZget_config_dictbase_config_key ValueErrorgethasattrr1loggerwarning from_dict)clsr.r/r*Z config_dictrrr-from_pretraineds    z!ClvpEncoderConfig.from_pretrained)r r r r r rrrrrTFrrrr)r)__name__ __module__ __qualname____doc__r1r3r( classmethodrstrosPathLiker; __classcell__rrr+r-rs<=' rcsbeZdZdZdZdZdddddd d d d d d d ddddd dd ddddddgdffdd ZZS)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 ```Z clvp_decoderdecoder_configi i`iiNZgelu_newrrg{Gz?Z cls_indexTi i Pr)S-rLc s||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_||_||_||_||_||_||_||_||_||_||_tjd||d|dSr)rmax_position_embeddingsmax_text_tokensrrrn_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)rrNrOrrrrPrQrRrSrTr"rUrVr&rWrXrZrYr[rrr\r%r!r]r*r+rr-r(s6zClvpDecoderConfig.__init__)r<r=r>r?r1r3r(rDrrr+r-rEs>\rEcsXeZdZdZdZeeedZ      dfdd Ze d ed ed efd dZ Z 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) ```r2rr rFNr /L F@rc stjdi||duri}td|duri}td|dur*i}tdtdi||_tdi||_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(r7inforrr rErFrlogit_scale_init_valuer!)r)rr rFrrbr!r*r+rr-r({s      zClvpConfig.__init__rr rFcKs"|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 r_Nr)to_dict)r:rr rFr*rrr-from_sub_model_configssz!ClvpConfig.from_sub_model_configs)NNNr r`r) r<r=r>r?r1rrEZ sub_configsr(r@rdrDrrr+r-r^@s,3 r^)r^rEr)r?rBtypingrrZconfiguration_utilsrutilsrZ get_loggerr<r7rrEr^__all__rrrr-s    {