Ë
    ÛÓ^jL_  ã                   óö  — d dl mZ d dlmZ d dlZd dlmZ ddlmZ	 ddl
mZ ddl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 ddlmZ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/m0Z0 ddl1m2Z2  G d„ dejf                  «      Z4 G d„ dejf                  «      Z5 G d„ dejf                  «      Z6d„ Z7 ed«      d=d„«       Z8dejr                  d e:d!ejr                  fd"„Z;	 	 	 d>d#ejf                  d$ejr                  d%ejr                  d&ejr                  d'ejr                  dz  d(e<e:z  d)e<dz  d*e<dz  d!e=ejr                  ejr                  f   fd+„Z> ee8«       G d,„ d-ejf                  «      «       Z? G d.„ d/e«      Z@ G d0„ d1ej‚                  «      ZBe* G d2„ d3e%«      «       ZCe* G d4„ d5eC«      «       ZDe* G d6„ d7eCe«      «       ZE G d8„ d9eeC«      ZF G d:„ d;eeC«      ZGg d<¢ZHy)?é    )ÚCallable)ÚOptionalNé   )Úinitialization)ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úuse_kernel_func_from_hubÚuse_kernelized_func)Úcreate_causal_maskÚ!create_sliding_window_causal_mask)ÚFlashAttentionKwargs)Ú GenericForSequenceClassificationÚGenericForTokenClassificationÚGradientCheckpointingLayer)ÚBaseModelOutputWithPastÚCausalLMOutputWithPast)ÚROPE_INIT_FUNCTIONSÚdynamic_rope_update)ÚALL_ATTENTION_FUNCTIONSÚPreTrainedModel)ÚUnpack)ÚTransformersKwargsÚauto_docstringÚcan_return_tuple)Úmaybe_autocastÚmerge_with_config_defaults)Úcapture_outputsé   )ÚGemma2Configc                   ó<   ‡ — e Zd Zddedefˆ fd„Zd„ Zd„ Zd„ Zˆ xZ	S )ÚGemma2RMSNormÚdimÚepsc                 óŠ   •— t         ‰| �  «        || _        t        j                  t        j                  |«      «      | _        y ©N)ÚsuperÚ__init__r%   ÚnnÚ	ParameterÚtorchÚzerosÚweight)Úselfr$   r%   Ú	__class__s      €úu/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/gemma2/modeling_gemma2.pyr)   zGemma2RMSNorm.__init__2   s.   ø€ Ü‰ÑÔØˆŒÜ—l‘l¤5§;¡;¨sÓ#3Ó4ˆ�ó    c                 óŒ   — |t        j                  |j                  d«      j                  dd¬«      | j                  z   «      z  S )Né   éÿÿÿÿT)Úkeepdim)r,   ÚrsqrtÚpowÚmeanr%   )r/   Úxs     r1   Ú_normzGemma2RMSNorm._norm7   s4   € Ø”5—;‘;˜qŸu™u Q›xŸ}™}¨R¸˜}Ó>ÀÇÁÑIÓJÑJÐJr2   c                 ó¢   — | j                  |j                  «       «      }|d| j                  j                  «       z   z  }|j                  |«      S )Nç      ð?)r;   Úfloatr.   Útype_as)r/   r:   Úoutputs      r1   ÚforwardzGemma2RMSNorm.forward:   sC   € Ø—‘˜AŸG™G›IÓ&ˆð ˜3 §¡×!2Ñ!2Ó!4Ñ4Ñ5ˆØ�~‰~˜aÓ Ð r2   c                 ó^   — t        | j                  j                  «      › d| j                  › �S )Nz, eps=)Útupler.   Úshaper%   )r/   s    r1   Ú
extra_reprzGemma2RMSNorm.extra_reprA   s'   € Ü˜Ÿ™×)Ñ)Ó*Ð+¨6°$·(±(°Ð<Ð<r2   )g�íµ ÷Æ°>)
Ú__name__Ú
__module__Ú__qualname__Úintr>   r)   r;   rA   rE   Ú__classcell__©r0   s   @r1   r#   r#   1   s&   ø„ ñ5˜Cð 5 eõ 5ò
Kò!ö=r2   r#   c                   ó$   ‡ — e Zd Zˆ fd„Zd„ Zˆ xZS )Ú	Gemma2MLPc                 óÊ  •— t         ‰| �  «        || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  d¬«      | _        t        j                  | j                  | j                  d¬«      | _        t        j                  | j                  | j                  d¬«      | _	        t        |j                     | _        y ©NF©Úbias)r(   r)   ÚconfigÚhidden_sizeÚintermediate_sizer*   ÚLinearÚ	gate_projÚup_projÚ	down_projr   Úhidden_activationÚact_fn©r/   rR   r0   s     €r1   r)   zGemma2MLP.__init__F   s¦   ø€ Ü‰ÑÔØˆŒØ!×-Ñ-ˆÔØ!'×!9Ñ!9ˆÔÜŸ™ 4×#3Ñ#3°T×5KÑ5KÐRWÔXˆŒÜ—y‘y ×!1Ñ!1°4×3IÑ3IÐPUÔVˆŒÜŸ™ 4×#9Ñ#9¸4×;KÑ;KÐRWÔXˆŒÜ˜V×5Ñ5Ñ6ˆ�r2   c                 óˆ   — | j                  | j                  | j                  |«      «      | j                  |«      z  «      }|S r'   )rX   rZ   rV   rW   )r/   r:   rX   s      r1   rA   zGemma2MLP.forwardP   s6   € Ø—N‘N 4§;¡;¨t¯~©~¸aÓ/@Ó#AÀDÇLÁLÐQRÃOÑ#SÓTˆ	ØÐr2   )rF   rG   rH   r)   rA   rJ   rK   s   @r1   rM   rM   E   s   ø„ ô7ör2   rM   c                   óÄ   ‡ — 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Gemma2RotaryEmbeddingÚinv_freqNrR   c                 ó®  •— t         ‰| �  «        |j                  | _        |j                  | _        || _        | j
                  j                  d   | _        | j                  }| j                  dk7  rt        | j                     } || j
                  |«      \  }| _
        | j                  d|d¬«       | j                  d|j                  «       d¬«       y )NÚ	rope_typeÚdefaultr_   F©Ú
persistentÚoriginal_inv_freq)r(   r)   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenrR   Úrope_parametersra   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)r/   rR   ÚdeviceÚrope_init_fnr_   r0   s        €r1   r)   zGemma2RotaryEmbedding.__init__X   s¶   ø€ Ü‰ÑÔØ"(×"@Ñ"@ˆÔØ$*×$BÑ$BˆÔ!àˆŒàŸ™×4Ñ4°[ÑAˆŒØ!%×!EÑ!EˆØ�>‰>˜YÒ&Ü.¨t¯~©~Ñ>ˆLÙ+7¸¿¹ÀVÓ+LÑ(ˆ�$Ô(à×Ñ˜Z¨¸eÐÔDØ×ÑÐ0°(·.±.Ó2BÈuÐÕUr2   rn   ztorch.deviceÚseq_lenÚreturnztorch.Tensorc                 ó  — | j                   d   }t        | dd«      xs | j                  | j                  z  }d}d|t	        j
                  d|dt        j                  ¬«      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_dimNr=   r   r4   ©Údtype)rn   rv   )	ri   ÚgetattrrS   Únum_attention_headsr,   ÚarangeÚint64Útor>   )rR   rn   rp   Úbaser$   Úattention_factorr_   s          r1   rj   z5Gemma2RotaryEmbedding.compute_default_rope_parametersh   s�   € ð& ×%Ñ% lÑ3ˆÜ�f˜j¨$Ó/Òc°6×3EÑ3EÈ×IcÑIcÑ3cˆàÐð Ø”U—\‘\ ! S¨!´5·;±;Ô?×BÑBÈ&ÔX]×XcÑXcÐBÓdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r2   c                 óN  — | j                   d d d …d f   j                  «       j                  |j                  d   dd«      j	                  |j
                  «      }|d d …d d d …f   j                  «       }t        |j
                  j                  t        «      r/|j
                  j                  dk7  r|j
                  j                  nd}t        |d¬«      5  |j                  «       |j                  «       z  j                  dd«      }t        j                  ||fd¬	«      }|j                  «       | j                  z  }|j                  «       | j                  z  }	d d d «       j	                  |j                   ¬
«      	j	                  |j                   ¬
«      fS # 1 sw Y   ŒAxY w)Nr   r5   r    ÚmpsÚcpuF)Údevice_typeÚenabledr4   ©r$   ru   )r_   r>   ÚexpandrD   r{   rn   Ú
isinstanceÚtypeÚstrr   Ú	transposer,   ÚcatÚcosrk   Úsinrv   )
r/   r:   Úposition_idsÚinv_freq_expandedÚposition_ids_expandedr�   ÚfreqsÚembrŠ   r‹   s
             r1   rA   zGemma2RotaryEmbedding.forward†   sR  € ð !ŸM™M¨$²°4¨-Ñ8×>Ñ>Ó@×GÑGÈ×HZÑHZÐ[\ÑH]Ð_aÐcdÓe×hÑhÐij×iqÑiqÓrÐØ ,ªQ°²a¨ZÑ 8× >Ñ >Ó @Ðä'1°!·(±(·-±-ÄÔ'EÈ!Ï(É(Ï-É-Ð[`ÒJ`�a—h‘h—m’mÐfkˆÜ¨¸UÔCñ 	5Ø&×,Ñ,Ó.Ð1F×1LÑ1LÓ1NÑN×YÑYÐZ[Ð]^Ó_ˆEÜ—)‘)˜U E˜N°Ô3ˆCØ—'‘'“)˜d×4Ñ4Ñ4ˆCØ—'‘'“)˜d×4Ñ4Ñ4ˆC÷		5ð �v‰v˜AŸG™GˆvÓ$ c§f¡f°1·7±7 fÓ&;Ð;Ð;÷	5ð 	5ús   ÃBFÆF$r'   ©NNN)rF   rG   rH   r,   ÚTensorÚ__annotations__r!   r)   Ústaticmethodr   rI   rC   r>   rj   Úno_gradr   rA   rJ   rK   s   @r1   r^   r^   U   s˜   ø… Ø�l‰lÓñV˜|õ Vð  à&*Ø+/Ø"ñ*Ø˜tÑ#ð*à˜Ñ(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ñ	%ò	*ó ð*ð: €U‡]�]ƒ_Øñ<ó ó ô<r2   r^   c                 óš   — | 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..Nr5   r4   rƒ   )rD   r,   r‰   )r:   Úx1Úx2s      r1   Úrotate_halfr™   –   sZ   € à	
ˆ3Ð"�!—'‘'˜"‘+ Ñ"Ð"Ð"Ñ	#€BØ	
ˆ3�—‘˜‘˜qÑ Ñ"Ð"Ñ	#€BÜ�9‰9�r�c˜2�Y BÔ'Ð'r2   Úrotary_pos_embc                 óž   — |j                  |«      }|j                  |«      }| |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krŠ   r‹   Úunsqueeze_dimÚq_embedÚk_embeds          r1   Úapply_rotary_pos_embr¢   �   sY   € ð& �-‰-˜Ó
&€CØ
�-‰-˜Ó
&€CØ�3‰wœ; q›>¨CÑ/Ñ0€GØ�3‰wœ; q›>¨CÑ/Ñ0€GØ�GÐÐr2   Úhidden_statesÚn_reprq   c                 óª   — | j                   \  }}}}|dk(  r| S | dd…dd…ddd…dd…f   j                  |||||«      } | j                  |||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)rD   r„   Úreshape)r£   r¤   ÚbatchÚnum_key_value_headsÚslenrt   s         r1   Ú	repeat_kvrª   ·   so   € ð
 2?×1DÑ1DÑ.€EÐ  hØ�‚zØÐØ!¢!¢Q¨ªa²Ð"2Ñ3×:Ñ:¸5ÐBUÐW\Ð^bÐdlÓm€MØ× Ñ  Ð(;¸eÑ(CÀTÈ8ÓTÐTr2   ÚmoduleÚqueryÚkeyÚvalueÚattention_maskÚdropoutÚscalingÚsoftcapc                 ó|  — |€| j                   dz  }t        || j                  «      }	t        || j                  «      }
t        j                  ||	j                  dd«      «      |z  }|�||z  }t        j                  |«      }||z  }|�||z   }t        j                  j                  |dt        j                  ¬«      j                  |j                  «      }t        j                  j                  ||| j                  ¬«      }t        j                  ||
«      }|j                  dd«      j                  «       }||fS )Nç      à¿r4   r   r5   )r$   rv   )ÚpÚtrainingr    )rt   rª   Únum_key_value_groupsr,   Úmatmulrˆ   Útanhr*   Ú
functionalÚsoftmaxÚfloat32r{   rv   r°   r¶   Ú
contiguous)r«   r¬   r­   r®   r¯   r°   r±   r²   ÚkwargsÚ
key_statesÚvalue_statesÚattn_weightsÚattn_outputs                r1   Úeager_attention_forwardrÃ   Ã   s  € ð €Ø—/‘/ 4Ñ'ˆä˜3 × ;Ñ ;Ó<€JÜ˜U F×$?Ñ$?Ó@€Lä—<‘<  z×';Ñ';¸A¸qÓ'AÓBÀWÑL€LàÐØ# gÑ-ˆÜ—z‘z ,Ó/ˆØ# gÑ-ˆØÐ!Ø# nÑ4ˆô —=‘=×(Ñ(¨¸2ÄUÇ]Á]Ð(ÓS×VÑVÐW\×WbÑWbÓc€LÜ—=‘=×(Ñ(¨¸È6Ï?É?Ð(Ó[€LÜ—,‘,˜|¨\Ó:€KØ×'Ñ'¨¨1Ó-×8Ñ8Ó:€KØ˜Ð$Ð$r2   c                   ó   ‡ — e Zd ZdZdedefˆ fd„Z	 	 	 ddej                  de	ej                  ej                  f   dz  dej                  dz  d	e
dz  d
ee   de	ej                  ej                  dz  e	ej                     dz  f   fd„Zˆ xZS )ÚGemma2Attentionz=Multi-headed attention from 'Attention Is All You Need' paperrR   Ú	layer_idxc                 óZ  •— t         ‰| �  «        t        |d«      r|j                  |   nd | _        || _        || _        t        |d|j                  |j                  z  «      | _
        |j                  |j                  z  | _        |j                  dz  | _        | j
                  j                  | _        t        |d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&                  ¬«      | _        | j
                  j0                  | _        | j                  dk(  r|j2                  | _        y d | _        y )NÚlayer_typesrt   r´   Úuse_bidirectional_attentionFrP   Úsliding_attention)r(   r)   ÚhasattrrÈ   Ú
layer_typerR   rÆ   rw   rS   rx   rt   r¨   r·   Úquery_pre_attn_scalarr±   Úattention_dropoutÚ	is_causalr*   rU   Úattention_biasÚq_projÚk_projÚv_projÚo_projÚattn_logit_softcappingÚsliding_window©r/   rR   rÆ   r0   s      €r1   r)   zGemma2Attention.__init__é   s´  ø€ Ü‰ÑÔÜ;BÀ6È=Ô;Y˜&×,Ñ,¨YÒ7Ð_cˆŒØˆŒØ"ˆŒÜ ¨
°F×4FÑ4FÈ&×JdÑJdÑ4dÓeˆŒØ$*×$>Ñ$>À&×B\ÑB\Ñ$\ˆÔ!Ø×3Ñ3°TÑ9ˆŒØ!%§¡×!>Ñ!>ˆÔÜ$ VÐ-JÈEÓRÐRˆŒä—i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×&Ñ&¨¯©Ñ6¸×8JÑ8JÐQW×QfÑQfô
ˆŒð '+§k¡k×&HÑ&HˆÔ#Ø7;·±ÐJ]Ò7]˜f×3Ñ3ˆÕÐcgˆÕr2   Nr£   Úposition_embeddingsr¯   Úpast_key_valuesr¾   rq   c                 ó6  — |j                   d d }g |¢d‘| j                  ‘­}| j                  |«      j                  |«      j	                  dd«      }| j                  |«      j                  |«      j	                  dd«      }	| j                  |«      j                  |«      j	                  dd«      }
|\  }}t        ||	||«      \  }}	|� |j                  |	|
| j                  «      \  }	}
t        j                  | j                  j                  t        «      } || ||	|
|f| j                  r| j                   nd| j"                  | j$                  | j&                  dœ|¤Ž\  }} |j(                  g |¢d‘­Ž j+                  «       }| j-                  |«      }||fS )Nr5   r    r4   ç        )r°   r±   rÖ   r²   )rD   rt   rÑ   Úviewrˆ   rÒ   rÓ   r¢   ÚupdaterÆ   r   Úget_interfacerR   Ú_attn_implementationrÃ   r¶   rÎ   r±   rÖ   rÕ   r¦   r½   rÔ   )r/   r£   rØ   r¯   rÙ   r¾   Úinput_shapeÚhidden_shapeÚquery_statesr¿   rÀ   rŠ   r‹   Úattention_interfacerÂ   rÁ   s                   r1   rA   zGemma2Attention.forward  s£  € ð $×)Ñ)¨#¨2Ð.ˆØ8˜Ð8 bÐ8¨$¯-©-Ñ8ˆà—{‘{ =Ó1×6Ñ6°|ÓD×NÑNÈqÐRSÓTˆØ—[‘[ Ó/×4Ñ4°\ÓB×LÑLÈQÐPQÓRˆ
Ø—{‘{ =Ó1×6Ñ6°|ÓD×NÑNÈqÐRSÓTˆà&‰ˆˆSÜ#7¸ÀjÐRUÐWZÓ#[Ñ ˆ�jàÐ&Ø'6×'=Ñ'=¸jÈ,ÐX\×XfÑXfÓ'gÑ$ˆJ˜ä(?×(MÑ(MØ�K‰K×,Ñ,Ô.Eó)
Ðñ %8ØØØØØð%
ð /3¯mªm�D×*Ò*ÀØ—L‘LØ×.Ñ.Ø×/Ñ/ñ%
ð ñ%
Ñ!ˆ�\ð *�k×)Ñ)Ð;¨;Ð;¸Ò;×FÑFÓHˆØ—k‘k +Ó.ˆØ˜LÐ(Ð(r2   r‘   )rF   rG   rH   Ú__doc__r!   rI   r)   r,   r’   rC   r   r   r   rA   rJ   rK   s   @r1   rÅ   rÅ   å   s½   ø„ áGðh˜|ð h¸õ hð: IMØ.2Ø(,ñ()à—|‘|ð()ð # 5§<¡<°·±Ð#=Ñ>ÀÑEð()ð Ÿ™ tÑ+ð	()ð
  ™ð()ð Ð-Ñ.ð()ð 
ˆu�|‰|˜UŸ\™\¨DÑ0°%¸¿¹Ñ2EÈÑ2LÐLÑ	M÷()r2   rÅ   c                   ó.  ‡ — e Zd Zdedefˆ fd„Z	 	 	 	 ddej                  deej                  ej                  f   dz  dej                  dz  dej                  dz  d	e
dz  d
eej                  eej                  ej                  f   dz  f   fd„Zˆ xZS )ÚGemma2DecoderLayerrR   rÆ   c                 óÆ  •— t         ‰| �  «        |j                  | _        || _        t	        ||¬«      | _        t        |«      | _        t        |j                  |j                  ¬«      | _
        t        |j                  |j                  ¬«      | _        t        |j                  |j                  ¬«      | _        t        |j                  |j                  ¬«      | _        y )N)rR   rÆ   ©r%   )r(   r)   rS   rR   rÅ   Ú	self_attnrM   Úmlpr#   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormÚpre_feedforward_layernormÚpost_feedforward_layernormr×   s      €r1   r)   zGemma2DecoderLayer.__init__/  sª   ø€ Ü‰ÑÔØ!×-Ñ-ˆÔØˆŒÜ(°À)ÔLˆŒÜ˜VÓ$ˆŒÜ,¨V×-?Ñ-?ÀV×EXÑEXÔYˆÔÜ(5°f×6HÑ6HÈf×NaÑNaÔ(bˆÔ%ä)6°v×7IÑ7IÈv×ObÑObÔ)cˆÔ&Ü*7¸×8JÑ8JÐPV×PcÑPcÔ*dˆÕ'r2   Nr£   rØ   r¯   rŒ   rÙ   rq   c           	      ó  — |}| j                  |«      } | j                  d|||||dœ|¤Ž\  }}| j                  |«      }||z   }|}| j                  |«      }| j	                  |«      }| j                  |«      }||z   }|S )N)r£   rØ   r¯   rŒ   rÙ   © )rì   ré   rí   rî   rê   rï   )	r/   r£   rØ   r¯   rŒ   rÙ   r¾   ÚresidualÚ_s	            r1   rA   zGemma2DecoderLayer.forward;  s­   € ð !ˆà×,Ñ,¨]Ó;ˆð *˜4Ÿ>™>ð 
Ø'Ø 3Ø)Ø%Ø+ñ
ð ñ
Ñˆ�qð ×5Ñ5°mÓDˆØ  =Ñ0ˆà ˆØ×6Ñ6°}ÓEˆØŸ™ Ó/ˆØ×7Ñ7¸ÓFˆØ  =Ñ0ˆàÐr2   )NNNN)rF   rG   rH   r!   rI   r)   r,   r’   rC   Ú
LongTensorr   ÚFloatTensorrA   rJ   rK   s   @r1   ræ   ræ   .  sÇ   ø„ ð
e˜|ð 
e¸õ 
eð IMØ.2Ø04Ø(,ñà—|‘|ðð # 5§<¡<°·±Ð#=Ñ>ÀÑEðð Ÿ™ tÑ+ð	ð
 ×&Ñ&¨Ñ-ðð  ™ðð 
ˆu× Ñ  %¨×(9Ñ(9¸5×;LÑ;LÐ(LÑ"MÐPTÑ"TÐTÑ	U÷r2   ræ   c            	       óZ   ‡ — e Zd ZdZd	dedededefˆ fd„Zdej                  fˆ fd„Z	ˆ xZ
S )
ÚGemma2TextScaledWordEmbeddingz\
    This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
    Únum_embeddingsÚembedding_dimÚpadding_idxÚembed_scalec                 ó„   •— t         ‰| �  |||«       || _        | j                  dt	        j
                  |«      d¬«       y )Nrû   Frc   )r(   r)   Úscalar_embed_scalerl   r,   Útensor)r/   rø   rù   rú   rû   r0   s        €r1   r)   z&Gemma2TextScaledWordEmbedding.__init__b  s;   ø€ Ü‰Ñ˜¨¸ÔDØ"-ˆÔØ×Ñ˜]¬E¯L©L¸Ó,EÐRWÐÕXr2   Ú	input_idsc                 ó‚   •— t         ‰| �  |«      | j                  j                  | j                  j
                  «      z  S r'   )r(   rA   rû   r{   r.   rv   )r/   rÿ   r0   s     €r1   rA   z%Gemma2TextScaledWordEmbedding.forwardg  s2   ø€ Ü‰w‰˜yÓ)¨D×,<Ñ,<×,?Ñ,?ÀÇÁ×@QÑ@QÓ,RÑRÐRr2   )r=   )rF   rG   rH   rä   rI   r>   r)   r,   r’   rA   rJ   rK   s   @r1   r÷   r÷   ]  sG   ø„ ññY sð Y¸3ð YÈSð YÐ_dõ Yð
S §¡÷ Sñ Sr2   r÷   c                   ó„   ‡ — 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 ej$                  «       ˆ fd„«       Zˆ xZS )ÚGemma2PreTrainedModelrR   ÚmodelTræ   rÙ   )r£   Ú
attentionsc                 ó
  •— t         ‰| �  |«       d|j                  j                  v r t	        j
                  |j                  «       y t        |t        «      r+t	        j                  |j                  |j                  «       y y )NÚRMSNorm)r(   Ú_init_weightsr0   rF   ÚinitÚzeros_r.   r…   r÷   Ú	constant_rû   rý   )r/   r«   r0   s     €r1   r  z#Gemma2PreTrainedModel._init_weights}  s^   ø€ ä‰Ñ˜fÔ%à˜×(Ñ(×1Ñ1Ñ1Ü�K‰K˜Ÿ™Õ&Ü˜Ô =Ô>Ü�N‰N˜6×-Ñ-¨v×/HÑ/HÕIð ?r2   )rF   rG   rH   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_backendræ   rÅ   Ú_can_record_outputsr,   r•   r  rJ   rK   s   @r1   r  r  k  sp   ø… àÓØÐØ&*Ð#Ø-Ð.ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà+Ø%ñÐð
 €U‡]�]ƒ_óJó ôJr2   r  c                   óò   ‡ — 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Gemma2ModelrR   c           	      ó(  •— t         ‰| �  |«       |j                  | _        |j                  | _        t        |j                  |j                  | j                  | j                  j                  dz  ¬«      | _        t        j                  t        |j                  «      D �cg c]  }t        ||«      ‘Œ c}«      | _        t        |j                  |j                   ¬«      | _        t%        |«      | _        d| _        | j+                  «        y c c}w )Ng      à?)rû   rè   F)r(   r)   Úpad_token_idrú   Ú
vocab_sizer÷   rS   rR   Úembed_tokensr*   Ú
ModuleListÚrangeÚnum_hidden_layersræ   Úlayersr#   rë   Únormr^   Ú
rotary_embÚgradient_checkpointingÚ	post_initr×   s      €r1   r)   zGemma2Model.__init__‰  sÛ   ø€ Ü‰Ñ˜Ô Ø!×.Ñ.ˆÔØ ×+Ñ+ˆŒä9Ø×Ñ˜v×1Ñ1°4×3CÑ3CÐQU×Q\ÑQ\×QhÑQhÐjmÑQmô
ˆÔô —m‘mÜDIÈ&×JbÑJbÓDcÖd°yÔ ¨	Õ2Òdó
ˆŒô " &×"4Ñ"4¸&×:MÑ:MÔNˆŒ	Ü/°Ó7ˆŒØ&+ˆÔ#ð 	�‰Õùò es   Â"DNrÿ   r¯   rŒ   rÙ   Úinputs_embedsÚ	use_cacher¾   rq   c           	      óÆ  — |d u |d uz  rt        d«      ‚|€| j                  |«      }|r|€t        | j                  ¬«      }|€V|�|j	                  «       nd}t        j                  |j                  d   |j                  ¬«      |z   }|j                  d«      }t        |x}	t        «      s)| j                  ||||dœ}
t        d
i |
¤Žt        d
i |
¤Ždœ}	|}| j                  ||«      }t        | j                   d | j                  j"                   «      D ]-  \  }} ||f|	| j                  j$                  |      |||dœ|¤Ž}Œ/ | j'                  |«      }t)        ||¬	«      S )Nz:You must specify exactly one of input_ids or inputs_embeds)rR   r   r    )rn   )rR   r#  r¯   rÙ   rŒ   )Úfull_attentionrÊ   )r¯   rØ   rŒ   rÙ   )Úlast_hidden_staterÙ   rñ   )Ú
ValueErrorr  r	   rR   Úget_seq_lengthr,   ry   rD   rn   rœ   r…   Údictr   r   r   Ú	enumerater  r  rÈ   r  r   )r/   rÿ   r¯   rŒ   rÙ   r#  r$  r¾   Úpast_seen_tokensÚcausal_mask_mappingÚmask_kwargsr£   rØ   ÚiÚdecoder_layers                  r1   rA   zGemma2Model.forward›  s–  € ð ˜Ð -°tÐ";Ò<ÜÐYÓZÐZàÐ Ø*.×*;Ñ*;¸IÓ*FˆMá˜Ð0Ü*°$·+±+Ô>ˆOàÐØCRÐC^˜×=Ñ=Ô?ÐdeÐÜ Ÿ<™<¨×(;Ñ(;¸AÑ(>À}×G[ÑG[Ô\Ð_oÑoˆLØ'×1Ñ1°!Ó4ˆLô °Ð?Ð-ÄÔFð Ÿ+™+Ø!.Ø"0Ø#2Ø ,ñˆKô #5Ñ"C°{Ñ"CÜ%FÑ%UÈÑ%Uñ#Ðð &ˆØ"Ÿo™o¨m¸\ÓJÐä )¨$¯+©+Ð6U¸¿¹×8UÑ8UÐ*VÓ Wò 	ÑˆAˆ}Ù)Øðà2°4·;±;×3JÑ3JÈ1Ñ3MÑNØ$7Ø)Ø /ñð ñ‰Mð	ð Ÿ	™	 -Ó0ˆä&Ø+Ø+ô
ð 	
r2   )NNNNNN)rF   rG   rH   r!   r)   r   r   r   r,   rô   r’   r   rõ   Úboolr   r   r   rA   rJ   rK   s   @r1   r  r  ‡  sÍ   ø„ ð˜|õ ð$  ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ñ;
à×#Ñ# dÑ*ð;
ð Ÿ™ tÑ+ð;
ð ×&Ñ&¨Ñ-ð	;
ð
  ™ð;
ð ×(Ñ(¨4Ñ/ð;
ð ˜$‘;ð;
ð Ð+Ñ,ð;
ð 
!ò;
ó ó ó  ô;
r2   r  c                   óB  ‡ — e Zd ZddiZddiZddgdgfiZˆ fd„Zee	 	 	 	 	 	 	 	 dd	e	j                  dz  d
e	j                  dz  de	j                  dz  dedz  de	j                  dz  de	j                  dz  dedz  dee	j                  z  dee   defd„«       «       Zˆ xZS )ÚGemma2ForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputr£   Úlogitsc                 óè   •— t         ‰| �  |«       t        |«      | _        |j                  | _        t        j                  |j                  |j                  d¬«      | _        | j                  «        y rO   )
r(   r)   r  r  r  r*   rU   rS   r4  r"  r[   s     €r1   r)   zGemma2ForCausalLM.__init__â  sU   ø€ Ü‰Ñ˜Ô Ü  Ó(ˆŒ
Ø ×+Ñ+ˆŒÜ—y‘y ×!3Ñ!3°V×5FÑ5FÈUÔSˆŒð 	�‰Õr2   Nrÿ   r¯   rŒ   rÙ   r#  Úlabelsr$  Úlogits_to_keepr¾   rq   c	           
      ó  —  | j                   d||||||dœ|	¤Ž}
|
j                  }t        |t        «      rt	        | d«      n|}| j                  |dd…|dd…f   «      }| j                  j                  �G|| j                  j                  z  }t        j                  |«      }|| j                  j                  z  }d}|� | j                  ||| j                  fi |	¤Ž}t        |||
j                  |
j                  |
j                  ¬«      S )a‚  
        Example:

        ```python
        >>> from transformers import AutoTokenizer, Gemma2ForCausalLM

        >>> model = Gemma2ForCausalLM.from_pretrained("google/gemma-2-9b")
        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")

        >>> prompt = "What is your favorite condiment?"
        >>> 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]
        "What is your favorite condiment?"
        ```)rÿ   r¯   rŒ   rÙ   r#  r$  N)Úlossr6  rÙ   r£   r  rñ   )r  r'  r…   rI   Úslicer4  rR   Úfinal_logit_softcappingr,   r¹   Úloss_functionr  r   rÙ   r£   r  )r/   rÿ   r¯   rŒ   rÙ   r#  r8  r$  r9  r¾   Úoutputsr£   Úslice_indicesr6  r;  s                  r1   rA   zGemma2ForCausalLM.forwardë  s  € ð@ ,6¨4¯:©:ð ,
ØØ)Ø%Ø+Ø'Øñ,
ð ñ,
ˆð  ×1Ñ1ˆä8BÀ>ÔSVÔ8Wœ˜~˜o¨tÔ4Ð]kˆØ—‘˜mªA¨}ºaÐ,?Ñ@ÓAˆØ�;‰;×.Ñ.Ð:Ø˜dŸk™k×AÑAÑAˆFÜ—Z‘Z Ó'ˆFØ˜dŸk™k×AÑAÑAˆFàˆØÐØ%�4×%Ñ% f¨f°d·o±oÑPÈÑPˆDä%ØØØ#×3Ñ3Ø!×/Ñ/Ø×)Ñ)ô
ð 	
r2   )NNNNNNNr   )rF   rG   rH   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr)   r   r   r,   rô   r’   r   rõ   r1  rI   r   r   r   rA   rJ   rK   s   @r1   r3  r3  Ü  s  ø„ à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hôð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø-.ñ;
à×#Ñ# dÑ*ð;
ð Ÿ™ tÑ+ð;
ð ×&Ñ&¨Ñ-ð	;
ð
  ™ð;
ð ×(Ñ(¨4Ñ/ð;
ð × Ñ  4Ñ'ð;
ð ˜$‘;ð;
ð ˜eŸl™lÑ*ð;
ð Ð+Ñ,ð;
ð 
 ò;
ó ó ô;
r2   r3  c                   ó   — e Zd Zy)ÚGemma2ForSequenceClassificationN©rF   rG   rH   rñ   r2   r1   rE  rE  +  ó   „ Ør2   rE  c                   ó   — e Zd Zy)ÚGemma2ForTokenClassificationNrF  rñ   r2   r1   rI  rI  /  rG  r2   rI  )r3  r  r  rE  rI  )r    )rÛ   NN)IÚcollections.abcr   Útypingr   r,   Útorch.nnr*   Ú r   r  Úactivationsr   Úcache_utilsr   r	   Ú
generationr
   Úintegrationsr   r   Úmasking_utilsr   r   Úmodeling_flash_attention_utilsr   Úmodeling_layersr   r   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   r   Úutils.output_capturingr   Úconfiguration_gemma2r!   ÚModuler#   rM   r^   r™   r¢   r’   rI   rª   r>   rC   rÃ   rÅ   ræ   Ú	Embeddingr÷   r  r  r3  rE  rI  Ú__all__rñ   r2   r1   ú<module>r`     s4  ðõ* %Ý ã Ý å &Ý !ß .Ý )ß Iß RÝ B÷ñ ÷
 Pß Kß FÝ &ß IÑ Iß GÝ 5Ý .ô=�B—I‘Iô =ô(�—	‘	ô ô ><˜BŸI™Iô ><òB(ñ Ð*Ó+òó ,ðð2	U˜UŸ\™\ð 	U°#ð 	U¸%¿,¹,ó 	Uð$ Ø Ø ñ%Ø�I‰Ið%à�<‰<ð%ð 
�‰ð%ð �<‰<ð	%ð
 —L‘L 4Ñ'ð%ð �S‰[ð%ð �T‰\ð%ð �T‰\ð%ð ˆ5�<‰<˜Ÿ™Ð%Ñ&ó%ñD Ð)Ó*ôE)�b—i‘ió E)ó +ðE)ôP,Ð3ô ,ô^S B§L¡Lô Sð ôJ˜Oó Jó ðJð6 ôQ
Ð'ó Q
ó ðQ
ðh ôK
Ð-¨ó K
ó ðK
ô\	Ð&FÐH]ô 	ô	Ð#@ÐBWô 	ò�r2   