Ë
    ÛÓ^j²T  ã                   óþ  — 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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' ddl(m)Z)m*Z* ddl+m,Z, ddl-m.Z.  G d„ dej^                  «      Z0 ed«       G d„ dej^                  «      «       Z1 G d„ dej^                  «      Z2d„ Z3 ed«      d@d„«       Z4d ejj                  d!e6d"ejj                  fd#„Z7	 dAd$ej^                  d%ejj                  d&ejj                  d'ejj                  d(ejj                  dz  d)e8d*e8d+e$e&   fd,„Z9 ee4«       G d-„ d.ej^                  «      «       Z: G d/„ d0e«      Z;e G d1„ d2e"«      «       Z<e G d3„ d4e<«      «       Z= ed5¬6«       G d7„ d8e<e«      «       Z> ed5¬6«       G d9„ d:ee<«      «       Z? ed5¬6«       G d;„ d<ee<«      «       Z@ ed5¬6«       G d=„ d>ee<«      «       ZAg d?¢ZBy)Bé    )ÚCallable)ÚOptionalN)Únn)Úauto_docstringé   )Ú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GenericForQuestionAnsweringÚ GenericForSequenceClassificationÚGenericForTokenClassificationÚGradientCheckpointingLayer)ÚBaseModelOutputWithPastÚCausalLMOutputWithPast)ÚROPE_INIT_FUNCTIONSÚdynamic_rope_update)ÚALL_ATTENTION_FUNCTIONSÚPreTrainedModel)ÚUnpack)ÚTransformersKwargsÚcan_return_tuple)Úmaybe_autocastÚmerge_with_config_defaults)Úcapture_outputsé   )ÚArceeConfigc                   ó$   ‡ — e Zd Zˆ fd„Zd„ Zˆ xZS )ÚArceeMLPc                 ó�  •— t         ‰| �  «        || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  |j                  ¬«      | _        t        j                  | j                  | j                  |j                  ¬«      | _	        t        |j                     | _        y )N©Úbias)ÚsuperÚ__init__ÚconfigÚhidden_sizeÚintermediate_sizer   ÚLinearÚmlp_biasÚup_projÚ	down_projr   Ú
hidden_actÚact_fn©Úselfr)   Ú	__class__s     €ús/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/arcee/modeling_arcee.pyr(   zArceeMLP.__init__3   s‘   ø€ Ü‰ÑÔØˆŒØ!×-Ñ-ˆÔØ!'×!9Ñ!9ˆÔÜ—y‘y ×!1Ñ!1°4×3IÑ3IÐPV×P_ÑP_Ô`ˆŒÜŸ™ 4×#9Ñ#9¸4×;KÑ;KÐRX×RaÑRaÔbˆŒÜ˜V×.Ñ.Ñ/ˆ�ó    c                 ó`   — | j                  | j                  | j                  |«      «      «      S ©N)r/   r1   r.   )r3   Úxs     r5   ÚforwardzArceeMLP.forward<   s"   € Ø�~‰~˜dŸk™k¨$¯,©,°q«/Ó:Ó;Ð;r6   )Ú__name__Ú
__module__Ú__qualname__r(   r:   Ú__classcell__©r4   s   @r5   r#   r#   2   s   ø„ ô0ö<r6   r#   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ArceeRMSNormÚepsÚreturnNc                 óŠ   •— t         ‰| �  «        t        j                  t	        j
                  |«      «      | _        || _        y)z;
        ArceeRMSNorm is equivalent to T5LayerNorm
        N)r'   r(   r   Ú	ParameterÚtorchÚonesÚweightÚvariance_epsilon)r3   r*   rC   r4   s      €r5   r(   zArceeRMSNorm.__init__B   s1   ø€ ô 	‰ÑÔÜ—l‘l¤5§:¡:¨kÓ#:Ó;ˆŒØ #ˆÕr6   Úhidden_statesc                 ó"  — |j                   }|j                  t        j                  «      }|j	                  d«      j                  dd¬«      }|t        j                  || j                  z   «      z  }| j                  |j                  |«      z  S )Né   éÿÿÿÿT)Úkeepdim)	ÚdtypeÚtorG   Úfloat32ÚpowÚmeanÚrsqrtrJ   rI   )r3   rK   Úinput_dtypeÚvariances       r5   r:   zArceeRMSNorm.forwardJ   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:r6   c                 ó^   — t        | j                  j                  «      › d| j                  › �S )Nz, eps=)ÚtuplerI   ÚshaperJ   )r3   s    r5   Ú
extra_reprzArceeRMSNorm.extra_reprQ   s*   € Ü˜Ÿ™×)Ñ)Ó*Ð+¨6°$×2GÑ2GÐ1HÐIÐIr6   )g�íµ ÷Æ°>)
r;   r<   r=   Úfloatr(   rG   ÚTensorr:   r[   r>   r?   s   @r5   rB   rB   @   s7   ø„ ñ$¨ð $¸$õ $ð; U§\¡\ð ;°e·l±ló ;öJr6   rB   c                   óÄ   ‡ — e Zd ZU ej                  ed<   ddefˆ fd„Ze	 	 	 ddedz  de	d   de
dz  ded	ef   fd
„«       Z ej                  «       ed„ «       «       Zˆ xZS )ÚArceeRotaryEmbeddingÚinv_freqNr)   c                 ó®  •— t         ‰| �  «        |j                  | _        |j                  | _        || _        | j
                  j                  d   | _        | j                  }| j                  dk7  rt        | j                     } || j
                  |«      \  }| _
        | j                  d|d¬«       | j                  d|j                  «       d¬«       y )NÚ	rope_typeÚdefaultr`   F)Ú
persistentÚoriginal_inv_freq)r'   r(   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenr)   Úrope_parametersrb   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)r3   r)   ÚdeviceÚrope_init_fnr`   r4   s        €r5   r(   zArceeRotaryEmbedding.__init__X   s¶   ø€ Ü‰ÑÔØ"(×"@Ñ"@ˆÔØ$*×$BÑ$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�$Ô(à×Ñ˜Z¨¸eÐÔDØ×ÑÐ0°(·.±.Ó2BÈuÐÕUr6   rn   ztorch.deviceÚseq_lenrD   ztorch.Tensorc                 ó  — | j                   d   }t        | dd«      xs | j                  | j                  z  }d}d|t	        j
                  d|dt        j                  ¬«      j                  |t        j                  ¬«      |z  z  z  }||fS )	a¨  
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        Ú
rope_thetaÚhead_dimNg      ð?r   rM   ©rP   )rn   rP   )	ri   Úgetattrr*   Únum_attention_headsrG   ÚarangeÚint64rQ   r\   )r)   rn   rp   ÚbaseÚdimÚattention_factorr`   s          r5   rj   z4ArceeRotaryEmbedding.compute_default_rope_parametersh   s�   € ð& ×%Ñ% lÑ3ˆÜ�f˜j¨$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)r6   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   rN   r    ÚmpsÚcpuF)Údevice_typeÚenabledrM   ©rz   rt   )r`   r\   ÚexpandrZ   rQ   rn   Ú
isinstanceÚtypeÚstrr   Ú	transposerG   ÚcatÚcosrk   ÚsinrP   )
r3   r9   Úposition_idsÚinv_freq_expandedÚposition_ids_expandedr   ÚfreqsÚembrˆ   r‰   s
             r5   r:   zArceeRotaryEmbedding.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$r8   ©NNN)r;   r<   r=   rG   r]   Ú__annotations__r!   r(   Ústaticmethodr   ÚintrY   r\   rj   Úno_gradr   r:   r>   r?   s   @r5   r_   r_   U   s˜   ø… Ø�l‰lÓñV˜{õ Vð  à%)Ø+/Ø"ñ*Ø˜dÑ"ð*à˜Ñ(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ñ	%ò	*ó ð*ð: €U‡]�]ƒ_Øñ<ó ó ô<r6   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..NrN   rM   r�   )rZ   rG   r‡   )r9   Úx1Úx2s      r5   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'r6   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krˆ   r‰   Úunsqueeze_dimÚq_embedÚk_embeds          r5   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r6   rK   Ún_reprD   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)rZ   r‚   Úreshape)rK   r¡   ÚbatchÚnum_key_value_headsÚslenrs   s         r5   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Tr6   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 )NrM   r   rN   )rz   rP   )ÚpÚtrainingr    )r§   Únum_key_value_groupsrG   Úmatmulr†   r   Ú
functionalÚsoftmaxrR   rQ   rP   r®   r²   Ú
contiguous)r¨   r©   rª   r«   r¬   r­   r®   r¯   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r5   Úeager_attention_forwardr¼   Ã   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$Ð$r6   c                   óø   ‡ — 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                  f   fd„Zˆ xZS )ÚArceeAttentionz=Multi-headed attention from 'Attention Is All You Need' paperr)   Ú	layer_idxc                 ód  •— 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                  ¬«      | _        y )Nrs   g      à¿Tr%   )r'   r(   r)   r¿   ru   r*   rv   rs   r¥   r³   r­   Úattention_dropoutÚ	is_causalr   r,   Úattention_biasÚq_projÚk_projÚv_projÚo_proj©r3   r)   r¿   r4   s      €r5   r(   zArceeAttention.__init__à   sM  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�r6   NrK   Úposition_embeddingsr¬   Úpast_key_valuesr¯   rD   c                 ó
  — |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                  sdn| j                   | j"                  dœ|¤Ž\  }} |j$                  g |¢d‘­Ž j'                  «       }| j)                  |«      }||fS )NrN   r    rM   ç        )r®   r­   )rZ   rs   rÄ   Úviewr†   rÅ   rÆ   r    Úupdater¿   r   Úget_interfacer)   Ú_attn_implementationr¼   r²   rÁ   r­   r£   r·   rÇ   )r3   rK   rÉ   r¬   rÊ   r¯   Úinput_shapeÚhidden_shapeÚquery_statesr¸   r¹   rˆ   r‰   Úattention_interfacer»   rº   s                   r5   r:   zArceeAttention.forward÷   s‘  € ð $×)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  $Ÿ}š}‘C°$×2HÑ2HØ—L‘Lñ	%
ð ñ	%
Ñ!ˆ�\ð *�k×)Ñ)Ð;¨;Ð;¸Ò;×FÑFÓHˆØ—k‘k +Ó.ˆØ˜LÐ(Ð(r6   r�   )r;   r<   r=   Ú__doc__r!   r’   r(   rG   r]   rY   r	   r   r   r:   r>   r?   s   @r5   r¾   r¾   Ü   s¥   ø„ áGð
˜{ð 
°sõ 
ð4 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\™\Ð)Ñ	*÷&)r6   r¾   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ArceeDecoderLayerr)   r¿   c                 ó   •— t         ‰| �  «        |j                  | _        t        ||¬«      | _        t        |«      | _        t        |j                  |j                  ¬«      | _	        t        |j                  |j                  ¬«      | _
        y )N)r)   r¿   ©rC   )r'   r(   r*   r¾   Ú	self_attnr#   ÚmlprB   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormrÈ   s      €r5   r(   zArceeDecoderLayer.__init__!  sl   ø€ Ü‰ÑÔØ!×-Ñ-ˆÔä'¨vÀÔKˆŒä˜FÓ#ˆŒÜ+¨F×,>Ñ,>ÀF×DWÑDWÔXˆÔÜ(4°V×5GÑ5GÈV×M`ÑM`Ô(aˆÕ%r6   NrK   r¬   rŠ   rÊ   Ú	use_cacherÉ   r¯   rD   c           
      óÀ   — |}| j                  |«      } | j                  d||||||dœ|¤Ž\  }}	||z   }|}| j                  |«      }| j                  |«      }||z   }|S )N)rK   r¬   rŠ   rÊ   rß   rÉ   © )rÝ   rÚ   rÞ   rÛ   )
r3   rK   r¬   rŠ   rÊ   rß   rÉ   r¯   ÚresidualÚ_s
             r5   r:   zArceeDecoderLayer.forward+  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r6   )NNNFN)r;   r<   r=   r!   r’   r(   rG   r]   Ú
LongTensorr	   ÚboolrY   r   r   r:   r>   r?   s   @r5   r×   r×      s¾   ø„ ðb˜{ð b°sõ bð /3Ø04Ø(,Ø!&ØHLñà—|‘|ðð Ÿ™ tÑ+ðð ×&Ñ&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5§<¡<°·±Ð#=Ñ>ÀÑEðð Ð+Ñ,ðð 
�‰÷r6   r×   c                   óJ   — e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedœZy)ÚArceePreTrainedModelr)   ÚmodelTr×   rÊ   )rK   Ú
attentionsN)r;   r<   r=   r!   r�   Úbase_model_prefixÚsupports_gradient_checkpointingÚ_no_split_modulesÚ_skip_keys_device_placementÚ_supports_flash_attnÚ_supports_sdpaÚ_supports_flex_attnÚ_can_compile_fullgraphÚ_supports_attention_backendr×   r¾   Ú_can_record_outputsrá   r6   r5   rç   rç   K  sQ   … àÓØÐØ&*Ð#Ø,Ð-ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà*Ø$ñÑr6   rç   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
ArceeModelr)   c           	      ó  •— t         ‰| �  |«       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                  «      | _        t        j                  t        |j                  «      D �cg c]  }t        ||«      ‘Œ c}«      | _        t        |j                  |j                  ¬«      | _        t#        |¬«      | _        d| _        | j)                  «        y c c}w )NrÙ   ©r)   F)r'   r(   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	Embeddingr*   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersr×   ÚlayersrB   rÜ   Únormr_   Ú
rotary_embÚgradient_checkpointingÚ	post_initrÈ   s      €r5   r(   zArceeModel.__init__`  sÆ   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‘mÜCHÈ×IaÑIaÓCbÖc°iÔ˜v yÕ1Òcó
ˆŒô ! ×!3Ñ!3¸×9LÑ9LÔMˆŒ	Ü.°fÔ=ˆŒØ&+ˆÔ#ð 	�‰Õùò ds   ÂDNÚ	input_idsr¬   rŠ   rÊ   Úinputs_embedsrß   r¯   rD   c           
      ó<  — |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        | j                  ||||¬«      }	|}
| j                  |
|¬«      }| j                  d | j                  j                   D ]  } ||
f|	||||dœ|¤Ž}
Œ | j                  |
«      }
t        |
|¬	«      S )
Nz:You must specify exactly one of input_ids or inputs_embedsr÷   r   r    )rn   )r)   r  r¬   rÊ   rŠ   )rŠ   )r¬   rÉ   rŠ   rÊ   rß   )Úlast_hidden_staterÊ   )Ú
ValueErrorrü   r
   r)   Úget_seq_lengthrG   rw   rZ   rn   rš   r   r  r   rÿ   r  r   )r3   r  r¬   rŠ   rÊ   r  rß   r¯   Úpast_seen_tokensÚcausal_maskrK   rÉ   Údecoder_layers                r5   r:   zArceeModel.forwardp  sL  € 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4ˆLä(Ø—;‘;Ø'Ø)Ø+Ø%ô
ˆð &ˆØ"Ÿo™o¨mÈ,˜oÓWÐà!Ÿ[™[Ð)H¨4¯;©;×+HÑ+HÐIò 		ˆMÙ)Øðà*Ø$7Ø)Ø /Ø#ñð ñ‰Mð		ð Ÿ	™	 -Ó0ˆÜ&Ø+Ø+ô
ð 	
r6   )NNNNNN)r;   r<   r=   r!   r(   r   r   r   rG   rä   r]   r	   ÚFloatTensorrå   r   r   r   r:   r>   r?   s   @r5   rõ   rõ   ^  sÍ   ø„ ð˜{õ ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ñ2
à×#Ñ# dÑ*ð2
ð Ÿ™ tÑ+ð2
ð ×&Ñ&¨Ñ-ð	2
ð
  ™ð2
ð ×(Ñ(¨4Ñ/ð2
ð ˜$‘;ð2
ð Ð+Ñ,ð2
ð 
!ò2
ó ó ó  ô2
r6   rõ   zarcee-ai/AFM-4.5B)Ú
checkpointc                   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ArceeForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputrK   Úlogitsc                 óè   •— t         ‰| �  |«       t        |«      | _        |j                  | _        t        j                  |j                  |j                  d¬«      | _        | j                  «        y )NFr%   )
r'   r(   rõ   rè   rú   r   r,   r*   r  r  r2   s     €r5   r(   zArceeForCausalLM.__init__®  sU   ø€ Ü‰Ñ˜Ô Ü Ó'ˆŒ
Ø ×+Ñ+ˆŒÜ—y‘y ×!3Ñ!3°V×5FÑ5FÈUÔSˆŒð 	�‰Õr6   Nr  r¬   rŠ   rÊ   r  Úlabelsrß   Úlogits_to_keepr¯   rD   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 )aÏ  
        Example:

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

        >>> model = ArceeForCausalLM.from_pretrained("meta-arcee/Arcee-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-arcee/Arcee-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¬   rŠ   rÊ   r  rß   N)r  r  rú   )Úlossr  rÊ   rK   ré   rá   )rè   r  rƒ   r’   Úslicer  Úloss_functionr)   rú   r   rÊ   rK   ré   )r3   r  r¬   rŠ   rÊ   r  r  rß   r  r¯   ÚoutputsrK   Úslice_indicesr  r  s                  r5   r:   zArceeForCausalLM.forward·  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 	
r6   )NNNNNNNr   )r;   r<   r=   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr(   r   r   rG   rä   r]   r	   r  rå   r’   r   r   r   r:   r>   r?   s   @r5   r  r  ¨  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 ˜$‘;ð6
ð ˜eŸl™lÑ*ð6
ð Ð+Ñ,ð6
ð 
 ò6
ó ó ô6
r6   r  c                   ó   — e Zd Zy)ÚArceeForSequenceClassificationN©r;   r<   r=   rá   r6   r5   r"  r"  ò  ó   „ àr6   r"  c                   ó   — e Zd ZdZy)ÚArceeForQuestionAnsweringÚtransformerN)r;   r<   r=   rê   rá   r6   r5   r&  r&  ÷  s   „ à%Ñr6   r&  c                   ó   — e Zd Zy)ÚArceeForTokenClassificationNr#  rá   r6   r5   r)  r)  ü  r$  r6   r)  )r  r&  r"  r)  rõ   rç   )r    )rÌ   )CÚcollections.abcr   Útypingr   rG   r   Útransformers.utilsr   Úactivationsr   Úcache_utilsr	   r
   Ú
generationr   Úintegrationsr   r   r   Úmasking_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   Úutils.genericr   r   Úutils.output_capturingr   Úconfiguration_arceer!   ÚModuler#   rB   r_   r—   r    r]   r’   r§   r\   r¼   r¾   r×   rç   rõ   r  r"  r&  r)  Ú__all__rá   r6   r5   ú<module>r=     s_  ðõ* %Ý ã Ý å -å !ß .Ý )ß fÑ fÝ /÷ó ÷ Pß Kß FÝ &ß 9ß GÝ 5Ý ,ô<ˆr�y‰yô <ñ ˜YÓ'ôJ�2—9‘9ó Jó (ðJô(><˜2Ÿ9™9ô ><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@)�R—Y‘Yó @)ó +ð@)ôF(Ð2ô (ðV ô˜?ó ó ðð$ ôF
Ð%ó F
ó ðF
ñR Ð.Ô/ôF
Ð+¨_ó F
ó 0ðF
ñR Ð.Ô/ô	Ð%EÐG[ó 	ó 0ð	ñ Ð.Ô/ô&Ð ;Ð=Qó &ó 0ð&ñ Ð.Ô/ô	Ð"?ÐAUó 	ó 0ð	ò�r6   