§
    ‚Štj²T  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	 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¦  «        dAd„¦   «         Z4d ej5        d!e6d"ej5        fd#„Z7	 dBd%ej/        d&ej5        d'ej5        d(ej5        d)ej5        dz  d*e8d+e8d,e$e&         fd-„Z9 ee4¦  «         G d.„ d/ej/        ¦  «        ¦   «         Z: G d0„ d1e¦  «        Z;e G d2„ d3e"¦  «        ¦   «         Z<e G d4„ d5e<¦  «        ¦   «         Z= ed6¬7¦  «         G d8„ d9e<e¦  «        ¦   «         Z> ed6¬7¦  «         G d:„ d;ee<¦  «        ¦   «         Z? ed6¬7¦  «         G d<„ d=ee<¦  «        ¦   «         Z@ ed6¬7¦  «         G d>„ d?ee<¦  «        ¦   «         ZAg d@¢ZBdS )Cé    )ÚCallable)ÚOptionalN)Únn)Úauto_docstringé   )ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úuse_kernel_forward_from_hubÚuse_kernel_func_from_hubÚuse_kernelized_func)Úcreate_causal_mask)ÚGenericForQuestionAnsweringÚ GenericForSequenceClassificationÚGenericForTokenClassificationÚGradientCheckpointingLayer)ÚBaseModelOutputWithPastÚCausalLMOutputWithPast)ÚROPE_INIT_FUNCTIONSÚdynamic_rope_update)ÚALL_ATTENTION_FUNCTIONSÚPreTrainedModel)ÚUnpack)ÚTransformersKwargsÚcan_return_tuple)Úmaybe_autocastÚmerge_with_config_defaults)Úcapture_outputsé   )ÚArceeConfigc                   ó$   ‡ — 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                 | _        d S )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     €úf/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/arcee/modeling_arcee.pyr(   zArceeMLP.__init__3   s�   ø€ Ý‰Œ×ÒÑÔÐØˆŒØ!Ô-ˆÔØ!'Ô!9ˆÔ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ó    c                 óx   — |                       |                      |                      |¦  «        ¦  «        ¦  «        S ©N)r/   r1   r.   )r3   Úxs     r5   ÚforwardzArceeMLP.forward<   s*   € Ø�~Š~˜dŸkšk¨$¯,ª,°q©/¬/Ñ:Ô:Ñ;Ô;Ð;r6   )Ú__name__Ú
__module__Ú__qualname__r(   r:   Ú__classcell__©r4   s   @r5   r#   r#   2   sG   ø€ € € € € ð0ð 0ð 0ð 0ð 0ð<ð <ð <ð <ð <ð <ð <r6   r#   ÚRMSNormc                   óT   ‡ — e Zd Zd	deddfˆ fd„Zdej        dej        fd„Zd„ Zˆ xZ	S )
ÚArceeRMSNormç�íµ ÷Æ°>ÚepsÚreturnNc                 ó¬   •— t          ¦   «                              ¦   «          t          j        t	          j        |¦  «        ¦  «        | _        || _        dS )z;
        ArceeRMSNorm is equivalent to T5LayerNorm
        N)r'   r(   r   Ú	ParameterÚtorchÚonesÚweightÚvariance_epsilon)r3   r*   rD   r4   s      €r5   r(   zArceeRMSNorm.__init__B   sD   ø€ õ 	‰Œ×ÒÑÔÐÝ”l¥5¤:¨kÑ#:Ô#:Ñ;Ô;ˆŒØ #ˆÔÐÐr6   Úhidden_statesc                 ó  — |j         }|                     t          j        ¦  «        }|                     d¦  «                             dd¬¦  «        }|t          j        || j        z   ¦  «        z  }| j        |                     |¦  «        z  S )Né   éÿÿÿÿT)Úkeepdim)	ÚdtypeÚtorH   Úfloat32ÚpowÚmeanÚrsqrtrK   rJ   )r3   rL   Úinput_dtypeÚvariances       r5   r:   zArceeRMSNorm.forwardJ   s|   € Ø#Ô)ˆØ%×(Ò(­¬Ñ7Ô7ˆØ ×$Ò$ QÑ'Ô'×,Ò,¨R¸Ð,Ñ>Ô>ˆØ%­¬°H¸tÔ?TÑ4TÑ(UÔ(UÑUˆØŒ{˜]×-Ò-¨kÑ:Ô:Ñ:Ð:r6   c                 óH   — t          | j        j        ¦  «        › d| j        › �S )Nz, eps=)ÚtuplerJ   ÚshaperK   )r3   s    r5   Ú
extra_reprzArceeRMSNorm.extra_reprQ   s&   € Ý˜œÔ)Ñ*Ô*ÐIÐI°$Ô2GÐIÐIÐIr6   )rC   )
r;   r<   r=   Úfloatr(   rH   ÚTensorr:   r\   r>   r?   s   @r5   rB   rB   @   sŒ   ø€ € € € € ð$ð $¨ð $¸$ð $ð $ð $ð $ð $ð $ð; U¤\ð ;°e´lð ;ð ;ð ;ð ;ðJð Jð Jð Jð Jð Jð Jr6   rB   c                   óÔ   ‡ — e Zd ZU ej        ed<   ddefˆ fd„Ze	 	 	 ddedz  de	d         de
dz  ded	ef         fd
„¦   «         Z ej        ¦   «         ed„ ¦   «         ¦   «         Zˆ xZS )ÚArceeRotaryEmbeddingÚinv_freqNr)   c                 ó²  •— t          ¦   «                              ¦   «          |j        | _        |j        | _        || _        | j        j        d         | _        | j        }| j        dk    rt          | j                 } || j        |¦  «        \  }| _
        |                      d|d¬¦  «         |                      d|                     ¦   «         d¬¦  «         d S )NÚ	rope_typeÚdefaultra   F)Ú
persistentÚoriginal_inv_freq)r'   r(   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenr)   Úrope_parametersrc   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)r3   r)   ÚdeviceÚrope_init_fnra   r4   s        €r5   r(   zArceeRotaryEmbedding.__init__X   sÊ   ø€ Ý‰Œ×ÒÑÔÐØ"(Ô"@ˆÔØ$*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Ur6   ro   ztorch.deviceÚseq_lenrE   ztorch.Tensorc                 óü   — | j         d         }t          | dd¦  «        p| j        | j        z  }d}d|t	          j        d|dt          j        ¬¦  «                             |t          j        ¬¦  «        |z  z  z  }||fS )	a¨  
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        Ú
rope_thetaÚhead_dimNg      ð?r   rN   ©rQ   )ro   rQ   )	rj   Úgetattrr*   Únum_attention_headsrH   ÚarangeÚint64rR   r]   )r)   ro   rq   ÚbaseÚdimÚattention_factorra   s          r5   rk   z4ArceeRotaryEmbedding.compute_default_rope_parametersh   sŒ   € ð& Ô% lÔ3ˆÝ�f˜j¨$Ñ/Ô/Ðc°6Ô3EÈÔIcÑ3cˆàÐð Ø•U”\ ! S¨!µ5´;Ð?Ñ?Ô?×BÒBÈ&ÕX]ÔXcÐBÑdÔdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r6   c                 óN  — | j         d d d …d f                              ¦   «                              |j        d         dd¦  «                             |j        ¦  «        }|d d …d d d …f                              ¦   «         }t          |j        j        t          ¦  «        r|j        j        dk    r|j        j        nd}t          |d¬¦  «        5  |                     ¦   «         |                     ¦   «         z   
                    dd¦  «        }t          j        ||fd¬	¦  «        }|                     ¦   «         | j        z  }|                     ¦   «         | j        z  }	d d d ¦  «         n# 1 swxY w Y   |                     |j        ¬
¦  «        |	                     |j        ¬
¦  «        fS )Nr   rO   r    ÚmpsÚcpuF)Údevice_typeÚenabledrN   ©r{   ru   )ra   r]   Úexpandr[   rR   ro   Ú
isinstanceÚtypeÚstrr   Ú	transposerH   ÚcatÚcosrl   ÚsinrQ   )
r3   r9   Úposition_idsÚinv_freq_expandedÚposition_ids_expandedr€   ÚfreqsÚembr‰   rŠ   s
             r5   r:   zArceeRotaryEmbedding.forward†   s·  € ð !œM¨$°°°°4¨-Ô8×>Ò>Ñ@Ô@×GÒGÈÔHZÐ[\ÔH]Ð_aÐcdÑeÔe×hÒhÐijÔiqÑrÔrÐØ ,¨Q¨Q¨Q°°a°a°a¨ZÔ 8× >Ò >Ñ @Ô @Ðå'1°!´(´-ÅÑ'EÔ'EÐkÈ!Ì(Ì-Ð[`ÒJ`ÐJ`�a”h”m�mÐfkˆÝ¨¸UÐCÑCÔCð 	5ð 	5Ø&×,Ò,Ñ.Ô.Ð1F×1LÒ1LÑ1NÔ1NÑN×YÒYÐZ[Ð]^Ñ_Ô_ˆEÝ”)˜U E˜N°Ð3Ñ3Ô3ˆCØ—'’'‘)”)˜dÔ4Ñ4ˆCØ—'’'‘)”)˜dÔ4Ñ4ˆCð		5ð 	5ð 	5ñ 	5ô 	5ð 	5ð 	5ð 	5ð 	5ð 	5ð 	5øøøð 	5ð 	5ð 	5ð 	5ð �vŠv˜AœGˆvÑ$Ô$ c§f¢f°1´7 fÑ&;Ô&;Ð;Ð;s   ÃBE&Å&E*Å-E*r8   ©NNN)r;   r<   r=   rH   r^   Ú__annotations__r!   r(   Ústaticmethodr   ÚintrZ   r]   rk   Úno_gradr   r:   r>   r?   s   @r5   r`   r`   U   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˜dÑ"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 <r6   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..NrO   rN   r‚   )r[   rH   rˆ   )r9   Úx1Úx2s      r5   Úrotate_halfr˜   –   s]   € à	
ˆ3Ð"�!”'˜"”+ Ñ"Ð"Ð"Ô	#€BØ	
ˆ3�”˜”˜qÑ Ð"Ð"Ð"Ô	#€BÝŒ9�r�c˜2�Y BÐ'Ñ'Ô'Ð'r6   Úrotary_pos_embc                 ó¾   — |                      |¦  «        }|                      |¦  «        }| |z  t          | ¦  «        |z  z   }||z  t          |¦  «        |z  z   }||fS )a…  Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    )Ú	unsqueezer˜   )ÚqÚkr‰   rŠ   Úunsqueeze_dimÚq_embedÚk_embeds          r5   Úapply_rotary_pos_embr¡   �   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r6   rL   Ún_reprE   c                 ó¸   — | j         \  }}}}|dk    r| S | dd…dd…ddd…dd…f                              |||||¦  «        } |                      |||z  ||¦  «        S )zÔ
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    r    N)r[   rƒ   Úreshape)rL   r¢   ÚbatchÚnum_key_value_headsÚslenrt   s         r5   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Tr6   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 )NrN   r   rO   )r{   rQ   )ÚpÚtrainingr    )r¨   Únum_key_value_groupsrH   Úmatmulr‡   r   Ú
functionalÚsoftmaxrS   rR   rQ   r°   r´   Ú
contiguous)rª   r«   r¬   r­   r®   r¯   r°   r±   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r5   Úeager_attention_forwardr¾   Ã   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$Ð$r6   c                   óÎ   ‡ — 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                 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        ¬¦  «        | _        d S )Nrt   g      à¿Tr%   )r'   r(   r)   rÁ   rv   r*   rw   rt   r¦   rµ   r¯   Úattention_dropoutÚ	is_causalr   r,   Úattention_biasÚq_projÚk_projÚv_projÚo_proj©r3   r)   rÁ   r4   s      €r5   r(   zArceeAttention.__init__à   sB  ø€ Ý‰Œ×ÒÑÔÐØˆŒØ"ˆŒÝ ¨
°FÔ4FÈ&ÔJdÑ4dÑeÔeˆŒØ$*Ô$>À&ÔB\Ñ$\ˆÔ!Ø”} dÑ*ˆŒØ!'Ô!9ˆÔØˆŒå”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ&¨¬Ñ6¸Ô8JÐQWÔQfð
ñ 
ô 
ˆŒˆˆr6   NrL   Úposition_embeddingsr®   Úpast_key_valuesr±   rE   c                 ó"  — |j         d d…         }g |¢d‘| j        ‘R }|                      |¦  «                             |¦  «                             dd¦  «        }|                      |¦  «                             |¦  «                             dd¦  «        }	|                      |¦  «                             |¦  «                             dd¦  «        }
|\  }}t          ||	||¦  «        \  }}	|�|                     |	|
| j	        ¦  «        \  }	}
t          j        | j        j        t          ¦  «        } || ||	|
|f| j        sdn| j        | j        dœ|¤Ž\  }} |j        g |¢d‘R Ž                      ¦   «         }|                      |¦  «        }||fS )NrO   r    rN   r©   )r°   r¯   )r[   rt   rÆ   Úviewr‡   rÇ   rÈ   r¡   ÚupdaterÁ   r   Úget_interfacer)   Ú_attn_implementationr¾   r´   rÃ   r¯   r¤   r¹   rÉ   )r3   rL   rË   r®   rÌ   r±   Úinput_shapeÚhidden_shapeÚquery_statesrº   r»   r‰   rŠ   Úattention_interfacer½   r¼   s                   r5   r:   zArceeAttention.forward÷   sÀ  € ð $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&‰ˆˆSÝ#7¸ÀjÐRUÐWZÑ#[Ô#[Ñ ˆ�jàÐ&Ø'6×'=Ò'=¸jÈ,ÐX\ÔXfÑ'gÔ'gÑ$ˆJ˜å(?Ô(MØŒKÔ,Õ.Eñ)
ô )
Ðð %8Ð$7ØØØØØð	%
ð  $œ}ÐH�C�C°$Ô2HØ”Lð	%
ð 	%
ð ð	%
ð 	%
Ñ!ˆ�\ð *�kÔ)Ð;¨;Ð;¸Ð;Ð;Ð;×FÒFÑHÔHˆØ—k’k +Ñ.Ô.ˆØ˜LÐ(Ð(r6   r�   )r;   r<   r=   Ú__doc__r!   r“   r(   rH   r^   rZ   r	   r   r   r:   r>   r?   s   @r5   rÀ   rÀ   Ü   så   ø€ € € € € àGÐGð
˜{ð 
°sð 
ð 
ð 
ð 
ð 
ð 
ð4 IMØ.2Ø(,ð&)ð &)à”|ð&)ð # 5¤<°´Ð#=Ô>ÀÑEð&)ð œ tÑ+ð	&)ð
  ™ð&)ð Ð+Ô,ð&)ð 
ˆuŒ|˜Uœ\Ð)Ô	*ð&)ð &)ð &)ð &)ð &)ð &)ð &)ð &)r6   rÀ   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ArceeDecoderLayerr)   rÁ   c                 ó4  •— t          ¦   «                              ¦   «          |j        | _        t          ||¬¦  «        | _        t          |¦  «        | _        t          |j        |j        ¬¦  «        | _	        t          |j        |j        ¬¦  «        | _
        d S )N)r)   rÁ   ©rD   )r'   r(   r*   rÀ   Ú	self_attnr#   ÚmlprB   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormrÊ   s      €r5   r(   zArceeDecoderLayer.__init__!  sƒ   ø€ Ý‰Œ×ÒÑÔÐØ!Ô-ˆÔå'¨vÀÐKÑKÔKˆŒå˜FÑ#Ô#ˆŒÝ+¨FÔ,>ÀFÔDWÐXÑXÔXˆÔÝ(4°VÔ5GÈVÔM`Ð(aÑ(aÔ(aˆÔ%Ð%Ð%r6   NFrL   r®   r‹   rÌ   Ú	use_cacherË   r±   rE   c           
      óÎ   — |}|                       |¦  «        } | j        d||||||dœ|¤Ž\  }}	||z   }|}|                      |¦  «        }|                      |¦  «        }||z   }|S )N)rL   r®   r‹   rÌ   rà   rË   © )rÞ   rÛ   rß   rÜ   )
r3   rL   r®   r‹   rÌ   rà   rË   r±   ÚresidualÚ_s
             r5   r:   zArceeDecoderLayer.forward+  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r6   )NNNFN)r;   r<   r=   r!   r“   r(   rH   r^   Ú
LongTensorr	   ÚboolrZ   r   r   r:   r>   r?   s   @r5   rØ   rØ      sÿ   ø€ € € € € ðb˜{ð b°sð bð bð bð bð bð bð /3Ø04Ø(,Ø!&ØHLðð à”|ðð œ tÑ+ðð Ô&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5¤<°´Ð#=Ô>ÀÑEðð Ð+Ô,ðð 
Œðð ð ð ð ð ð ð r6   rØ   c                   óL   — e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedœZdS )ÚArceePreTrainedModelr)   ÚmodelTrØ   rÌ   )rL   Ú
attentionsN)r;   r<   r=   r!   r‘   Úbase_model_prefixÚsupports_gradient_checkpointingÚ_no_split_modulesÚ_skip_keys_device_placementÚ_supports_flash_attnÚ_supports_sdpaÚ_supports_flex_attnÚ_can_compile_fullgraphÚ_supports_attention_backendrØ   rÀ   Ú_can_record_outputsrâ   r6   r5   rè   rè   K  sl   € € € € € € àÐÐÑØÐØ&*Ð#Ø,Ð-ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà*Ø$ðð ÐÐÐr6   rè   c                   óâ   ‡ — e Zd Zdefˆ fd„Zeee	 	 	 	 	 	 ddej	        dz  dej
        dz  dej	        dz  dedz  dej        dz  d	edz  d
ee         defd„¦   «         ¦   «         ¦   «         Zˆ xZS )Ú
ArceeModelr)   c                 óÞ  •‡— t          ¦   «                              ‰¦  «         ‰j        | _        ‰j        | _        t          j        ‰j        ‰j        | j        ¦  «        | _        t          j	        ˆfd„t          ‰j        ¦  «        D ¦   «         ¦  «        | _        t          ‰j        ‰j        ¬¦  «        | _        t!          ‰¬¦  «        | _        d| _        |                      ¦   «          d S )Nc                 ó0   •— g | ]}t          ‰|¦  «        ‘ŒS râ   )rØ   )Ú.0rÁ   r)   s     €r5   ú
<listcomp>z'ArceeModel.__init__.<locals>.<listcomp>g  s$   ø€ ÐcÐcÐc°iÕ˜v yÑ1Ô1ÐcÐcÐcr6   rÚ   ©r)   F)r'   r(   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	Embeddingr*   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersÚlayersrB   rÝ   Únormr`   Ú
rotary_embÚgradient_checkpointingÚ	post_initr2   s    `€r5   r(   zArceeModel.__init__`  sÔ   øø€ Ý‰Œ×Ò˜Ñ Ô Ð Ø!Ô.ˆÔØ Ô+ˆŒåœL¨Ô):¸FÔ<NÐPTÔP`ÑaÔaˆÔÝ”mØcÐcÐcÐcÅ5ÈÔIaÑCbÔCbÐcÑcÔcñ
ô 
ˆŒõ ! Ô!3¸Ô9LÐMÑMÔMˆŒ	Ý.°fÐ=Ñ=Ô=ˆŒØ&+ˆÔ#ð 	�ŠÑÔÐÐÐr6   NÚ	input_idsr®   r‹   rÌ   Úinputs_embedsrà   r±   rE   c           
      óH  — |d u |d uz  rt          d¦  «        ‚|€|                      |¦  «        }|r|€t          | j        ¬¦  «        }|€V|�|                     ¦   «         nd}t          j        |j        d         |j        ¬¦  «        |z   }| 	                    d¦  «        }t          | j        ||||¬¦  «        }	|}
|                      |
|¬¦  «        }| j        d | j        j        …         D ]} ||
f|	||||dœ|¤Ž}
Œ|                      |
¦  «        }
t          |
|¬	¦  «        S )
Nz:You must specify exactly one of input_ids or inputs_embedsrû   r   r    )ro   )r)   r
  r®   rÌ   r‹   )r‹   )r®   rË   r‹   rÌ   rà   )Úlast_hidden_staterÌ   )Ú
ValueErrorr   r
   r)   Úget_seq_lengthrH   rx   r[   ro   r›   r   r  r  r  r  r   )r3   r	  r®   r‹   rÌ   r
  rà   r±   Úpast_seen_tokensÚcausal_maskrL   rË   Údecoder_layers                r5   r:   zArceeModel.forwardp  sŠ  € ð ˜Ð -°tÐ";Ñ<ð 	[ÝÐYÑZÔZÐZàÐ Ø*.×*;Ò*;¸IÑ*FÔ*FˆMàð 	?˜Ð0Ý*°$´+Ð>Ñ>Ô>ˆOàÐØCRÐC^˜×=Ò=Ñ?Ô?Ð?ÐdeÐÝ œ<¨Ô(;¸AÔ(>À}ÔG[Ð\Ñ\Ô\Ð_oÑoˆLØ'×1Ò1°!Ñ4Ô4ˆLå(Ø”;Ø'Ø)Ø+Ø%ð
ñ 
ô 
ˆð &ˆØ"Ÿošo¨mÈ,˜oÑWÔWÐà!œ[Ð)H¨4¬;Ô+HÐ)HÔIð 		ð 		ˆMØ)˜MØðà*Ø$7Ø)Ø /Ø#ðð ð ðð ˆMˆMð Ÿ	š	 -Ñ0Ô0ˆÝ&Ø+Ø+ð
ñ 
ô 
ð 	
r6   )NNNNNN)r;   r<   r=   r!   r(   r   r   r   rH   rå   r^   r	   ÚFloatTensorræ   r   r   r   r:   r>   r?   s   @r5   rö   rö   ^  s  ø€ € € € € ð˜{ð ð ð ð ð ð ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ð2
ð 2
àÔ# dÑ*ð2
ð œ tÑ+ð2
ð Ô&¨Ñ-ð	2
ð
  ™ð2
ð Ô(¨4Ñ/ð2
ð ˜$‘;ð2
ð Ð+Ô,ð2
ð 
!ð2
ð 2
ð 2
ñ „^ñ „_ñ  Ôð2
ð 2
ð 2
ð 2
ð 2
r6   rö   zarcee-ai/AFM-4.5B)Ú
checkpointc                   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ArceeForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputrL   Úlogitsc                 óþ   •— t          ¦   «                              |¦  «         t          |¦  «        | _        |j        | _        t          j        |j        |j        d¬¦  «        | _        |  	                    ¦   «          d S )NFr%   )
r'   r(   rö   ré   rþ   r   r,   r*   r  r  r2   s     €r5   r(   zArceeForCausalLM.__init__®  sj   ø€ Ý‰Œ×Ò˜Ñ Ô Ð Ý Ñ'Ô'ˆŒ
Ø Ô+ˆŒÝ”y Ô!3°VÔ5FÈUÐSÑSÔSˆŒð 	�ŠÑÔÐÐÐr6   Nr   r	  r®   r‹   rÌ   r
  Úlabelsrà   Úlogits_to_keepr±   rE   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 )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®   r‹   rÌ   r
  rà   N)r  r  rþ   )Úlossr  rÌ   rL   rê   râ   )ré   r  r„   r“   Úslicer  Úloss_functionr)   rþ   r   rÌ   rL   rê   )r3   r	  r®   r‹   rÌ   r
  r  rà   r  r±   ÚoutputsrL   Úslice_indicesr  r  s                  r5   r:   zArceeForCausalLM.forward·  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 	
r6   )NNNNNNNr   )r;   r<   r=   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr(   r   r   rH   rå   r^   r	   r  ræ   r“   r   r   r   r:   r>   r?   s   @r5   r  r  ¨  sK  ø€ € € € € à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hðð ð ð ð ð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø-.ð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 ˜$‘;ð6
ð ˜eœlÑ*ð6
ð Ð+Ô,ð6
ð 
 ð6
ð 6
ð 6
ñ „^ñ Ôð6
ð 6
ð 6
ð 6
ð 6
r6   r  c                   ó   — e Zd ZdS )ÚArceeForSequenceClassificationN©r;   r<   r=   râ   r6   r5   r&  r&  ò  ó   € € € € € à€Dr6   r&  c                   ó   — e Zd ZdZdS )ÚArceeForQuestionAnsweringÚtransformerN)r;   r<   r=   rë   râ   r6   r5   r*  r*  ÷  s   € € € € € à%ÐÐÐr6   r*  c                   ó   — e Zd ZdS )ÚArceeForTokenClassificationNr'  râ   r6   r5   r-  r-  ü  r(  r6   r-  )r  r*  r&  r-  rö   rè   )r    )r©   )CÚcollections.abcr   Útypingr   rH   r   Útransformers.utilsr   Úactivationsr   Úcache_utilsr	   r
   Ú
generationr   Úintegrationsr   r   r   Úmasking_utilsr   Úmodeling_layersr   r   r   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   Úutils.genericr   r   Úutils.output_capturingr   Úconfiguration_arceer!   ÚModuler#   rB   r`   r˜   r¡   r^   r“   r¨   r]   r¾   rÀ   rØ   rè   rö   r  r&  r*  r-  Ú__all__râ   r6   r5   ú<module>rA     sv  ðð* %Ð $Ð $Ð $Ð $Ð $Ø Ð Ð Ð Ð Ð à €€€Ø Ð Ð Ð Ð Ð à -Ð -Ð -Ð -Ð -Ð -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 /Ð /Ð /Ð /Ð /Ð /ð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 9Ð 9Ð 9Ð 9Ð 9Ð 9Ð 9Ð 9Ø GÐ GÐ GÐ GÐ GÐ GÐ GÐ GØ 5Ð 5Ð 5Ð 5Ð 5Ð 5Ø ,Ð ,Ð ,Ð ,Ð ,Ð ,ð<ð <ð <ð <ð <ˆrŒyñ <ô <ð <ð Ð˜YÑ'Ô'ðJð Jð Jð Jð J�2”9ñ Jô Jñ (Ô'ðJð(><ð ><ð ><ð ><ð ><˜2œ9ñ ><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@)ð @)ð @)ð @)ð @)�R”Yñ @)ô @)ñ +Ô*ð@)ðF(ð (ð (ð (ð (Ð2ñ (ô (ð (ðV ðð ð ð ð ˜?ñ ô ñ „ðð$ ðF
ð F
ð F
ð F
ð F
Ð%ñ F
ô F
ñ „ðF
ðR €Ð.Ð/Ñ/Ô/ðF
ð F
ð F
ð F
ð F
Ð+¨_ñ F
ô F
ñ 0Ô/ðF
ðR €Ð.Ð/Ñ/Ô/ð	ð 	ð 	ð 	ð 	Ð%EÐG[ñ 	ô 	ñ 0Ô/ð	ð €Ð.Ð/Ñ/Ô/ð&ð &ð &ð &ð &Ð ;Ð=Qñ &ô &ñ 0Ô/ð&ð €Ð.Ð/Ñ/Ô/ð	ð 	ð 	ð 	ð 	Ð"?ÐAUñ 	ô 	ñ 0Ô/ð	ðð ð €€€r6   