§
    ‚Štj0  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DeBERTa model configurationé    )Ústricté   )ÚPreTrainedConfig)Úauto_docstringzmicrosoft/deberta-base)Ú
checkpointc                   ó¸  ‡ — e Zd ZU dZdZdZeed<   dZeed<   dZ	eed<   dZ
eed	<   d
Ze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d<   dZeed<   dZeed<   dZeed<   dZeed<   dZedz  ed<   dZedz  ed<   dZeee         z  dz  ed <   d!Zeed"<   dZeee         z  dz  ed#<   d$Zeez  ed%<   dZeed&<   d!Z eed'<   d!Z!eed(<   ˆ fd)„Z"ˆ xZ#S )*ÚDebertaConfigaí  
    relative_attention (`bool`, *optional*, defaults to `False`):
        Whether use relative position encoding.
    max_relative_positions (`int`, *optional*, defaults to -1):
        The range of relative positions `[-max_position_embeddings, max_position_embeddings]`. Use the same value
        as `max_position_embeddings`.
    position_biased_input (`bool`, *optional*, defaults to `True`):
        Whether add absolute position embedding to content embedding.
    pos_att_type (`list[str]`, *optional*):
        The type of relative position attention, it can be a combination of `["p2c", "c2p"]`, e.g. `["p2c"]`,
        `["p2c", "c2p"]`.
    pooler_dropout (`float`, *optional*, defaults to `0`):
        Dropout rate in the pooler module.
    pooler_hidden_act (`str`, *optional*, defaults to `"gelu"`):
        Activation function used in the dropout module.
    legacy (`bool`, *optional*, defaults to `True`):
        Whether or not the model should use the legacy `LegacyDebertaOnlyMLMHead`, which does not work properly
        for mask infilling tasks.

    Example:

    ```python
    >>> from transformers import DebertaConfig, DebertaModel

    >>> # Initializing a DeBERTa microsoft/deberta-base style configuration
    >>> configuration = DebertaConfig()

    >>> # Initializing a model (with random weights) from the microsoft/deberta-base style configuration
    >>> model = DebertaModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```ÚdebertaiYÄ  Ú
vocab_sizei   Úhidden_sizeé   Únum_hidden_layersÚnum_attention_headsi   Úintermediate_sizeÚgeluÚ
hidden_actgš™™™™™¹?Úhidden_dropout_probÚattention_probs_dropout_probi   Úmax_position_embeddingsr   Útype_vocab_sizeg{®Gáz”?Úinitializer_rangegH¯¼šò×z>Úlayer_norm_epsFÚrelative_attentionéÿÿÿÿÚmax_relative_positionsNÚpad_token_idÚbos_token_idÚeos_token_idTÚposition_biased_inputÚpos_att_typeg        Úpooler_dropoutÚpooler_hidden_actÚlegacyÚtie_word_embeddingsc                 ó$  •— t          | j        t          ¦  «        r;d„ | j                             ¦   «                              d¦  «        D ¦   «         | _        |                     d| j        ¦  «        | _         t          ¦   «         j	        di |¤Ž d S )Nc                 ó6   — g | ]}|                      ¦   «         ‘ŒS © )Ústrip)Ú.0Úxs     úo/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/deberta/configuration_deberta.pyú
<listcomp>z/DebertaConfig.__post_init__.<locals>.<listcomp>X   s    € Ð YÐ YÐ Y¨q §¢¡¤Ð YÐ YÐ Yó    ú|Úpooler_hidden_sizer'   )
Ú
isinstancer    ÚstrÚlowerÚsplitÚgetr   r/   ÚsuperÚ__post_init__)ÚselfÚkwargsÚ	__class__s     €r+   r6   zDebertaConfig.__post_init__U   sŠ   ø€ å�dÔ'­Ñ-Ô-ð 	ZØ YÐ Y°DÔ4E×4KÒ4KÑ4MÔ4M×4SÒ4SÐTWÑ4XÔ4XÐ YÑ YÔ YˆDÔà"(§*¢*Ð-AÀ4ÔCSÑ"TÔ"TˆÔØ�‰ŒÔÐ'Ð' Ð'Ð'Ð'Ð'Ð'r-   )$Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typer   ÚintÚ__annotations__r   r   r   r   r   r1   r   Úfloatr   r   r   r   r   r   Úboolr   r   r   r   Úlistr   r    r!   r"   r#   r$   r6   Ú__classcell__)r9   s   @r+   r	   r	      sö  ø€ € € € € € ð ð  ðD €Jà€J�ÐÐÑØ€K�ÐÐÑØÐ�sÐÐÑØ!Ð˜Ð!Ð!Ñ!Ø!Ð�sÐ!Ð!Ñ!Ø€J�ÐÐÑØ'*Ð˜ ™Ð*Ð*Ñ*Ø03Ð  %¨#¡+Ð3Ð3Ñ3Ø#&Ð˜SÐ&Ð&Ñ&Ø€O�SÐÐÑØ#Ð�uÐ#Ð#Ñ#Ø €N�EÐ Ð Ñ Ø$Ð˜Ð$Ð$Ñ$Ø"$Ð˜CÐ$Ð$Ñ$Ø €L�#˜‘*Ð Ð Ñ Ø#€L�#˜‘*Ð#Ð#Ñ#Ø+/€L�#˜˜Sœ	‘/ DÑ(Ð/Ð/Ñ/Ø"&Ð˜4Ð&Ð&Ñ&Ø+/€L�#˜˜Sœ	‘/ DÑ(Ð/Ð/Ñ/Ø"%€N�E˜C‘KÐ%Ð%Ñ%Ø#Ð�sÐ#Ð#Ñ#Ø€FˆDÐÐÑØ $Ð˜Ð$Ð$Ñ$ð(ð (ð (ð (ð (ð (ð (ð (ð (r-   r	   N)	r=   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r	   Ú__all__r'   r-   r+   ú<module>rI      sª   ðð "Ð !à .Ð .Ð .Ð .Ð .Ð .à 3Ð 3Ð 3Ð 3Ð 3Ð 3Ø #Ð #Ð #Ð #Ð #Ð #ð €Ð3Ð4Ñ4Ô4ØðC(ð C(ð C(ð C(ð C(Ð$ñ C(ô C(ñ „ñ 5Ô4ðC(ðL Ð
€€€r-   