§
    ‚ŠtjNY  ã                   óÂ  — 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$m%Z% ddl&m'Z'm(Z(m)Z) ddl*m+Z+m,Z, ddl-m.Z. ddl/m0Z0  G d„ dej1        ¦  «        Z2d„ Z3 ed¦  «        d?d„¦   «         Z4dej5        de6dej5        fd„Z7	 d@d ej1        d!ej5        d"ej5        d#ej5        d$ej5        dz  d%e8d&e8d'e%e'         fd(„Z9 ee4¦  «         G d)„ d*ej1        ¦  «        ¦   «         Z: ed+¦  «         G d,„ d-ej1        ¦  «        ¦   «         Z; G d.„ d/ej1        ¦  «        Z< G d0„ d1e¦  «        Z=e( G d2„ d3e#¦  «        ¦   «         Z>e( G d4„ d5e>¦  «        ¦   «         Z?e( G d6„ d7e>e¦  «        ¦   «         Z@ G d8„ d9ee>¦  «        ZA G d:„ d;ee>¦  «        ZB G d<„ d=ee>¦  «        ZCg d>¢ZDdS )Aé    )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                   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SmolLM3RotaryEmbeddingÚinv_freqNÚconfigc                 ó²  •— t          ¦   «                              ¦   «          |j        | _        |j        | _        || _        | j        j        d         | _        | j        }| j        dk    rt          | j                 } || j        |¦  «        \  }| _
        |                      d|d¬¦  «         |                      d|                     ¦   «         d¬¦  «         d S )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        €új/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/smollm3/modeling_smollm3.pyr.   zSmolLM3RotaryEmbedding.__init__4   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�$Ô(à×Ò˜Z¨¸eÐÑDÔDÐDØ×ÒÐ0°(·.².Ñ2BÔ2BÈuÐÑUÔUÐUÐUÐUó    r8   ztorch.deviceÚseq_lenÚreturnztorch.Tensorc                 óü   — | j         d         }t          | dd¦  «        p| j        | j        z  }d}d|t	          j        d|dt          j        ¬¦  «                             |t          j        ¬¦  «        |z  z  z  }||fS )	a¨  
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        Ú
rope_thetaÚhead_dimNg      ð?r   é   ©Údtype)r8   rD   )	r2   ÚgetattrÚhidden_sizeÚnum_attention_headsÚtorchÚarangeÚint64ÚtoÚfloat)r'   r8   r=   ÚbaseÚdimÚattention_factorr&   s          r;   r3   z6SmolLM3RotaryEmbedding.compute_default_rope_parametersD   sŒ   € ð& Ô% lÔ3ˆÝ�f˜j¨$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)r<   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   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   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*©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   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 <r<   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..NrQ   rB   rV   )rX   rH   r]   )r`   Úx1Úx2s      r;   Úrotate_halfrv   r   s]   € à	
ˆ3Ð"�!”'˜"”+ Ñ"Ð"Ð"Ô	#€BØ	
ˆ3�”˜”˜qÑ Ð"Ð"Ð"Ô	#€BÝŒ9�r�c˜2�Y BÐ'Ñ'Ô'Ð'r<   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	unsqueezerv   )ÚqÚkr^   r_   Úunsqueeze_dimÚq_embedÚk_embeds          r;   Úapply_rotary_pos_embr   y   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r<   Úhidden_statesÚn_repr>   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)rX   rW   Úreshape)r€   r�   ÚbatchÚnum_key_value_headsÚslenrA   s         r;   Ú	repeat_kvr‡   “   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Tr<   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 )NrB   r   rQ   )rN   rD   )ÚpÚtrainingr"   )r‡   Únum_key_value_groupsrH   Úmatmulr\   r   Ú
functionalÚsoftmaxÚfloat32rK   rD   r�   r“   Ú
contiguous)r‰   rŠ   r‹   rŒ   r�   rŽ   r�   r�   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r;   Úeager_attention_forwardrž   Ÿ   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$Ð$r<   c                   óÊ   ‡ — 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                 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        ¬¦  «        | _        |j        |         | _        |j        r|j        |         dk    r|j        nd | _        d S )NrA   g      à¿T©ÚbiasÚsliding_attention)r-   r.   r'   r¡   rE   rF   rG   rA   r…   r”   rŽ   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   sŒ  ø€ Ý‰Œ×ÒÑÔÐØˆŒØ"ˆŒÝ ¨
°FÔ4FÈ&ÔJdÑ4dÑeÔeˆŒØ$*Ô$>À&ÔB\Ñ$\ˆÔ!Ø”} dÑ*ˆŒØ!'Ô!9ˆÔØˆŒå”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ&¨¬Ñ6¸Ô8JÐQWÔQfð
ñ 
ô 
ˆŒð Ô-¨iÔ8ˆŒð Ô(ðØ-3Ô-?À	Ô-JÐNaÒ-aÐ-að Ô!Ð!àð 	ÔÐÐr<   Nr€   Úposition_embeddingsr�   Úpast_key_valuesr�   r>   c                 ó<  — |j         d d…         }g |¢d‘| j        ‘R }|                      |¦  «                             |¦  «                             dd¦  «        }|                      |¦  «                             |¦  «                             dd¦  «        }	|                      |¦  «                             |¦  «                             dd¦  «        }
| j        r|\  }}t          ||	||¦  «        \  }}	|�| 	                    |	|
| j
        ¦  «        \  }	}
t          j        | j        j        t          ¦  «        } || ||	|
|f| j        sdn| j        | j        | j        dœ|¤Ž\  }} |j        g |¢d‘R Ž                      ¦   «         }|                      |¦  «        }||fS )NrQ   r"   rB   rˆ   )r�   rŽ   r²   )rX   rA   rª   Úviewr\   r«   r¬   r¯   r   Úupdater¡   r   Úget_interfacer'   Ú_attn_implementationrž   r“   r¦   rŽ   r²   rƒ   r™   r­   )r7   r€   r´   r�   rµ   r�   Úinput_shapeÚhidden_shapeÚquery_statesrš   r›   r^   r_   Úattention_interfacer�   rœ   s                   r;   rf   zSmolLM3Attention.forwardÚ   sÑ  € ð $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àŒ=ð 	`Ø*‰HˆC�Ý';¸LÈ*ÐVYÐ[^Ñ'_Ô'_Ñ$ˆL˜*à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”LØÔ.ð
%
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(r<   rg   )rh   ri   rj   Ú__doc__r#   rn   r.   rH   rk   ro   r   r   r   rf   rq   rr   s   @r;   r    r    ¸   sÞ   ø€ € € € € àGÐGð
˜}ð 
¸ð 
ð 
ð 
ð 
ð 
ð 
ðF )-ð()ð ()à”|ð()ð # 5¤<°´Ð#=Ô>ð()ð œ tÑ+ð	()ð
  ™ð()ð Ð-Ô.ð()ð 
ˆuŒ|˜Uœ\¨DÑ0Ð0Ô	1ð()ð ()ð ()ð ()ð ()ð ()ð ()ð ()r<   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SmolLM3RMSNormç�íµ ÷Æ°>Úepsr>   Nc                 ó¬   •— t          ¦   «                              ¦   «          t          j        t	          j        |¦  «        ¦  «        | _        || _        dS )z=
        SmolLM3RMSNorm is equivalent to T5LayerNorm
        N)r-   r.   r   Ú	ParameterrH   ÚonesÚweightÚvariance_epsilon)r7   rF   rÄ   r:   s      €r;   r.   zSmolLM3RMSNorm.__init__  sD   ø€ õ 	‰Œ×ÒÑÔÐÝ”l¥5¤:¨kÑ#:Ô#:Ñ;Ô;ˆŒØ #ˆÔÐÐr<   r€   c                 ó  — |j         }|                     t          j        ¦  «        }|                     d¦  «                             dd¬¦  «        }|t          j        || j        z   ¦  «        z  }| j        |                     |¦  «        z  S )NrB   rQ   T)Úkeepdim)	rD   rK   rH   r˜   ÚpowÚmeanÚrsqrtrÉ   rÈ   )r7   r€   Úinput_dtypeÚvariances       r;   rf   zSmolLM3RMSNorm.forward  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:r<   c                 óH   — t          | j        j        ¦  «        › d| j        › �S )Nz, eps=)ro   rÈ   rX   rÉ   )r7   s    r;   Ú
extra_reprzSmolLM3RMSNorm.extra_repr  s&   € Ý˜œÔ)Ñ*Ô*ÐIÐI°$Ô2GÐIÐIÐIr<   )rÃ   )
rh   ri   rj   rL   r.   rH   rk   rf   rÒ   rq   rr   s   @r;   rÂ   rÂ     sŒ   ø€ € € € € ð$ð $¨ð $¸$ð $ð $ð $ð $ð $ð $ð; U¤\ð ;°e´lð ;ð ;ð ;ð ;ðJð Jð Jð Jð Jð Jð Jr<   rÂ   c                   ó$   ‡ — 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                 | _        d S )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€ Ý‰Œ×ÒÑÔÐØˆŒØ!Ô-ˆÔØ!'Ô!9ˆÔÝœ 4Ô#3°TÔ5KÐRXÔRaÐbÑbÔbˆŒÝ”y Ô!1°4Ô3IÐPVÔP_Ð`Ñ`Ô`ˆŒÝœ 4Ô#9¸4Ô;KÐRXÔRaÐbÑbÔbˆŒÝ˜VÔ.Ô/ˆŒˆˆr<   c                 ó¨   — |                       |                      |                      |¦  «        ¦  «        |                      |¦  «        z  ¦  «        }|S rg   )rÚ   rÜ   rØ   rÙ   )r7   r`   rÚ   s      r;   rf   zSmolLM3MLP.forward%  sA   € Ø—N’N 4§;¢;¨t¯~ª~¸aÑ/@Ô/@Ñ#AÔ#AÀDÇLÂLÐQRÁOÄOÑ#SÑTÔTˆ	ØÐr<   )rh   ri   rj   r.   rf   rq   rr   s   @r;   rÔ   rÔ     sG   ø€ € € € € ð0ð 0ð 0ð 0ð 0ðð ð ð ð ð ð r<   rÔ   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SmolLM3DecoderLayerr'   r¡   c                 ó4  •— t          ¦   «                              ¦   «          |j        | _        t          ||¬¦  «        | _        t          |¦  «        | _        t          |j        |j        ¬¦  «        | _	        t          |j        |j        ¬¦  «        | _
        d S )N)r'   r¡   ©rÄ   )r-   r.   rF   r    Ú	self_attnrÔ   ÚmlprÂ   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormr³   s      €r;   r.   zSmolLM3DecoderLayer.__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-¨fÔ.@ÀfÔFYÐZÑZÔZˆÔÝ(6°vÔ7IÈvÔObÐ(cÑ(cÔ(cˆÔ%Ð%Ð%r<   NFr€   r�   ra   rµ   Ú	use_cacher´   r�   r>   c           
      óÎ   — |}|                       |¦  «        } | j        d||||||dœ|¤Ž\  }}	||z   }|}|                      |¦  «        }|                      |¦  «        }||z   }|S )N)r€   r�   ra   rµ   rè   r´   © )ræ   rã   rç   rä   )
r7   r€   r�   ra   rµ   rè   r´   r�   ÚresidualÚ_s
             r;   rf   zSmolLM3DecoderLayer.forward5  s¡   € ð !ˆØ×,Ò,¨]Ñ;Ô;ˆà)˜4œ>ð 
Ø'Ø)Ø%Ø+ØØ 3ð
ð 
ð ð
ð 
Ñˆ�qð ! =Ñ0ˆð !ˆØ×5Ò5°mÑDÔDˆØŸš Ñ/Ô/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;   rà   rà   *  sÿ   ø€ € € € € ðd˜}ð d¸ð dð dð dð dð dð dð /3Ø04Ø(,Ø!&ØHLðð à”|ðð œ tÑ+ðð Ô&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5¤<°´Ð#=Ô>ÀÑEðð Ð+Ô,ðð 
Œðð ð ð ð ð ð ð r<   rà   c                   óL   — e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedœZdS )ÚSmolLM3PreTrainedModelr'   ÚmodelTrà   rµ   )r€   Ú
attentionsN)rh   ri   rj   r#   rl   Úbase_model_prefixÚsupports_gradient_checkpointingÚ_no_split_modulesÚ_skip_keys_device_placementÚ_supports_flash_attnÚ_supports_sdpaÚ_supports_flex_attnÚ_can_compile_fullgraphÚ_supports_attention_backendrà   r    Ú_can_record_outputsrê   r<   r;   rð   rð   U  sl   € € € € € € àÐÐÑØÐØ&*Ð#Ø.Ð/ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà,Ø&ðð ÐÐÐr<   rð   c                   óâ   ‡ — e Zd Zdefˆ fd„Zeee	 	 	 	 	 	 ddej	        dz  dej
        dz  dej	        dz  dedz  dej        dz  d	edz  d
ee         defd„¦   «         ¦   «         ¦   «         Zˆ xZS )ÚSmolLM3Modelr'   c                 ó  •‡— t          ¦   «                              ‰¦  «         ‰j        | _        ‰j        | _        t          j        ‰j        ‰j        | j        ¦  «        | _        t          j	        ˆfd„t          ‰j        ¦  «        D ¦   «         ¦  «        | _        t          ‰j        ‰j        ¬¦  «        | _        t!          ‰¬¦  «        | _        d| _        d| j        j        v | _        |                      ¦   «          d S )Nc                 ó0   •— g | ]}t          ‰|¦  «        ‘ŒS rê   )rà   )Ú.0r¡   r'   s     €r;   ú
<listcomp>z)SmolLM3Model.__init__.<locals>.<listcomp>q  s$   ø€ ÐeÐeÐe¸	Õ  ¨Ñ3Ô3ÐeÐeÐer<   râ   ©r'   Fr¥   )r-   r.   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	EmbeddingrF   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersÚlayersrÂ   rå   Únormr%   Ú
rotary_embÚgradient_checkpointingr'   r±   Úhas_sliding_layersÚ	post_initrÝ   s    `€r;   r.   zSmolLM3Model.__init__j  sæ   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#Ø"5¸¼Ô9PÐ"PˆÔð 	�ŠÑÔÐÐÐr<   NÚ	input_idsr�   ra   rµ   Úinputs_embedsrè   r�   r>   c           
      óø  — |d u |d uz  rt          d¦  «        ‚|€|                      |¦  «        }|r|€t          | j        ¬¦  «        }|€V|�|                     ¦   «         nd}t          j        |j        d         |j        ¬¦  «        |z   }| 	                    d¦  «        }t          |x}	t          ¦  «        s1| j        ||||dœ}
dt          di |
¤Ži}	| j        rt          di |
¤Ž|	d<   |}|                      ||¦  «        }t!          | j        d | j        j        …         ¦  «        D ]*\  }} ||f|	| j        j        |                  ||||d	œ|¤Ž}Œ+|                      |¦  «        }t+          ||r|nd ¬
¦  «        S )Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r"   )r8   )r'   r  r�   rµ   ra   Úfull_attentionr¥   )r�   r´   ra   rµ   rè   )Úlast_hidden_staterµ   rê   )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  r�   ra   rµ   r  rè   r�   Úpast_seen_tokensÚcausal_mask_mappingÚmask_kwargsr€   r´   ÚiÚdecoder_layers                  r;   rf   zSmolLM3Model.forward{  s  € ð ˜Ð -°tÐ";Ñ<ð 	[ÝÐYÑZÔZÐZàÐ Ø ×-Ò-¨iÑ8Ô8ˆMà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4ˆLõ °Ð?Ð-ÅÑFÔFð 	lð œ+Ø!.Ø"0Ø#2Ø ,ðð ˆKð !Õ"4Ð"CÐ"C°{Ð"CÐ"Cð#Ðð Ô&ð lÝ;\Ð;kÐ;kÐ_jÐ;kÐ;kÐ#Ð$7Ñ8à%ˆØ"Ÿošo¨m¸\ÑJÔJÐå )¨$¬+Ð6U¸¼Ô8UÐ6UÔ*VÑ WÔ Wð 		ð 		ÑˆAˆ}Ø)˜MØðà2°4´;Ô3JÈ1Ô3MÔNØ$7Ø)Ø /Ø#ðð ð ðð ˆMˆMð Ÿ	š	 -Ñ0Ô0ˆÝ&Ø+Ø/8ÐB˜O˜O¸dð
ñ 
ô 
ð 	
r<   )NNNNNN)rh   ri   rj   r#   r.   r    r!   r   rH   rí   rk   r   ÚFloatTensorrî   r   r   r   rf   rq   rr   s   @r;   rþ   rþ   h  s  ø€ € € € € ð˜}ð ð ð ð ð ð ð"  ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ð<
ð <
àÔ# dÑ*ð<
ð œ tÑ+ð<
ð Ô&¨Ñ-ð	<
ð
  ™ð<
ð Ô(¨4Ñ/ð<
ð ˜$‘;ð<
ð Ð+Ô,ð<
ð 
!ð<
ð <
ð <
ñ „^ñ „_ñ  Ôð<
ð <
ð <
ð <
ð <
r<   rþ   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SmolLM3ForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputr€   Úlogitsc                 óþ   •— t          ¦   «                              |¦  «         t          |¦  «        | _        |j        | _        t          j        |j        |j        d¬¦  «        | _        |  	                    ¦   «          d S )NFr£   )
r-   r.   rþ   rñ   r  r   r¨   rF   r#  r  rÝ   s     €r;   r.   zSmolLM3ForCausalLM.__init__Ã  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r<   Nr   r  r�   ra   rµ   r  Úlabelsrè   Úlogits_to_keepr�   r>   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 )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  r�   ra   rµ   r  rè   N)r%  r'  r  )Úlossr%  rµ   r€   rò   rê   )rñ   r  rY   rn   Úslicer#  Úloss_functionr'   r  r   rµ   r€   rò   )r7   r  r�   ra   rµ   r  r'  rè   r(  r�   Úoutputsr€   Úslice_indicesr%  r*  s                  r;   rf   zSmolLM3ForCausalLM.forwardÌ  sô   € ð> ,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 	
r<   )NNNNNNNr   )rh   ri   rj   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr.   r   r   rH   rí   rk   r   r   rî   rn   r   r   r   rf   rq   rr   s   @r;   r"  r"  ½  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6
ð 6
àÔ# dÑ*ð6
ð œ tÑ+ð6
ð Ô&¨Ñ-ð	6
ð
  ™ð6
ð Ô(¨4Ñ/ð6
ð Ô  4Ñ'ð6
ð ˜$‘;ð6
ð ˜eœlÑ*ð6
ð Ð+Ô,ð6
ð 
 ð6
ð 6
ð 6
ñ „^ñ Ôð6
ð 6
ð 6
ð 6
ð 6
r<   r"  c                   ó   — e Zd ZdS )Ú SmolLM3ForSequenceClassificationN©rh   ri   rj   rê   r<   r;   r3  r3    ó   € € € € € Ø€Dr<   r3  c                   ó   — e Zd ZdS )ÚSmolLM3ForTokenClassificationNr4  rê   r<   r;   r7  r7    r5  r<   r7  c                   ó   — e Zd ZdZdS )ÚSmolLM3ForQuestionAnsweringÚtransformerN)rh   ri   rj   ró   rê   r<   r;   r9  r9    s   € € € € € Ø%ÐÐÐr<   r9  )rð   rþ   r"  r3  r7  r9  )r"   )rˆ   )EÚcollections.abcr   Útypingr   rH   r   Úactivationsr   Úcache_utilsr   r	   Ú
generationr
   Úintegrationsr   r   r   Úmasking_utilsr   r   Úmodeling_flash_attention_utilsr   Úmodeling_layersr   r   r   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   r    Úutils.output_capturingr!   Úconfiguration_smollm3r#   ÚModuler%   rv   r   rk   rn   r‡   rL   rž   r    rÂ   rÔ   rà   rð   rþ   r"  r3  r7  r9  Ú__all__rê   r<   r;   ú<module>rN     s"  ðð* %Ð $Ð $Ð $Ð $Ð $Ø Ð Ð Ð Ð Ð à €€€Ø Ð Ð Ð Ð Ð à !Ð !Ð !Ð !Ð !Ð !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 RÐ RÐ RÐ RÐ RÐ RÐ RÐ RØ BÐ BÐ BÐ BÐ BÐ Bðð ð ð ð ð ð ð ð ð ð ð ð PÐ 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 ><˜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I)ð I)ð I)ð I)ð I)�r”yñ I)ô I)ñ +Ô*ðI)ðX 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 �”ñ ô ð ð (ð (ð (ð (ð (Ð4ñ (ô (ð (ðV ðð ð ð ð ˜_ñ ô ñ „ðð$ ðQ
ð Q
ð Q
ð Q
ð Q
Ð)ñ Q
ô Q
ñ „ðQ
ðh ðF
ð F
ð F
ð F
ð F
Ð/°ñ F
ô F
ñ „ðF
ðR	ð 	ð 	ð 	ð 	Ð'GÐI_ñ 	ô 	ð 	ð	ð 	ð 	ð 	ð 	Ð$AÐCYñ 	ô 	ð 	ð&ð &ð &ð &ð &Ð"=Ð?Uñ &ô &ð &ðð ð €€€r<   