§
    ‚Štj]U  ã                   ór  — d dl mZ d dlmZ d dlZd dlmZ ddlmZmZ ddl	m
Z
mZ ddlmZ dd	lmZmZmZ dd
lmZ ddlmZmZ ddlmZmZ ddlmZmZ ddlmZmZ ddl m!Z! ddl"m#Z#m$Z$m%Z% ddl&m'Z'm(Z( ddl)m*Z* ddl+m,Z,  G d„ dej-        ¦  «        Z. ed¦  «         G d„ dej-        ¦  «        ¦   «         Z/ G d„ dej-        ¦  «        Z0d„ Z1 ed¦  «        d:d„¦   «         Z2dej3        d e4d!ej3        fd"„Z5	 d;d$ej-        d%ej3        d&ej3        d'ej3        d(ej3        dz  d)e6d*e6d+e!e#         fd,„Z7 ee2¦  «         G d-„ d.ej-        ¦  «        ¦   «         Z8 G d/„ d0e¦  «        Z9e$ G d1„ d2e¦  «        ¦   «         Z:e$ G d3„ d4e:¦  «        ¦   «         Z;e$ G d5„ d6e:e¦  «        ¦   «         Z< G d7„ d8ee:¦  «        Z=g d9¢Z>dS )<é    )ÚCallable)ÚOptionalN)Únné   )ÚACT2CLSÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úuse_kernel_forward_from_hubÚuse_kernel_func_from_hubÚuse_kernelized_func)Úcreate_causal_mask)ÚGenericForTokenClassificationÚGradientCheckpointingLayer)ÚBaseModelOutputWithPastÚCausalLMOutputWithPast)ÚROPE_INIT_FUNCTIONSÚdynamic_rope_update)ÚALL_ATTENTION_FUNCTIONSÚPreTrainedModel)ÚUnpack)ÚTransformersKwargsÚauto_docstringÚcan_return_tuple)Úmaybe_autocastÚmerge_with_config_defaults)Úcapture_outputsé   )ÚApertusConfigc                   ó$   ‡ — e Zd Zˆ fd„Zd„ Zˆ xZS )Ú
ApertusMLPc                 ó¦  •— t          ¦   «                              ¦   «          || _        |j        | _        |j        | _        t          j        | j        | j        d¬¦  «        | _        t          j        | j        | j        d¬¦  «        | _        t          |j
                 | _        |j
        dk    r"t          d         |j        ¬¦  «        | _        d S d S )NF©ÚbiasÚxielu©Údtype)ÚsuperÚ__init__ÚconfigÚhidden_sizeÚintermediate_sizer   ÚLinearÚup_projÚ	down_projr   Ú
hidden_actÚact_fnr   r(   ©Úselfr+   Ú	__class__s     €új/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/apertus/modeling_apertus.pyr*   zApertusMLP.__init__,   s¶   ø€ Ý‰Œ×ÒÑÔÐØˆŒØ!Ô-ˆÔØ!'Ô!9ˆÔÝ”y Ô!1°4Ô3IÐPUÐVÑVÔVˆŒÝœ 4Ô#9¸4Ô;KÐRWÐXÑXÔXˆŒÝ˜VÔ.Ô/ˆŒØÔ Ò'Ð'Ý! 'Ô*°´Ð>Ñ>Ô>ˆDŒKˆKˆKð (Ð'ó    c                 óx   — |                       |                      |                      |¦  «        ¦  «        ¦  «        S ©N)r0   r2   r/   )r4   Úxs     r6   ÚforwardzApertusMLP.forward7   s*   € Ø�~Š~˜dŸkšk¨$¯,ª,°q©/¬/Ñ:Ô:Ñ;Ô;Ð;r7   )Ú__name__Ú
__module__Ú__qualname__r*   r;   Ú__classcell__©r5   s   @r6   r"   r"   +   sG   ø€ € € € € ð	?ð 	?ð 	?ð 	?ð 	?ð<ð <ð <ð <ð <ð <ð <r7   r"   ÚRMSNormc                   óT   ‡ — e Zd Zd	deddfˆ fd„Zdej        dej        fd„Zd„ Zˆ xZ	S )
ÚApertusRMSNormç�íµ ÷Æ°>ÚepsÚreturnNc                 ó¬   •— t          ¦   «                              ¦   «          t          j        t	          j        |¦  «        ¦  «        | _        || _        dS )z=
        ApertusRMSNorm is equivalent to T5LayerNorm
        N)r)   r*   r   Ú	ParameterÚtorchÚonesÚweightÚvariance_epsilon)r4   r,   rE   r5   s      €r6   r*   zApertusRMSNorm.__init__=   sD   ø€ õ 	‰Œ×ÒÑÔÐÝ”l¥5¤:¨kÑ#:Ô#:Ñ;Ô;ˆŒØ #ˆÔÐÐr7   Úhidden_statesc                 ó  — |j         }|                     t          j        ¦  «        }|                     d¦  «                             dd¬¦  «        }|t          j        || j        z   ¦  «        z  }| j        |                     |¦  «        z  S )Né   éÿÿÿÿT)Úkeepdim)	r(   ÚtorI   Úfloat32ÚpowÚmeanÚrsqrtrL   rK   )r4   rM   Úinput_dtypeÚvariances       r6   r;   zApertusRMSNorm.forwardE   s|   € Ø#Ô)ˆØ%×(Ò(­¬Ñ7Ô7ˆØ ×$Ò$ QÑ'Ô'×,Ò,¨R¸Ð,Ñ>Ô>ˆØ%­¬°H¸tÔ?TÑ4TÑ(UÔ(UÑUˆØŒ{˜]×-Ò-¨kÑ:Ô:Ñ:Ð:r7   c                 óH   — t          | j        j        ¦  «        › d| j        › �S )Nz, eps=)ÚtuplerK   ÚshaperL   )r4   s    r6   Ú
extra_reprzApertusRMSNorm.extra_reprL   s&   € Ý˜œÔ)Ñ*Ô*ÐIÐI°$Ô2GÐIÐIÐIr7   )rD   )
r<   r=   r>   Úfloatr*   rI   ÚTensorr;   r\   r?   r@   s   @r6   rC   rC   ;   sŒ   ø€ € € € € ð$ð $¨ð $¸$ð $ð $ð $ð $ð $ð $ð; U¤\ð ;°e´lð ;ð ;ð ;ð ;ðJð Jð Jð Jð Jð Jð Jr7   rC   c                   óÔ   ‡ — e Zd ZU ej        ed<   ddefˆ fd„Ze	 	 	 ddedz  de	d         de
dz  ded	ef         fd
„¦   «         Z ej        ¦   «         ed„ ¦   «         ¦   «         Zˆ xZS )ÚApertusRotaryEmbeddingÚinv_freqNr+   c                 ó²  •— t          ¦   «                              ¦   «          |j        | _        |j        | _        || _        | j        j        d         | _        | j        }| j        dk    rt          | j                 } || j        |¦  «        \  }| _
        |                      d|d¬¦  «         |                      d|                     ¦   «         d¬¦  «         d S )NÚ	rope_typeÚdefaultra   F)Ú
persistentÚoriginal_inv_freq)r)   r*   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenr+   Úrope_parametersrc   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)r4   r+   ÚdeviceÚrope_init_fnra   r5   s        €r6   r*   zApertusRotaryEmbedding.__init__S   sÊ   ø€ Ý‰Œ×ÒÑÔÐØ"(Ô"@ˆÔØ$*Ô$BˆÔ!àˆŒàœÔ4°[ÔAˆŒØ!%Ô!EˆØŒ>˜YÒ&Ð&Ý.¨t¬~Ô>ˆLØ+7¨<¸¼ÀVÑ+LÔ+LÑ(ˆ�$Ô(à×Ò˜Z¨¸eÐÑDÔDÐDØ×ÒÐ0°(·.².Ñ2BÔ2BÈuÐÑUÔUÐUÐUÐUr7   ro   ztorch.deviceÚseq_lenrF   ztorch.Tensorc                 óü   — | j         d         }t          | dd¦  «        p| j        | j        z  }d}d|t	          j        d|dt          j        ¬¦  «                             |t          j        ¬¦  «        |z  z  z  }||fS )	a¨  
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        Ú
rope_thetaÚhead_dimNg      ð?r   rO   r'   )ro   r(   )	rj   Úgetattrr,   Únum_attention_headsrI   ÚarangeÚint64rR   r]   )r+   ro   rq   ÚbaseÚdimÚattention_factorra   s          r6   rk   z6ApertusRotaryEmbedding.compute_default_rope_parametersc   sŒ   € ð& Ô% lÔ3ˆÝ�f˜j¨$Ñ/Ô/Ðc°6Ô3EÈÔIcÑ3cˆàÐð Ø•U”\ ! S¨!µ5´;Ð?Ñ?Ô?×BÒBÈ&ÕX]ÔXcÐBÑdÔdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r7   c                 óN  — | j         d d d …d f                              ¦   «                              |j        d         dd¦  «                             |j        ¦  «        }|d d …d d d …f                              ¦   «         }t          |j        j        t          ¦  «        r|j        j        dk    r|j        j        nd}t          |d¬¦  «        5  |                     ¦   «         |                     ¦   «         z   
                    dd¦  «        }t          j        ||fd¬	¦  «        }|                     ¦   «         | j        z  }|                     ¦   «         | j        z  }	d d d ¦  «         n# 1 swxY w Y   |                     |j        ¬
¦  «        |	                     |j        ¬
¦  «        fS )Nr   rP   r   ÚmpsÚcpuF)Údevice_typeÚenabledrO   ©rz   r'   )ra   r]   Úexpandr[   rR   ro   Ú
isinstanceÚtypeÚstrr   Ú	transposerI   ÚcatÚcosrl   Úsinr(   )
r4   r:   Úposition_idsÚinv_freq_expandedÚposition_ids_expandedr   ÚfreqsÚembrˆ   r‰   s
             r6   r;   zApertusRotaryEmbedding.forward�   s·  € ð !œM¨$°°°°4¨-Ô8×>Ò>Ñ@Ô@×GÒGÈÔHZÐ[\ÔH]Ð_aÐcdÑeÔe×hÒhÐijÔiqÑrÔrÐØ ,¨Q¨Q¨Q°°a°a°a¨ZÔ 8× >Ò >Ñ @Ô @Ðå'1°!´(´-ÅÑ'EÔ'EÐkÈ!Ì(Ì-Ð[`ÒJ`ÐJ`�a”h”m�mÐfkˆÝ¨¸UÐCÑCÔCð 	5ð 	5Ø&×,Ò,Ñ.Ô.Ð1F×1LÒ1LÑ1NÔ1NÑN×YÒYÐZ[Ð]^Ñ_Ô_ˆEÝ”)˜U E˜N°Ð3Ñ3Ô3ˆCØ—'’'‘)”)˜dÔ4Ñ4ˆCØ—'’'‘)”)˜dÔ4Ñ4ˆCð		5ð 	5ð 	5ñ 	5ô 	5ð 	5ð 	5ð 	5ð 	5ð 	5ð 	5øøøð 	5ð 	5ð 	5ð 	5ð �vŠv˜AœGˆvÑ$Ô$ c§f¢f°1´7 fÑ&;Ô&;Ð;Ð;s   ÃBE&Å&E*Å-E*r9   )NNN)r<   r=   r>   rI   r^   Ú__annotations__r    r*   Ústaticmethodr   ÚintrZ   r]   rk   Úno_gradr   r;   r?   r@   s   @r6   r`   r`   P   sù   ø€ € € € € € ØŒlÐÐÑðVð V˜}ð Vð Vð Vð Vð Vð Vð  à'+Ø+/Ø"ð*ð *Ø Ñ$ð*à˜Ô(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ô	%ð	*ð *ð *ñ „\ð*ð: €U„]�_„_Øð<ð <ñ Ôñ „_ð<ð <ð <ð <ð <r7   r`   c                 óœ   — | dd| j         d         dz  …f         }| d| j         d         dz  d…f         }t          j        | |fd¬¦  «        S )z*Rotates half the hidden dims of the input..NrP   rO   r�   )r[   rI   r‡   )r:   Úx1Úx2s      r6   Úrotate_halfr–   ‘   s]   € à	
ˆ3Ð"�!”'˜"”+ Ñ"Ð"Ð"Ô	#€BØ	
ˆ3�”˜”˜qÑ Ð"Ð"Ð"Ô	#€BÝŒ9�r�c˜2�Y BÐ'Ñ'Ô'Ð'r7   Úrotary_pos_embc                 ó¾   — |                      |¦  «        }|                      |¦  «        }| |z  t          | ¦  «        |z  z   }||z  t          |¦  «        |z  z   }||fS )a…  Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    )Ú	unsqueezer–   )ÚqÚkrˆ   r‰   Úunsqueeze_dimÚq_embedÚk_embeds          r6   Úapply_rotary_pos_embrŸ   ˜   sc   € ð& �-Š-˜Ñ
&Ô
&€CØ
�-Š-˜Ñ
&Ô
&€CØ�3‰w�; q™>œ>¨CÑ/Ñ0€GØ�3‰w�; q™>œ>¨CÑ/Ñ0€GØ�GÐÐr7   rM   Ún_reprF   c                 ó¸   — | j         \  }}}}|dk    r| S | dd…dd…ddd…dd…f                              |||||¦  «        } |                      |||z  ||¦  «        S )zÔ
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    r   N)r[   r‚   Úreshape)rM   r    ÚbatchÚnum_key_value_headsÚslenrt   s         r6   Ú	repeat_kvr¦   ²   s„   € ð
 2?Ô1DÑ.€EÐ  hØ�‚z€zØÐØ! ! ! ! Q Q Q¨¨a¨a¨a°°°Ð"2Ô3×:Ò:¸5ÐBUÐW\Ð^bÐdlÑmÔm€MØ× Ò  Ð(;¸eÑ(CÀTÈ8ÑTÔTÐTr7   ç        ÚmoduleÚqueryÚkeyÚvalueÚattention_maskÚscalingÚdropoutÚkwargsc                 ó  — t          || j        ¦  «        }t          || j        ¦  «        }	t          j        ||                     dd¦  «        ¦  «        |z  }
|�|
|z   }
t
          j                             |
dt          j        ¬¦  «         	                    |j
        ¦  «        }
t
          j                             |
|| j        ¬¦  «        }
t          j        |
|	¦  «        }|                     dd¦  «                             ¦   «         }||
fS )NrO   r   rP   )rz   r(   )ÚpÚtrainingr   )r¦   Únum_key_value_groupsrI   Úmatmulr†   r   Ú
functionalÚsoftmaxrS   rR   r(   r®   r²   Ú
contiguous)r¨   r©   rª   r«   r¬   r­   r®   r¯   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r6   Úeager_attention_forwardr¼   ¾   sé   € õ ˜3 Ô ;Ñ<Ô<€JÝ˜U FÔ$?Ñ@Ô@€Lå”<  z×';Ò';¸A¸qÑ'AÔ'AÑBÔBÀWÑL€LØÐ!Ø# nÑ4ˆå”=×(Ò(¨¸2ÅUÄ]Ð(ÑSÔS×VÒVÐW\ÔWbÑcÔc€LÝ”=×(Ò(¨¸È6Ì?Ð(Ñ[Ô[€LÝ”,˜|¨\Ñ:Ô:€KØ×'Ò'¨¨1Ñ-Ô-×8Ò8Ñ:Ô:€Kà˜Ð$Ð$r7   c                   óÌ   ‡ — e Zd ZdZddededz  fˆ fd„Z	 ddej        de	ej        ej        f         dej        dz  d	e
dz  d
ee         de	ej        ej        f         fd„Zˆ xZS )ÚApertusAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNr+   Ú	layer_idxc                 ó*  •— t          ¦   «                              ¦   «          || _        || _        t	          |d|j        |j        z  ¦  «        | _        |j        |j        z  | _	        | j        dz  | _
        |j        | _        d| _        t          j        |j        |j        | j        z  |j        ¬¦  «        | _        t          j        |j        |j        | j        z  |j        ¬¦  «        | _        t          j        |j        |j        | j        z  |j        ¬¦  «        | _        t          j        |j        | j        z  |j        |j        ¬¦  «        | _        t)          | j        |j        ¦  «        | _        t)          | j        |j        ¦  «        | _        d S )Nrt   g      à¿Tr$   )r)   r*   r+   r¿   ru   r,   rv   rt   r¤   r³   r­   Úattention_dropoutÚ	is_causalr   r.   Úattention_biasÚq_projÚk_projÚv_projÚo_projrC   Úrms_norm_epsÚq_normÚk_norm©r4   r+   r¿   r5   s      €r6   r*   zApertusAttention.__init__Û   sp  ø€ Ý‰Œ×ÒÑÔÐØˆŒØ"ˆŒÝ ¨
°FÔ4FÈ&ÔJdÑ4dÑeÔeˆŒØ$*Ô$>À&ÔB\Ñ$\ˆÔ!Ø”} dÑ*ˆŒØ!'Ô!9ˆÔØˆŒå”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ&¨¬Ñ6¸Ô8JÐQWÔQfð
ñ 
ô 
ˆŒõ % T¤]°FÔ4GÑHÔHˆŒÝ$ T¤]°FÔ4GÑHÔHˆŒˆˆr7   rM   Úposition_embeddingsr¬   Úpast_key_valuesr¯   rF   c                 óv  — |j         d d…         }g |¢d‘| j        ‘R }|                      |¦  «                             |¦  «                             dd¦  «        }|                      |¦  «                             |¦  «                             dd¦  «        }	|                      |¦  «                             |¦  «                             dd¦  «        }
|                      |¦  «        }|                      |	¦  «        }	|\  }}t          ||	||¦  «        \  }}	|�| 
                    |	|
| j        ¦  «        \  }	}
t          j        | j        j        t           ¦  «        } || ||	|
|f| j        sdn| j        | j        dœ|¤Ž\  }} |j        g |¢d‘R Ž                      ¦   «         }|                      |¦  «        }||fS )NrP   r   rO   r§   )r®   r­   )r[   rt   rÄ   Úviewr†   rÅ   rÆ   rÉ   rÊ   rŸ   Úupdater¿   r   Úget_interfacer+   Ú_attn_implementationr¼   r²   rÁ   r­   r¢   r·   rÇ   )r4   rM   rÌ   r¬   rÍ   r¯   Úinput_shapeÚhidden_shapeÚquery_statesr¸   r¹   rˆ   r‰   Úattention_interfacer»   rº   s                   r6   r;   zApertusAttention.forwardô   sâ  € ð $Ô)¨#¨2¨#Ô.ˆØ8˜Ð8 bÐ8¨$¬-Ð8Ð8ˆà—{’{ =Ñ1Ô1×6Ò6°|ÑDÔD×NÒNÈqÐRSÑTÔTˆØ—[’[ Ñ/Ô/×4Ò4°\ÑBÔB×LÒLÈQÐPQÑRÔRˆ
Ø—{’{ =Ñ1Ô1×6Ò6°|ÑDÔD×NÒNÈqÐRSÑTÔTˆØ—{’{ <Ñ0Ô0ˆØ—[’[ Ñ,Ô,ˆ
à&‰ˆˆSÝ#7¸ÀjÐRUÐWZÑ#[Ô#[Ñ ˆ�jàÐ&Ø'6×'=Ò'=¸jÈ,ÐX\ÔXfÑ'gÔ'gÑ$ˆJ˜å(?Ô(MØŒKÔ,Õ.Eñ)
ô )
Ðð %8Ð$7ØØØØØð	%
ð  $œ}ÐH�C�C°$Ô2HØ”Lð	%
ð 	%
ð ð	%
ð 	%
Ñ!ˆ�\ð *�kÔ)Ð;¨;Ð;¸Ð;Ð;Ð;×FÒFÑHÔHˆØ—k’k +Ñ.Ô.ˆØ˜LÐ(Ð(r7   r9   )r<   r=   r>   Ú__doc__r    r‘   r*   rI   r^   rZ   r	   r   r   r;   r?   r@   s   @r6   r¾   r¾   ×   sê   ø€ € € € € àGÐGðIð I˜}ð I¸¸t¹ð Ið Ið Ið Ið Ið Ið< )-ð()ð ()à”|ð()ð # 5¤<°´Ð#=Ô>ð()ð œ tÑ+ð	()ð
  ™ð()ð Ð+Ô,ð()ð 
ˆuŒ|˜Uœ\Ð)Ô	*ð()ð ()ð ()ð ()ð ()ð ()ð ()ð ()r7   r¾   c                   óÒ   ‡ — e Zd Zdedefˆ fd„Z	 	 	 	 	 ddej        dej        dz  dej        dz  d	e	dz  d
e
dz  deej        ej        f         dz  dee         dej        fd„Zˆ xZS )ÚApertusDecoderLayerr+   r¿   c                 ó4  •— t          ¦   «                              ¦   «          |j        | _        t          ||¬¦  «        | _        t          |¦  «        | _        t          |j        |j        ¬¦  «        | _	        t          |j        |j        ¬¦  «        | _
        d S )N)r+   r¿   ©rE   )r)   r*   r,   r¾   Ú	self_attnr"   ÚmlprC   rÈ   Úattention_layernormÚfeedforward_layernormrË   s      €r6   r*   zApertusDecoderLayer.__init__   sƒ   ø€ Ý‰Œ×ÒÑÔÐØ!Ô-ˆÔå)°À9ÐMÑMÔMˆŒå˜fÑ%Ô%ˆŒÝ#1°&Ô2DÈ&ÔJ]Ð#^Ñ#^Ô#^ˆÔ Ý%3°FÔ4FÈFÔL_Ð%`Ñ%`Ô%`ˆÔ"Ð"Ð"r7   NFrM   r¬   rŠ   rÍ   Ú	use_cacherÌ   r¯   rF   c           
      óÎ   — |}|                       |¦  «        } | j        d||||||dœ|¤Ž\  }}	||z   }|}|                      |¦  «        }|                      |¦  «        }||z   }|S )N)rM   r¬   rŠ   rÍ   rà   rÌ   © )rÞ   rÜ   rß   rÝ   )
r4   rM   r¬   rŠ   rÍ   rà   rÌ   r¯   ÚresidualÚ_s
             r6   r;   zApertusDecoderLayer.forward*  s¡   € ð !ˆØ×0Ò0°Ñ?Ô?ˆØ)˜4œ>ð 
Ø'Ø)Ø%Ø+ØØ 3ð
ð 
ð ð
ð 
Ñˆ�qð ! =Ñ0ˆð !ˆØ×2Ò2°=ÑAÔAˆØŸš Ñ/Ô/ˆØ  =Ñ0ˆØÐr7   )NNNFN)r<   r=   r>   r    r‘   r*   rI   r^   Ú
LongTensorr	   ÚboolrZ   r   r   r;   r?   r@   s   @r6   rÙ   rÙ     sÿ   ø€ € € € € ða˜}ð a¸ð að að að að að að /3Ø04Ø(,Ø!&ØHLðð à”|ðð œ tÑ+ðð Ô&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5¤<°´Ð#=Ô>ÀÑEðð Ð+Ô,ðð 
Œðð ð ð ð ð ð ð r7   rÙ   c                   óL   — e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedœZdS )ÚApertusPreTrainedModelr+   ÚmodelTrÙ   rÍ   )rM   Ú
attentionsN)r<   r=   r>   r    r�   Úbase_model_prefixÚsupports_gradient_checkpointingÚ_no_split_modulesÚ_skip_keys_device_placementÚ_supports_flash_attnÚ_supports_sdpaÚ_supports_flex_attnÚ_can_compile_fullgraphÚ_supports_attention_backendrÙ   r¾   Ú_can_record_outputsrâ   r7   r6   rè   rè   I  sl   € € € € € € àÐÐÑØÐØ&*Ð#Ø.Ð/ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà,Ø&ðð ÐÐÐr7   rè   c                   óâ   ‡ — e Zd Zdefˆ fd„Zeee	 	 	 	 	 	 ddej	        dz  dej
        dz  dej	        dz  dedz  dej        dz  d	edz  d
ee         defd„¦   «         ¦   «         ¦   «         Zˆ xZS )ÚApertusModelr+   c                 óÞ  •‡— t          ¦   «                              ‰¦  «         ‰j        | _        ‰j        | _        t          j        ‰j        ‰j        | j        ¦  «        | _        t          j	        ˆfd„t          ‰j        ¦  «        D ¦   «         ¦  «        | _        t          ‰j        ‰j        ¬¦  «        | _        t!          ‰¬¦  «        | _        d| _        |                      ¦   «          d S )Nc                 ó0   •— g | ]}t          ‰|¦  «        ‘ŒS râ   )rÙ   )Ú.0r¿   r+   s     €r6   ú
<listcomp>z)ApertusModel.__init__.<locals>.<listcomp>e  s$   ø€ ÐeÐeÐe¸	Õ  ¨Ñ3Ô3ÐeÐeÐer7   rÛ   ©r+   F)r)   r*   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	Embeddingr,   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersÚlayersrC   rÈ   Únormr`   Ú
rotary_embÚgradient_checkpointingÚ	post_initr3   s    `€r6   r*   zApertusModel.__init__^  sÔ   øø€ Ý‰Œ×Ò˜Ñ Ô Ð Ø!Ô.ˆÔØ Ô+ˆŒåœL¨Ô):¸FÔ<NÐPTÔP`ÑaÔaˆÔÝ”mØeÐeÐeÐeÅUÈ6ÔKcÑEdÔEdÐeÑeÔeñ
ô 
ˆŒõ # 6Ô#5¸6Ô;NÐOÑOÔOˆŒ	Ý0¸Ð?Ñ?Ô?ˆŒØ&+ˆÔ#ð 	�ŠÑÔÐÐÐr7   NÚ	input_idsr¬   rŠ   rÍ   Úinputs_embedsrà   r¯   rF   c           
      óH  — |d u |d uz  rt          d¦  «        ‚|€|                      |¦  «        }|r|€t          | j        ¬¦  «        }|€V|�|                     ¦   «         nd}t          j        |j        d         |j        ¬¦  «        |z   }| 	                    d¦  «        }t          | j        ||||¬¦  «        }	|}
|                      |
|¬¦  «        }| j        d | j        j        …         D ]} ||
f|	||||dœ|¤Ž}
Œ|                      |
¦  «        }
t          |
|¬	¦  «        S )
Nz:You must specify exactly one of input_ids or inputs_embedsrû   r   r   )ro   )r+   r
  r¬   rÍ   rŠ   )rŠ   )r¬   rÌ   rŠ   rÍ   rà   )Úlast_hidden_staterÍ   )Ú
ValueErrorr   r
   r+   Úget_seq_lengthrI   rw   r[   ro   r™   r   r  r  r  r  r   )r4   r	  r¬   rŠ   rÍ   r
  rà   r¯   Úpast_seen_tokensÚcausal_maskrM   rÌ   Údecoder_layers                r6   r;   zApertusModel.forwardn  sŠ  € ð ˜Ð -°tÐ";Ñ<ð 	[ÝÐYÑZÔZÐZàÐ Ø*.×*;Ò*;¸IÑ*FÔ*FˆMàð 	?˜Ð0Ý*°$´+Ð>Ñ>Ô>ˆOàÐØCRÐC^˜×=Ò=Ñ?Ô?Ð?ÐdeÐÝ œ<¨Ô(;¸AÔ(>À}ÔG[Ð\Ñ\Ô\Ð_oÑoˆLØ'×1Ò1°!Ñ4Ô4ˆLå(Ø”;Ø'Ø)Ø+Ø%ð
ñ 
ô 
ˆð &ˆØ"Ÿošo¨mÈ,˜oÑWÔWÐà!œ[Ð)H¨4¬;Ô+HÐ)HÔIð 		ð 		ˆMØ)˜MØðà*Ø$7Ø)Ø /Ø#ðð ð ðð ˆMˆMð Ÿ	š	 -Ñ0Ô0ˆÝ&Ø+Ø+ð
ñ 
ô 
ð 	
r7   )NNNNNN)r<   r=   r>   r    r*   r   r   r   rI   rå   r^   r	   ÚFloatTensorræ   r   r   r   r;   r?   r@   s   @r6   rö   rö   \  s  ø€ € € € € ð˜}ð ð ð ð ð ð ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ð2
ð 2
àÔ# dÑ*ð2
ð œ tÑ+ð2
ð Ô&¨Ñ-ð	2
ð
  ™ð2
ð Ô(¨4Ñ/ð2
ð ˜$‘;ð2
ð Ð+Ô,ð2
ð 
!ð2
ð 2
ð 2
ñ „^ñ „_ñ  Ôð2
ð 2
ð 2
ð 2
ð 2
r7   rö   c                   ó  ‡ — e Zd ZddiZddiZddgdgfiZˆ fd„Zee	 	 	 	 	 	 	 	 dd
e	j
        dz  de	j        dz  de	j
        dz  dedz  de	j        dz  de	j
        dz  dedz  dee	j        z  dee         defd„¦   «         ¦   «         Zˆ xZS )ÚApertusForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputrM   Úlogitsc                 óþ   •— t          ¦   «                              |¦  «         t          |¦  «        | _        |j        | _        t          j        |j        |j        d¬¦  «        | _        |  	                    ¦   «          d S )NFr$   )
r)   r*   rö   ré   rþ   r   r.   r,   r  r  r3   s     €r6   r*   zApertusForCausalLM.__init__¬  sj   ø€ Ý‰Œ×Ò˜Ñ Ô Ð Ý! &Ñ)Ô)ˆŒ
Ø Ô+ˆŒÝ”y Ô!3°VÔ5FÈUÐSÑSÔSˆŒð 	�ŠÑÔÐÐÐr7   Nr   r	  r¬   rŠ   rÍ   r
  Úlabelsrà   Úlogits_to_keepr¯   rF   c	           
      óP  —  | j         d||||||dœ|	¤Ž}
|
j        }t          |t          ¦  «        rt	          | d¦  «        n|}|                      |dd…|dd…f         ¦  «        }d}|� | j        d||| j        j        dœ|	¤Ž}t          |||
j
        |
j        |
j        ¬¦  «        S )aŠ  
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Example:

        ```python
        >>> from transformers import AutoTokenizer, ApertusForCausalLM

        >>> model = ApertusForCausalLM.from_pretrained("swiss-ai/Apertus-8B-Instruct-2509")
        >>> tokenizer = AutoTokenizer.from_pretrained("swiss-ai/Apertus-8B-Instruct-2509")

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```)r	  r¬   rŠ   rÍ   r
  rà   N)r  r  rþ   )Úlossr  rÍ   rM   rê   râ   )ré   r  rƒ   r‘   Úslicer  Úloss_functionr+   rþ   r   rÍ   rM   rê   )r4   r	  r¬   rŠ   rÍ   r
  r  rà   r  r¯   ÚoutputsrM   Úslice_indicesr  r  s                  r6   r;   zApertusForCausalLM.forwardµ  sõ   € ðH ,6¨4¬:ð ,
ØØ)Ø%Ø+Ø'Øð,
ð ,
ð ð,
ð ,
ˆð  Ô1ˆå8BÀ>ÕSVÑ8WÔ8WÐk�˜~˜o¨tÑ4Ô4Ð4Ð]kˆØ—’˜m¨A¨A¨A¨}¸a¸a¸aÐ,?Ô@ÑAÔAˆàˆØÐØ%�4Ô%Ðp¨V¸FÈtÌ{ÔOeÐpÐpÐioÐpÐpˆDå%ØØØ#Ô3Ø!Ô/ØÔ)ð
ñ 
ô 
ð 	
r7   )NNNNNNNr   )r<   r=   r>   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr*   r   r   rI   rå   r^   r	   r  ræ   r‘   r   r   r   r;   r?   r@   s   @r6   r  r  ¦  sK  ø€ € € € € à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hðð ð ð ð ð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø-.ð;
ð ;
àÔ# dÑ*ð;
ð œ tÑ+ð;
ð Ô&¨Ñ-ð	;
ð
  ™ð;
ð Ô(¨4Ñ/ð;
ð Ô  4Ñ'ð;
ð ˜$‘;ð;
ð ˜eœlÑ*ð;
ð Ð+Ô,ð;
ð 
 ð;
ð ;
ð ;
ñ „^ñ Ôð;
ð ;
ð ;
ð ;
ð ;
r7   r  c                   ó   — e Zd ZdS )ÚApertusForTokenClassificationN)r<   r=   r>   râ   r7   r6   r%  r%  õ  s   € € € € € Ø€Dr7   r%  )rö   r  r%  rè   )r   )r§   )?Úcollections.abcr   Útypingr   rI   r   Úactivationsr   r   Úcache_utilsr	   r
   Ú
generationr   Úintegrationsr   r   r   Úmasking_utilsr   Úmodeling_layersr   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   r   Úutils.output_capturingr   Úconfiguration_apertusr    ÚModuler"   rC   r`   r–   rŸ   r^   r‘   r¦   r]   r¼   r¾   rÙ   rè   rö   r  r%  Ú__all__râ   r7   r6   ú<module>r8     sŠ  ðð* %Ð $Ð $Ð $Ð $Ð $Ø Ð Ð Ð Ð Ð à €€€Ø Ð Ð Ð Ð Ð à *Ð *Ð *Ð *Ð *Ð *Ð *Ð *Ø .Ð .Ð .Ð .Ð .Ð .Ð .Ð .Ø )Ð )Ð )Ð )Ð )Ð )Ø fÐ fÐ fÐ fÐ fÐ fÐ fÐ fÐ fÐ fØ /Ð /Ð /Ð /Ð /Ð /Ø XÐ XÐ XÐ XÐ XÐ XÐ XÐ XØ OÐ OÐ OÐ OÐ OÐ OÐ OÐ OØ KÐ KÐ KÐ KÐ KÐ KÐ KÐ KØ FÐ FÐ FÐ FÐ FÐ FÐ FÐ FØ &Ð &Ð &Ð &Ð &Ð &Ø IÐ IÐ IÐ IÐ IÐ IÐ IÐ IÐ IÐ IØ GÐ GÐ GÐ GÐ GÐ GÐ GÐ GØ 5Ð 5Ð 5Ð 5Ð 5Ð 5Ø 0Ð 0Ð 0Ð 0Ð 0Ð 0ð<ð <ð <ð <ð <�”ñ <ô <ð <ð  Ð˜YÑ'Ô'ðJð Jð Jð Jð J�R”Yñ Jô Jñ (Ô'ðJð(><ð ><ð ><ð ><ð ><˜RœYñ ><ô ><ð ><ðB(ð (ð (ð ÐÐ*Ñ+Ô+ðð ð ñ ,Ô+ðð2	U˜Uœ\ð 	U°#ð 	U¸%¼,ð 	Uð 	Uð 	Uð 	Uð& ð%ð %ØŒIð%àŒ<ð%ð 
Œð%ð Œ<ð	%ð
 ”L 4Ñ'ð%ð ð%ð ð%ð Ð'Ô(ð%ð %ð %ð %ð2 ÐÐ)Ñ*Ô*ðD)ð D)ð D)ð D)ð D)�r”yñ D)ô D)ñ +Ô*ðD)ðN'ð 'ð 'ð 'ð 'Ð4ñ 'ô 'ð 'ðT ðð ð ð ð ˜_ñ ô ñ „ðð$ ðF
ð F
ð F
ð F
ð F
Ð)ñ F
ô F
ñ „ðF
ðR ðK
ð K
ð K
ð K
ð K
Ð/°ñ K
ô K
ñ „ðK
ð\	ð 	ð 	ð 	ð 	Ð$AÐCYñ 	ô 	ð 	ð lÐ
kÐ
k€€€r7   