Ë
    ÛÓ^jFU  ã                   óP  — 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 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jX                  «      Z- G d„ dejX                  «      Z.dej^                  de0dej^                  fd„Z1	 d9dejX                  dej^                  dej^                  dej^                  d ej^                  dz  d!e2d"e2d#e e"   fd$„Z3d%„ Z4d:d&„Z5 G d'„ d(ejX                  «      Z6 ed)«       G d*„ d+ejX                  «      «       Z7 G d,„ d-e«      Z8e# G d.„ d/e«      «       Z9e# G d0„ d1e9«      «       Z:e# G d2„ d3e9e«      «       Z; G d4„ d5ee9«      Z< G d6„ d7ee9«      Z=g d8¢Z>y);é    )ÚCallable)ÚOptionalNé   )ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úuse_kernel_forward_from_hub)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	GlmConfigc                   óV   ‡ — e Zd Zˆ fd„Zdej
                  dej
                  fd„Zˆ xZS )ÚGlmMLPc                 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configÚnnÚLinearÚhidden_sizeÚintermediate_sizeÚgate_up_projÚ	down_projr   Ú
hidden_actÚactivation_fn©Úselfr&   Ú	__class__s     €úo/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/glm/modeling_glm.pyr%   zGlmMLP.__init__0   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$5Ñ6ˆÕó    Úhidden_statesÚreturnc                 óš   — | j                  |«      }|j                  dd¬«      \  }}|| j                  |«      z  }| j                  |«      S )Nr!   éÿÿÿÿ©Údim)r+   Úchunkr.   r,   )r0   r4   Ú	up_statesÚgates       r2   ÚforwardzGlmMLP.forward8   sL   € Ø×%Ñ% mÓ4ˆ	à#Ÿ/™/¨!°˜/Ó4‰ˆˆiØ × 2Ñ 2°4Ó 8Ñ8ˆ	à�~‰~˜iÓ(Ð(r3   )Ú__name__Ú
__module__Ú__qualname__r%   ÚtorchÚFloatTensorr=   Ú__classcell__©r1   s   @r2   r   r   /   s'   ø„ ô7ð) U×%6Ñ%6ð )¸5×;LÑ;L÷ )r3   r   c                   óÄ   ‡ — e Zd ZU ej                  ed<   ddefˆ fd„Ze	 	 	 ddedz  de	d   de
dz  ded	ef   fd
„«       Z ej                  «       ed„ «       «       Zˆ xZS )ÚGlmRotaryEmbeddingÚinv_freqNr&   c                 ó®  •— t         ‰| �  «        |j                  | _        |j                  | _        || _        | j
                  j                  d   | _        | j                  }| j                  dk7  rt        | j                     } || j
                  |«      \  }| _
        | j                  d|d¬«       | j                  d|j                  «       d¬«       y )NÚ	rope_typeÚdefaultrG   F)Ú
persistentÚoriginal_inv_freq)r$   r%   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenr&   Úrope_parametersrI   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)r0   r&   ÚdeviceÚrope_init_fnrG   r1   s        €r2   r%   zGlmRotaryEmbedding.__init__D   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Ur3   rU   ztorch.deviceÚseq_lenr5   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)rU   r]   )rP   ÚgetÚgetattrr)   Únum_attention_headsÚintrA   ÚarangeÚint64ÚtoÚfloat)	r&   rU   rW   ÚbaserZ   r[   r9   Úattention_factorrG   s	            r2   rQ   z2GlmRotaryEmbedding.compute_default_rope_parametersT   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)r3   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   r7   r   ÚmpsÚcpuF)Údevice_typeÚenabledr!   r8   r\   )rG   re   ÚexpandÚshaperd   rU   Ú
isinstanceÚtypeÚstrr   Ú	transposerA   ÚcatÚcosrR   Úsinr]   )
r0   ÚxÚposition_idsÚinv_freq_expandedÚposition_ids_expandedrk   ÚfreqsÚembrt   ru   s
             r2   r=   zGlmRotaryEmbedding.forwardt   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$©N©NNN)r>   r?   r@   rA   ÚTensorÚ__annotations__r   r%   Ústaticmethodr   ra   Útuplere   rQ   Úno_gradr   r=   rC   rD   s   @r2   rF   rF   A   s˜   ø… Ø�l‰lÓñV˜yõ Vð  à#'Ø+/Ø"ñ*Ø˜DÑ ð*à˜Ñ(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ñ	%ò	*ó ð*ð> €U‡]�]ƒ_Øñ<ó ó ô<r3   rF   r4   Ún_repr5   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)rn   rm   Úreshape)r4   rƒ   ÚbatchÚnum_key_value_headsÚslenr[   s         r2   Ú	repeat_kvr‰   „   so   € ð
 2?×1DÑ1DÑ.€EÐ  hØ�‚zØÐØ!¢!¢Q¨ªa²Ð"2Ñ3×:Ñ:¸5ÐBUÐW\Ð^bÐdlÓm€MØ× Ñ  Ð(;¸eÑ(CÀTÈ8ÓTÐTr3   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   r7   )r9   r]   )ÚpÚtrainingr   )r‰   Únum_key_value_groupsrA   Úmatmulrr   r'   Ú
functionalÚsoftmaxÚfloat32rd   r]   r�   r”   Ú
contiguous)rŠ   r‹   rŒ   r�   rŽ   r�   r�   r‘   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r2   Úeager_attention_forwardrŸ   �   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$Ð$r3   c                 ó|   — | dddd…f   }| dddd…f   }t        j                  | |fd¬«      j                  d«      S )	z*Rotates half the hidden dims of the input..r   Nr!   r   r7   r8   éþÿÿÿ)rA   ÚstackÚflatten)rv   Úx1Úx2s      r2   Úrotate_halfr¦   ©   sJ   € à	
ˆ3���1�ˆ9‰€BØ	
ˆ3���1�ˆ9‰€BÜ�;‰;˜˜˜R�y bÔ)×1Ñ1°"Ó5Ð5r3   c                 ó  — |j                  |«      }|j                  |«      }|dd|j                  d   dz  …f   j                  dd¬«      }|dd|j                  d   dz  …f   j                  dd¬«      }|j                  d   }| dd|…f   | d|d…f   }}|dd|…f   |d|d…f   }	}||z  t        |«      |z  z   }
||z  t        |«      |z  z   }t	        j
                  |
|gd¬«      }
t	        j
                  ||	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.
    .Nr7   r!   r8   )Ú	unsqueezern   Úrepeat_interleaver¦   rA   rs   )ÚqÚkrt   ru   Úunsqueeze_dimÚ
rotary_dimÚq_rotÚq_passÚk_rotÚk_passÚq_embedÚk_embeds               r2   Úapply_rotary_pos_embr´   °   sD  € ð$ �-‰-˜Ó
&€CØ
�-‰-˜Ó
&€Cð ˆcÐ'�S—Y‘Y˜r‘] aÑ'Ð'Ð'Ñ
(×
:Ñ
:¸1À"Ð
:Ó
E€CØ
ˆcÐ'�S—Y‘Y˜r‘] aÑ'Ð'Ð'Ñ
(×
:Ñ
:¸1À"Ð
:Ó
E€Cð —‘˜2‘€JØ�c˜;˜J˜;Ð&Ñ'¨¨3°
±Ð+;Ñ)<ˆ6€EØ�c˜;˜J˜;Ð&Ñ'¨¨3°
±Ð+;Ñ)<ˆ6€Eð �s‰{œ{¨5Ó1°CÑ7Ñ8€GØ�s‰{œ{¨5Ó1°CÑ7Ñ8€Gô �i‰i˜ &Ð)¨rÔ2€GÜ�i‰i˜ &Ð)¨rÔ2€GØ�GÐÐr3   c                   ó   ‡ — e Zd ZdZddededz  fˆ fd„Z	 	 	 ddej                  de	ej                  ej                  f   dz  dej                  dz  d	e
dz  d
ee   de	ej                  ej                  f   fd„Zˆ xZS )ÚGlmAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNr&   Ú	layer_idxc                 óP  •— t         ‰| �  «        || _        || _        t	        |d|j
                  |j                  z  «      | _        |j                  |j                  z  | _	        | j                  dz  | _
        |j                  | _        d| _        t        j                  |j
                  |j                  | j                  z  |j                  ¬«      | _        t        j                  |j
                  |j                  | j                  z  |j                  ¬«      | _        t        j                  |j
                  |j                  | j                  z  |j                  ¬«      | _        t        j                  |j                  | j                  z  |j
                  d¬«      | _        y )Nr[   g      à¿Tr"   F)r$   r%   r&   r·   r_   r)   r`   r[   r‡   r•   r�   Úattention_dropoutÚ	is_causalr'   r(   Úattention_biasÚq_projÚk_projÚv_projÚo_proj©r0   r&   r·   r1   s      €r2   r%   zGlmAttention.__init__Û   sD  ø€ Ü‰ÑÔØˆŒØ"ˆŒÜ ¨
°F×4FÑ4FÈ&×JdÑJdÑ4dÓeˆŒØ$*×$>Ñ$>À&×B\ÑB\Ñ$\ˆÔ!Ø—}‘} dÑ*ˆŒØ!'×!9Ñ!9ˆÔØˆŒä—i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘i × :Ñ :¸T¿]¹]Ñ JÈF×L^ÑL^ÐejÔkˆ�r3   r4   Úposition_embeddingsrŽ   Úpast_key_valuesr‘   r5   c                 ó
  — |j                   d d }g |¢d‘| j                  ‘­}| j                  |«      j                  |«      j	                  dd«      }| j                  |«      j                  |«      j	                  dd«      }	| j                  |«      j                  |«      j	                  dd«      }
|\  }}t        ||	||«      \  }}	|� |j                  |	|
| j                  «      \  }	}
t        j                  | j                  j                  t        «      } || ||	|
|f| j                  sdn| j                   | j"                  dœ|¤Ž\  }} |j$                  g |¢d‘­Ž j'                  «       }| j)                  |«      }||fS )Nr7   r   r!   ç        )r�   r�   )rn   r[   r¼   Úviewrr   r½   r¾   r´   Úupdater·   r   Úget_interfacer&   Ú_attn_implementationrŸ   r”   r¹   r�   r…   rš   r¿   )r0   r4   rÁ   rŽ   rÂ   r‘   Úinput_shapeÚhidden_shapeÚquery_statesr›   rœ   rt   ru   Úattention_interfacerž   r�   s                   r2   r=   zGlmAttention.forwardð   s‘  € ð $×)Ñ)¨#¨2Ð.ˆØ8˜Ð8 bÐ8¨$¯-©-Ñ8ˆà—{‘{ =Ó1×6Ñ6°|ÓD×NÑNÈqÐRSÓTˆØ—[‘[ Ó/×4Ñ4°\ÓB×LÑLÈQÐPQÓRˆ
Ø—{‘{ =Ó1×6Ñ6°|ÓD×NÑNÈqÐRSÓTˆà&‰ˆˆSÜ#7¸ÀjÐRUÐWZÓ#[Ñ ˆ�jàÐ&Ø'6×'=Ñ'=¸jÈ,ÐX\×XfÑXfÓ'gÑ$ˆJ˜ä(?×(MÑ(MØ�K‰K×,Ñ,Ô.Eó)
Ðñ %8ØØØØØð	%
ð  $Ÿ}š}‘C°$×2HÑ2HØ—L‘Lñ	%
ð ñ	%
Ñ!ˆ�\ð *�k×)Ñ)Ð;¨;Ð;¸Ò;×FÑFÓHˆØ—k‘k +Ó.ˆØ˜LÐ(Ð(r3   r|   r}   )r>   r?   r@   Ú__doc__r   ra   r%   rA   r~   r�   r   r   r   r=   rC   rD   s   @r2   r¶   r¶   Ø   s¬   ø„ ÙGñl˜yð l°S¸4±Zõ lð0 IMØ.2Ø(,ñ&)à—|‘|ð&)ð # 5§<¡<°·±Ð#=Ñ>ÀÑEð&)ð Ÿ™ tÑ+ð	&)ð
  ™ð&)ð Ð+Ñ,ð&)ð 
ˆu�|‰|˜UŸ\™\Ð)Ñ	*÷&)r3   r¶   ÚRMSNormc                   óh   ‡ — e Zd Zddeddfˆ fd„Zdej                  dej                  fd„Zd„ Zˆ xZ	S )	Ú
GlmRMSNormÚepsr5   Nc                 óŠ   •— t         ‰| �  «        t        j                  t	        j
                  |«      «      | _        || _        y)z9
        GlmRMSNorm is equivalent to T5LayerNorm
        N)r$   r%   r'   Ú	ParameterrA   ÚonesÚweightÚvariance_epsilon)r0   r)   rÑ   r1   s      €r2   r%   zGlmRMSNorm.__init__  s1   ø€ ô 	‰ÑÔÜ—l‘l¤5§:¡:¨kÓ#:Ó;ˆŒØ #ˆÕr3   r4   c                 ó"  — |j                   }|j                  t        j                  «      }|j	                  d«      j                  dd¬«      }|t        j                  || j                  z   «      z  }| j                  |j                  |«      z  S )Nr!   r7   T)Úkeepdim)	r]   rd   rA   r™   ÚpowÚmeanÚrsqrtrÖ   rÕ   )r0   r4   Úinput_dtypeÚvariances       r2   r=   zGlmRMSNorm.forward#  sy   € Ø#×)Ñ)ˆØ%×(Ñ(¬¯©Ó7ˆØ ×$Ñ$ QÓ'×,Ñ,¨R¸Ð,Ó>ˆØ%¬¯©°H¸t×?TÑ?TÑ4TÓ(UÑUˆØ�{‰{˜]×-Ñ-¨kÓ:Ñ:Ð:r3   c                 ó^   — t        | j                  j                  «      › d| j                  › �S )Nz, eps=)r�   rÕ   rn   rÖ   )r0   s    r2   Ú
extra_reprzGlmRMSNorm.extra_repr*  s*   € Ü˜Ÿ™×)Ñ)Ó*Ð+¨6°$×2GÑ2GÐ1HÐIÐIr3   )g�íµ ÷Æ°>)
r>   r?   r@   re   r%   rA   r~   r=   rß   rC   rD   s   @r2   rÐ   rÐ     s7   ø„ ñ$¨ð $¸$õ $ð; U§\¡\ð ;°e·l±ló ;öJr3   rÐ   c                   ó  ‡ — e Zd Zdedefˆ fd„Z	 	 	 	 	 ddej                  dej                  dz  dej                  dz  de	dz  d	e
dz  d
eej                  ej                  f   dz  dee   dej                  fd„Zˆ xZS )ÚGlmDecoderLayerr&   r·   c                 ó   •— t         ‰| �  «        |j                  | _        t        ||¬«      | _        t        |«      | _        t        |j                  |j                  ¬«      | _	        t        |j                  |j                  ¬«      | _
        y )N)r&   r·   ©rÑ   )r$   r%   r)   r¶   Ú	self_attnr   ÚmlprÐ   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormrÀ   s      €r2   r%   zGlmDecoderLayer.__init__/  sk   ø€ Ü‰ÑÔØ!×-Ñ-ˆÔä%¨V¸yÔIˆŒä˜&“>ˆŒÜ)¨&×*<Ñ*<À&×BUÑBUÔVˆÔÜ(2°6×3EÑ3EÈ6×K^ÑK^Ô(_ˆÕ%r3   Nr4   rŽ   rw   rÂ   Ú	use_cacherÁ   r‘   r5   c           
      óÀ   — |}| j                  |«      } | j                  d||||||dœ|¤Ž\  }}	||z   }|}| j                  |«      }| j                  |«      }||z   }|S )N)r4   rŽ   rw   rÂ   ré   rÁ   © )rç   rä   rè   rå   )
r0   r4   rŽ   rw   rÂ   ré   rÁ   r‘   ÚresidualÚ_s
             r2   r=   zGlmDecoderLayer.forward9  s�   € ð !ˆØ×,Ñ,¨]Ó;ˆà)˜4Ÿ>™>ð 
Ø'Ø)Ø%Ø+ØØ 3ñ
ð ñ
Ñˆ�qð ! =Ñ0ˆð !ˆØ×5Ñ5°mÓDˆØŸ™ Ó/ˆØ  =Ñ0ˆØÐr3   )NNNFN)r>   r?   r@   r   ra   r%   rA   r~   Ú
LongTensorr   Úboolr�   r   r   r=   rC   rD   s   @r2   rá   rá   .  s¾   ø„ ð`˜yð `°Sõ `ð /3Ø04Ø(,Ø!&ØHLñà—|‘|ðð Ÿ™ tÑ+ðð ×&Ñ&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5§<¡<°·±Ð#=Ñ>ÀÑEðð Ð+Ñ,ðð 
�‰÷r3   rá   c                   óJ   — e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedœZy)ÚGlmPreTrainedModelr&   ÚmodelTrá   rÂ   )r4   Ú
attentionsN)r>   r?   r@   r   r   Úbase_model_prefixÚsupports_gradient_checkpointingÚ_no_split_modulesÚ_skip_keys_device_placementÚ_supports_flash_attnÚ_supports_sdpaÚ_supports_flex_attnÚ_can_compile_fullgraphÚ_supports_attention_backendrá   r¶   Ú_can_record_outputsrë   r3   r2   rñ   rñ   Y  sQ   … àÓØÐØ&*Ð#Ø*Ð+ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà(Ø"ñÑr3   rñ   c                   óò   ‡ — e Zd Zdefˆ fd„Zeee	 	 	 	 	 	 ddej                  dz  dej                  dz  dej                  dz  dedz  dej                  dz  d	edz  d
ee   defd„«       «       «       Zˆ xZS )ÚGlmModelr&   c           	      ó  •— t         ‰| �  |«       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                  «      | _        t        j                  t        |j                  «      D �cg c]  }t        ||«      ‘Œ c}«      | _        t        |j                  |j                  ¬«      | _        t#        |¬«      | _        d| _        | j)                  «        y c c}w )Nrã   ©r&   F)r$   r%   Úpad_token_idÚpadding_idxÚ
vocab_sizer'   Ú	Embeddingr)   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersrá   ÚlayersrÐ   ræ   ÚnormrF   Ú
rotary_embÚgradient_checkpointingÚ	post_initrÀ   s      €r2   r%   zGlmModel.__init__n  sÅ   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‘mÜAFÀv×G_ÑG_ÓA`Öa°IŒ_˜V YÕ/Òaó
ˆŒô ˜v×1Ñ1°v×7JÑ7JÔKˆŒ	Ü,°FÔ;ˆŒØ&+ˆÔ#ð 	�‰Õùò bs   ÂDNÚ	input_idsrŽ   rw   rÂ   Úinputs_embedsré   r‘   r5   c           
      ó<  — |d u |d uz  rt        d«      ‚|€| j                  |«      }|r|€t        | j                  ¬«      }|€V|�|j	                  «       nd}t        j                  |j                  d   |j                  ¬«      |z   }|j                  d«      }t        | j                  ||||¬«      }	|}
| j                  |
|¬«      }| j                  d | j                  j                   D ]  } ||
f|	||||dœ|¤Ž}
Œ | j                  |
«      }
t        |
|¬	«      S )
Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r   )rU   )r&   r  rŽ   rÂ   rw   )rw   )rŽ   rÁ   rw   rÂ   ré   )Úlast_hidden_staterÂ   )Ú
ValueErrorr  r   r&   Úget_seq_lengthrA   rb   rn   rU   r¨   r   r  r
  r	  r  r   )r0   r  rŽ   rw   rÂ   r  ré   r‘   Úpast_seen_tokensÚcausal_maskr4   rÁ   Údecoder_layers                r2   r=   zGlmModel.forward~  sL  € ð ˜Ð -°tÐ";Ò<ÜÐYÓZÐZàÐ Ø*.×*;Ñ*;¸IÓ*FˆMá˜Ð0Ü*°$·+±+Ô>ˆOàÐØCRÐC^˜×=Ñ=Ô?ÐdeÐÜ Ÿ<™<¨×(;Ñ(;¸AÑ(>À}×G[ÑG[Ô\Ð_oÑoˆLØ'×1Ñ1°!Ó4ˆLä(Ø—;‘;Ø'Ø)Ø+Ø%ô
ˆð &ˆØ"Ÿo™o¨mÈ,˜oÓWÐà!Ÿ[™[Ð)H¨4¯;©;×+HÑ+HÐIò 		ˆMÙ)Øðà*Ø$7Ø)Ø /Ø#ñð ñ‰Mð		ð Ÿ	™	 -Ó0ˆÜ&Ø+Ø+ô
ð 	
r3   )NNNNNN)r>   r?   r@   r   r%   r   r   r   rA   rî   r~   r   rB   rï   r   r   r   r=   rC   rD   s   @r2   rÿ   rÿ   l  sÍ   ø„ ð˜yõ ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ñ2
à×#Ñ# dÑ*ð2
ð Ÿ™ tÑ+ð2
ð ×&Ñ&¨Ñ-ð	2
ð
  ™ð2
ð ×(Ñ(¨4Ñ/ð2
ð ˜$‘;ð2
ð Ð+Ñ,ð2
ð 
!ò2
ó ó ó  ô2
r3   rÿ   c                   óB  ‡ — e Zd ZddiZddiZddgdgfiZˆ fd„Zee	 	 	 	 	 	 	 	 dd	e	j                  dz  d
e	j                  dz  de	j                  dz  dedz  de	j                  dz  de	j                  dz  dedz  dee	j                  z  dee   defd„«       «       Zˆ xZS )ÚGlmForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputr4   Úlogitsc                 óè   •— t         ‰| �  |«       t        |«      | _        |j                  | _        t        j                  |j                  |j                  d¬«      | _        | j                  «        y )NFr"   )
r$   r%   rÿ   rò   r  r'   r(   r)   r  r  r/   s     €r2   r%   zGlmForCausalLM.__init__¼  sU   ø€ Ü‰Ñ˜Ô Ü˜fÓ%ˆŒ
Ø ×+Ñ+ˆŒÜ—y‘y ×!3Ñ!3°V×5FÑ5FÈUÔSˆŒð 	�‰Õr3   Nr  rŽ   rw   rÂ   r  Úlabelsré   Úlogits_to_keepr‘   r5   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 )aÃ  
        Example:

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

        >>> model = GlmForCausalLM.from_pretrained("meta-glm/Glm-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-glm/Glm-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  rŽ   rw   rÂ   r  ré   N)r  r  r  )Úlossr  rÂ   r4   ró   rë   )rò   r  ro   ra   Úslicer  Úloss_functionr&   r  r   rÂ   r4   ró   )r0   r  rŽ   rw   rÂ   r  r  ré   r  r‘   Úoutputsr4   Úslice_indicesr  r!  s                  r2   r=   zGlmForCausalLM.forwardÅ  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 	
r3   )NNNNNNNr   )r>   r?   r@   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr%   r   r   rA   rî   r~   r   rB   rï   ra   r   r   r   r=   rC   rD   s   @r2   r  r  ¶  s  ø„ à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hôð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø-.ñ6
à×#Ñ# dÑ*ð6
ð Ÿ™ tÑ+ð6
ð ×&Ñ&¨Ñ-ð	6
ð
  ™ð6
ð ×(Ñ(¨4Ñ/ð6
ð × Ñ  4Ñ'ð6
ð ˜$‘;ð6
ð ˜eŸl™lÑ*ð6
ð Ð+Ñ,ð6
ð 
 ò6
ó ó ô6
r3   r  c                   ó   — e Zd Zy)ÚGlmForSequenceClassificationN©r>   r?   r@   rë   r3   r2   r*  r*     ó   „ Ør3   r*  c                   ó   — e Zd Zy)ÚGlmForTokenClassificationNr+  rë   r3   r2   r.  r.    r,  r3   r.  )rñ   rÿ   r  r*  r.  )rÄ   )r   )?Úcollections.abcr   Útypingr   rA   Útorch.nnr'   Úactivationsr   Úcache_utilsr   r   Ú
generationr	   Úintegrationsr
   Úmasking_utilsr   Úmodeling_layersr   r   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   r   Úutils.output_capturingr   Úconfiguration_glmr   ÚModuler   rF   r~   ra   r‰   re   rŸ   r¦   r´   r¶   rÐ   rá   rñ   rÿ   r  r*  r.  Ú__all__rë   r3   r2   ú<module>rB     sÝ  ðõ* %Ý ã Ý å !ß .Ý )Ý 7Ý /÷ñ ÷
 Pß Kß FÝ &ß IÑ Iß GÝ 5Ý (ô)ˆR�Y‰Yô )ô$@<˜Ÿ™ô @<ðF	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%ò26ó%ôP>)�2—9‘9ô >)ñB ˜YÓ'ôJ�—‘ó Jó (ðJô((Ð0ô (ðV ô˜ó ó ðð$ ôF
Ð!ó F
ó ðF
ðR ôF
Ð'¨ó F
ó ðF
ôR	Ð#CÐEWô 	ô	Ð =Ð?Qô 	ò�r3   