§
    ‚Štjúž  ã                   óø  — d dl mZ d dlmZ d dlZd dlmc m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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/m0Z0 ddl1m2Z2m3Z3 ddl4m5Z5m6Z6 ddl7m8Z8  ed¦  «         G d„ dej9        ¦  «        ¦   «         Z: G d„ de¦  «        Z; G d„ dej9        ¦  «        Z< G d„ dej9        ¦  «        Z=d „ Z> ed!¦  «        dLd"„¦   «         Z?d#ej@        d$eAd%ej@        fd&„ZB	 dMd(ej9        d)ej@        d*ej@        d+ej@        d,ej@        dz  d-eCd.eCd/e,e.         fd0„ZD ee?¦  «         G d1„ d2ej9        ¦  «        ¦   «         ZE G d3„ d4ej9        ¦  «        ZFe G d5„ d6ej9        ¦  «        ¦   «         ZG G d7„ d8ej9        ¦  «        ZH G d9„ d:e!¦  «        ZIe/ G d;„ d<e*¦  «        ¦   «         ZJe/ G d=„ d>eJ¦  «        ¦   «         ZK	 	 	 dNd@ej@        eLej@                 z  dz  dAeAdz  d,ej@        dz  d%ej@        eAz  fdB„ZMe/ G dC„ dDeJe¦  «        ¦   «         ZN G dE„ dFeeJ¦  «        ZO G dG„ dHe eJ¦  «        ZP G dI„ dJeeJ¦  «        ZQg dK¢ZRdS )Oé    )ÚCallable)ÚOptionalN)Únné   )Úinitialization)ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úuse_experts_implementationÚ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MoeCausalLMOutputWithPastÚMoeModelOutputWithPast)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OutputRecorderÚcapture_outputsé   )ÚMiniMaxConfigÚ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MiniMaxRMSNormç�íµ ÷Æ°>ÚepsÚreturnNc                 ó¬   •— t          ¦   «                              ¦   «          t          j        t	          j        |¦  «        ¦  «        | _        || _        dS )z=
        MiniMaxRMSNorm is equivalent to T5LayerNorm
        N)ÚsuperÚ__init__r   Ú	ParameterÚtorchÚonesÚweightÚvariance_epsilon)ÚselfÚhidden_sizer+   Ú	__class__s      €új/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/minimax/modeling_minimax.pyr/   zMiniMaxRMSNorm.__init__;   sD   ø€ õ 	‰Œ×ÒÑÔÐÝ”l¥5¤:¨kÑ#:Ô#:Ñ;Ô;ˆŒØ #ˆÔÐÐó    Úhidden_statesc                 ó  — |j         }|                     t          j        ¦  «        }|                     d¦  «                             dd¬¦  «        }|t          j        || j        z   ¦  «        z  }| j        |                     |¦  «        z  S )Né   éÿÿÿÿT)Úkeepdim)	ÚdtypeÚtor1   Úfloat32ÚpowÚmeanÚrsqrtr4   r3   )r5   r:   Úinput_dtypeÚvariances       r8   ÚforwardzMiniMaxRMSNorm.forwardC   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:r9   c                 óH   — t          | j        j        ¦  «        › d| j        › �S )Nz, eps=)Útupler3   Úshaper4   )r5   s    r8   Ú
extra_reprzMiniMaxRMSNorm.extra_reprJ   s&   € Ý˜œÔ)Ñ*Ô*ÐIÐI°$Ô2GÐIÐIÐIr9   )r*   )
Ú__name__Ú
__module__Ú__qualname__Úfloatr/   r1   ÚTensorrG   rK   Ú__classcell__©r7   s   @r8   r)   r)   9   sŒ   ø€ € € € € ð$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r9   r)   c                   óh   ‡ — e Zd Zˆ fd„Zd„ Zdefd„Zˆ fd„Zdefd„Zde	j
        fd	„Zd
efd„Zˆ xZS )ÚMiniMaxCachec                 óV   •— t          ¦   «                              ¦   «          g | _        d S ©N)r.   r/   Úlinear_cache©r5   r7   s    €r8   r/   zMiniMaxCache.__init__O   s'   ø€ Ý‰Œ×ÒÑÔÐØ02ˆÔÐÐr9   c                 óž   — t          t          | j        ¦  «        |dz   ¦  «        D ]}| j                             g ¦  «         Œ|| j        |<   d S )Nr%   )ÚrangeÚlenrW   Úappend)r5   Ú	layer_idxrW   Ú_s       r8   Úset_linear_cachezMiniMaxCache.set_linear_cacheS   sW   € å•s˜4Ô,Ñ-Ô-¨y¸1©}Ñ=Ô=ð 	)ð 	)ˆAØÔ×$Ò$ RÑ(Ô(Ð(Ð(Ø'3ˆÔ˜)Ñ$Ð$Ð$r9   r]   c                 óF   — |t          | ¦  «        k     r| j        |         S d S rV   )r[   rW   )r5   r]   s     r8   Úget_linear_cachezMiniMaxCache.get_linear_cacheY   s&   € Ø•s˜4‘y”yÒ Ð ØÔ$ YÔ/Ð/Øˆtr9   c                 ó„   •— t          t          ¦   «                              ¦   «         t          | j        ¦  «        ¦  «        S rV   )Úmaxr.   Ú__len__r[   rW   rX   s    €r8   rd   zMiniMaxCache.__len__^   s,   ø€ Ý•5‘7”7—?’?Ñ$Ô$¥c¨$Ô*;Ñ&<Ô&<Ñ=Ô=Ð=r9   Úrepeatsc                 óü   — t          t          | ¦  «        ¦  «        D ]^}| j        |         g k    r+| j        |                              |d¬¦  «        | j        |<   Œ>| j        |                              |¦  «         Œ_d S )Nr   ©Údim)rZ   r[   rW   Úrepeat_interleaveÚlayersÚbatch_repeat_interleave)r5   re   r]   s      r8   rk   z$MiniMaxCache.batch_repeat_interleavea   s‹   € Ý�s 4™yœyÑ)Ô)ð 	Hð 	HˆIØÔ  Ô+¨rÒ1Ð1Ø/3Ô/@ÀÔ/K×/]Ò/]Ð^eÐklÐ/]Ñ/mÔ/m�Ô! )Ñ,Ð,à”˜IÔ&×>Ò>¸wÑGÔGÐGÐGð		Hð 	Hr9   Úindicesc                 óâ   — t          t          | ¦  «        ¦  «        D ]Q}| j        |         g k    r| j        |         |df         | j        |<   Œ1| j        |                              |¦  «         ŒRd S )N.)rZ   r[   rW   rj   Úbatch_select_indices)r5   rl   r]   s      r8   rn   z!MiniMaxCache.batch_select_indicesh   s€   € Ý�s 4™yœyÑ)Ô)ð 	Eð 	EˆIØÔ  Ô+¨rÒ1Ð1Ø/3Ô/@ÀÔ/KÈGÐUXÈLÔ/Y�Ô! )Ñ,Ð,à”˜IÔ&×;Ò;¸GÑDÔDÐDÐDð		Eð 	Er9   Ú
max_lengthc                 ó    — t          d¦  «        ‚)Nz*MiniMaxCache doesnot support `crop` method)ÚRuntimeError)r5   ro   s     r8   ÚcropzMiniMaxCache.cropo   s   € ÝÐGÑHÔHÐHr9   )rL   rM   rN   r/   r_   Úintra   rd   rk   r1   rP   rn   rr   rQ   rR   s   @r8   rT   rT   N   sá   ø€ € € € € ð3ð 3ð 3ð 3ð 3ð4ð 4ð 4ð¨#ð ð ð ð ð
>ð >ð >ð >ð >ðH¨sð Hð Hð Hð HðE¨E¬Lð Eð Eð Eð EðI˜sð Ið Ið Ið Ið Ið Ið Ið Ir9   rT   c                   óð   ‡ — e Zd Zdedefˆ fd„Zd„ Z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  e
ej	                 dz  f         fd„Zˆ xZS )ÚMiniMaxLightningAttentionÚconfigr]   c                 ó   •— t          ¦   «                              ¦   «          || _        t          |dd ¦  «        p|j        |j        z  | _        |j        | _        |j        | _        |j        | _        t          |j
                 | _        t          | j        | j        z  ¦  «        | _        t          j        |j        | j        | j        z  dz  d¬¦  «        | _        t          j        | j        | j        z  |j        d¬¦  «        | _        t          j        |j        | j        | j        z  d¬¦  «        | _        |                      ¦   «         }|                      |¦  «        \  }}}|                      d|¦  «         |                      d|¦  «         |                      d|¦  «         |                      d|¦  «         |j        |         | _        d S )	NÚhead_dimr   F©ÚbiasÚ
slope_rateÚquery_decayÚ	key_decayÚdiagonal_decay)r.   r/   r]   Úgetattrr6   Únum_attention_headsrx   Únum_hidden_layersÚ
block_sizer   Ú
hidden_actÚact_fnr)   Únormr   ÚLinearÚqkv_projÚout_projÚoutput_gateÚget_slope_rateÚdecay_factorsÚregister_bufferÚlayer_typesÚ
layer_type)r5   rv   r]   r{   r|   r}   r~   r7   s          €r8   r/   z"MiniMaxLightningAttention.__init__t   s   ø€ Ý‰Œ×ÒÑÔÐØ"ˆŒÝ ¨
°DÑ9Ô9Ðm¸VÔ=OÐSYÔSmÑ=mˆŒØ#)Ô#=ˆÔ Ø!'Ô!9ˆÔØ Ô+ˆŒå˜VÔ.Ô/ˆŒÝ" 4¤=°4Ô3KÑ#KÑLÔLˆŒ	Ýœ	 &Ô"4°dÔ6NÐQUÔQ^Ñ6^ÐabÑ6bÐinÐoÑoÔoˆŒÝœ	 $Ô":¸T¼]Ñ"JÈFÔL^ÐejÐkÑkÔkˆŒÝœ9 VÔ%7¸Ô9QÐTXÔTaÑ9aÐhmÐnÑnÔnˆÔà×(Ò(Ñ*Ô*ˆ
Ø15×1CÒ1CÀJÑ1OÔ1OÑ.ˆ�Y à×Ò˜\¨:Ñ6Ô6Ð6Ø×Ò˜]¨KÑ8Ô8Ð8Ø×Ò˜[¨)Ñ4Ô4Ð4Ø×ÒÐ-¨~Ñ>Ô>Ð>à Ô,¨YÔ7ˆŒˆˆr9   c                 óÂ   — ddd| j         z  z  z  }t          j        | j         ¦  «        dz   }d| j        | j        dz
  dz   z  z
  dz   }||z  }||z  }|d d …d d f         }|S )Nr%   r<   é   gñhãˆµøä>)r€   r1   Úaranger]   r�   )r5   ÚbaseÚexponentÚfactorÚrates        r8   rŠ   z(MiniMaxLightningAttention.get_slope_rateŒ   s~   € Ø�A˜!˜dÔ6Ñ6Ñ7Ñ8ˆÝ”< Ô 8Ñ9Ô9¸AÑ=ˆØ�T”^ tÔ'=ÀÑ'AÀDÑ'HÑIÑIÈDÑPˆà�X‰~ˆØ�f‰}ˆØ�A�A�A�t˜T�MÔ"ˆàˆr9   c                 ó°  — t          j        | j        ¦  «        dz   }t          j        | |d d …d f         z  ¦  «        }t          j        | | j        |d d …d f         z
  z  ¦  «        }|d d …d f         |d d d …f         z
  }|d d d d …d d …f         }||z  }t          j        |dk    | t          d¦  «        ¦  «        }t          j        |¦  «        }|||fS )Nr%   r   z-inf)r1   r‘   r‚   ÚexpÚwhererO   )r5   r{   Úblock_size_ranger|   r}   r~   s         r8   r‹   z'MiniMaxLightningAttention.decay_factors—   sô   € Ý œ<¨¬Ñ8Ô8¸1Ñ<Ðå”i  Ð.>¸q¸q¸qÀ$¸wÔ.GÑ GÑHÔHˆÝ”I˜z˜k¨T¬_Ð?OÐPQÐPQÐPQÐSWÐPWÔ?XÑ-XÑYÑZÔZˆ	à)¨!¨!¨!¨T¨'Ô2Ð5EÀdÈAÈAÈAÀgÔ5NÑNˆØ'¨¨d°A°A°A°q°q°qÐ(8Ô9ˆØ# nÑ4ˆÝœ ^°qÒ%8¸>¸/Í5ÐQWÉ=Ì=ÑYÔYˆÝœ >Ñ2Ô2ˆà˜I ~Ð5Ð5r9   Nr:   Úposition_embeddingsÚattention_maskÚpast_key_valuesÚkwargsr,   c                 ó	  — |j         \  }}}|| j        z   dz
  | j        z  }	|                      |                      |¦  «        ¦  «        }
|
                     ||| j        d| j        z  ¦  «        }
t          j        |
| j        d¬¦  «        \  }}}| 	                    dd¦  «        }| 	                    dd¦  «        }| 	                    dd¦  «        }d }|�| 
                    | j        ¦  «        }|�€t          j        || j        | j        | j        ¦  «                             |¦  «        }|�]|                     t          j        ¬¦  «        }|                     |                     d¦  «                             d¦  «         d¦  «        }g }t#          |	¦  «        D �]a}|| j        z  }t%          || j        z   |¦  «        }||z
  }|d d …d d …||…f         }|d d …d d …||…f         }|d d …d d …||…f         }| j        d d …d |…f         }| j        d d …| d …f         }| j        d d …d d …d |…d |…f         }t          j        | j         |z  ¦  «        }t          j        || 	                    dd¦  «        ¦  «        }t          j        ||z  |¦  «        }t          j        ||z  |¦  «        }||z   }|                     |¦  «         t          j        ||z   	                    dd¦  «        |¦  «        }||z  |z   }�ŒcnÅt          j        | j         ¦  «        } g }t#          |¦  «        D ]™}|d d …d d …||dz   …f         }|d d …d d …||dz   …f         }|d d …d d …||dz   …f         }t          j        | 	                    dd¦  «        |¦  «        }!| |z  |!z   }t          j        ||¦  «        }|                     |¦  «         Œšt          j        |d¬¦  «        }| 	                    dd¦  «        }|                     ||| j        | j        z  ¦  «        }|                      |¦  «        }t9          j        |                      |¦  «        ¦  «        |z  }|                      |¦  «        }|�|                      | j        |¦  «         ||fS )	Nr%   r   rg   r<   ©r?   r=   r   éþÿÿÿ)!rJ   r‚   r„   r‡   Úreshaper€   rx   r1   ÚsplitÚ	transposera   r]   Úzerosr@   ÚboolÚmasked_fillÚ	unsqueezerZ   Úminr|   r}   r~   r—   r{   Úmatmulr\   Úcatr…   ÚFÚsigmoidr‰   rˆ   r_   )"r5   r:   rš   r›   rœ   r�   Ú
batch_sizeÚseq_lenr6   Ú
num_blocksÚ
qkv_statesÚquery_statesÚ
key_statesÚvalue_statesÚattn_weights_interÚattn_outputÚiÚ	start_idxÚend_idxÚcurrent_block_sizeÚcurrent_query_statesÚcurrent_key_statesÚcurrent_value_statesÚcurrent_query_decayÚcurrent_key_decayÚcurrent_diagonal_decayÚblock_decayÚattn_weights_intraÚattn_output_intraÚattn_output_interÚcurrent_attn_outputÚnext_attn_weights_interÚratioÚcurrent_attn_weights_inters"                                     r8   rG   z!MiniMaxLightningAttention.forward¥   s5  € ð ,9Ô+>Ñ(ˆ
�G˜[Ø ¤Ñ/°!Ñ3¸¼ÑGˆ
à—[’[ §¢¨}Ñ!=Ô!=Ñ>Ô>ˆ
Ø×'Ò'¨
°G¸TÔ=UÐWXÐ[_Ô[hÑWhÑiÔiˆ
å16´¸ZÈÌÐ\]Ð1^Ñ1^Ô1^Ñ.ˆ�j ,à#×-Ò-¨a°Ñ3Ô3ˆØ×)Ò)¨!¨QÑ/Ô/ˆ
Ø#×-Ò-¨a°Ñ3Ô3ˆð "ÐØÐ&Ø!0×!AÒ!AÀ$Ä.Ñ!QÔ!QÐàÑ%Ý!&¤¨Z¸Ô9QÐSWÔS`ÐbfÔboÑ!pÔ!p×!sÒ!sØñ"ô "Ðð
 Ð)Ø!/×!2Ò!2½¼Ð!2Ñ!DÔ!D�Ø+×7Ò7¸×9QÒ9QÐRSÑ9TÔ9T×9^Ò9^Ð_aÑ9bÔ9bÐ8bÐdeÑfÔf�àˆKÝ˜:Ñ&Ô&ð `ñ `�Ø ¤Ñ/�	Ý˜i¨$¬/Ñ9¸7ÑCÔC�Ø%,¨yÑ%8Ð"à'3°A°A°A°q°q°q¸)ÀGÐ:KÐ4KÔ'LÐ$Ø%/°°°°1°1°1°iÀÐ6GÐ0GÔ%HÐ"Ø'3°A°A°A°q°q°q¸)ÀGÐ:KÐ4KÔ'LÐ$à&*Ô&6°q°q°qÐ:MÐ;MÐ:MÐ7MÔ&NÐ#Ø$(¤N°1°1°1Ð7IÐ6IÐ6JÐ6JÐ3JÔ$KÐ!Ø)-Ô)<¸Q¸Q¸QÀÀÀÐCVÐDVÐCVÐXkÐYkÐXkÐ=kÔ)lÐ&Ý#œi¨¬Ð(8Ð;MÑ(MÑNÔN�õ &+¤\Ð2FÐHZ×HdÒHdÐegÐikÑHlÔHlÑ%mÔ%mÐ"Ý$)¤LÐ1CÐF\Ñ1\Ð^rÑ$sÔ$sÐ!õ %*¤LÐ1EÐH[Ñ1[Ð]oÑ$pÔ$pÐ!ð '8Ð:KÑ&KÐ#Ø×"Ò"Ð#6Ñ7Ô7Ð7õ +0¬,Ø'Ð*;Ñ;×FÒFÀrÈ2ÑNÔNÐPdñ+ô +Ð'ð &8¸+Ñ%EÐH_Ñ%_Ð"Ñ"ð;`õ@ ”I˜tœÐ.Ñ/Ô/ˆEØˆKÝ˜7‘^”^ð 	8ð 	8�Ø'3°A°A°A°q°q°q¸!¸aÀ!¹e¸)°OÔ'DÐ$Ø%/°°°°1°1°1°a¸!¸a¹%°i°Ô%@Ð"Ø'3°A°A°A°q°q°q¸!¸aÀ!¹e¸)°OÔ'DÐ$å-2¬\Ð:L×:VÒ:VÐWYÐ[]Ñ:^Ô:^Ð`tÑ-uÔ-uÐ*Ø%*Ð-?Ñ%?ÐB\Ñ%\Ð"Ý&+¤lÐ3GÐI[Ñ&\Ô&\Ð#à×"Ò"Ð#6Ñ7Ô7Ð7Ð7õ ”i °Ð4Ñ4Ô4ˆð "×+Ò+¨A¨qÑ1Ô1ˆØ!×)Ò)¨*°g¸tÔ?WÐZ^ÔZgÑ?gÑhÔhˆØ—i’i Ñ,Ô,ˆÝ”i × 0Ò 0°Ñ ?Ô ?Ñ@Ô@À;ÑNˆØ—m’m KÑ0Ô0ˆð Ð&Ø×,Ò,¨T¬^Ð=OÑPÔPÐPàÐ.Ð.Ð.r9   rV   )rL   rM   rN   r&   rs   r/   rŠ   r‹   r1   rP   rI   r	   r   r   rG   rQ   rR   s   @r8   ru   ru   s   s  ø€ € € € € ð8˜}ð 8¸ð 8ð 8ð 8ð 8ð 8ð 8ð0	ð 	ð 	ð6ð 6ð 6ð& )-ð_/ð _/à”|ð_/ð # 5¤<°´Ð#=Ô>ð_/ð œ tÑ+ð	_/ð
  ™ð_/ð Ð-Ô.ð_/ð 
ˆuŒ|˜Uœ\¨DÑ0°%¸¼Ô2EÈÑ2LÐLÔ	Mð_/ð _/ð _/ð _/ð _/ð _/ð _/ð _/r9   ru   c                   óÔ   ‡ — e Zd ZU ej        ed<   ddefˆ fd„Ze	 	 	 ddedz  de	d         de
dz  ded	ef         fd
„¦   «         Z ej        ¦   «         ed„ ¦   «         ¦   «         Zˆ xZS )ÚMiniMaxRotaryEmbeddingÚinv_freqNrv   c                 ó²  •— t          ¦   «                              ¦   «          |j        | _        |j        | _        || _        | j        j        d         | _        | j        }| j        dk    rt          | j                 } || j        |¦  «        \  }| _
        |                      d|d¬¦  «         |                      d|                     ¦   «         d¬¦  «         d S )NÚ	rope_typeÚdefaultrÊ   F)Ú
persistentÚoriginal_inv_freq)r.   r/   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenrv   Úrope_parametersrÌ   Úcompute_default_rope_parametersr   Úattention_scalingrŒ   Úclone)r5   rv   ÚdeviceÚrope_init_fnrÊ   r7   s        €r8   r/   zMiniMaxRotaryEmbedding.__init__
  sÊ   ø€ Ý‰Œ×ÒÑÔÐØ"(Ô"@ˆÔØ$*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Ur9   r×   ztorch.devicer®   r,   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_thetarx   Nç      ð?r   r<   rŸ   )r×   r?   )	rÓ   r   r6   r€   r1   r‘   Úint64r@   rO   )rv   r×   r®   r’   rh   Úattention_factorrÊ   s          r8   rÔ   z6MiniMaxRotaryEmbedding.compute_default_rope_parameters  sŒ   € ð& Ô% lÔ3ˆÝ�f˜j¨$Ñ/Ô/Ðc°6Ô3EÈÔIcÑ3cˆàÐð Ø•U”\ ! S¨!µ5´;Ð?Ñ?Ô?×BÒBÈ&ÕX]ÔXcÐBÑdÔdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r9   c                 óN  — | j         d d d …d f                              ¦   «                              |j        d         dd¦  «                             |j        ¦  «        }|d d …d d d …f                              ¦   «         }t          |j        j        t          ¦  «        r|j        j        dk    r|j        j        nd}t          |d¬¦  «        5  |                     ¦   «         |                     ¦   «         z   
                    dd¦  «        }t          j        ||fd¬	¦  «        }|                     ¦   «         | j        z  }|                     ¦   «         | j        z  }	d d d ¦  «         n# 1 swxY w Y   |                     |j        ¬
¦  «        |	                     |j        ¬
¦  «        fS )Nr   r=   r%   ÚmpsÚcpuF)Údevice_typeÚenabledr<   rg   rŸ   )rÊ   rO   ÚexpandrJ   r@   r×   Ú
isinstanceÚtypeÚstrr!   r£   r1   rª   ÚcosrÕ   Úsinr?   )
r5   ÚxÚposition_idsÚinv_freq_expandedÚposition_ids_expandedrá   ÚfreqsÚembrç   rè   s
             r8   rG   zMiniMaxRotaryEmbedding.forward8  s·  € ð !œM¨$°°°°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*rV   )NNN)rL   rM   rN   r1   rP   Ú__annotations__r&   r/   Ústaticmethodr   rs   rI   rO   rÔ   Úno_gradr   rG   rQ   rR   s   @r8   rÉ   rÉ     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 <r9   rÉ   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..Nr=   r<   rg   )rJ   r1   rª   )ré   Úx1Úx2s      r8   Úrotate_halfrõ   H  s]   € à	
ˆ3Ð"�!”'˜"”+ Ñ"Ð"Ð"Ô	#€BØ	
ˆ3�”˜”˜qÑ Ð"Ð"Ð"Ô	#€BÝŒ9�r�c˜2�Y BÐ'Ñ'Ô'Ð'r9   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.
    )r§   rõ   )ÚqÚkrç   rè   Úunsqueeze_dimÚq_embedÚk_embeds          r8   Úapply_rotary_pos_embrý   O  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r9   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)rJ   rã   r¡   )r:   rþ   ÚbatchÚnum_key_value_headsÚslenrx   s         r8   Ú	repeat_kvr  i  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Tr9   ç        ÚmoduleÚqueryÚkeyÚvaluer›   ÚscalingÚdropoutr�   c                 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 )Nr<   r   r=   )rh   r?   )ÚpÚtrainingr%   )r  Únum_key_value_groupsr1   r©   r£   r   Ú
functionalÚsoftmaxrA   r@   r?   r
  r  Ú
contiguous)r  r  r  r  r›   r	  r
  r�   r²   r³   Úattn_weightsrµ   s               r8   Úeager_attention_forwardr  u  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$Ð$r9   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MiniMaxAttentionz=Multi-headed attention from 'Attention Is All You Need' paperrv   r]   c                 óŠ  •— t          ¦   «                              ¦   «          || _        || _        t	          |dd ¦  «        p|j        |j        z  | _        |j        |j        z  | _	        | j        dz  | _
        |j        | _        d| _        t          j        |j        |j        | j        z  d¬¦  «        | _        t          j        |j        |j        | j        z  d¬¦  «        | _        t          j        |j        |j        | j        z  d¬¦  «        | _        t          j        |j        | j        z  |j        d¬¦  «        | _        d S )Nrx   g      à¿TFry   )r.   r/   rv   r]   r   r6   r€   rx   r  r  r	  Úattention_dropoutÚ	is_causalr   r†   Úq_projÚk_projÚv_projÚo_proj©r5   rv   r]   r7   s      €r8   r/   zMiniMaxAttention.__init__’  s   ø€ Ý‰Œ×ÒÑÔÐØˆŒØ"ˆŒÝ ¨
°DÑ9Ô9Ðm¸VÔ=OÐSYÔSmÑ=mˆŒØ$*Ô$>À&ÔB\Ñ$\ˆÔ!Ø”} dÑ*ˆŒØ!'Ô!9ˆÔØˆŒÝ”i Ô 2°FÔ4NÐQUÔQ^Ñ4^ÐejÐkÑkÔkˆŒÝ”i Ô 2°FÔ4NÐQUÔQ^Ñ4^ÐejÐkÑkÔkˆŒÝ”i Ô 2°FÔ4NÐQUÔQ^Ñ4^ÐejÐkÑkÔkˆŒÝ”i Ô :¸T¼]Ñ JÈFÔL^ÐejÐkÑkÔkˆŒˆˆr9   Nr:   rš   r›   rœ   r�   r,   c           
      óL  — |j         d d…         }g |¢d‘| j        ‘R }|                      |¦  «                             |¦  «                             dd¦  «        }|                      |¦  «                             |¦  «                             dd¦  «        }	|                      |¦  «                             |¦  «                             dd¦  «        }
|\  }}t          ||	||¦  «        \  }}	|�|                     |	|
| j	        ¦  «        \  }	}
t          j        | j        j        t          ¦  «        } || ||	|
|f| j        sdn| j        | j        t%          | j        dd ¦  «        dœ|¤Ž\  }} |j        g |¢d‘R Ž                      ¦   «         }|                      |¦  «        }||fS )Nr=   r%   r<   r  Úsliding_window)r
  r	  r  )rJ   rx   r  Úviewr£   r  r  rý   Úupdater]   r   Úget_interfacerv   Ú_attn_implementationr  r  r  r	  r   r¡   r  r  )r5   r:   rš   r›   rœ   r�   Úinput_shapeÚhidden_shaper±   r²   r³   rç   rè   Úattention_interfacerµ   r  s                   r8   rG   zMiniMaxAttention.forward   sÒ  € ð $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”LÝ" 4¤;Ð0@À$ÑGÔGð
%
ð 
%
ð ð
%
ð 
%
Ñ!ˆ�\ð *�kÔ)Ð;¨;Ð;¸Ð;Ð;Ð;×FÒFÑHÔHˆØ—k’k +Ñ.Ô.ˆØ˜LÐ(Ð(r9   rV   )rL   rM   rN   Ú__doc__r&   rs   r/   r1   rP   rI   r	   r   r   rG   rQ   rR   s   @r8   r  r  Ž  så   ø€ € € € € àGÐGðl˜}ð l¸ð lð lð lð lð lð lð& )-ð')ð ')à”|ð')ð # 5¤<°´Ð#=Ô>ð')ð œ tÑ+ð	')ð
  ™ð')ð Ð-Ô.ð')ð 
ˆuŒ|˜Uœ\¨DÑ0Ð0Ô	1ð')ð ')ð ')ð ')ð ')ð ')ð ')ð ')r9   r  c                   ó$   ‡ — e Zd Zˆ fd„Zd„ Zˆ xZS )ÚMiniMaxTopKRouterc                 óü   •— t          ¦   «                              ¦   «          |j        | _        |j        | _        |j        | _        t          j	        t          j        | j        | j        ¦  «        ¦  «        | _        d S rV   )r.   r/   Únum_experts_per_tokÚtop_kÚnum_local_expertsÚnum_expertsr6   Ú
hidden_dimr   r0   r1   Úemptyr3   ©r5   rv   r7   s     €r8   r/   zMiniMaxTopKRouter.__init__Ë  s^   ø€ Ý‰Œ×ÒÑÔÐØÔ/ˆŒ
Ø!Ô3ˆÔØ Ô,ˆŒÝ”l¥5¤;¨tÔ/?ÀÄÑ#QÔ#QÑRÔRˆŒˆˆr9   c                 ó\  — |                      d| j        ¦  «        }t          j        || j        ¦  «        }t
          j        j                             | 	                    ¦   «         d¬¦  «        }t          j
        || j        d¬¦  «        \  }}||                     dd¬¦  «        z  }|}|||fS )Nr=   rg   T)rh   r>   )r¡   r/  r«   Úlinearr3   r1   r   r  r  rO   Útopkr,  Úsum)r5   r:   Úrouter_logitsÚrouter_probsÚrouter_top_valueÚrouter_indicesÚrouter_scoress          r8   rG   zMiniMaxTopKRouter.forwardÒ  s¥   € Ø%×-Ò-¨b°$´/ÑBÔBˆÝœ °´Ñ<Ô<ˆÝ”xÔ*×2Ò2°=×3FÒ3FÑ3HÔ3HÈbÐ2ÑQÔQˆÝ+0¬:°lÀDÄJÐTVÐ+WÑ+WÔ+WÑ(Ð˜.ØÐ,×0Ò0°RÀÐ0ÑFÔFÑFÐØ(ˆØ˜m¨^Ð;Ð;r9   )rL   rM   rN   r/   rG   rQ   rR   s   @r8   r)  r)  Ê  sL   ø€ € € € € ðSð Sð Sð Sð Sð<ð <ð <ð <ð <ð <ð <r9   r)  c                   óh   ‡ — e Zd ZdZdefˆ fd„Zdej        dej        dej        dej        fd„Zˆ xZ	S )	ÚMiniMaxExpertsz2Collection of expert weights stored as 3D tensors.rv   c                 ó´  •— t          ¦   «                              ¦   «          |j        | _        |j        | _        |j        | _        t          j	        t          j        | j        d| j        z  | j        ¦  «        ¦  «        | _        t          j	        t          j        | j        | j        | j        ¦  «        ¦  «        | _        t          |j                 | _        d S )Nr<   )r.   r/   r-  r.  r6   r/  Úintermediate_sizeÚintermediate_dimr   r0   r1   r0  Úgate_up_projÚ	down_projr   rƒ   r„   r1  s     €r8   r/   zMiniMaxExperts.__init__à  s£   ø€ Ý‰Œ×ÒÑÔÐØ!Ô3ˆÔØ Ô,ˆŒØ &Ô 8ˆÔÝœL­¬°TÔ5EÀqÈ4ÔK`ÑG`ÐbfÔbqÑ)rÔ)rÑsÔsˆÔÝœ¥e¤k°$Ô2BÀDÄOÐUYÔUjÑ&kÔ&kÑlÔlˆŒÝ˜VÔ.Ô/ˆŒˆˆr9   r:   Útop_k_indexÚtop_k_weightsr,   c                 ó€  — t          j        |¦  «        }t          j        ¦   «         5  t           j        j                             || j        ¬¦  «        }|                     ddd¦  «        }t          j        | 	                    d¬¦  «        d¦  «         
                    ¦   «         }d d d ¦  «         n# 1 swxY w Y   |D ]þ}|d         }|| j        k    rŒt          j        ||         ¦  «        \  }}	||	         }
t          j                             |
| j        |         ¦  «                             dd¬¦  «        \  }}|                      |¦  «        |z  }t          j                             || j        |         ¦  «        }|||	|d f         z  }|                     d|	|                     |j        ¦  «        ¦  «         Œÿ|S )N)Únum_classesr<   r%   r   )r=   r    rg   r=   )r1   Ú
zeros_likerñ   r   r  Úone_hotr.  ÚpermuteÚgreaterr5  Únonzeror˜   r3  r@  Úchunkr„   rA  Ú
index_add_r@   r?   )r5   r:   rB  rC  Úfinal_hidden_statesÚexpert_maskÚ
expert_hitÚ
expert_idxÚ	top_k_posÚ	token_idxÚcurrent_stateÚgateÚupÚcurrent_hidden_statess                 r8   rG   zMiniMaxExperts.forwardé  sø  € õ $Ô.¨}Ñ=Ô=ÐÝŒ]‰_Œ_ð 	Sð 	SÝœ(Ô-×5Ò5°kÈtÔO_Ð5Ñ`Ô`ˆKØ%×-Ò-¨a°°AÑ6Ô6ˆKÝœ {§¢¸8 Ñ'DÔ'DÀaÑHÔH×PÒPÑRÔRˆJð	Sð 	Sð 	Sñ 	Sô 	Sð 	Sð 	Sð 	Sð 	Sð 	Sð 	Søøøð 	Sð 	Sð 	Sð 	Sð
 %ð 
	nð 
	nˆJØ# AœˆJØ˜TÔ-Ò-Ð-ØÝ#(¤;¨{¸:Ô/FÑ#GÔ#GÑ ˆI�yØ)¨)Ô4ˆMÝ”}×+Ò+¨M¸4Ô;LÈZÔ;XÑYÔY×_Ò_Ð`aÐgiÐ_ÑjÔj‰HˆD�"Ø$(§K¢K°Ñ$5Ô$5¸Ñ$:Ð!Ý$&¤M×$8Ò$8Ð9NÐPTÔP^Ð_iÔPjÑ$kÔ$kÐ!Ø$9¸MÈ)ÐU^Ð`dÐJdÔ<eÑ$eÐ!Ø×*Ò*¨1¨iÐ9N×9QÒ9QÐReÔRkÑ9lÔ9lÑmÔmÐmÐmà"Ð"s   ¨A>B2Â2B6Â9B6)
rL   rM   rN   r'  r&   r/   r1   rP   rG   rQ   rR   s   @r8   r<  r<  Ü  sŒ   ø€ € € € € à<Ð<ð0˜}ð 0ð 0ð 0ð 0ð 0ð 0ð#à”|ð#ð ”\ð#ð ”|ð	#ð
 
Œð#ð #ð #ð #ð #ð #ð #ð #r9   r<  c                   ó\   ‡ — e Zd Zˆ fd„Zdej        deej        ej        f         fd„Zˆ xZS )ÚMiniMaxSparseMoeBlockc                 óÈ   •— t          ¦   «                              ¦   «          |j        | _        |j        | _        t          |¦  «        | _        t          |¦  «        | _	        d S rV   )
r.   r/   r+  r,  Úrouter_jitter_noiseÚjitter_noiser)  rT  r<  Úexpertsr1  s     €r8   r/   zMiniMaxSparseMoeBlock.__init__  sP   ø€ Ý‰Œ×ÒÑÔÐØÔ/ˆŒ
Ø"Ô6ˆÔÝ% fÑ-Ô-ˆŒ	Ý% fÑ-Ô-ˆŒˆˆr9   r:   r,   c                 ó†  — |j         \  }}}| j        rF| j        dk    r;|t          j        |¦  «                             d| j        z
  d| j        z   ¦  «        z  }|                     d|j         d         ¦  «        }|                      |¦  «        \  }}}|                      |||¦  «        }| 	                    |||¦  «        }|S )Nr   rÛ   r=   )
rJ   r  r[  r1   Ú
empty_likeÚuniform_r   rT  r\  r¡   )r5   r:   r­   Úsequence_lengthr/  r^   rC  rB  s           r8   rG   zMiniMaxSparseMoeBlock.forward  sÇ   € Ø2?Ô2EÑ/ˆ
�O ZØŒ=ð 	x˜TÔ.°Ò2Ð2Ø�UÔ-¨mÑ<Ô<×EÒEÀcÈDÔL]ÑF]Ð_bÐeiÔevÑ_vÑwÔwÑwˆMØ%×*Ò*¨2¨}Ô/BÀ2Ô/FÑGÔGˆØ(,¯	ª	°-Ñ(@Ô(@Ñ%ˆˆ=˜+ØŸš ]°KÀÑOÔOˆØ%×-Ò-¨j¸/È:ÑVÔVˆØÐr9   )	rL   rM   rN   r/   r1   rP   rI   rG   rQ   rR   s   @r8   rX  rX    sj   ø€ € € € € ð.ð .ð .ð .ð .ð U¤\ð °e¸E¼LÈ%Ì,Ð<VÔ6Wð ð ð ð ð ð ð ð r9   rX  c                   ó  ‡ — e 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j	        dz  d
e
dz  dedz  dee         deej        eej        ej        f         dz  f         fd„Zˆ xZS )ÚMiniMaxDecoderLayerrv   r]   c                 ó†  •— t          ¦   «                              ¦   «          |j        | _        t          ||¦  «        | _        t          |j        |j        ¬¦  «        | _        t          |j        |j        ¬¦  «        | _        || _	        t          |d¦  «        r|j        |         nd | _        |j        | _        |j        | _        t          |¦  «        | _        | j        dk    r/t#          ||¦  «        | _        |j        | _        |j        | _        d S t          ||¦  «        | _        |j        | _        |j        | _        d S )N©r+   r�   Úlinear_attention)r.   r/   r6   r  Ú	self_attnr)   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormr]   Úhasattrr�   Ú
block_typeÚmlp_alpha_factorÚmlp_beta_factorrX  Úmlpru   Úlinear_attn_alpha_factorÚattn_alpha_factorÚlinear_attn_beta_factorÚattn_beta_factorÚfull_attn_alpha_factorÚfull_attn_beta_factorr  s      €r8   r/   zMiniMaxDecoderLayer.__init__  s#  ø€ Ý‰Œ×ÒÑÔÐØ!Ô-ˆÔå)¨&°)Ñ<Ô<ˆŒÝ-¨fÔ.@ÀfÔFYÐZÑZÔZˆÔÝ(6°vÔ7IÈvÔObÐ(cÑ(cÔ(cˆÔ%à"ˆŒÝ;BÀ6È=Ñ;YÔ;YÐc˜&Ô,¨YÔ7Ð7Ð_cˆŒØ &Ô 7ˆÔØ%Ô5ˆÔÝ(¨Ñ0Ô0ˆŒØŒ?Ð0Ò0Ð0Ý6°v¸yÑIÔIˆDŒNØ%+Ô%DˆDÔ"Ø$*Ô$BˆDÔ!Ð!Ð!å-¨f°iÑ@Ô@ˆDŒNØ%+Ô%BˆDÔ"Ø$*Ô$@ˆDÔ!Ð!Ð!r9   NFr:   rš   r›   rê   rœ   Ú	use_cacher�   r,   c           
      ó  — |                       |¦  «        }|} | j        d||||||dœ|¤Ž\  }}	|| j        z  || j        z  z   }|                      |¦  «        }|}|                      |¦  «        }|| j        z  || j        z  z   }|S )N)r:   rš   r›   rê   rœ   ru  © )rh  rf  rp  rr  ri  rn  rl  rm  )
r5   r:   rš   r›   rê   rœ   ru  r�   Úresidualr^   s
             r8   rG   zMiniMaxDecoderLayer.forward.  s¿   € ð ×,Ò,¨]Ñ;Ô;ˆØ ˆØ)˜4œ>ð 
Ø'Ø 3Ø)Ø%Ø+Øð
ð 
ð ð
ð 
Ñˆ�qð ! 4Ô#9Ñ9¸MÈDÔLaÑ<aÑaˆØ×5Ò5°mÑDÔDˆØ ˆØŸš Ñ/Ô/ˆØ  4Ô#8Ñ8¸=È4ÔK_Ñ;_Ñ_ˆàÐr9   )NNNNF)rL   rM   rN   r&   rs   r/   r1   rP   rI   Ú
LongTensorr	   r¥   r   r   ÚFloatTensorrG   rQ   rR   s   @r8   rb  rb    s"  ø€ € € € € ðA˜}ð A¸ð Að Að Að Að Að Að2 IMØ.2Ø04Ø(,Ø!&ðð à”|ðð # 5¤<°´Ð#=Ô>ÀÑEðð œ tÑ+ð	ð
 Ô&¨Ñ-ðð  ™ðð ˜$‘;ðð Ð-Ô.ðð 
ˆuÔ  %¨Ô(9¸5Ô;LÐ(LÔ"MÐPTÑ"TÐTÔ	Uðð ð ð ð ð ð ð r9   rb  c                   ó¤   ‡ — e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZ eedd¬	¦  «        eeegd
œZ ej        ¦   «         ˆ fd„¦   «         Zˆ xZS )ÚMiniMaxPreTrainedModelrv   ÚmodelTrb  rœ   Fzmlp.gater   )Ú
layer_nameÚindex)r6  r:   Ú
attentionsc                 ó¸  •— t          ¦   «                              |¦  «         | j        j        }t	          |t
          ¦  «        r9t          j        |j        d|¬¦  «         t          j        |j	        d|¬¦  «         n1t	          |t          ¦  «        rt          j        |j        d|¬¦  «         t	          |t          ¦  «        r—|                     ¦   «         }|                     |¦  «        \  }}}t          j        |j        |¦  «         t          j        |j        |¦  «         t          j        |j        |¦  «         t          j        |j        |¦  «         d S d S )Nr  )rC   Ústd)r.   Ú_init_weightsrv   Úinitializer_rangerä   r<  ÚinitÚnormal_r@  rA  r)  r3   ru   rŠ   r‹   Úcopy_r{   r|   r}   r~   )r5   r  r‚  r{   r|   r}   r~   r7   s          €r8   rƒ  z$MiniMaxPreTrainedModel._init_weights^  s<  ø€ å‰Œ×Ò˜fÑ%Ô%Ð%ØŒkÔ+ˆÝ�f�nÑ-Ô-ð 	;ÝŒL˜Ô,°3¸CÐ@Ñ@Ô@Ð@ÝŒL˜Ô)°¸Ð=Ñ=Ô=Ð=Ð=Ý˜Õ 1Ñ2Ô2ð 	;ÝŒL˜œ¨S°cÐ:Ñ:Ô:Ð:Ý�fÕ7Ñ8Ô8ð 	>Ø×.Ò.Ñ0Ô0ˆJØ5;×5IÒ5IÈ*Ñ5UÔ5UÑ2ˆK˜ NÝŒJ�vÔ(¨*Ñ5Ô5Ð5ÝŒJ�vÔ)¨;Ñ7Ô7Ð7ÝŒJ�vÔ'¨Ñ3Ô3Ð3ÝŒJ�vÔ,¨nÑ=Ô=Ð=Ð=Ð=ð	>ð 	>r9   )rL   rM   rN   r&   rï   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#   r)  rb  r  ru   Ú_can_record_outputsr1   rñ   rƒ  rQ   rR   s   @r8   r|  r|  L  sÄ   ø€ € € € € € àÐÐÑØÐØ&*Ð#Ø.Ð/ÐØ#4Ð"5ÐØÐØ€NØÐØ"ÐØ"&Ðà'˜Ð(9ÀjÐXYÐZÑZÔZØ,Ø'Ð)BÐCðð Ðð €U„]�_„_ð>ð >ð >ð >ñ „_ð>ð >ð >ð >ð >r9   r|  c                   óØ   ‡ — e Zd Zdef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dz  d
ee         deez  fd„¦   «         ¦   «         Zˆ xZS )ÚMiniMaxModelrv   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 rw  )rb  )Ú.0r]   rv   s     €r8   ú
<listcomp>z)MiniMaxModel.__init__.<locals>.<listcomp>y  s$   ø€ ÐeÐeÐe¸	Õ  ¨Ñ3Ô3ÐeÐeÐer9   rd  )rv   F)r.   r/   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	Embeddingr6   Úembed_tokensÚ
ModuleListrZ   r�   rj   r)   rg  r…   rÉ   Ú
rotary_embÚgradient_checkpointingÚ	post_initr1  s    `€r8   r/   zMiniMaxModel.__init__r  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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#ð 	�ŠÑÔÐÐÐr9   NÚ	input_idsr›   rê   rœ   Úinputs_embedsru  r�   r,   c           
      ó  — |d u |d uz  rt          d¦  «        ‚|r|€t          ¦   «         }n7|r5t          |t          ¦  «        s t          dt          |¦  «        › d�¦  «        ‚|€|                      |¦  «        }|€V|�|                     ¦   «         nd}t          j        |j        d         |j	        ¬¦  «        |z   }| 
                    d¦  «        }| j        j        €t          nt          }	 |	| j        ||||¬¦  «        }
|}|                      ||¦  «        }t!          | j        ¦  «        D ]/\  }}| j        j        |         dk    r|
}n|} ||f|||||d	œ|¤Ž}Œ0|                      |¦  «        }t)          ||¬
¦  «        S )Nz:You must specify exactly one of input_ids or inputs_embedszSMiniMax uses cache of its own and is not compatible with `past_key_values` of type ú.r   r%   )r×   )rv   r¢  r›   rœ   rê   Úfull_attention)r›   rš   rê   rœ   ru  )Úlast_hidden_staterœ   )Ú
ValueErrorrT   rä   rå   rœ  Úget_seq_lengthr1   r‘   rJ   r×   r§   rv   r  r   r   rž  Ú	enumeraterj   r�   r…   r   )r5   r¡  r›   rê   rœ   r¢  ru  r�   Úpast_seen_tokensÚmask_functionÚcausal_maskr:   rš   r¶   Údecoder_layerÚinput_attention_masks                   r8   rG   zMiniMaxModel.forward‚  s  € ð ˜Ð -°tÐ";Ñ<ð 	[ÝÐYÑZÔZÐZàð 	˜Ð0Ý*™nœnˆOˆOØð 	�z¨/½<ÑHÔHð 	ÝØ~ÕfjÐkzÑf{Ôf{Ð~Ð~Ð~ñô ð ð Ð Ø ×-Ò-¨iÑ8Ô8ˆMàÐØCRÐC^˜×=Ò=Ñ?Ô?Ð?ÐdeÐÝ œ<¨Ô(;¸AÔ(>À}ÔG[Ð\Ñ\Ô\Ð_oÑoˆLØ'×1Ò1°!Ñ4Ô4ˆLà.2¬kÔ.HÐ.PÕ*Ð*ÕVwˆØ#�mØ”;Ø'Ø)Ø+Ø%ð
ñ 
ô 
ˆð &ˆØ"Ÿošo¨m¸\ÑJÔJÐå )¨$¬+Ñ 6Ô 6ð 	ð 	ÑˆAˆ}ØŒ{Ô& qÔ)Ð-=Ò=Ð=Ø'2Ð$Ð$ð (6Ð$à)˜MØðà3Ø$7Ø)Ø /Ø#ðð ð ðð ˆMˆMð Ÿ	š	 -Ñ0Ô0ˆå%Ø+Ø+ð
ñ 
ô 
ð 	
r9   )NNNNNN)rL   rM   rN   r&   r/   r"   r$   r1   ry  rP   rT   rz  r¥   r   r   rI   r   rG   rQ   rR   s   @r8   r“  r“  p  s
  ø€ € € € € ð˜}ð ð ð ð ð ð ð   Øð .2Ø.2Ø04Ø/3Ø26Ø!%ð>
ð >
àÔ# dÑ*ð>
ð œ tÑ+ð>
ð Ô&¨Ñ-ð	>
ð
 &¨Ñ,ð>
ð Ô(¨4Ñ/ð>
ð ˜$‘;ð>
ð Ð+Ô,ð>
ð 
Ð'Ñ	'ð>
ð >
ð >
ñ „_ñ  Ôð>
ð >
ð >
ð >
ð >
r9   r“  r<   Úgate_logitsr.  c                 óÆ  ‡— | �t          | t          ¦  «        sdS t          | t          ¦  «        r/| d         j        Št          j        ˆfd„| D ¦   «         d¬¦  «        }t          j        j                             |d¬¦  «        }t          j        ||d¬¦  «        \  }}t          j        j         	                    ||¦  «        }|€@t          j
        |                     ¦   «         d¬¦  «        }	t          j
        |d¬¦  «        }
�n.|j        \  }}|j        d         ||z  z  }|ddd…dd…ddf                              |||||f¦  «                             d||¦  «                             ‰¦  «        }t          j        |                     ¦   «         |z  d¬¦  «        t          j        |d¬¦  «        z  }	|ddd…dd…df                              ||||f¦  «                             d|¦  «                             ‰¦  «        }t          j        ||z  d¬¦  «        t          j        |d¬¦  «        z  }
t          j        |	|
                     d¦  «        z  ¦  «        }||z  S )aÄ  
    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.

    See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
    experts is too unbalanced.

    Args:
        gate_logits:
            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
            shape [batch_size X sequence_length, num_experts].
        num_experts:
            Number of experts
        top_k:
            The number of experts to route per-token, can be also interpreted as the `top-k` routing
            parameter.
        attention_mask (`torch.Tensor`, *optional*):
            The attention_mask used in forward function
            shape [batch_size X sequence_length] if not None.

    Returns:
        The auxiliary loss.
    Nr   c                 ó:   •— g | ]}|                      ‰¦  «        ‘ŒS rw  )r@   )r–  Ú
layer_gateÚcompute_devices     €r8   r—  z,load_balancing_loss_func.<locals>.<listcomp>ç  s&   ø€ Ð-jÐ-jÐ-jÐPZ¨j¯mªm¸NÑ.KÔ.KÐ-jÐ-jÐ-jr9   rg   r=   )rä   rI   r×   r1   rª   r   r  r  r4  rG  rC   rO   rJ   rã   r¡   r@   r5  r§   )r¯  r.  r,  r›   Úconcatenated_gate_logitsÚrouting_weightsr^   Úselected_expertsrN  Útokens_per_expertÚrouter_prob_per_expertr­   r`  r�   Úexpert_attention_maskÚ router_per_expert_attention_maskÚoverall_lossr³  s                    @r8   Úload_balancing_loss_funcr¼  Å  s�  ø€ ð: Ð¥*¨[½%Ñ"@Ô"@ÐØˆqå�+�uÑ%Ô%ð sØ$ QœÔ.ˆÝ#(¤9Ð-jÐ-jÐ-jÐ-jÐ^iÐ-jÑ-jÔ-jÐpqÐ#rÑ#rÔ#rÐ å”hÔ)×1Ò1Ð2JÐPRÐ1ÑSÔS€Oåœ* _°eÀÐDÑDÔDÑ€AÐå”(Ô%×-Ò-Ð.>ÀÑLÔL€KàÐå!œJ {×'8Ò'8Ñ':Ô':ÀÐBÑBÔBÐõ "'¤¨OÀÐ!CÑ!CÔ!CÐÑà&4Ô&:Ñ#ˆ
�OØ4Ô:¸1Ô=À*ÈÑB^Ñ_Ðð ˜4    A A A t¨TÐ1Ô2ßŠVÐ&¨
°OÀUÈKÐXÑYÔYßŠW�R˜ Ñ,Ô,ßŠR�ÑÔð	 	õ "œI k×&7Ò&7Ñ&9Ô&9Ð<QÑ&QÐWXÐYÑYÔYÕ\aÔ\eØ! qð]
ñ ]
ô ]
ñ 
Ðð ˜4    A A A tÐ+Ô,ßŠVÐ&¨
°OÀ[ÐQÑRÔRßŠW�R˜Ñ%Ô%ßŠR�ÑÔð	 	)õ "'¤¨?Ð=]Ñ+]ÐcdÐ!eÑ!eÔ!eÕhmÔhqØ,°!ði
ñ i
ô i
ñ "
Ðõ ”9Ð.Ð1G×1QÒ1QÐRSÑ1TÔ1TÑTÑUÔU€LØ˜+Ñ%Ð%r9   c                   ó$  ‡ — e Zd ZddiZddiZddgdgfiZˆ fd„Zee	 	 	 	 	 	 	 	 	 dd
e	j
        dz  de	j        dz  de	j
        dz  dedz  de	j        dz  de	j
        dz  dedz  dedz  dee	j        z  dee         defd„¦   «         ¦   «         Zˆ xZS )ÚMiniMaxForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputr:   Úlogitsc                 óF  •— t          ¦   «                              |¦  «         t          |¦  «        | _        |j        | _        t          j        |j        |j        d¬¦  «        | _        |j	        | _	        |j
        | _        |j        | _        |                      ¦   «          d S )NFry   )r.   r/   r“  r}  rš  r   r†   r6   r¿  Úrouter_aux_loss_coefr-  r.  r+  r   r1  s     €r8   r/   zMiniMaxForCausalLM.__init__  s‹   ø€ Ý‰Œ×Ò˜Ñ Ô Ð Ý! &Ñ)Ô)ˆŒ
Ø Ô+ˆŒÝ”y Ô!3°VÔ5FÈUÐSÑSÔSˆŒØ$*Ô$?ˆÔ!Ø!Ô3ˆÔØ#)Ô#=ˆÔ ð 	�ŠÑÔÐÐÐr9   Nr   r¡  r›   rê   rœ   r¢  Úlabelsru  Úoutput_router_logitsÚlogits_to_keepr�   r,   c
                 ó  — |�|n| j         j        } | j        d|||||||dœ|
¤Ž}|j        }t	          |	t
          ¦  «        rt          |	 d¦  «        n|	}|                      |dd…|dd…f         ¦  «        }d}|� | j        ||| j	        fi |
¤Ž}d}|rHt          |j        | j        | j        |¦  «        }|�%|| j        |                     |j        ¦  «        z  z  }t#          ||||j        |j        |j        |j        ¬¦  «        S )a€  
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Example:

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

        >>> model = MiniMaxForCausalLM.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-Text-01-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."
        ```N)r¡  r›   rê   rœ   r¢  ru  rÅ  )ÚlossÚaux_lossrÁ  rœ   r:   r€  r6  rw  )rv   rÅ  r}  r¦  rä   rs   Úslicer¿  Úloss_functionrš  r¼  r6  r.  r+  rÃ  r@   r×   r   rœ   r:   r€  )r5   r¡  r›   rê   rœ   r¢  rÄ  ru  rÅ  rÆ  r�   Úoutputsr:   Úslice_indicesrÁ  rÈ  rÉ  s                    r8   rG   zMiniMaxForCausalLM.forward)  sn  € ðN %9Ð$DÐ Ð È$Ì+ÔJjð 	ð
 +5¨$¬*ð 	+
ØØ)Ø%Ø+Ø'ØØ!5ð	+
ð 	+
ð ð	+
ð 	+
ˆð  Ô1ˆå8BÀ>ÕSVÑ8WÔ8WÐk�˜~˜o¨tÑ4Ô4Ð4Ð]kˆØ—’˜m¨A¨A¨A¨}¸a¸a¸aÐ,?Ô@ÑAÔAˆàˆØÐØ%�4Ô% f¨f°d´oÐPÐPÈÐPÐPˆDàˆØð 	MÝ/ØÔ%ØÔ ØÔ(Øñ	ô ˆHð Ð!Ø˜Ô1°H·K²KÀÄÑ4LÔ4LÑLÑL�å(ØØØØ#Ô3Ø!Ô/ØÔ)Ø!Ô/ð
ñ 
ô 
ð 	
r9   )	NNNNNNNNr   )rL   rM   rN   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr/   r    r   r1   ry  rP   r	   rz  r¥   rs   r   r   r   rG   rQ   rR   s   @r8   r¾  r¾    sp  ø€ € € € € à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hð
ð 
ð 
ð 
ð 
ð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø,0Ø-.ðP
ð P
àÔ# dÑ*ðP
ð œ tÑ+ðP
ð Ô&¨Ñ-ð	P
ð
  ™ðP
ð Ô(¨4Ñ/ðP
ð Ô  4Ñ'ðP
ð ˜$‘;ðP
ð # T™kðP
ð ˜eœlÑ*ðP
ð Ð+Ô,ðP
ð 
#ðP
ð P
ð P
ñ „^ñ ÔðP
ð P
ð P
ð P
ð P
r9   r¾  c                   ó   — e Zd ZdS )Ú MiniMaxForSequenceClassificationN©rL   rM   rN   rw  r9   r8   rÒ  rÒ  ~  ó   € € € € € Ø€Dr9   rÒ  c                   ó   — e Zd ZdS )ÚMiniMaxForTokenClassificationNrÓ  rw  r9   r8   rÖ  rÖ  ‚  rÔ  r9   rÖ  c                   ó   — e Zd ZdS )ÚMiniMaxForQuestionAnsweringNrÓ  rw  r9   r8   rØ  rØ  †  rÔ  r9   rØ  )r|  r“  r¾  rÒ  rÖ  rØ  )r%   )r  )Nr<   N)SÚcollections.abcr   Útypingr   r1   Útorch.nn.functionalr   r  r«   Ú r   r…  Úactivationsr   Úcache_utilsr	   r
   Ú
generationr   Úintegrationsr   r   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#   r$   Úconfiguration_minimaxr&   ÚModuler)   rT   ru   rÉ   rõ   rý   rP   rs   r  rO   r  r  r)  r<  rX  rb  r|  r“  rI   r¼  r¾  rÒ  rÖ  rØ  Ú__all__rw  r9   r8   ú<module>rî     s´  ðð, %Ð $Ð $Ð $Ð $Ð $Ø Ð Ð Ð Ð Ð à €€€Ø Ð Ð Ð Ð Ð Ð Ð Ð Ø Ð Ð Ð Ð Ð à &Ð &Ð &Ð &Ð &Ð &Ø !Ð !Ð !Ð !Ð !Ð !Ø .Ð .Ð .Ð .Ð .Ð .Ð .Ð .Ø )Ð )Ð )Ð )Ð )Ð )ðð ð ð ð ð ð ð ð ð ð ð ð SÐ 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 RÐ QÐ QÐ QÐ QÐ QÐ QÐ QØ 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 EÐ EÐ EÐ EÐ EÐ EÐ EÐ EØ 0Ð 0Ð 0Ð 0Ð 0Ð 0ð Ð˜YÑ'Ô'ðJð Jð Jð Jð J�R”Yñ Jô Jñ (Ô'ðJð("Ið "Ið "Ið "Ið "I�<ñ "Iô "Ið "IðJQ/ð Q/ð Q/ð Q/ð Q/ ¤	ñ Q/ô Q/ð Q/ðh><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8)ð 8)ð 8)ð 8)ð 8)�r”yñ 8)ô 8)ñ +Ô*ð8)ðv<ð <ð <ð <ð <˜œ	ñ <ô <ð <ð$ ð$#ð $#ð $#ð $#ð $#�R”Yñ $#ô $#ñ Ôð$#ðNð ð ð ð ˜BœIñ ô ð ð&2ð 2ð 2ð 2ð 2Ð4ñ 2ô 2ð 2ðj ð >ð  >ð  >ð  >ð  >˜_ñ  >ô  >ñ „ð >ðF ðQ
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ð Q
Ð)ñ Q
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ñ „ðQ
ðl #Ø
Ø*.ð	O&ð O&Ø”  e¤lÔ 3Ñ3°dÑ:ðO&à�t‘ðO&ð ”L 4Ñ'ð	O&ð
 „\�CÑðO&ð O&ð O&ð O&ðd ðc
ð c
ð c
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Ð/°ñ c
ô c
ñ „ðc
ðL	ð 	ð 	ð 	ð 	Ð'GÐI_ñ 	ô 	ð 	ð	ð 	ð 	ð 	ð 	Ð$AÐCYñ 	ô 	ð 	ð	ð 	ð 	ð 	ð 	Ð"=Ð?Uñ 	ô 	ð 	ðð ð €€€r9   