§
    ‚Štj°R  ã                   ó  — d dl Z 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 dd	lmZmZmZ dd
lmZmZ ddlmZmZ ddlmZmZ ddlmZ ddl m!Z!m"Z"m#Z# ddl$m%Z%m&Z& ddl'm(Z( ddl)m*Z*  G d„ dej+        ¦  «        Z, G d„ dej+        ¦  «        Z- G d„ dej+        ¦  «        Z.dej/        de0dej/        fd„Z1	 d8dej+        dej/        d ej/        d!ej/        d"ej/        dz  d#e2d$e2d%ee!         fd&„Z3d'„ Z4d9d(„Z5 G d)„ d*ej+        ¦  «        Z6 G d+„ d,e¦  «        Z7e" G d-„ d.e¦  «        ¦   «         Z8e" G d/„ d0e8¦  «        ¦   «         Z9e" G d1„ d2e8e¦  «        ¦   «         Z: G d3„ d4ee8¦  «        Z; G d5„ d6ee8¦  «        Z<g d7¢Z=dS ):é    N)ÚCallable)ÚOptionalé   )ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úcreate_causal_mask)Ú GenericForSequenceClassificationÚGenericForTokenClassificationÚGradientCheckpointingLayer)ÚBaseModelOutputWithPastÚCausalLMOutputWithPast)ÚROPE_INIT_FUNCTIONSÚdynamic_rope_update)ÚALL_ATTENTION_FUNCTIONSÚPreTrainedModel)ÚUnpack)ÚTransformersKwargsÚauto_docstringÚcan_return_tuple)Úmaybe_autocastÚmerge_with_config_defaults)Úcapture_outputsé   )ÚHeliumConfigc                   ó,   ‡ — e Zd Zdˆ fd„	Zd„ Zd„ Zˆ xZS )ÚHeliumRMSNormç�íµ ÷Æ°>c                 ó¬   •— t          ¦   «                              ¦   «          t          j        t	          j        |¦  «        ¦  «        | _        || _        d S ©N)ÚsuperÚ__init__ÚnnÚ	ParameterÚtorchÚonesÚweightÚvariance_epsilon)ÚselfÚhidden_sizeÚepsÚ	__class__s      €úh/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/helium/modeling_helium.pyr#   zHeliumRMSNorm.__init__0   sB   ø€ Ý‰Œ×ÒÑÔÐÝ”l¥5¤:¨kÑ#:Ô#:Ñ;Ô;ˆŒØ #ˆÔÐÐó    c                 óT  — |j         }|                     t          j        ¦  «        }|                     d¦  «                             dd¬¦  «        }|t          j        || j        z   ¦  «        z  }| j                             t          j        ¦  «        |z                       |¦  «        S )Né   éÿÿÿÿT)Úkeepdim)	ÚdtypeÚtor&   Úfloat32ÚpowÚmeanÚrsqrtr)   r(   )r*   Úhidden_statesÚinput_dtypeÚvariances       r.   ÚforwardzHeliumRMSNorm.forward5   sŠ   € 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”—’�uœ}Ñ-Ô-°Ñ=×AÒAÀ+ÑNÔNÐNr/   c                 óH   — t          | j        j        ¦  «        › d| j        › �S )Nz, eps=)Útupler(   Úshaper)   )r*   s    r.   Ú
extra_reprzHeliumRMSNorm.extra_repr<   s&   € Ý˜œÔ)Ñ*Ô*ÐIÐI°$Ô2GÐIÐIÐIr/   )r   )Ú__name__Ú
__module__Ú__qualname__r#   r=   rA   Ú__classcell__©r-   s   @r.   r   r   /   se   ø€ € € € € ð$ð $ð $ð $ð $ð $ð
Oð Oð OðJð Jð Jð Jð Jð Jð Jr/   r   c                   óÔ   ‡ — e Zd ZU ej        ed<   ddefˆ fd„Ze	 	 	 ddedz  de	d         de
dz  ded	ef         fd
„¦   «         Z ej        ¦   «         ed„ ¦   «         ¦   «         Zˆ xZS )ÚHeliumRotaryEmbeddingÚinv_freqNÚconfigc                 ó²  •— t          ¦   «                              ¦   «          |j        | _        |j        | _        || _        | j        j        d         | _        | j        }| j        dk    rt          | j                 } || j        |¦  «        \  }| _
        |                      d|d¬¦  «         |                      d|                     ¦   «         d¬¦  «         d S )NÚ	rope_typeÚdefaultrI   F)Ú
persistentÚoriginal_inv_freq)r"   r#   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenrJ   Úrope_parametersrL   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)r*   rJ   ÚdeviceÚrope_init_fnrI   r-   s        €r.   r#   zHeliumRotaryEmbedding.__init__C   sÊ   ø€ Ý‰Œ×ÒÑÔÐØ"(Ô"@ˆÔØ$*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Ur/   rX   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   r1   ©r4   )rX   r4   )	rS   Úgetattrr+   Únum_attention_headsr&   ÚarangeÚint64r5   Úfloat)rJ   rX   rZ   ÚbaseÚdimÚattention_factorrI   s          r.   rT   z5HeliumRotaryEmbedding.compute_default_rope_parametersS   sŒ   € ð& Ô% lÔ3ˆÝ�f˜j¨$Ñ/Ô/Ðc°6Ô3EÈÔIcÑ3cˆàÐð Ø•U”\ ! S¨!µ5´;Ð?Ñ?Ô?×BÒBÈ&ÕX]ÔXcÐBÑdÔdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r/   c                 óN  — | j         d d d …d f                              ¦   «                              |j        d         dd¦  «                             |j        ¦  «        }|d d …d d d …f                              ¦   «         }t          |j        j        t          ¦  «        r|j        j        dk    r|j        j        nd}t          |d¬¦  «        5  |                     ¦   «         |                     ¦   «         z   
                    dd¦  «        }t          j        ||fd¬	¦  «        }|                     ¦   «         | j        z  }|                     ¦   «         | j        z  }	d d d ¦  «         n# 1 swxY w Y   |                     |j        ¬
¦  «        |	                     |j        ¬
¦  «        fS )Nr   r2   r   ÚmpsÚcpuF)Údevice_typeÚenabledr1   ©rf   r_   )rI   rd   Úexpandr@   r5   rX   Ú
isinstanceÚtypeÚstrr   Ú	transposer&   ÚcatÚcosrU   Úsinr4   )
r*   ÚxÚposition_idsÚinv_freq_expandedÚposition_ids_expandedrk   ÚfreqsÚembrt   ru   s
             r.   r=   zHeliumRotaryEmbedding.forwardq   s·  € ð !œM¨$°°°°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*r!   ©NNN)rB   rC   rD   r&   ÚTensorÚ__annotations__r   r#   Ústaticmethodr   Úintr?   rd   rT   Úno_gradr   r=   rE   rF   s   @r.   rH   rH   @   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˜tÑ#ð*à˜Ô(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ô	%ð	*ð *ð *ñ „\ð*ð: €U„]�_„_Øð<ð <ñ Ôñ „_ð<ð <ð <ð <ð <r/   rH   c                   ó$   ‡ — e Zd Zˆ fd„Zd„ Zˆ xZS )Ú	HeliumMLPc                 ó¶  •— t          ¦   «                              ¦   «          || _        |j        | _        |j        | _        t          j        | j        | j        |j        ¬¦  «        | _        t          j        | j        | j        |j        ¬¦  «        | _	        t          j        | j        | j        |j        ¬¦  «        | _
        t          |j                 | _        d S )N©Úbias)r"   r#   rJ   r+   Úintermediate_sizer$   ÚLinearÚmlp_biasÚ	gate_projÚup_projÚ	down_projr   Ú
hidden_actÚact_fn©r*   rJ   r-   s     €r.   r#   zHeliumMLP.__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 r!   )rŒ   rŽ   rŠ   r‹   )r*   rv   rŒ   s      r.   r=   zHeliumMLP.forwardŒ   sA   € Ø—N’N 4§;¢;¨t¯~ª~¸aÑ/@Ô/@Ñ#AÔ#AÀDÇLÂLÐQRÁOÄOÑ#SÑTÔTˆ	ØÐr/   )rB   rC   rD   r#   r=   rE   rF   s   @r.   rƒ   rƒ   �   sG   ø€ € € € € ð0ð 0ð 0ð 0ð 0ðð ð ð ð ð ð r/   rƒ   r:   Ú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)r@   rn   Úreshape)r:   r‘   ÚbatchÚnum_key_value_headsÚslenr^   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 )Nr1   r   r2   )rf   r4   )ÚpÚtrainingr   )r—   Únum_key_value_groupsr&   Úmatmulrr   r$   Ú
functionalÚsoftmaxr6   r5   r4   rŸ   r£   Ú
contiguous)r™   rš   r›   rœ   r�   rž   rŸ   r    Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r.   Úeager_attention_forwardr­   �   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                 óŽ   — | dddd…f         }| dddd…f         }t          j        | |fd¬¦  «                             d¦  «        S )	z*Rotates half the hidden dims of the input..r   Nr1   r   r2   rm   éþÿÿÿ)r&   ÚstackÚflatten)rv   Úx1Úx2s      r.   Úrotate_halfr´   ¶   sQ   € à	
ˆ3���1�ˆ9Œ€BØ	
ˆ3���1�ˆ9Œ€BÝŒ;˜˜˜R�y bÐ)Ñ)Ô)×1Ò1°"Ñ5Ô5Ð5r/   c                 óz  — |                      |¦  «        }|                      |¦  «        }|dd|j        d         dz  …f                              dd¬¦  «        }|dd|j        d         dz  …f                              dd¬¦  «        }| |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.
    .Nr2   r1   rm   )Ú	unsqueezer@   Úrepeat_interleaver´   )ÚqÚkrt   ru   Úunsqueeze_dimÚq_embedÚk_embeds          r.   Úapply_rotary_pos_embr½   ½   sË   € ð$ �-Š-˜Ñ
&Ô
&€CØ
�-Š-˜Ñ
&Ô
&€Cð ˆcÐ'�S”Y˜r”] aÑ'Ð'Ð'Ô
(×
:Ò
:¸1À"Ð
:Ñ
EÔ
E€CØ
ˆcÐ'�S”Y˜r”] aÑ'Ð'Ð'Ô
(×
:Ò
:¸1À"Ð
:Ñ
EÔ
E€Cà�3‰w�; q™>œ>¨CÑ/Ñ0€GØ�3‰w�; q™>œ>¨CÑ/Ñ0€Gà�GÐÐr/   c                   óÖ   ‡ — e Zd ZdZddededz  fˆ fd„Z	 	 	 ddej        de	ej        ej        f         dz  dej        dz  d	e
dz  d
ee         de	ej        ej        f         fd„Zˆ xZS )ÚHeliumAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNrJ   Ú	layer_idxc                 ó¸  •— t          ¦   «                              ¦   «          || _        || _        t	          |d|j        |j        z  ¦  «        | _        |j        |j        z  | _	        dt          j        | j        ¦  «        z  | _        |j        | _        d| _        t          j        |j        |j        | j        z  |j        ¬¦  «        | _        t          j        |j        |j        | j        z  |j        ¬¦  «        | _        t          j        |j        |j        | j        z  |j        ¬¦  «        | _        t          j        |j        |j        d¬¦  «        | _        d S )Nr^   r   Tr…   F)r"   r#   rJ   rÀ   r`   r+   ra   r^   r•   r¤   ÚmathÚsqrtrž   Úattention_dropoutÚ	is_causalr$   rˆ   Úattention_biasÚq_projÚk_projÚv_projÚo_proj©r*   rJ   rÀ   r-   s      €r.   r#   zHeliumAttention.__init__ß   s:  ø€ Ý‰Œ×ÒÑÔÐØˆŒØ"ˆŒÝ ¨
°FÔ4FÈ&ÔJdÑ4dÑeÔeˆŒØ$*Ô$>À&ÔB\Ñ$\ˆÔ!Ø�4œ9 T¤]Ñ3Ô3Ñ3ˆŒØ!'Ô!9ˆÔØˆŒå”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”i Ô 2°FÔ4FÈUÐSÑSÔSˆŒˆˆr/   r:   Úposition_embeddingsr�   Úpast_key_valuesr    r[   c                 ó"  — |j         d d…         }g |¢d‘| j        ‘R }|                      |¦  «                             |¦  «                             dd¦  «        }|                      |¦  «                             |¦  «                             dd¦  «        }	|                      |¦  «                             |¦  «                             dd¦  «        }
|\  }}t          ||	||¦  «        \  }}	|�|                     |	|
| j	        ¦  «        \  }	}
t          j        | j        j        t          ¦  «        } || ||	|
|f| j        sdn| j        | j        dœ|¤Ž\  }} |j        g |¢d‘R Ž                      ¦   «         }|                      |¦  «        }||fS )Nr2   r   r1   r˜   )rŸ   rž   )r@   r^   rÇ   Úviewrr   rÈ   rÉ   r½   ÚupdaterÀ   r   Úget_interfacerJ   Ú_attn_implementationr­   r£   rÄ   rž   r“   r¨   rÊ   )r*   r:   rÌ   r�   rÍ   r    Úinput_shapeÚhidden_shapeÚquery_statesr©   rª   rt   ru   Úattention_interfacer¬   r«   s                   r.   r=   zHeliumAttention.forwardô   sÀ  € ð $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(r/   r!   r|   )rB   rC   rD   Ú__doc__r   r€   r#   r&   r}   r?   r   r   r   r=   rE   rF   s   @r.   r¿   r¿   Ü   s÷   ø€ € € € € ØGÐGðTð T˜|ð T¸¸d¹
ð Tð Tð Tð Tð Tð Tð0 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 &)r/   r¿   c                   óÚ   ‡ — e Zd Zddededz  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HeliumDecoderLayerNrJ   rÀ   c                 ó4  •— t          ¦   «                              ¦   «          |j        | _        t          ||¬¦  «        | _        t          |¦  «        | _        t          |j        |j        ¬¦  «        | _	        t          |j        |j        ¬¦  «        | _
        d S )N)rJ   rÀ   ©r,   )r"   r#   r+   r¿   Ú	self_attnrƒ   Úmlpr   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormrË   s      €r.   r#   zHeliumDecoderLayer.__init__  sƒ   ø€ Ý‰Œ×ÒÑÔÐØ!Ô-ˆÔå(°À)ÐLÑLÔLˆŒå˜VÑ$Ô$ˆŒÝ,¨VÔ-?ÀVÔEXÐYÑYÔYˆÔÝ(5°fÔ6HÈfÔNaÐ(bÑ(bÔ(bˆÔ%Ð%Ð%r/   Fr:   r�   rw   rÍ   Ú	use_cacherÌ   r    r[   c           
      óÎ   — |}|                       |¦  «        } | j        d||||||dœ|¤Ž\  }}	||z   }|}|                      |¦  «        }|                      |¦  «        }||z   }|S )N)r:   r�   rw   rÍ   rá   rÌ   © )rß   rÜ   rà   rÝ   )
r*   r:   r�   rw   rÍ   rá   rÌ   r    ÚresidualÚ_s
             r.   r=   zHeliumDecoderLayer.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r/   r!   )NNNFN)rB   rC   rD   r   r€   r#   r&   r}   Ú
LongTensorr   Úboolr?   r   r   r=   rE   rF   s   @r.   rÙ   rÙ     s	  ø€ € € € € ðcð c˜|ð c¸¸d¹
ð cð cð cð cð cð cð /3Ø04Ø(,Ø!&ØHLðð à”|ðð œ tÑ+ðð Ô&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5¤<°´Ð#=Ô>ÀÑEðð Ð+Ô,ðð 
Œðð ð ð ð ð ð ð r/   rÙ   c                   óL   — e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedœZdS )ÚHeliumPreTrainedModelrJ   ÚmodelTrÙ   rÍ   )r:   Ú
attentionsN)rB   rC   rD   r   r~   Úbase_model_prefixÚsupports_gradient_checkpointingÚ_no_split_modulesÚ_skip_keys_device_placementÚ_supports_flash_attnÚ_supports_sdpaÚ_supports_flex_attnÚ_can_compile_fullgraphÚ_supports_attention_backendrÙ   r¿   Ú_can_record_outputsrã   r/   r.   ré   ré   H  sl   € € € € € € àÐÐÑØÐØ&*Ð#Ø-Ð.ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà+Ø%ðð ÐÐÐr/   ré   c                   óâ   ‡ — e Zd Zdefˆ fd„Zeee	 	 	 	 	 	 ddej	        dz  dej
        dz  dej	        dz  dedz  dej        dz  d	edz  d
ee         defd„¦   «         ¦   «         ¦   «         Zˆ xZS )ÚHeliumModelrJ   c                 óÞ  •‡— t          ¦   «                              ‰¦  «         ‰j        | _        ‰j        | _        t          j        ‰j        ‰j        | j        ¦  «        | _        t          j	        ˆfd„t          ‰j        ¦  «        D ¦   «         ¦  «        | _        t          ‰j        ‰j        ¬¦  «        | _        t!          ‰¬¦  «        | _        d| _        |                      ¦   «          d S )Nc                 ó0   •— g | ]}t          ‰|¦  «        ‘ŒS rã   )rÙ   )Ú.0rÀ   rJ   s     €r.   ú
<listcomp>z(HeliumModel.__init__.<locals>.<listcomp>d  s$   ø€ ÐdÐdÐd°yÕ ¨	Ñ2Ô2ÐdÐdÐdr/   rÛ   ©rJ   F)r"   r#   Úpad_token_idÚpadding_idxÚ
vocab_sizer$   Ú	Embeddingr+   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersÚlayersr   rÞ   ÚnormrH   Ú
rotary_embÚgradient_checkpointingÚ	post_initr�   s    `€r.   r#   zHeliumModel.__init__]  sÔ   øø€ Ý‰Œ×Ò˜Ñ Ô Ð Ø!Ô.ˆÔØ Ô+ˆŒåœL¨Ô):¸FÔ<NÐPTÔP`ÑaÔaˆÔÝ”mØdÐdÐdÐdÅEÈ&ÔJbÑDcÔDcÐdÑdÔdñ
ô 
ˆŒõ " &Ô"4¸&Ô:MÐNÑNÔNˆŒ	Ý/°vÐ>Ñ>Ô>ˆŒØ&+ˆÔ#ð 	�ŠÑÔÐÐÐr/   NÚ	input_idsr�   rw   rÍ   Úinputs_embedsrá   r    r[   c           
      óH  — |d u |d uz  rt          d¦  «        ‚|€|                      |¦  «        }|r|€t          | j        ¬¦  «        }|€V|�|                     ¦   «         nd}t          j        |j        d         |j        ¬¦  «        |z   }| 	                    d¦  «        }t          | j        ||||¬¦  «        }	|}
|                      |
|¬¦  «        }| j        d | j        j        …         D ]} ||
f|	||||dœ|¤Ž}
Œ|                      |
¦  «        }
t          |
|¬	¦  «        S )
Nz:You must specify exactly one of input_ids or inputs_embedsrü   r   r   )rX   )rJ   r  r�   rÍ   rw   )rw   )r�   rÌ   rw   rÍ   rá   )Úlast_hidden_staterÍ   )Ú
ValueErrorr  r   rJ   Úget_seq_lengthr&   rb   r@   rX   r¶   r
   r  r  r  r  r   )r*   r
  r�   rw   rÍ   r  rá   r    Úpast_seen_tokensÚcausal_maskr:   rÌ   Údecoder_layers                r.   r=   zHeliumModel.forwardm  sŠ  € ð ˜Ð -°tÐ";Ñ<ð 	[ÝÐYÑZÔZÐZàÐ Ø*.×*;Ò*;¸IÑ*FÔ*FˆMàð 	?˜Ð0Ý*°$´+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$7Ø)Ø /Ø#ðð ð ðð ˆMˆMð Ÿ	š	 -Ñ0Ô0ˆÝ&Ø+Ø+ð
ñ 
ô 
ð 	
r/   )NNNNNN)rB   rC   rD   r   r#   r   r   r   r&   ræ   r}   r   ÚFloatTensorrç   r   r   r   r=   rE   rF   s   @r.   r÷   r÷   [  s  ø€ € € € € ð˜|ð ð ð ð ð ð ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ð2
ð 2
àÔ# dÑ*ð2
ð œ tÑ+ð2
ð Ô&¨Ñ-ð	2
ð
  ™ð2
ð Ô(¨4Ñ/ð2
ð ˜$‘;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
r/   r÷   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HeliumForCausalLMz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#   r÷   rê   rÿ   r$   rˆ   r+   r  r	  r�   s     €r.   r#   zHeliumForCausalLM.__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r/   Nr   r
  r�   rw   rÍ   r  Úlabelsrá   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 )a€  
        Example:

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

        >>> model = HeliumForCausalLM.from_pretrained("google/helium-7b")
        >>> tokenizer = AutoTokenizer.from_pretrained("google/helium-7b")

        >>> prompt = "What is your favorite condiment?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "What is your favorite condiment?"
        ```)r
  r�   rw   rÍ   r  rá   N)r  r  rÿ   )Úlossr  rÍ   r:   rë   rã   )rê   r  ro   r€   Úslicer  Úloss_functionrJ   rÿ   r   rÍ   r:   rë   )r*   r
  r�   rw   rÍ   r  r  rá   r  r    Úoutputsr:   Úslice_indicesr  r  s                  r.   r=   zHeliumForCausalLM.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 	
r/   )NNNNNNNr   )rB   rC   rD   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr#   r   r   r&   ræ   r}   r   r  rç   r€   r   r   r   r=   rE   rF   s   @r.   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 ˜$‘;ð6
ð ˜eœlÑ*ð6
ð Ð+Ô,ð6
ð 
 ð6
ð 6
ð 6
ñ „^ñ Ôð6
ð 6
ð 6
ð 6
ð 6
r/   r  c                   ó   — e Zd ZdS )ÚHeliumForSequenceClassificationN©rB   rC   rD   rã   r/   r.   r&  r&  ï  ó   € € € € € Ø€Dr/   r&  c                   ó   — e Zd ZdS )ÚHeliumForTokenClassificationNr'  rã   r/   r.   r*  r*  ó  r(  r/   r*  )ré   r÷   r  r&  r*  )r˜   )r   )>rÂ   Úcollections.abcr   Útypingr   r&   Útorch.nnr$   Úactivationsr   Úcache_utilsr   r   Ú
generationr	   Úmasking_utilsr
   Úmodeling_layersr   r   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   r   Úutils.output_capturingr   Úconfiguration_heliumr   ÚModuler   rH   rƒ   r}   r€   r—   rd   r­   r´   r½   r¿   rÙ   ré   r÷   r  r&  r*  Ú__all__rã   r/   r.   ú<module>r=     sk  ðð* €€€Ø $Ð $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 /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 IÐ IÐ IÐ IÐ IÐ IÐ IÐ IÐ IÐ IØ GÐ GÐ GÐ GÐ GÐ GÐ GÐ GØ 5Ð 5Ð 5Ð 5Ð 5Ð 5Ø .Ð .Ð .Ð .Ð .Ð .ðJð Jð Jð Jð J�B”Iñ Jô Jð Jð"><ð ><ð ><ð ><ð ><˜BœIñ ><ô ><ð ><ðBð ð ð ð �”	ñ ô ð ð 	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6ð 6ð 6ðð ð ð ð>>)ð >)ð >)ð >)ð >)�b”iñ >)ô >)ð >)ðB(ð (ð (ð (ð (Ð3ñ (ô (ð (ðV ðð ð ð ð ˜Oñ ô ñ „ðð$ ðF
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ñ „ðF
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ñ „ðF
ðR	ð 	ð 	ð 	ð 	Ð&FÐH]ñ 	ô 	ð 	ð	ð 	ð 	ð 	ð 	Ð#@ÐBWñ 	ô 	ð 	ðð ð €€€r/   