Ë
    ÜÓ^jNY  ã                   ó°  — 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	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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$m%Z% ddl&m'Z'm(Z(m)Z) ddl*m+Z+m,Z, ddl-m.Z. ddl/m0Z0  G d„ dejb                  «      Z2d„ Z3 ed«      d>d„«       Z4dejj                  de6dejj                  fd„Z7	 d?dejb                  d ejj                  d!ejj                  d"ejj                  d#ejj                  dz  d$e8d%e8d&e%e'   fd'„Z9 ee4«       G d(„ d)ejb                  «      «       Z: ed*«       G d+„ d,ejb                  «      «       Z; G d-„ d.ejb                  «      Z< G d/„ d0e«      Z=e( G d1„ d2e#«      «       Z>e( G d3„ d4e>«      «       Z?e( G d5„ d6e>e«      «       Z@ G d7„ d8ee>«      ZA G d9„ d:ee>«      ZB G d;„ d<ee>«      ZCg d=¢ZDy)@é    )ÚCallable)ÚOptionalN)Únné   )ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úuse_kernel_forward_from_hubÚuse_kernel_func_from_hubÚuse_kernelized_func)Úcreate_causal_maskÚ!create_sliding_window_causal_mask)ÚFlashAttentionKwargs)ÚGenericForQuestionAnsweringÚ 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SmolLM3Configc                   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SmolLM3RotaryEmbeddingÚinv_freqNÚconfigc                 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)ÚsuperÚ__init__Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenr'   Úrope_parametersr)   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)Úselfr'   ÚdeviceÚrope_init_fnr&   Ú	__class__s        €úw/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/smollm3/modeling_smollm3.pyr.   zSmolLM3RotaryEmbedding.__init__4   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Uó    r8   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_dimNg      ð?r   é   ©Údtype)r8   rD   )	r2   ÚgetattrÚhidden_sizeÚnum_attention_headsÚtorchÚarangeÚint64ÚtoÚfloat)r'   r8   r=   ÚbaseÚdimÚattention_factorr&   s          r;   r3   z6SmolLM3RotaryEmbedding.compute_default_rope_parametersD   s�   € ð& ×%Ñ% lÑ3ˆÜ�f˜j¨$Ó/Òc°6×3EÑ3EÈ×IcÑIcÑ3cˆàÐð Ø”U—\‘\ ! S¨!´5·;±;Ô?×BÑBÈ&ÔX]×XcÑXcÐBÓdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r<   c                 óN  — | j                   d d d …d f   j                  «       j                  |j                  d   dd«      j	                  |j
                  «      }|d d …d d d …f   j                  «       }t        |j
                  j                  t        «      r/|j
                  j                  dk7  r|j
                  j                  nd}t        |d¬«      5  |j                  «       |j                  «       z  j                  dd«      }t        j                  ||fd¬	«      }|j                  «       | j                  z  }|j                  «       | j                  z  }	d d d «       j	                  |j                   ¬
«      	j	                  |j                   ¬
«      fS # 1 sw Y   ŒAxY w)Nr   éÿÿÿÿr"   ÚmpsÚcpuF)Údevice_typeÚenabledrB   ©rN   rC   )r&   rL   ÚexpandÚshaperK   r8   Ú
isinstanceÚtypeÚstrr   Ú	transposerH   ÚcatÚcosr4   ÚsinrD   )
r7   ÚxÚposition_idsÚinv_freq_expandedÚposition_ids_expandedrT   ÚfreqsÚembr^   r_   s
             r;   ÚforwardzSmolLM3RotaryEmbedding.forwardb   sR  € ð !ŸM™M¨$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$©N)NNN)Ú__name__Ú
__module__Ú__qualname__rH   ÚTensorÚ__annotations__r#   r.   Ústaticmethodr   ÚintÚtuplerL   r3   Úno_gradr   rf   Ú__classcell__©r:   s   @r;   r%   r%   1   s˜   ø… Ø�l‰lÓñV˜}õ Vð  à'+Ø+/Ø"ñ*Ø Ñ$ð*à˜Ñ(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ñ	%ò	*ó ð*ð: €U‡]�]ƒ_Øñ<ó ó ô<r<   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..NrQ   rB   rV   )rX   rH   r]   )r`   Úx1Úx2s      r;   Úrotate_halfrv   r   sZ   € à	
ˆ3Ð"�!—'‘'˜"‘+ Ñ"Ð"Ð"Ñ	#€BØ	
ˆ3�—‘˜‘˜qÑ Ñ"Ð"Ñ	#€BÜ�9‰9�r�c˜2�Y BÔ'Ð'r<   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	unsqueezerv   )ÚqÚkr^   r_   Úunsqueeze_dimÚq_embedÚk_embeds          r;   Úapply_rotary_pos_embr   y   sY   € ð& �-‰-˜Ó
&€CØ
�-‰-˜Ó
&€CØ�3‰wœ; q›>¨CÑ/Ñ0€GØ�3‰wœ; q›>¨CÑ/Ñ0€GØ�GÐÐr<   Úhidden_statesÚn_repr>   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)rX   rW   Úreshape)r€   r�   ÚbatchÚnum_key_value_headsÚslenrA   s         r;   Ú	repeat_kvr‡   “   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Tr<   ÚmoduleÚqueryÚkeyÚvalueÚattention_maskÚscalingÚdropoutÚkwargsc                 ó  — t        || j                  «      }t        || j                  «      }	t        j                  ||j	                  dd«      «      |z  }
|�|
|z   }
t
        j                  j                  |
dt        j                  ¬«      j                  |j                  «      }
t
        j                  j                  |
|| j                  ¬«      }
t        j                  |
|	«      }|j	                  dd«      j                  «       }||
fS )NrB   r   rQ   )rN   rD   )ÚpÚtrainingr"   )r‡   Únum_key_value_groupsrH   Úmatmulr\   r   Ú
functionalÚsoftmaxÚfloat32rK   rD   rŽ   r’   Ú
contiguous)rˆ   r‰   rŠ   r‹   rŒ   r�   rŽ   r�   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r;   Úeager_attention_forwardr�   Ÿ   sà   € ô ˜3 × ;Ñ ;Ó<€JÜ˜U F×$?Ñ$?Ó@€Lä—<‘<  z×';Ñ';¸A¸qÓ'AÓBÀWÑL€LØÐ!Ø# nÑ4ˆä—=‘=×(Ñ(¨¸2ÄUÇ]Á]Ð(ÓS×VÑVÐW\×WbÑWbÓc€LÜ—=‘=×(Ñ(¨¸È6Ï?É?Ð(Ó[€LÜ—,‘,˜|¨\Ó:€KØ×'Ñ'¨¨1Ó-×8Ñ8Ó:€Kà˜Ð$Ð$r<   c                   óô   ‡ — 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ej                  dz  d	e
dz  d
ee   de	ej                  ej                  dz  f   fd„Zˆ xZS )ÚSmolLM3Attentionz=Multi-headed attention from 'Attention Is All You Need' paperr'   Ú	layer_idxc                 óú  •— 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                  ¬«      | _        |j(                  |   | _        |j,                  r$|j.                  |   dk(  r|j0                  | _        y d | _        y )NrA   g      à¿T©ÚbiasÚsliding_attention)r-   r.   r'   r    rE   rF   rG   rA   r…   r“   r�   Úattention_dropoutÚ	is_causalr   ÚLinearÚattention_biasÚq_projÚk_projÚv_projÚo_projÚno_rope_layersÚuse_ropeÚuse_sliding_windowÚlayer_typesÚsliding_window©r7   r'   r    r:   s      €r;   r.   zSmolLM3Attention.__init__¼   sŸ  ø€ Ü‰ÑÔØˆŒØ"ˆŒÜ ¨
°F×4FÑ4FÈ&×JdÑJdÑ4dÓeˆŒØ$*×$>Ñ$>À&×B\ÑB\Ñ$\ˆÔ!Ø—}‘} dÑ*ˆŒØ!'×!9Ñ!9ˆÔØˆŒä—i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×&Ñ&¨¯©Ñ6¸×8JÑ8JÐQW×QfÑQfô
ˆŒð ×-Ñ-¨iÑ8ˆŒð ×(Ò(¨V×-?Ñ-?À	Ñ-JÐNaÒ-að ×!Ñ!ð 	Õð ð 	Õr<   Nr€   Úposition_embeddingsrŒ   Úpast_key_valuesr�   r>   c                 ó8  — |j                   d d }g |¢d‘| j                  ‘­}| j                  |«      j                  |«      j	                  dd«      }| j                  |«      j                  |«      j	                  dd«      }	| j                  |«      j                  |«      j	                  dd«      }
| j                  r|\  }}t        ||	||«      \  }}	|� |j                  |	|
| j                  «      \  }	}
t        j                  | j                  j                  t        «      } || ||	|
|f| j                   sdn| j"                  | j$                  | j&                  dœ|¤Ž\  }} |j(                  g |¢d‘­Ž j+                  «       }| j-                  |«      }||fS )NrQ   r"   rB   ç        )rŽ   r�   r±   )rX   rA   r©   Úviewr\   rª   r«   r®   r   Úupdater    r   Úget_interfacer'   Ú_attn_implementationr�   r’   r¥   r�   r±   rƒ   r˜   r¬   )r7   r€   r³   rŒ   r´   r�   Úinput_shapeÚhidden_shapeÚquery_statesr™   rš   r^   r_   Úattention_interfacerœ   r›   s                   r;   rf   zSmolLM3Attention.forwardÚ   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�=Š=Ø*‰HˆC�Ü';¸LÈ*ÐVYÐ[^Ó'_Ñ$ˆL˜*à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  $Ÿ}š}‘C°$×2HÑ2HØ—L‘LØ×.Ñ.ñ
%
ð ñ
%
Ñ!ˆ�\ð *�k×)Ñ)Ð;¨;Ð;¸Ò;×FÑFÓHˆØ—k‘k +Ó.ˆØ˜LÐ(Ð(r<   rg   )rh   ri   rj   Ú__doc__r#   rn   r.   rH   rk   ro   r   r   r   rf   rq   rr   s   @r;   rŸ   rŸ   ¸   sž   ø„ áGð
˜}ð 
¸õ 
ðF )-ñ()à—|‘|ð()ð # 5§<¡<°·±Ð#=Ñ>ð()ð Ÿ™ tÑ+ð	()ð
  ™ð()ð Ð-Ñ.ð()ð 
ˆu�|‰|˜UŸ\™\¨DÑ0Ð0Ñ	1÷()r<   rŸ   ÚRMSNormc                   óh   ‡ — 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SmolLM3RMSNormÚepsr>   Nc                 óŠ   •— t         ‰| �  «        t        j                  t	        j
                  |«      «      | _        || _        y)z=
        SmolLM3RMSNorm is equivalent to T5LayerNorm
        N)r-   r.   r   Ú	ParameterrH   ÚonesÚweightÚvariance_epsilon)r7   rF   rÃ   r:   s      €r;   r.   zSmolLM3RMSNorm.__init__  s1   ø€ ô 	‰ÑÔÜ—l‘l¤5§:¡:¨kÓ#:Ó;ˆŒØ #ˆÕr<   r€   c                 ó"  — |j                   }|j                  t        j                  «      }|j	                  d«      j                  dd¬«      }|t        j                  || j                  z   «      z  }| j                  |j                  |«      z  S )NrB   rQ   T)Úkeepdim)	rD   rK   rH   r—   ÚpowÚmeanÚrsqrtrÈ   rÇ   )r7   r€   Úinput_dtypeÚvariances       r;   rf   zSmolLM3RMSNorm.forward  sy   € Ø#×)Ñ)ˆØ%×(Ñ(¬¯©Ó7ˆØ ×$Ñ$ QÓ'×,Ñ,¨R¸Ð,Ó>ˆØ%¬¯©°H¸t×?TÑ?TÑ4TÓ(UÑUˆØ�{‰{˜]×-Ñ-¨kÓ:Ñ:Ð:r<   c                 ó^   — t        | j                  j                  «      › d| j                  › �S )Nz, eps=)ro   rÇ   rX   rÈ   )r7   s    r;   Ú
extra_reprzSmolLM3RMSNorm.extra_repr  s*   € Ü˜Ÿ™×)Ñ)Ó*Ð+¨6°$×2GÑ2GÐ1HÐIÐIr<   )g�íµ ÷Æ°>)
rh   ri   rj   rL   r.   rH   rk   rf   rÑ   rq   rr   s   @r;   rÂ   rÂ     s7   ø„ ñ$¨ð $¸$õ $ð; U§\¡\ð ;°e·l±ló ;öJr<   rÂ   c                   ó$   ‡ — e Zd Zˆ fd„Zd„ Zˆ xZS )Ú
SmolLM3MLPc                 ó  •— t         ‰| �  «        || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  |j                  ¬«      | _        t        j                  | j                  | j                  |j                  ¬«      | _	        t        j                  | j                  | j                  |j                  ¬«      | _
        t        |j                     | _        y )Nr¢   )r-   r.   r'   rF   Úintermediate_sizer   r§   Úmlp_biasÚ	gate_projÚup_projÚ	down_projr   Ú
hidden_actÚact_fn©r7   r'   r:   s     €r;   r.   zSmolLM3MLP.__init__  s¸   ø€ Ü‰ÑÔØˆŒØ!×-Ñ-ˆÔØ!'×!9Ñ!9ˆÔÜŸ™ 4×#3Ñ#3°T×5KÑ5KÐRX×RaÑRaÔbˆŒÜ—y‘y ×!1Ñ!1°4×3IÑ3IÐPV×P_ÑP_Ô`ˆŒÜŸ™ 4×#9Ñ#9¸4×;KÑ;KÐRX×RaÑRaÔbˆŒÜ˜V×.Ñ.Ñ/ˆ�r<   c                 óˆ   — | j                  | j                  | j                  |«      «      | j                  |«      z  «      }|S rg   )rÙ   rÛ   r×   rØ   )r7   r`   rÙ   s      r;   rf   zSmolLM3MLP.forward%  s6   € Ø—N‘N 4§;¡;¨t¯~©~¸aÓ/@Ó#AÀDÇLÁLÐQRÃOÑ#SÓTˆ	ØÐr<   )rh   ri   rj   r.   rf   rq   rr   s   @r;   rÓ   rÓ     s   ø„ ô0ör<   rÓ   c                   ó  ‡ — 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SmolLM3DecoderLayerr'   r    c                 ó   •— t         ‰| �  «        |j                  | _        t        ||¬«      | _        t        |«      | _        t        |j                  |j                  ¬«      | _	        t        |j                  |j                  ¬«      | _
        y )N)r'   r    ©rÃ   )r-   r.   rF   rŸ   Ú	self_attnrÓ   ÚmlprÂ   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormr²   s      €r;   r.   zSmolLM3DecoderLayer.__init__+  sl   ø€ Ü‰ÑÔØ!×-Ñ-ˆÔä)°À9ÔMˆŒä˜fÓ%ˆŒÜ-¨f×.@Ñ.@Àf×FYÑFYÔZˆÔÜ(6°v×7IÑ7IÈv×ObÑObÔ(cˆÕ%r<   Nr€   rŒ   ra   r´   Ú	use_cacher³   r�   r>   c           
      óÀ   — |}| j                  |«      } | j                  d||||||dœ|¤Ž\  }}	||z   }|}| j                  |«      }| j                  |«      }||z   }|S )N)r€   rŒ   ra   r´   rç   r³   © )rå   râ   ræ   rã   )
r7   r€   rŒ   ra   r´   rç   r³   r�   ÚresidualÚ_s
             r;   rf   zSmolLM3DecoderLayer.forward5  s�   € ð !ˆØ×,Ñ,¨]Ó;ˆà)˜4Ÿ>™>ð 
Ø'Ø)Ø%Ø+ØØ 3ñ
ð ñ
Ñˆ�qð ! =Ñ0ˆð !ˆØ×5Ñ5°mÓDˆØŸ™ Ó/ˆØ  =Ñ0ˆØÐr<   )NNNFN)rh   ri   rj   r#   rn   r.   rH   rk   Ú
LongTensorr   Úboolro   r   r   rf   rq   rr   s   @r;   rß   rß   *  s¾   ø„ ðd˜}ð d¸õ dð /3Ø04Ø(,Ø!&ØHLñà—|‘|ðð Ÿ™ tÑ+ðð ×&Ñ&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5§<¡<°·±Ð#=Ñ>ÀÑEðð Ð+Ñ,ðð 
�‰÷r<   rß   c                   óJ   — 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y)ÚSmolLM3PreTrainedModelr'   ÚmodelTrß   r´   )r€   Ú
attentionsN)rh   ri   rj   r#   rl   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_backendrß   rŸ   Ú_can_record_outputsré   r<   r;   rï   rï   U  sQ   … àÓØÐØ&*Ð#Ø.Ð/ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà,Ø&ñÑr<   rï   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SmolLM3Modelr'   c           	      óF  •— t         ‰| �  |«       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                  «      | _        t        j                  t        |j                  «      D �cg c]  }t        ||«      ‘Œ c}«      | _        t        |j                  |j                  ¬«      | _        t#        |¬«      | _        d| _        d| j(                  j*                  v | _        | j/                  «        y c c}w )Nrá   ©r'   Fr¤   )r-   r.   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	EmbeddingrF   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersrß   ÚlayersrÂ   rä   Únormr%   Ú
rotary_embÚgradient_checkpointingr'   r°   Úhas_sliding_layersÚ	post_initr²   s      €r;   r.   zSmolLM3Model.__init__j  sÝ   ø€ Ü‰Ñ˜Ô Ø!×.Ñ.ˆÔØ ×+Ñ+ˆŒäŸL™L¨×):Ñ):¸F×<NÑ<NÐPT×P`ÑP`ÓaˆÔÜ—m‘mÜEJÈ6×KcÑKcÓEdÖe¸	Ô  ¨Õ3Òeó
ˆŒô # 6×#5Ñ#5¸6×;NÑ;NÔOˆŒ	Ü0¸Ô?ˆŒØ&+ˆÔ#Ø"5¸¿¹×9PÑ9PÐ"PˆÔð 	�‰Õùò fs   ÂDNÚ	input_idsrŒ   ra   r´   Úinputs_embedsrç   r�   r>   c           
      óú  — |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        «      s9| j                  ||||dœ}
dt        di |
¤Ži}	| j                  rt        di |
¤Ž|	d<   |}| j                  ||«      }t!        | j"                  d | j                  j$                   «      D ].  \  }} ||f|	| j                  j&                  |      ||||d	œ|¤Ž}Œ0 | j)                  |«      }t+        ||r|¬
«      S d ¬
«      S )Nz:You must specify exactly one of input_ids or inputs_embedsrÿ   r   r"   )r8   )r'   r  rŒ   r´   ra   Úfull_attentionr¤   )rŒ   r³   ra   r´   rç   )Úlast_hidden_stater´   ré   )Ú
ValueErrorr  r	   r'   Úget_seq_lengthrH   rI   rX   r8   ry   rY   Údictr   r  r   r
  Ú	enumerater  r  r°   r	  r   )r7   r  rŒ   ra   r´   r  rç   r�   Úpast_seen_tokensÚcausal_mask_mappingÚmask_kwargsr€   r³   ÚiÚdecoder_layers                  r;   rf   zSmolLM3Model.forward{  s¾  € ð ˜Ð -°tÐ";Ò<ÜÐYÓZÐZàÐ Ø ×-Ñ-¨iÓ8ˆMá˜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ˆKð !Ô"4Ñ"C°{Ñ"Cð#Ðð ×&Ò&Ü;\Ñ;kÐ_jÑ;kÐ#Ð$7Ñ8à%ˆØ"Ÿo™o¨m¸\ÓJÐä )¨$¯+©+Ð6U¸¿¹×8UÑ8UÐ*VÓ Wò 		ÑˆAˆ}Ù)Øðà2°4·;±;×3JÑ3JÈ1Ñ3MÑNØ$7Ø)Ø /Ø#ñð ñ‰Mð		ð Ÿ	™	 -Ó0ˆÜ&Ø+Ù/8˜Oô
ð 	
à>Bô
ð 	
r<   )NNNNNN)rh   ri   rj   r#   r.   r    r!   r   rH   rì   rk   r   ÚFloatTensorrí   r   r   r   rf   rq   rr   s   @r;   rý   rý   h  sÍ   ø„ ð˜}õ ð"  ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ñ<
à×#Ñ# dÑ*ð<
ð Ÿ™ tÑ+ð<
ð ×&Ñ&¨Ñ-ð	<
ð
  ™ð<
ð ×(Ñ(¨4Ñ/ð<
ð ˜$‘;ð<
ð Ð+Ñ,ð<
ð 
!ò<
ó ó ó  ô<
r<   rý   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SmolLM3ForCausalLMz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 )NFr¢   )
r-   r.   rý   rð   r  r   r§   rF   r  r  rÜ   s     €r;   r.   zSmolLM3ForCausalLM.__init__Ã  sU   ø€ Ü‰Ñ˜Ô Ü! &Ó)ˆŒ
Ø ×+Ñ+ˆŒÜ—y‘y ×!3Ñ!3°V×5FÑ5FÈUÔSˆŒð 	�‰Õr<   Nr  rŒ   ra   r´   r  Úlabelsrç   Úlogits_to_keepr�   r>   c	           
      óx  —  | j                   d||||||dœ|	¤Ž}
|
j                  }t        |t        «      rt	        | d«      n|}| j                  |dd…|dd…f   «      }d}|�* | j                  d||| j                  j                  dœ|	¤Ž}t        |||
j                  |
j                  |
j                  ¬«      S )aÛ  
        Example:

        ```python
        >>> from transformers import AutoTokenizer, SmolLM3ForCausalLM

        >>> model = SmolLM3ForCausalLM.from_pretrained("meta-smollm3/SmolLM3-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-smollm3/SmolLM3-2-7b-hf")

        >>> 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  rŒ   ra   r´   r  rç   N)r!  r#  r  )Úlossr!  r´   r€   rñ   ré   )rð   r  rY   rn   Úslicer  Úloss_functionr'   r  r   r´   r€   rñ   )r7   r  rŒ   ra   r´   r  r#  rç   r$  r�   Úoutputsr€   Úslice_indicesr!  r&  s                  r;   rf   zSmolLM3ForCausalLM.forwardÌ  sÙ   € ð> ,6¨4¯:©:ð ,
ØØ)Ø%Ø+Ø'Øñ,
ð ñ,
ˆð  ×1Ñ1ˆä8BÀ>ÔSVÔ8Wœ˜~˜o¨tÔ4Ð]kˆØ—‘˜mªA¨}ºaÐ,?Ñ@ÓAˆàˆØÐØ%�4×%Ñ%Ðp¨V¸FÈtÏ{É{×OeÑOeÑpÐioÑpˆDä%ØØØ#×3Ñ3Ø!×/Ñ/Ø×)Ñ)ô
ð 	
r<   )NNNNNNNr   )rh   ri   rj   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr.   r   r   rH   rì   rk   r   r  rí   rn   r   r   r   rf   rq   rr   s   @r;   r  r  ½  s  ø„ à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hôð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø-.ñ6
à×#Ñ# dÑ*ð6
ð Ÿ™ tÑ+ð6
ð ×&Ñ&¨Ñ-ð	6
ð
  ™ð6
ð ×(Ñ(¨4Ñ/ð6
ð × Ñ  4Ñ'ð6
ð ˜$‘;ð6
ð ˜eŸl™lÑ*ð6
ð Ð+Ñ,ð6
ð 
 ò6
ó ó ô6
r<   r  c                   ó   — e Zd Zy)Ú SmolLM3ForSequenceClassificationN©rh   ri   rj   ré   r<   r;   r/  r/    ó   „ Ør<   r/  c                   ó   — e Zd Zy)ÚSmolLM3ForTokenClassificationNr0  ré   r<   r;   r3  r3    r1  r<   r3  c                   ó   — e Zd ZdZy)ÚSmolLM3ForQuestionAnsweringÚtransformerN)rh   ri   rj   rò   ré   r<   r;   r5  r5    s   „ Ø%Ñr<   r5  )rï   rý   r  r/  r3  r5  )r"   )r¶   )EÚcollections.abcr   Útypingr   rH   r   Úactivationsr   Úcache_utilsr   r	   Ú
generationr
   Úintegrationsr   r   r   Úmasking_utilsr   r   Úmodeling_flash_attention_utilsr   Úmodeling_layersr   r   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_smollm3r#   ÚModuler%   rv   r   rk   rn   r‡   rL   r�   rŸ   rÂ   rÓ   rß   rï   rý   r  r/  r3  r5  Ú__all__ré   r<   r;   ú<module>rJ     s  ðõ* %Ý ã Ý å !ß .Ý )ß fÑ fß RÝ B÷ó ÷ Pß Kß FÝ &ß IÑ Iß GÝ 5Ý 0ô><˜RŸY™Yô ><òB(ñ Ð*Ó+òó ,ðð2	U˜UŸ\™\ð 	U°#ð 	U¸%¿,¹,ó 	Uð& ñ%Ø�I‰Ið%à�<‰<ð%ð 
�‰ð%ð �<‰<ð	%ð
 —L‘L 4Ñ'ð%ð ð%ð ð%ð Ð'Ñ(ó%ñ2 Ð)Ó*ôI)�r—y‘yó I)ó +ðI)ñX ˜YÓ'ôJ�R—Y‘Yó Jó (ðJô(�—‘ô ô (Ð4ô (ðV ô˜_ó ó ðð$ ôQ
Ð)ó Q
ó ðQ
ðh ôF
Ð/°ó F
ó ðF
ôR	Ð'GÐI_ô 	ô	Ð$AÐCYô 	ô&Ð"=Ð?Uô &ò�r<   