qi!`dZddlmZddlmZej eZGddeZdgZ y)z&Funnel Transformer model configuration)PreTrainedConfig)loggingceZdZdZdZdddZdgddd d d d d ddddddddddddddffd ZedZejdZedZ e jdZ xZ S) FunnelConfiga This is the configuration class to store the configuration of a [`FunnelModel`]. It is used to instantiate a Funnel Transformer 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 Funnel Transformer [funnel-transformer/small](https://huggingface.co/funnel-transformer/small) 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 30522): Vocabulary size of the Funnel transformer. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FunnelModel`]. block_sizes (`list[int]`, *optional*, defaults to `[4, 4, 4]`): The sizes of the blocks used in the model. block_repeats (`list[int]`, *optional*): If passed along, each layer of each block is repeated the number of times indicated. num_decoder_layers (`int`, *optional*, defaults to 2): The number of layers in the decoder (when not using the base model). d_model (`int`, *optional*, defaults to 768): Dimensionality of the model's hidden states. n_head (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. d_head (`int`, *optional*, defaults to 64): Dimensionality of the model's heads. d_inner (`int`, *optional*, defaults to 3072): Inner dimension in the feed-forward blocks. hidden_act (`str` or `callable`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout probability used between the two layers of the feed-forward blocks. initializer_range (`float`, *optional*, defaults to 0.1): The upper bound of the *uniform initializer* for initializing all weight matrices in attention layers. initializer_std (`float`, *optional*): The standard deviation of the *normal initializer* for initializing the embedding matrix and the weight of linear layers. Will default to 1 for the embedding matrix and the value given by Xavier initialization for linear layers. layer_norm_eps (`float`, *optional*, defaults to 1e-09): The epsilon used by the layer normalization layers. pooling_type (`str`, *optional*, defaults to `"mean"`): Possible values are `"mean"` or `"max"`. The way pooling is performed at the beginning of each block. attention_type (`str`, *optional*, defaults to `"relative_shift"`): Possible values are `"relative_shift"` or `"factorized"`. The former is faster on CPU/GPU while the latter is faster on TPU. separate_cls (`bool`, *optional*, defaults to `True`): Whether or not to separate the cls token when applying pooling. truncate_seq (`bool`, *optional*, defaults to `True`): When using `separate_cls`, whether or not to truncate the last token when pooling, to avoid getting a sequence length that is not a multiple of 2. pool_q_only (`bool`, *optional*, defaults to `True`): Whether or not to apply the pooling only to the query or to query, key and values for the attention layers. pad_token_id (`int`, *optional*): Padding token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings funneld_modeln_head) hidden_sizenum_attention_headsi:w)r r Ni @i gelu_newg?gg& .>meanrelative_shiftTc ||_||_||_||_|dgt |zn||_t |t |j k(sJd||_||_||_||_ ||_ | |_ | |_ | |_ | |_| |_||_||_|dvs Jd|d||_|dvs Jd|d||_||_||_||_t/|`di|y) Nz>`block_sizes` and `block_repeats` should have the same length.)rmaxzGot z< for `pooling_type` but only 'mean' and 'max' are supported.)r factorizedzO for `attention_type` but only 'relative_shift' and 'factorized' are supported.) pad_token_idtie_word_embeddings vocab_size block_sizeslen block_repeatsnum_decoder_layersrr d_headd_inner hidden_acthidden_dropoutattention_dropoutactivation_dropoutinitializer_rangeinitializer_stdlayer_norm_eps pooling_typeattention_type separate_cls truncate_seq pool_q_onlysuper__init__)selfrrrrrr rr r!r"r#r$r%r&r'r(r)r*r+r,rrkwargs __class__s a/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/funnel/configuration_funnel.pyr.zFunnelConfig.__init__\s\4)#6 $&7D7LaS3{#33R_;3t'9'9#:: L :#5    $,!2"4!2.,   ],[ \ ] )"   r.!!p q r -((& "6"c,t|jSN)sumrr/s r2num_hidden_layerszFunnelConfig.num_hidden_layers4##$$r3ctd)NzYThis model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.NotImplementedErrorr/values r2r8zFunnelConfig.num_hidden_layerss! g  r3c,t|jSr5)rrr7s r2 num_blockszFunnelConfig.num_blocksr9r3ctd)NzRThis model does not support the setting of `num_blocks`. Please set `block_sizes`.r;r=s r2r@zFunnelConfig.num_blockss!"vwwr3) __name__ __module__ __qualname____doc__ model_type attribute_mapr.propertyr8setterr@ __classcell__)r1s@r2rrs<|J 'M' /<#|%%  %%xxr3rN) rEconfiguration_utilsrutilsr get_loggerrBloggerr__all__rr3r2rPs@-3   H %Sx#Sxl  r3