# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/lasr/modular_lasr.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_lasr.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 The HuggingFace Inc. team and Google LLC. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...configuration_utils import PreTrainedConfig class LasrEncoderConfig(PreTrainedConfig): r""" 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). """ model_type = "lasr_encoder" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, hidden_size=512, num_hidden_layers=17, num_attention_heads=8, intermediate_size=2048, hidden_act="silu", attention_bias=False, convolution_bias=False, conv_kernel_size=32, subsampling_conv_channels=256, subsampling_conv_kernel_size=5, subsampling_conv_stride=2, num_mel_bins=128, dropout=0.1, dropout_positions=0.0, layerdrop=0.1, activation_dropout=0.1, attention_dropout=0.1, max_position_embeddings=10000, initializer_range=0.02, layer_norm_eps=1e-6, feed_forward_residual_weights=[1.5, 0.5], conv_residual_weights=[2.0, 1.0], batch_norm_momentum=0.01, rope_parameters=None, **kwargs, ): self.rope_parameters = rope_parameters self.layer_norm_eps = layer_norm_eps self.feed_forward_residual_weights = feed_forward_residual_weights self.conv_residual_weights = conv_residual_weights self.batch_norm_momentum = batch_norm_momentum self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_attention_heads # LlamaAttention compatibility self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.attention_bias = attention_bias self.convolution_bias = convolution_bias self.conv_kernel_size = conv_kernel_size self.subsampling_conv_kernel_size = subsampling_conv_kernel_size self.subsampling_conv_stride = subsampling_conv_stride self.subsampling_conv_channels = subsampling_conv_channels self.num_mel_bins = num_mel_bins self.dropout = dropout self.dropout_positions = dropout_positions self.layerdrop = layerdrop self.activation_dropout = activation_dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range super().__init__( **kwargs, ) class LasrCTCConfig(PreTrainedConfig): r""" 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). """ model_type = "lasr_ctc" sub_configs = {"encoder_config": LasrEncoderConfig} def __init__( self, vocab_size=512, ctc_loss_reduction="mean", ctc_zero_infinity=True, encoder_config: dict | LasrEncoderConfig = None, pad_token_id=0, **kwargs, ): self.vocab_size = vocab_size self.ctc_loss_reduction = ctc_loss_reduction self.ctc_zero_infinity = ctc_zero_infinity if isinstance(encoder_config, dict): self.encoder_config = LasrEncoderConfig(**encoder_config) elif encoder_config is None: self.encoder_config = LasrEncoderConfig() self.encoder_config = self.encoder_config self.initializer_range = self.encoder_config.initializer_range self.pad_token_id = pad_token_id super().__init__(**kwargs) @classmethod def from_encoder_config(cls, encoder_config: LasrEncoderConfig, **kwargs): r""" Instantiate a [`LasrCTCConfig`] (or a derived class) from lasr encoder model configuration. Returns: [`LasrCTCConfig`]: An instance of a configuration object """ return cls(encoder_config=encoder_config.to_dict(), **kwargs) @property def inputs_to_logits_ratio(self): return self.encoder_config.subsampling_conv_stride**2 __all__ = ["LasrEncoderConfig", "LasrCTCConfig"]