§
    ‚Štj<  ã                   ó„   — d Z ddlmZ ddlmZ ddlmZ  ed¬¦  «        e G d„ d	e¦  «        ¦   «         ¦   «         Zd	gZd
S )zPvt model configurationé    )Ústricté   )ÚPreTrainedConfig)Úauto_docstringzXrenya/pvt-tiny-224)Ú
checkpointc                   ó<  — e Zd ZU dZdZdZeee         z  eeef         z  e	d<   dZ
ee	d<   dZee	d<   d	Zee         eed
f         z  e	d<   dZee         eed
f         z  e	d<   dZee         eed
f         z  e	d<   dZee         eed
f         z  e	d<   dZee         eed
f         z  e	d<   dZee         eed
f         z  e	d<   dZee         eed
f         z  e	d<   dZee	d<   dZeez  e	d<   dZeez  e	d<   dZee	d<   dZeez  e	d<   dZee	d <   d!Zee	d"<   d#Zee	d$<   d%S )&Ú	PvtConfiga×  
    num_encoder_blocks (`int`, *optional*, defaults to 4):
        The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
    depths (`list[int]`, *optional*, defaults to `[2, 2, 2, 2]`):
        The number of layers in each encoder block.
    sequence_reduction_ratios (`list[int]`, *optional*, defaults to `[8, 4, 2, 1]`):
        Sequence reduction ratios in each encoder block.
    patch_sizes (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
        Patch size before each encoder block.
    strides (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
        Stride before each encoder block.
    num_attention_heads (`list[int]`, *optional*, defaults to `[1, 2, 5, 8]`):
        Number of attention heads for each attention layer in each block of the Transformer encoder.
    mlp_ratios (`list[int]`, *optional*, defaults to `[8, 8, 4, 4]`):
        Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
        encoder blocks.
    num_labels ('int', *optional*, defaults to 1000):
        The number of classes.

    Example:

    ```python
    >>> from transformers import PvtModel, PvtConfig

    >>> # Initializing a PVT Xrenya/pvt-tiny-224 style configuration
    >>> configuration = PvtConfig()

    >>> # Initializing a model from the Xrenya/pvt-tiny-224 style configuration
    >>> model = PvtModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Úpvtéà   Ú
image_sizer   Únum_channelsé   Únum_encoder_blocks)é   r   r   r   .Údepths)é   r   r   é   Úsequence_reduction_ratios)é@   é€   i@  i   Úhidden_sizes)r   r   r   r   Úpatch_sizesÚstrides)r   r   é   r   Únum_attention_heads)r   r   r   r   Ú
mlp_ratiosÚgeluÚ
hidden_actg        Úhidden_dropout_probÚattention_probs_dropout_probg{®Gáz”?Úinitializer_rangeÚdrop_path_rateg�íµ ÷Æ°>Úlayer_norm_epsTÚqkv_biasiè  Ú
num_labelsN)Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typer   ÚintÚlistÚtupleÚ__annotations__r   r   r   r   r   r   r   r   r   r   Ústrr   Úfloatr    r!   r"   r#   r$   Úboolr%   © ó    úg/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/pvt/configuration_pvt.pyr	   r	      sâ  € € € € € € ð ð  ðD €Jà47€J��d˜3”i‘ %¨¨S¨¤/Ñ1Ð7Ð7Ñ7Ø€L�#ÐÐÑØÐ˜ÐÐÑØ*6€FˆD�ŒI˜˜c 3˜hœÑ'Ð6Ð6Ñ6Ø=IÐ˜t Cœy¨5°°c°¬?Ñ:ÐIÐIÑIØ0C€L�$�s”)˜e C¨ HœoÑ-ÐCÐCÑCØ/;€K��c”˜U 3¨ 8œ_Ñ,Ð;Ð;Ñ;Ø+7€GˆT�#ŒY˜˜s C˜xœÑ(Ð7Ð7Ñ7Ø7CÐ˜˜cœ U¨3°¨8¤_Ñ4ÐCÐCÑCØ.:€J��S”	˜E # s (œOÑ+Ð:Ð:Ñ:Ø€J�ÐÐÑØ'*Ð˜ ™Ð*Ð*Ñ*Ø03Ð  %¨#¡+Ð3Ð3Ñ3Ø#Ð�uÐ#Ð#Ñ#Ø"%€N�E˜C‘KÐ%Ð%Ñ%Ø €N�EÐ Ð Ñ Ø€HˆdÐÐÑØ€J�ÐÐÑÐÐr3   r	   N)	r)   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r	   Ú__all__r2   r3   r4   ú<module>r9      s¡   ðð  Ð à .Ð .Ð .Ð .Ð .Ð .à 3Ð 3Ð 3Ð 3Ð 3Ð 3Ø #Ð #Ð #Ð #Ð #Ð #ð €Ð0Ð1Ñ1Ô1Øð6ð 6ð 6ð 6ð 6Ð ñ 6ô 6ñ „ñ 2Ô1ð6ðr ˆ-€€€r3   