i /DddlmZGddeZGddeZddgZy))PreTrainedConfigcfeZdZdZdZdgZdddddd d d d d d dddddddddddgddgddffd ZxZS)LasrEncoderConfiga This is the configuration class to store the configuration of a [`LasrEncoder`]. It is used to instantiate a `LasrEncoder` model according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 512): Dimension of the layers and the hidden states. num_hidden_layers (`int`, *optional*, defaults to 17): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 2048): Dimension of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the encoder and pooler. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in the attention layers. convolution_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in convolutions of the conformer's convolution module. conv_kernel_size (`int`, *optional*, defaults to 32): The kernel size of the convolution layers in the Conformer block. subsampling_conv_channels (`int`, *optional*, defaults to 256): The number of channels in the subsampling convolution layers. subsampling_conv_kernel_size (`int`, *optional*, defaults to 5): The kernel size of the subsampling convolution layers. subsampling_conv_stride (`int`, *optional*, defaults to 2): The stride of the subsampling convolution layers. num_mel_bins (`int`, *optional*, defaults to 128): Number of mel features. dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for all fully connected layers in the embeddings, encoder, and pooler. dropout_positions (`float`, *optional*, defaults to 0.0): The dropout ratio for the positions in the input sequence. layerdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the layers in the encoder. activation_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for activations inside the fully connected layer. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention layers. max_position_embeddings (`int`, *optional*, defaults to 10000): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. feed_forward_residual_weights (`tuple[float, float]`, *optional*, defaults to `[1.5, 0.5]`): The residual weights for the feed forward layers. conv_residual_weights (`tuple[float, float]`, *optional*, defaults to `[2.0, 1.0]`): The residual weights for the convolution layers. batch_norm_momentum (`float`, *optional*, defaults to 0.01): The momentum for the batch normalization layers. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. Example: ```python >>> from transformers import LasrEncoderModel, LasrEncoderConfig >>> # Initializing a `LasrEncoder` configuration >>> configuration = LasrEncoderConfig() >>> # Initializing a model from the configuration >>> model = LasrEncoderModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` This configuration class is based on the LasrEncoder architecture from Google Health AI. You can find more details and pre-trained models at [TODO/TODO](https://huggingface.co/TODO/TODO). lasr_encoderpast_key_valuesisiluF g?gi'g{Gz?gư>g?g?g@g?g{Gz?Nc ||_||_||_||_||_||_||_||_||_||_ ||_ ||_ ||_ ||_ | |_| |_| |_| |_| |_||_||_||_||_||_||_t3|hdi|yN)rope_parameterslayer_norm_epsfeed_forward_residual_weightsconv_residual_weightsbatch_norm_momentum hidden_sizenum_hidden_layersnum_attention_headsnum_key_value_headsintermediate_size hidden_actattention_biasconvolution_biasconv_kernel_sizesubsampling_conv_kernel_sizesubsampling_conv_stridesubsampling_conv_channels num_mel_binsdropoutdropout_positions layerdropactivation_dropoutattention_dropoutmax_position_embeddingsinitializer_rangesuper__init__)selfrrrrrrr r!r$r"r#r%r&r'r(r)r*r+r,rrrrrkwargs __class__s g/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/models/lasr/configuration_lasr.pyr.zLasrEncoderConfig.__init__js8 /,-J*%:"#6 &!2#6 #6 !2$, 0 0,H)'>$)B&( !2""4!2'>$!2   )__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencer. __classcell__r1s@r2rrsvKZ J#4"5"%%& ! %'*Cj"Cj 3: : r3rcleZdZdZdZdeiZ ddeezffd Ze defdZ e dZ xZ S) LasrCTCConfiga This is the configuration class to store the configuration of a [`LasrForCTC`]. It is used to instantiate a Lasr CTC model according to the specified arguments, defining the model 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 512): Vocabulary size of the model. ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`): Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an instance of [`LasrForCTC`]. ctc_zero_infinity (`bool`, *optional*, defaults to `True`): 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 [`LasrForCTC`]. encoder_config (`Union[dict, LasrEncoderConfig]`, *optional*): The config object or dictionary of the encoder. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. Also used as blank token id. Example: ```python >>> from transformers import LasrForCTC, LasrCTCConfig >>> # Initializing a Lasr configuration >>> configuration = LasrCTCConfig() >>> # Initializing a model from the configuration >>> model = LasrForCTC(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` This configuration class is based on the Lasr CTC architecture from Google Health AI. You can find more details and pre-trained models at [TODO/TODO](https://huggingface.co/TODO/TODO). lasr_ctcencoder_configc ||_||_||_t|trt di||_n|t |_|j |_|j j|_||_t|(di|yr) vocab_sizectc_loss_reductionctc_zero_infinity isinstancedictrr?r, pad_token_idr-r.)r/rArBrCr?rFr0r1s r2r.zLasrCTCConfig.__init__s%"4!2 nd +"3"En"ED   #"3"5D "11!%!4!4!F!F( "6"r3c 2|dd|ji|S)z Instantiate a [`LasrCTCConfig`] (or a derived class) from lasr encoder model configuration. Returns: [`LasrCTCConfig`]: An instance of a configuration object r?r)to_dict)clsr?r0s r2from_encoder_configz!LasrCTCConfig.from_encoder_configs E."8"8":EfEEr3c4|jjdzS)Nr)r?r#)r/s r2inputs_to_logits_ratioz$LasrCTCConfig.inputs_to_logits_ratios""::A==r3)rmeanTN)r4r5r6r7r8r sub_configsrEr. classmethodrJpropertyrLr:r;s@r2r=r=stBJ#%67K!37 # 00 #0F1BFF>>r3r=N)configuration_utilsrrr=__all__rr3r2rTs6,4K (K \J>$J>Z  0r3