Ë
    ÜÓ^jT\  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	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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\                  «      Z0d„ Z1dejd                  de3dejd                  fd„Z4	 d;dej\                  d ejd                  d!ejd                  d"ejd                  d#ejd                  dz  d$e5d%e5d&e"e$   fd'„Z6d<d(„Z7 G d)„ d*ej\                  «      Z8 ed+«       G d,„ d-ej\                  «      «       Z9 G d.„ d/e«      Z:e% G d0„ d1e «      «       Z;e% G d2„ d3e;«      «       Z<e% G d4„ d5e;e«      «       Z= G d6„ d7ee;«      Z> G d8„ d9ee;«      Z?g d:¢Z@y)=é    )ÚCallable)ÚOptionalN)Únné   )ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úuse_kernel_forward_from_hub)Úcreate_causal_maskÚ!create_sliding_window_causal_mask)ÚFlashAttentionKwargs)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
Phi3Configc                   óV   ‡ — e Zd Zˆ fd„Zdej
                  dej
                  fd„Zˆ xZS )ÚPhi3MLPc                 ó*  •— t         ‰| �  «        || _        t        j                  |j
                  d|j                  z  d¬«      | _        t        j                  |j                  |j
                  d¬«      | _        t        |j                     | _        y )Né   F©Úbias)ÚsuperÚ__init__Úconfigr   ÚLinearÚhidden_sizeÚintermediate_sizeÚgate_up_projÚ	down_projr   Ú
hidden_actÚactivation_fn©Úselfr)   Ú	__class__s     €úq/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/phi3/modeling_phi3.pyr(   zPhi3MLP.__init__2   sp   ø€ Ü‰ÑÔàˆŒÜŸI™I f×&8Ñ&8¸!¸f×>VÑ>VÑ:VÐ]bÔcˆÔÜŸ™ 6×#;Ñ#;¸V×=OÑ=OÐV[Ô\ˆŒÜ# F×$5Ñ$5Ñ6ˆÕó    Úhidden_statesÚreturnc                 óš   — | j                  |«      }|j                  dd¬«      \  }}|| j                  |«      z  }| j                  |«      S )Nr$   éÿÿÿÿ©Údim)r-   Úchunkr0   r.   )r2   r6   Ú	up_statesÚgates       r4   ÚforwardzPhi3MLP.forward:   sL   € Ø×%Ñ% mÓ4ˆ	à#Ÿ/™/¨!°˜/Ó4‰ˆˆiØ × 2Ñ 2°4Ó 8Ñ8ˆ	à�~‰~˜iÓ(Ð(r5   )Ú__name__Ú
__module__Ú__qualname__r(   ÚtorchÚFloatTensorr?   Ú__classcell__©r3   s   @r4   r"   r"   1   s'   ø„ ô7ð) U×%6Ñ%6ð )¸5×;LÑ;L÷ )r5   r"   c                   óÄ   ‡ — e Zd ZU ej                  ed<   ddefˆ fd„Ze	 	 	 ddedz  de	d   de
dz  ded	ef   fd
„«       Z ej                  «       ed„ «       «       Zˆ xZS )ÚPhi3RotaryEmbeddingÚinv_freqNr)   c                 ó®  •— t         ‰| �  «        |j                  | _        |j                  | _        || _        | j
                  j                  d   | _        | j                  }| j                  dk7  rt        | j                     } || j
                  |«      \  }| _
        | j                  d|d¬«       | j                  d|j                  «       d¬«       y )NÚ	rope_typeÚdefaultrI   F)Ú
persistentÚoriginal_inv_freq)r'   r(   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenr)   Úrope_parametersrK   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)r2   r)   ÚdeviceÚrope_init_fnrI   r3   s        €r4   r(   zPhi3RotaryEmbedding.__init__F   s¶   ø€ Ü‰ÑÔØ"(×"@Ñ"@ˆÔØ$*×$BÑ$BˆÔ!àˆŒàŸ™×4Ñ4°[ÑAˆŒØ!%×!EÑ!EˆØ�>‰>˜YÒ&Ü.¨t¯~©~Ñ>ˆLÙ+7¸¿¹ÀVÓ+LÑ(ˆ�$Ô(à×Ñ˜Z¨¸eÐÔDØ×ÑÐ0°(·.±.Ó2BÈuÐÕUr5   rW   ztorch.deviceÚseq_lenr7   ztorch.Tensorc                 ón  — | j                   d   }| j                   j                  dd«      }t        | dd«      xs | j                  | j                  z  }t        ||z  «      }d}d|t        j                  d|dt        j                  ¬«      j                  |t        j                  ¬	«      |z  z  z  }||fS )
a¨  
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        Ú
rope_thetaÚpartial_rotary_factorg      ð?Úhead_dimNr   r$   ©Údtype)rW   r_   )rR   ÚgetÚgetattrr+   Únum_attention_headsÚintrC   ÚarangeÚint64ÚtoÚfloat)	r)   rW   rY   Úbaser\   r]   r;   Úattention_factorrI   s	            r4   rS   z3Phi3RotaryEmbedding.compute_default_rope_parametersV   s»   € ð& ×%Ñ% lÑ3ˆØ &× 6Ñ 6× :Ñ :Ð;RÐTWÓ XÐÜ˜6 :¨tÓ4Òh¸×8JÑ8JÈf×NhÑNhÑ8hˆÜ�(Ð2Ñ2Ó3ˆàÐð Ø”U—\‘\ ! S¨!´5·;±;Ô?×BÑBÈ&ÔX]×XcÑXcÐBÓdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r5   c                 óN  — | j                   d d d …d f   j                  «       j                  |j                  d   dd«      j	                  |j
                  «      }|d d …d d d …f   j                  «       }t        |j
                  j                  t        «      r/|j
                  j                  dk7  r|j
                  j                  nd}t        |d¬«      5  |j                  «       |j                  «       z  j                  dd«      }t        j                  ||fd¬	«      }|j                  «       | j                  z  }|j                  «       | j                  z  }	d d d «       j	                  |j                   ¬
«      	j	                  |j                   ¬
«      fS # 1 sw Y   ŒAxY w)Nr   r9   r   ÚmpsÚcpuF)Údevice_typeÚenabledr$   r:   r^   )rI   rg   ÚexpandÚshaperf   rW   Ú
isinstanceÚtypeÚstrr   Ú	transposerC   ÚcatÚcosrT   Úsinr_   )
r2   ÚxÚposition_idsÚinv_freq_expandedÚposition_ids_expandedrm   ÚfreqsÚembrv   rw   s
             r4   r?   zPhi3RotaryEmbedding.forwardv   sR  € ð !ŸM™M¨$²°4¨-Ñ8×>Ñ>Ó@×GÑGÈ×HZÑHZÐ[\ÑH]Ð_aÐcdÓe×hÑhÐij×iqÑiqÓrÐØ ,ªQ°²a¨ZÑ 8× >Ñ >Ó @Ðä'1°!·(±(·-±-ÄÔ'EÈ!Ï(É(Ï-É-Ð[`ÒJ`�a—h‘h—m’mÐfkˆÜ¨¸UÔCñ 	5Ø&×,Ñ,Ó.Ð1F×1LÑ1LÓ1NÑN×YÑYÐZ[Ð]^Ó_ˆEÜ—)‘)˜U E˜N°Ô3ˆCØ—'‘'“)˜d×4Ñ4Ñ4ˆCØ—'‘'“)˜d×4Ñ4Ñ4ˆC÷		5ð �v‰v˜AŸG™GˆvÓ$ c§f¡f°1·7±7 fÓ&;Ð;Ð;÷	5ð 	5ús   ÃBFÆF$©N)NNN)r@   rA   rB   rC   ÚTensorÚ__annotations__r    r(   Ústaticmethodr   rc   Útuplerg   rS   Úno_gradr   r?   rE   rF   s   @r4   rH   rH   C   s˜   ø… Ø�l‰lÓñV˜zõ Vð  à$(Ø+/Ø"ñ*Ø˜TÑ!ð*à˜Ñ(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ñ	%ò	*ó ð*ð> €U‡]�]ƒ_Øñ<ó ó ô<r5   rH   c                 óš   — | dd| j                   d   dz  …f   }| d| j                   d   dz  d…f   }t        j                  | |fd¬«      S )z*Rotates half the hidden dims of the input..Nr9   r$   r:   )rp   rC   ru   )rx   Úx1Úx2s      r4   Úrotate_halfr‡   †   sZ   € à	
ˆ3Ð"�!—'‘'˜"‘+ Ñ"Ð"Ð"Ñ	#€BØ	
ˆ3�—‘˜‘˜qÑ Ñ"Ð"Ñ	#€BÜ�9‰9�r�c˜2�Y BÔ'Ð'r5   r6   Ún_repr7   c                 óª   — | j                   \  }}}}|dk(  r| S | dd…dd…ddd…dd…f   j                  |||||«      } | j                  |||z  ||«      S )zÔ
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    r   N)rp   ro   Úreshape)r6   rˆ   ÚbatchÚnum_key_value_headsÚslenr]   s         r4   Ú	repeat_kvrŽ   �   so   € ð
 2?×1DÑ1DÑ.€EÐ  hØ�‚zØÐØ!¢!¢Q¨ªa²Ð"2Ñ3×:Ñ:¸5ÐBUÐW\Ð^bÐdlÓm€MØ× Ñ  Ð(;¸eÑ(CÀTÈ8ÓTÐTr5   ÚmoduleÚqueryÚkeyÚvalueÚattention_maskÚscalingÚdropoutÚkwargsc                 ó  — t        || j                  «      }t        || j                  «      }	t        j                  ||j	                  dd«      «      |z  }
|�|
|z   }
t
        j                  j                  |
dt        j                  ¬«      j                  |j                  «      }
t
        j                  j                  |
|| j                  ¬«      }
t        j                  |
|	«      }|j	                  dd«      j                  «       }||
fS )Nr$   r   r9   )r;   r_   )ÚpÚtrainingr   )rŽ   Únum_key_value_groupsrC   Úmatmulrt   r   Ú
functionalÚsoftmaxÚfloat32rf   r_   r•   r™   Ú
contiguous)r�   r�   r‘   r’   r“   r”   r•   r–   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r4   Úeager_attention_forwardr¤   ™   sà   € ô ˜3 × ;Ñ ;Ó<€JÜ˜U F×$?Ñ$?Ó@€Lä—<‘<  z×';Ñ';¸A¸qÓ'AÓBÀWÑL€LØÐ!Ø# nÑ4ˆä—=‘=×(Ñ(¨¸2ÄUÇ]Á]Ð(ÓS×VÑVÐW\×WbÑWbÓc€LÜ—=‘=×(Ñ(¨¸È6Ï?É?Ð(Ó[€LÜ—,‘,˜|¨\Ó:€KØ×'Ñ'¨¨1Ó-×8Ñ8Ó:€Kà˜Ð$Ð$r5   c                 ó`  — |j                  |«      }|j                  |«      }|j                  d   }| dd|…f   | d|d…f   }}|dd|…f   |d|d…f   }	}t        j                  ||z  t	        |«      |z  z   |gd¬«      }
t        j                  ||z  t	        |«      |z  z   |	gd¬«      }|
|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.
    r9   .Nr:   )Ú	unsqueezerp   rC   ru   r‡   )ÚqÚkrv   rw   Úunsqueeze_dimÚ
rotary_dimÚq_rotÚq_passÚk_rotÚk_passÚq_embedÚk_embeds               r4   Úapply_rotary_pos_embr±   ²   sÐ   € ð$ �-‰-˜Ó
&€CØ
�-‰-˜Ó
&€Cà—‘˜2‘€JØ�c˜;˜J˜;Ð&Ñ'¨¨3°
±Ð+;Ñ)<ˆ6€EØ�c˜;˜J˜;Ð&Ñ'¨¨3°
±Ð+;Ñ)<ˆ6€Eä�i‰i˜% #™+¬+°eÓ*<¸sÑ*BÑCÀVÐLÐRTÔU€GÜ�i‰i˜% #™+¬+°eÓ*<¸sÑ*BÑCÀVÐLÐRTÔU€GØ�GÐÐr5   c                   ó  ‡ — e Zd ZdZddededz  fˆ fd„Z	 ddej                  de	ej                  ej                  f   dej                  dz  d	e
dz  d
ee   de	ej                  ej                  dz  e	ej                     dz  f   fd„Zˆ xZS )ÚPhi3Attentionz=Multi-headed attention from 'Attention Is All You Need' paperNr)   Ú	layer_idxc                 ó|  •— t         ‰| �  «        || _        || _        t	        |d|j
                  |j                  z  «      | _        |j                  |j                  z  | _	        |j                  | _        | j                  dz  | _
        |j                  | _        d| _        |j                  | j                  z  d|j                  | j                  z  z  z   }t        j                  |j                  | j                  z  |j
                  d¬«      | _        t        j                  |j
                  |d¬«      | _        y )Nr]   g      à¿Tr$   Fr%   )r'   r(   r)   r´   ra   r+   rb   r]   rŒ   rš   r”   Úattention_dropoutÚ	is_causalr   r*   Úo_projÚqkv_proj)r2   r)   r´   Úop_sizer3   s       €r4   r(   zPhi3Attention.__init__Ó   sý   ø€ Ü‰ÑÔØˆŒØ"ˆŒÜ ¨
°F×4FÑ4FÈ&×JdÑJdÑ4dÓeˆŒØ$*×$>Ñ$>À&×B\ÑB\Ñ$\ˆÔ!Ø#)×#=Ñ#=ˆÔ Ø—}‘} dÑ*ˆŒØ!'×!9Ñ!9ˆÔØˆŒà×,Ñ,¨t¯}©}Ñ<¸qÀF×D^ÑD^Ðae×anÑanÑDnÑ?oÑoˆÜ—i‘i × :Ñ :¸T¿]¹]Ñ JÈF×L^ÑL^ÐejÔkˆŒÜŸ	™	 &×"4Ñ"4°gÀEÔJˆ�r5   r6   Úposition_embeddingsr“   Úpast_key_valuesr–   r7   c           
      óâ  — |j                   d d }g |¢d‘| j                  ‘­}| j                  |«      }| j                  j                  | j                  z  }	|dd |	…f   }
|d|	|	| j
                  | j                  z  z   …f   }|d|	| j
                  | j                  z  z   d …f   }|
j                  |«      j                  dd«      }
|j                  |«      j                  dd«      }|j                  |«      j                  dd«      }|\  }}t        |
|||«      \  }
}|� |j                  ||| j                  «      \  }}t        j                  | j                  j                  t        «      } || |
|||f| j                  sdn| j                   | j"                  t%        | j                  dd «      dœ|¤Ž\  }} |j&                  g |¢d‘­Ž j)                  «       }| j+                  |«      }||fS )Nr9   .r   r$   ç        Úsliding_window)r•   r”   r¿   )rp   r]   r¹   r)   rb   rŒ   Úviewrt   r±   Úupdater´   r   Úget_interfaceÚ_attn_implementationr¤   r™   r¶   r”   ra   rŠ   rŸ   r¸   )r2   r6   r»   r“   r¼   r–   Úinput_shapeÚhidden_shapeÚqkvÚ	query_posÚquery_statesr    r¡   rv   rw   Úattention_interfacer£   r¢   s                     r4   r?   zPhi3Attention.forwardâ   s  € ð $×)Ñ)¨#¨2Ð.ˆØ8˜Ð8 bÐ8¨$¯-©-Ñ8ˆà�m‰m˜MÓ*ˆØ—K‘K×3Ñ3°d·m±mÑCˆ	Ø˜3 
  
˜?Ñ+ˆØ˜˜i¨)°d×6NÑ6NÐQU×Q^ÑQ^Ñ6^Ñ*^Ð^Ð^Ñ_ˆ
Ø˜3 	¨D×,DÑ,DÀtÇ}Á}Ñ,TÑ TÑ VÐVÑWˆà#×(Ñ(¨Ó6×@Ñ@ÀÀAÓFˆØ—_‘_ \Ó2×<Ñ<¸QÀÓBˆ
Ø#×(Ñ(¨Ó6×@Ñ@ÀÀAÓFˆà&‰ˆˆSÜ#7¸ÀjÐRUÐWZÓ#[Ñ ˆ�jàÐ&Ø'6×'=Ñ'=¸jÈ,ÐX\×XfÑXfÓ'gÑ$ˆJ˜ä(?×(MÑ(MØ�K‰K×,Ñ,Ô.Eó)
Ðñ %8ØØØØØð
%
ð  $Ÿ}š}‘C°$×2HÑ2HØ—L‘LÜ" 4§;¡;Ð0@À$ÓGñ
%
ð ñ
%
Ñ!ˆ�\ð *�k×)Ñ)Ð;¨;Ð;¸Ò;×FÑFÓHˆØ—k‘k +Ó.ˆØ˜LÐ(Ð(r5   r~   )r@   rA   rB   Ú__doc__r    rc   r(   rC   r   r‚   r   r   r   r?   rE   rF   s   @r4   r³   r³   Ð   s´   ø„ ÙGñK˜zð K°c¸D±jõ Kð( )-ñ-)à—|‘|ð-)ð # 5§<¡<°·±Ð#=Ñ>ð-)ð Ÿ™ tÑ+ð	-)ð
  ™ð-)ð Ð-Ñ.ð-)ð 
ˆu�|‰|˜UŸ\™\¨DÑ0°%¸¿¹Ñ2EÈÑ2LÐLÑ	M÷-)r5   r³   ÚRMSNormc                   óh   ‡ — e Zd Zddeddfˆ fd„Zdej                  dej                  fd„Zd„ Zˆ xZ	S )	ÚPhi3RMSNormÚepsr7   Nc                 óŠ   •— t         ‰| �  «        t        j                  t	        j
                  |«      «      | _        || _        y)z:
        Phi3RMSNorm is equivalent to T5LayerNorm
        N)r'   r(   r   Ú	ParameterrC   ÚonesÚweightÚvariance_epsilon)r2   r+   rÎ   r3   s      €r4   r(   zPhi3RMSNorm.__init__  s1   ø€ ô 	‰ÑÔÜ—l‘l¤5§:¡:¨kÓ#:Ó;ˆŒØ #ˆÕr5   r6   c                 ó"  — |j                   }|j                  t        j                  «      }|j	                  d«      j                  dd¬«      }|t        j                  || j                  z   «      z  }| j                  |j                  |«      z  S )Nr$   r9   T)Úkeepdim)	r_   rf   rC   rž   ÚpowÚmeanÚrsqrtrÓ   rÒ   )r2   r6   Úinput_dtypeÚvariances       r4   r?   zPhi3RMSNorm.forward  sy   € Ø#×)Ñ)ˆØ%×(Ñ(¬¯©Ó7ˆØ ×$Ñ$ QÓ'×,Ñ,¨R¸Ð,Ó>ˆØ%¬¯©°H¸t×?TÑ?TÑ4TÓ(UÑUˆØ�{‰{˜]×-Ñ-¨kÓ:Ñ:Ð:r5   c                 ó^   — t        | j                  j                  «      › d| j                  › �S )Nz, eps=)r‚   rÒ   rp   rÓ   )r2   s    r4   Ú
extra_reprzPhi3RMSNorm.extra_repr#  s*   € Ü˜Ÿ™×)Ñ)Ó*Ð+¨6°$×2GÑ2GÐ1HÐIÐIr5   )g�íµ ÷Æ°>)
r@   rA   rB   rg   r(   rC   r   r?   rÜ   rE   rF   s   @r4   rÍ   rÍ     s7   ø„ ñ$¨ð $¸$õ $ð; U§\¡\ð ;°e·l±ló ;öJr5   rÍ   c                   óD  ‡ — e Zd Zdedefˆ fd„Z	 	 	 	 	 ddej                  dej                  dz  dej                  dz  de	dz  d	e
dz  d
eej                  ej                  f   dz  dee   deej                  eej                  ej                  f   dz  f   fd„Zˆ xZS )ÚPhi3DecoderLayerr)   r´   c                 ó¾  •— t         ‰| �  «        |j                  | _        t        ||¬«      | _        t        |«      | _        t        |j                  |j                  ¬«      | _	        t        |j                  |j                  ¬«      | _
        || _        t        j                  |j                  «      | _        t        j                  |j                  «      | _        y )N)r)   r´   ©rÎ   )r'   r(   r+   r³   Ú	self_attnr"   ÚmlprÍ   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormr)   r   ÚDropoutÚresid_pdropÚresid_attn_dropoutÚresid_mlp_dropout©r2   r)   r´   r3   s      €r4   r(   zPhi3DecoderLayer.__init__(  s    ø€ Ü‰ÑÔØ!×-Ñ-ˆÔÜ&¨fÀ	ÔJˆŒÜ˜6“?ˆŒÜ*¨6×+=Ñ+=À6×CVÑCVÔWˆÔÜ(3°F×4FÑ4FÈF×L_ÑL_Ô(`ˆÔ%ØˆŒÜ"$§*¡*¨V×-?Ñ-?Ó"@ˆÔÜ!#§¡¨F×,>Ñ,>Ó!?ˆÕr5   Nr6   r“   ry   r¼   Ú	use_cacher»   r–   r7   c           
      óü   — |}| j                  |«      } | j                  d||||||dœ|¤Ž\  }}	|| j                  |«      z   }|}| j                  |«      }| j	                  |«      }|| j                  |«      z   }|S )N)r6   r“   ry   r¼   rë   r»   © )rä   rá   rè   rå   râ   ré   )
r2   r6   r“   ry   r¼   rë   r»   r–   ÚresidualÚself_attn_weightss
             r4   r?   zPhi3DecoderLayer.forward3  s¥   € ð !ˆØ×,Ñ,¨]Ó;ˆà+9¨4¯>©>ð ,
Ø'Ø)Ø%Ø+ØØ 3ñ,
ð ñ,
Ñ(ˆÐ(ð ! 4×#:Ñ#:¸=Ó#IÑIˆà ˆØ×5Ñ5°mÓDˆØŸ™ Ó/ˆØ  4×#9Ñ#9¸-Ó#HÑHˆØÐr5   )NNNFN)r@   rA   rB   r    rc   r(   rC   r   Ú
LongTensorr   Úboolr‚   r   r   rD   r?   rE   rF   s   @r4   rÞ   rÞ   '  sæ   ø„ ð	@˜zð 	@°cõ 	@ð /3Ø04Ø(,Ø!&ØHLñà—|‘|ðð Ÿ™ tÑ+ðð ×&Ñ&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5§<¡<°·±Ð#=Ñ>ÀÑEðð Ð-Ñ.ðð 
ˆu× Ñ  %¨×(9Ñ(9¸5×;LÑ;LÐ(LÑ"MÐPTÑ"TÐTÑ	U÷r5   rÞ   c                   óN   — 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Zy)	ÚPhi3PreTrainedModelr)   ÚmodelTrÞ   r¼   )r6   Ú
attentionsz0.0.5N)r@   rA   rB   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_backendrÞ   r³   Ú_can_record_outputsÚ_versionrí   r5   r4   ró   ró   R  sX   … àÓØÐØ&*Ð#Ø+Ð,ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà)Ø#ñÐð �Hr5   ró   c                   óò   ‡ — e Zd Zdefˆ fd„Zeee	 	 	 	 	 	 ddej                  dz  dej                  dz  dej                  dz  dedz  dej                  dz  d	edz  d
ee   defd„«       «       «       Zˆ xZS )Ú	Phi3Modelr)   c           	      ó  •— t         ‰| �  |«       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                  «      | _        t        j                  t        |j                  «      D �cg c]  }t        ||«      ‘Œ c}«      | _        t        |j                  |j                  ¬«      | _        t#        |¬«      | _        d| _        | j)                  «        y c c}w )Nrà   ©r)   F)r'   r(   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	Embeddingr+   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersrÞ   ÚlayersrÍ   rã   ÚnormrH   Ú
rotary_embÚgradient_checkpointingÚ	post_initrê   s      €r4   r(   zPhi3Model.__init__h  sÆ   ø€ Ü‰Ñ˜Ô Ø!×.Ñ.ˆÔØ ×+Ñ+ˆŒäŸL™L¨×):Ñ):¸F×<NÑ<NÐPT×P`ÑP`ÓaˆÔÜ—m‘mÜBGÈ×H`ÑH`ÓBaÖb°YÔ˜f iÕ0Òbó
ˆŒô   × 2Ñ 2¸×8KÑ8KÔLˆŒ	Ü-°VÔ<ˆŒØ&+ˆÔ#ð 	�‰Õùò cs   ÂDNÚ	input_idsr“   ry   r¼   Úinputs_embedsrë   r–   r7   c           
      óŒ  — |d u |d uz  rt        d«      ‚|€| j                  |«      }|r|€t        | j                  ¬«      }|€V|�|j	                  «       nd}t        j                  |j                  d   |j                  ¬«      |z   }|j                  d«      }| j                  j                  €t        nt        }	 |	| j                  ||||¬«      }
|}| j                  ||¬«      }| j                  d | j                  j                   D ]  } ||f|
||||dœ|¤Ž}Œ | j!                  |«      }t#        ||r|¬	«      S d ¬	«      S )
Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r   )rW   )r)   r  r“   r¼   ry   )ry   )r“   ry   r¼   rë   r»   )Úlast_hidden_stater¼   )Ú
ValueErrorr	  r	   r)   Úget_seq_lengthrC   rd   rp   rW   r¦   r¿   r   r   r  r  r  r  r   )r2   r  r“   ry   r¼   r  rë   r–   Úpast_seen_tokensÚmask_functionÚcausal_maskr6   r»   Údecoder_layers                 r4   r?   zPhi3Model.forwardx  ss  € ð ˜Ð -°tÐ";Ò<ÜÐYÓZÐZàÐ Ø ×-Ñ-¨iÓ8ˆMá˜Ð0Ü*°$·+±+Ô>ˆOàÐØCRÐC^˜×=Ñ=Ô?ÐdeÐÜ Ÿ<™<¨×(;Ñ(;¸AÑ(>À}×G[ÑG[Ô\Ð_oÑoˆLØ'×1Ñ1°!Ó4ˆLà.2¯k©k×.HÑ.HÐ.PÕ*ÔVwˆÙ#Ø—;‘;Ø'Ø)Ø+Ø%ô
ˆð &ˆØ"Ÿo™o¨mÈ,˜oÓWÐà!Ÿ[™[Ð)H¨4¯;©;×+HÑ+HÐIò 		ˆMÙ)Øðà*Ø)Ø /Ø#Ø$7ñð ñ‰Mð		ð Ÿ	™	 -Ó0ˆÜ&Ø+Ù/8˜Oô
ð 	
à>Bô
ð 	
r5   )NNNNNN)r@   rA   rB   r    r(   r   r   r   rC   rð   r   r   rD   rñ   r   r   r   r?   rE   rF   s   @r4   r  r  f  sÍ   ø„ ð˜zõ ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ñ2
à×#Ñ# dÑ*ð2
ð Ÿ™ tÑ+ð2
ð ×&Ñ&¨Ñ-ð	2
ð
  ™ð2
ð ×(Ñ(¨4Ñ/ð2
ð ˜$‘;ð2
ð Ð+Ñ,ð2
ð 
!ò2
ó ó ó  ô2
r5   r  c                   óZ  ‡ — 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	 	 	 	 	 	 dˆ fd„	Zˆ xZS )ÚPhi3ForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputr6   Úlogitsc                 óè   •— t         ‰| �  |«       t        |«      | _        |j                  | _        t        j                  |j                  |j                  d¬«      | _        | j                  «        y )NFr%   )
r'   r(   r  rô   r  r   r*   r+   r  r  r1   s     €r4   r(   zPhi3ForCausalLM.__init__¶  sU   ø€ Ü‰Ñ˜Ô Ü˜vÓ&ˆŒ
Ø ×+Ñ+ˆŒÜ—y‘y ×!3Ñ!3°V×5FÑ5FÈUÔSˆŒð 	�‰Õr5   Nr  r“   ry   r¼   r  Úlabelsrë   Úlogits_to_keepr–   r7   c	           
      óx  —  | j                   d||||||dœ|	¤Ž}
|
j                  }t        |t        «      rt	        | d«      n|}| j                  |dd…|dd…f   «      }d}|�* | j                  d||| j                  j                  dœ|	¤Ž}t        |||
j                  |
j                  |
j                  ¬«      S )aÉ  
        Example:

        ```python
        >>> from transformers import AutoTokenizer, Phi3ForCausalLM

        >>> model = Phi3ForCausalLM.from_pretrained("meta-phi3/Phi3-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-phi3/Phi3-2-7b-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“   ry   r¼   r  rë   N)r   r"  r  )Úlossr   r¼   r6   rõ   rí   )rô   r  rq   rc   Úslicer  Úloss_functionr)   r  r   r¼   r6   rõ   )r2   r  r“   ry   r¼   r  r"  rë   r#  r–   Úoutputsr6   Úslice_indicesr   r%  s                  r4   r?   zPhi3ForCausalLM.forward¿  sÙ   € ð> ,6¨4¯:©:ð ,
ØØ)Ø%Ø+Ø'Øñ,
ð ñ,
ˆð  ×1Ñ1ˆä8BÀ>ÔSVÔ8Wœ˜~˜o¨tÔ4Ð]kˆØ—‘˜mªA¨}ºaÐ,?Ñ@ÓAˆàˆØÐØ%�4×%Ñ%Ðp¨V¸FÈtÏ{É{×OeÑOeÑpÐioÑpˆDä%ØØØ#×3Ñ3Ø!×/Ñ/Ø×)Ñ)ô
ð 	
r5   c                 ó  •— |rjt        | j                  d«      rT|j                  d   | j                  j                  dz   k\  r+|j	                  «       }	|	| j                  j                  k  rd }t        ‰| �  d|||||||dœ|¤Ž}
|
S )NÚ original_max_position_embeddingsr   )r  r¼   r“   r  ry   rë   r#  rí   )Úhasattrr)   rp   r+  r  r'   Úprepare_inputs_for_generation)r2   r  r¼   r“   r  ry   rë   r#  r–   Úpast_lengthÚmodel_inputsr3   s              €r4   r-  z-Phi3ForCausalLM.prepare_inputs_for_generationù  s˜   ø€ ñ" Ü˜Ÿ™Ð%GÔHØ—‘ Ñ" d§k¡k×&RÑ&RÐUVÑ&VÒVà)×8Ñ8Ó:ˆKØ˜dŸk™k×JÑJÒJØ"&�ä‘wÑ<ð 	
ØØ+Ø)Ø'Ø%ØØ)ñ	
ð ñ	
ˆð Ðr5   )NNNNNNNr   )NNNNTN)r@   rA   rB   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr(   r   r   rC   rð   r   r   rD   rñ   rc   r   r   r   r?   r-  rE   rF   s   @r4   r  r  °  s6  ø„ à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hôð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø-.ñ6
à×#Ñ# dÑ*ð6
ð Ÿ™ tÑ+ð6
ð ×&Ñ&¨Ñ-ð	6
ð
  ™ð6
ð ×(Ñ(¨4Ñ/ð6
ð × Ñ  4Ñ'ð6
ð ˜$‘;ð6
ð ˜eŸl™lÑ*ð6
ð Ð+Ñ,ð6
ð 
 ò6
ó ó ð6
ðv ØØØØØ÷#ñ #r5   r  c                   ó   — e Zd Zy)ÚPhi3ForSequenceClassificationN©r@   rA   rB   rí   r5   r4   r4  r4    ó   „ Ør5   r4  c                   ó   — e Zd Zy)ÚPhi3ForTokenClassificationNr5  rí   r5   r4   r8  r8  #  r6  r5   r8  )ró   r  r  r4  r8  )r¾   )r   )AÚcollections.abcr   Útypingr   rC   r   Úactivationsr   Úcache_utilsr   r	   Ú
generationr
   Úintegrationsr   Úmasking_utilsr   r   Úmodeling_flash_attention_utilsr   Úmodeling_layersr   r   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   r   Úutils.output_capturingr   Úconfiguration_phi3r    ÚModuler"   rH   r‡   r   rc   rŽ   rg   r¤   r±   r³   rÍ   rÞ   ró   r  r  r4  r8  Ú__all__rí   r5   r4   ú<module>rL     sß  ðõ, %Ý ã Ý å !ß .Ý )Ý 7ß RÝ B÷ñ ÷
 Pß Kß FÝ &ß IÑ Iß GÝ 5Ý *ô)ˆb�i‰iô )ô$@<˜"Ÿ)™)ô @<òF(ð	U˜UŸ\™\ð 	U°#ð 	U¸%¿,¹,ó 	Uð& ñ%Ø�I‰Ið%à�<‰<ð%ð 
�‰ð%ð �<‰<ð	%ð
 —L‘L 4Ñ'ð%ð ð%ð ð%ð Ð'Ñ(ó%ó2ô<?)�B—I‘Iô ?)ñD ˜YÓ'ôJ�"—)‘)ó Jó (ðJô((Ð1ô (ðV ô˜/ó ó ðð& ôF
Ð#ó F
ó ðF
ðR ôkÐ)¨?ó kó ðkô\	Ð$DÐFYô 	ô	Ð!>Ð@Sô 	ò�r5   