§
    ‚ŠtjFU  ã                   óD  — 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j,        ¦  «        Z- G d„ dej,        ¦  «        Z.dej/        de0dej/        fd„Z1	 d:dej,        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j,        ¦  «        Z6 ed*¦  «         G d+„ d,ej,        ¦  «        ¦   «         Z7 G d-„ d.e¦  «        Z8e# G d/„ d0e¦  «        ¦   «         Z9e# G d1„ d2e9¦  «        ¦   «         Z:e# G d3„ d4e9e¦  «        ¦   «         Z; G d5„ d6ee9¦  «        Z< G d7„ d8ee9¦  «        Z=g d9¢Z>dS )<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                   óB   ‡ — 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
                 | _        d S )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     €úb/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/glm/modeling_glm.pyr%   zGlmMLP.__init__0   sz   ø€ Ý‰Œ×ÒÑÔÐàˆŒÝœI fÔ&8¸!¸fÔ>VÑ:VÐ]bÐcÑcÔcˆÔÝœ 6Ô#;¸VÔ=OÐV[Ð\Ñ\Ô\ˆŒÝ# FÔ$5Ô6ˆÔÐÐó    Úhidden_statesÚreturnc                 óº   — |                       |¦  «        }|                     dd¬¦  «        \  }}||                      |¦  «        z  }|                      |¦  «        S )Nr!   éÿÿÿÿ©Údim)r+   Úchunkr.   r,   )r0   r4   Ú	up_statesÚgates       r2   ÚforwardzGlmMLP.forward8   sX   € Ø×%Ò% mÑ4Ô4ˆ	à#Ÿ/š/¨!°˜/Ñ4Ô4‰ˆˆiØ × 2Ò 2°4Ñ 8Ô 8Ñ8ˆ	à�~Š~˜iÑ(Ô(Ð(r3   )Ú__name__Ú
__module__Ú__qualname__r%   ÚtorchÚFloatTensorr=   Ú__classcell__©r1   s   @r2   r   r   /   s`   ø€ € € € € ð7ð 7ð 7ð 7ð 7ð) UÔ%6ð )¸5Ô;Lð )ð )ð )ð )ð )ð )ð )ð )r3   r   c                   óÔ   ‡ — e Zd ZU ej        ed<   ddefˆ fd„Ze	 	 	 ddedz  de	d         de
dz  ded	ef         fd
„¦   «         Z ej        ¦   «         ed„ ¦   «         ¦   «         Zˆ xZS )ÚGlmRotaryEmbeddingÚinv_freqNr&   c                 ó²  •— t          ¦   «                              ¦   «          |j        | _        |j        | _        || _        | j        j        d         | _        | j        }| j        dk    rt          | j                 } || j        |¦  «        \  }| _
        |                      d|d¬¦  «         |                      d|                     ¦   «         d¬¦  «         d S )NÚ	rope_typeÚdefaultrG   F)Ú
persistentÚoriginal_inv_freq)r$   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   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Ur3   rU   ztorch.deviceÚseq_lenr5   ztorch.Tensorc                 óV  — | j         d         }| j                              dd¦  «        }t          | dd¦  «        p| j        | j        z  }t          ||z  ¦  «        }d}d|t          j        d|dt          j        ¬¦  «         	                    |t          j
        ¬	¦  «        |z  z  z  }||fS )
a¨  
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        Ú
rope_thetaÚ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   sº   € ð& Ô% lÔ3ˆØ &Ô 6× :Ò :Ð;RÐTWÑ XÔ XÐÝ˜6 :¨tÑ4Ô4Ðh¸Ô8JÈfÔNhÑ8hˆÝ�(Ð2Ñ2Ñ3Ô3ˆàÐð Ø•U”\ ! S¨!µ5´;Ð?Ñ?Ô?×BÒBÈ&ÕX]ÔXcÐBÑdÔdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r3   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   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   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*©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   sù   ø€ € € € € € ØŒlÐÐÑðVð V˜yð Vð Vð Vð Vð Vð Vð  à#'Ø+/Ø"ð*ð *Ø˜DÑ ð*à˜Ô(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ô	%ð	*ð *ð *ñ „\ð*ð> €U„]�_„_Øð<ð <ñ Ôñ „_ð<ð <ð <ð <ð <r3   rF   r4   Ún_repr5   c                 ó¸   — | j         \  }}}}|dk    r| S | dd…dd…ddd…dd…f                              |||||¦  «        } |                      |||z  ||¦  «        S )zÔ
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    r   N)rn   rm   Úreshape)r4   rƒ   ÚbatchÚnum_key_value_headsÚslenr[   s         r2   Ú	repeat_kvr‰   „   s„   € ð
 2?Ô1DÑ.€EÐ  hØ�‚z€zØÐØ! ! ! ! Q Q Q¨¨a¨a¨a°°°Ð"2Ô3×:Ò:¸5ÐBUÐW\Ð^bÐdlÑmÔm€MØ× Ò  Ð(;¸eÑ(CÀTÈ8ÑTÔTÐTr3   ç        ÚmoduleÚqueryÚkeyÚvalueÚattention_maskÚscalingÚdropoutÚkwargsc                 ó  — t          || j        ¦  «        }t          || j        ¦  «        }	t          j        ||                     dd¦  «        ¦  «        |z  }
|�|
|z   }
t
          j                             |
dt          j        ¬¦  «         	                    |j
        ¦  «        }
t
          j                             |
|| j        ¬¦  «        }
t          j        |
|	¦  «        }|                     dd¦  «                             ¦   «         }||
fS )Nr!   r   r7   )r9   r]   )ÚpÚtrainingr   )r‰   Únum_key_value_groupsrA   Úmatmulrr   r'   Ú
functionalÚsoftmaxÚfloat32rd   r]   r‘   r•   Ú
contiguous)r‹   rŒ   r�   rŽ   r�   r�   r‘   r’   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r2   Úeager_attention_forwardr    �   sé   € õ ˜3 Ô ;Ñ<Ô<€JÝ˜U FÔ$?Ñ@Ô@€Lå”<  z×';Ò';¸A¸qÑ'AÔ'AÑBÔBÀWÑL€LØÐ!Ø# nÑ4ˆå”=×(Ò(¨¸2ÅUÄ]Ð(ÑSÔS×VÒVÐW\ÔWbÑcÔc€LÝ”=×(Ò(¨¸È6Ì?Ð(Ñ[Ô[€LÝ”,˜|¨\Ñ:Ô:€KØ×'Ò'¨¨1Ñ-Ô-×8Ò8Ñ:Ô:€Kà˜Ð$Ð$r3   c                 óŽ   — | dddd…f         }| dddd…f         }t          j        | |fd¬¦  «                             d¦  «        S )	z*Rotates half the hidden dims of the input..r   Nr!   r   r7   r8   éþÿÿÿ)rA   ÚstackÚflatten)rv   Úx1Úx2s      r2   Úrotate_halfr§   ©   sQ   € à	
ˆ3���1�ˆ9Œ€BØ	
ˆ3���1�ˆ9Œ€BÝŒ;˜˜˜R�y bÐ)Ñ)Ô)×1Ò1°"Ñ5Ô5Ð5r3   c                 óT  — |                      |¦  «        }|                      |¦  «        }|dd|j        d         dz  …f                              dd¬¦  «        }|dd|j        d         dz  …f                              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_embrµ   °   s\  € ð$ �-Š-˜Ñ
&Ô
&€CØ
�-Š-˜Ñ
&Ô
&€Cð ˆcÐ'�S”Y˜r”] aÑ'Ð'Ð'Ô
(×
:Ò
:¸1À"Ð
:Ñ
EÔ
E€CØ
ˆcÐ'�S”Y˜r”] aÑ'Ð'Ð'Ô
(×
:Ò
:¸1À"Ð
:Ñ
EÔ
E€Cð ”˜2”€JØ�c˜;˜J˜;Ð&Ô'¨¨3°
°°Ð+;Ô)<ˆ6€EØ�c˜;˜J˜;Ð&Ô'¨¨3°
°°Ð+;Ô)<ˆ6€Eð �s‰{�{¨5Ñ1Ô1°CÑ7Ñ8€GØ�s‰{�{¨5Ñ1Ô1°CÑ7Ñ8€Gõ Œi˜ &Ð)¨rÐ2Ñ2Ô2€GÝŒi˜ &Ð)¨rÐ2Ñ2Ô2€GØ�GÐÐr3   c                   óÖ   ‡ — e Zd ZdZddededz  fˆ fd„Z	 	 	 ddej        de	ej        ej        f         dz  dej        dz  d	e
dz  d
ee         de	ej        ej        f         fd„Zˆ xZS )ÚGlmAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNr&   Ú	layer_idxc                 ó¤  •— t          ¦   «                              ¦   «          || _        || _        t	          |d|j        |j        z  ¦  «        | _        |j        |j        z  | _	        | j        dz  | _
        |j        | _        d| _        t          j        |j        |j        | j        z  |j        ¬¦  «        | _        t          j        |j        |j        | j        z  |j        ¬¦  «        | _        t          j        |j        |j        | j        z  |j        ¬¦  «        | _        t          j        |j        | j        z  |j        d¬¦  «        | _        d S )Nr[   g      à¿Tr"   F)r$   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   s8  ø€ Ý‰Œ×ÒÑÔÐØˆŒØ"ˆŒÝ ¨
°FÔ4FÈ&ÔJdÑ4dÑeÔeˆŒØ$*Ô$>À&ÔB\Ñ$\ˆÔ!Ø”} dÑ*ˆŒØ!'Ô!9ˆÔØˆŒå”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”iØÔ Ô :¸T¼]Ñ JÐQWÔQfð
ñ 
ô 
ˆŒõ ”i Ô :¸T¼]Ñ JÈFÔL^ÐejÐkÑkÔkˆŒˆˆr3   r4   Úposition_embeddingsr�   Úpast_key_valuesr’   r5   c                 ó"  — |j         d d…         }g |¢d‘| j        ‘R }|                      |¦  «                             |¦  «                             dd¦  «        }|                      |¦  «                             |¦  «                             dd¦  «        }	|                      |¦  «                             |¦  «                             dd¦  «        }
|\  }}t          ||	||¦  «        \  }}	|�|                     |	|
| j	        ¦  «        \  }	}
t          j        | j        j        t          ¦  «        } || ||	|
|f| j        sdn| j        | j        dœ|¤Ž\  }} |j        g |¢d‘R Ž                      ¦   «         }|                      |¦  «        }||fS )Nr7   r   r!   rŠ   )r‘   r�   )rn   r[   r½   Úviewrr   r¾   r¿   rµ   Úupdater¸   r   Úget_interfacer&   Ú_attn_implementationr    r•   rº   r�   r…   r›   rÀ   )r0   r4   rÂ   r�   rÃ   r’   Úinput_shapeÚhidden_shapeÚquery_statesrœ   r�   rt   ru   Úattention_interfacerŸ   rž   s                   r2   r=   zGlmAttention.forwardð   sÀ  € ð $Ô)¨#¨2¨#Ô.ˆØ8˜Ð8 bÐ8¨$¬-Ð8Ð8ˆà—{’{ =Ñ1Ô1×6Ò6°|ÑDÔD×NÒNÈqÐRSÑTÔTˆØ—[’[ Ñ/Ô/×4Ò4°\ÑBÔB×LÒLÈQÐPQÑRÔRˆ
Ø—{’{ =Ñ1Ô1×6Ò6°|ÑDÔD×NÒNÈqÐRSÑTÔTˆà&‰ˆˆSÝ#7¸ÀjÐRUÐWZÑ#[Ô#[Ñ ˆ�jàÐ&Ø'6×'=Ò'=¸jÈ,ÐX\ÔXfÑ'gÔ'gÑ$ˆJ˜å(?Ô(MØŒKÔ,Õ.Eñ)
ô )
Ðð %8Ð$7ØØØØØð	%
ð  $œ}ÐH�C�C°$Ô2HØ”Lð	%
ð 	%
ð ð	%
ð 	%
Ñ!ˆ�\ð *�kÔ)Ð;¨;Ð;¸Ð;Ð;Ð;×FÒFÑHÔHˆØ—k’k +Ñ.Ô.ˆØ˜LÐ(Ð(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€ € € € € ØGÐGðlð l˜yð l°S¸4±Zð lð lð lð lð lð lð0 IMØ.2Ø(,ð&)ð &)à”|ð&)ð # 5¤<°´Ð#=Ô>ÀÑEð&)ð œ tÑ+ð	&)ð
  ™ð&)ð Ð+Ô,ð&)ð 
ˆuŒ|˜Uœ\Ð)Ô	*ð&)ð &)ð &)ð &)ð &)ð &)ð &)ð &)r3   r·   ÚRMSNormc                   óT   ‡ — e Zd Zd	deddfˆ fd„Zdej        dej        fd„Zd„ Zˆ xZ	S )
Ú
GlmRMSNormç�íµ ÷Æ°>Úepsr5   Nc                 ó¬   •— t          ¦   «                              ¦   «          t          j        t	          j        |¦  «        ¦  «        | _        || _        dS )z9
        GlmRMSNorm is equivalent to T5LayerNorm
        N)r$   r%   r'   Ú	ParameterrA   ÚonesÚweightÚvariance_epsilon)r0   r)   rÒ   r1   s      €r2   r%   zGlmRMSNorm.__init__  sD   ø€ õ 	‰Œ×ÒÑÔÐÝ”l¥5¤:¨kÑ#:Ô#:Ñ;Ô;ˆŒØ #ˆÔÐÐr3   r4   c                 ó  — |j         }|                     t          j        ¦  «        }|                     d¦  «                             dd¬¦  «        }|t          j        || j        z   ¦  «        z  }| j        |                     |¦  «        z  S )Nr!   r7   T)Úkeepdim)	r]   rd   rA   rš   ÚpowÚmeanÚrsqrtr×   rÖ   )r0   r4   Úinput_dtypeÚvariances       r2   r=   zGlmRMSNorm.forward#  s|   € Ø#Ô)ˆØ%×(Ò(­¬Ñ7Ô7ˆØ ×$Ò$ QÑ'Ô'×,Ò,¨R¸Ð,Ñ>Ô>ˆØ%­¬°H¸tÔ?TÑ4TÑ(UÔ(UÑUˆØŒ{˜]×-Ò-¨kÑ:Ô:Ñ:Ð:r3   c                 óH   — t          | j        j        ¦  «        › d| j        › �S )Nz, eps=)r�   rÖ   rn   r×   )r0   s    r2   Ú
extra_reprzGlmRMSNorm.extra_repr*  s&   € Ý˜œÔ)Ñ*Ô*ÐIÐI°$Ô2GÐIÐIÐIr3   )rÑ   )
r>   r?   r@   re   r%   rA   r~   r=   rà   rC   rD   s   @r2   rÐ   rÐ     sŒ   ø€ € € € € ð$ð $¨ð $¸$ð $ð $ð $ð $ð $ð $ð; U¤\ð ;°e´lð ;ð ;ð ;ð ;ðJð Jð Jð Jð Jð Jð Jr3   rÐ   c                   óÒ   ‡ — e Zd Zdedefˆ fd„Z	 	 	 	 	 ddej        dej        dz  dej        dz  d	e	dz  d
e
dz  deej        ej        f         dz  dee         dej        fd„Zˆ xZS )ÚGlmDecoderLayerr&   r¸   c                 ó4  •— t          ¦   «                              ¦   «          |j        | _        t          ||¬¦  «        | _        t          |¦  «        | _        t          |j        |j        ¬¦  «        | _	        t          |j        |j        ¬¦  «        | _
        d S )N)r&   r¸   ©rÒ   )r$   r%   r)   r·   Ú	self_attnr   ÚmlprÐ   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormrÁ   s      €r2   r%   zGlmDecoderLayer.__init__/  s�   ø€ Ý‰Œ×ÒÑÔÐØ!Ô-ˆÔå%¨V¸yÐIÑIÔIˆŒå˜&‘>”>ˆŒÝ)¨&Ô*<À&ÔBUÐVÑVÔVˆÔÝ(2°6Ô3EÈ6ÔK^Ð(_Ñ(_Ô(_ˆÔ%Ð%Ð%r3   NFr4   r�   rw   rÃ   Ú	use_cacherÂ   r’   r5   c           
      óÎ   — |}|                       |¦  «        } | j        d||||||dœ|¤Ž\  }}	||z   }|}|                      |¦  «        }|                      |¦  «        }||z   }|S )N)r4   r�   rw   rÃ   rê   rÂ   © )rè   rå   ré   ræ   )
r0   r4   r�   rw   rÃ   rê   rÂ   r’   ÚresidualÚ_s
             r2   r=   zGlmDecoderLayer.forward9  s¡   € ð !ˆØ×,Ò,¨]Ñ;Ô;ˆà)˜4œ>ð 
Ø'Ø)Ø%Ø+ØØ 3ð
ð 
ð ð
ð 
Ñˆ�qð ! =Ñ0ˆð !ˆØ×5Ò5°mÑDÔDˆØŸš Ñ/Ô/ˆØ  =Ñ0ˆØÐr3   )NNNFN)r>   r?   r@   r   ra   r%   rA   r~   Ú
LongTensorr   Úboolr�   r   r   r=   rC   rD   s   @r2   râ   râ   .  sÿ   ø€ € € € € ð`˜yð `°Sð `ð `ð `ð `ð `ð `ð /3Ø04Ø(,Ø!&ØHLðð à”|ðð œ tÑ+ðð Ô&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5¤<°´Ð#=Ô>ÀÑEðð Ð+Ô,ðð 
Œðð ð ð ð ð ð ð r3   râ   c                   óL   — e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedœZdS )ÚGlmPreTrainedModelr&   ÚmodelTrâ   rÃ   )r4   Ú
attentionsN)r>   r?   r@   r   r   Úbase_model_prefixÚsupports_gradient_checkpointingÚ_no_split_modulesÚ_skip_keys_device_placementÚ_supports_flash_attnÚ_supports_sdpaÚ_supports_flex_attnÚ_can_compile_fullgraphÚ_supports_attention_backendrâ   r·   Ú_can_record_outputsrì   r3   r2   rò   rò   Y  sl   € € € € € € àÐÐÑØÐØ&*Ð#Ø*Ð+ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà(Ø"ðð ÐÐÐr3   rò   c                   óâ   ‡ — e Zd Zdefˆ fd„Zeee	 	 	 	 	 	 ddej	        dz  dej
        dz  dej	        dz  dedz  dej        dz  d	edz  d
ee         defd„¦   «         ¦   «         ¦   «         Zˆ xZS )ÚGlmModelr&   c                 óÞ  •‡— t          ¦   «                              ‰¦  «         ‰j        | _        ‰j        | _        t          j        ‰j        ‰j        | j        ¦  «        | _        t          j	        ˆfd„t          ‰j        ¦  «        D ¦   «         ¦  «        | _        t          ‰j        ‰j        ¬¦  «        | _        t!          ‰¬¦  «        | _        d| _        |                      ¦   «          d S )Nc                 ó0   •— g | ]}t          ‰|¦  «        ‘ŒS rì   )râ   )Ú.0r¸   r&   s     €r2   ú
<listcomp>z%GlmModel.__init__.<locals>.<listcomp>u  s#   ø€ ÐaÐaÐa°I�_˜V YÑ/Ô/ÐaÐaÐar3   rä   ©r&   F)r$   r%   Úpad_token_idÚpadding_idxÚ
vocab_sizer'   Ú	Embeddingr)   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersÚlayersrÐ   rç   ÚnormrF   Ú
rotary_embÚgradient_checkpointingÚ	post_initr/   s    `€r2   r%   zGlmModel.__init__n  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aÐaÐaÐaÅÀvÔG_ÑA`ÔA`ÐaÑaÔañ
ô 
ˆŒõ ˜vÔ1°vÔ7JÐKÑKÔKˆŒ	Ý,°FÐ;Ñ;Ô;ˆŒØ&+ˆÔ#ð 	�ŠÑÔÐÐÐr3   NÚ	input_idsr�   rw   rÃ   Úinputs_embedsrê   r’   r5   c           
      óH  — |d u |d uz  rt          d¦  «        ‚|€|                      |¦  «        }|r|€t          | j        ¬¦  «        }|€V|�|                     ¦   «         nd}t          j        |j        d         |j        ¬¦  «        |z   }| 	                    d¦  «        }t          | j        ||||¬¦  «        }	|}
|                      |
|¬¦  «        }| j        d | j        j        …         D ]} ||
f|	||||dœ|¤Ž}
Œ|                      |
¦  «        }
t          |
|¬	¦  «        S )
Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r   )rU   )r&   r  r�   rÃ   rw   )rw   )r�   rÂ   rw   rÃ   rê   )Úlast_hidden_staterÃ   )Ú
ValueErrorr
  r   r&   Úget_seq_lengthrA   rb   rn   rU   r©   r   r  r  r  r  r   )r0   r  r�   rw   rÃ   r  rê   r’   Úpast_seen_tokensÚcausal_maskr4   rÂ   Údecoder_layers                r2   r=   zGlmModel.forward~  sŠ  € ð ˜Ð -°tÐ";Ñ<ð 	[ÝÐYÑZÔZÐZàÐ Ø*.×*;Ò*;¸IÑ*FÔ*FˆMàð 	?˜Ð0Ý*°$´+Ð>Ñ>Ô>ˆOàÐØCRÐC^˜×=Ò=Ñ?Ô?Ð?ÐdeÐÝ œ<¨Ô(;¸AÔ(>À}ÔG[Ð\Ñ\Ô\Ð_oÑoˆLØ'×1Ò1°!Ñ4Ô4ˆLå(Ø”;Ø'Ø)Ø+Ø%ð
ñ 
ô 
ˆð &ˆØ"Ÿošo¨mÈ,˜oÑWÔWÐà!œ[Ð)H¨4¬;Ô+HÐ)HÔIð 		ð 		ˆMØ)˜MØðà*Ø$7Ø)Ø /Ø#ðð ð ðð ˆMˆMð Ÿ	š	 -Ñ0Ô0ˆÝ&Ø+Ø+ð
ñ 
ô 
ð 	
r3   )NNNNNN)r>   r?   r@   r   r%   r   r   r   rA   rï   r~   r   rB   rð   r   r   r   r=   rC   rD   s   @r2   r   r   l  s  ø€ € € € € ð˜yð ð ð ð ð ð ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ð2
ð 2
àÔ# dÑ*ð2
ð œ tÑ+ð2
ð Ô&¨Ñ-ð	2
ð
  ™ð2
ð Ô(¨4Ñ/ð2
ð ˜$‘;ð2
ð Ð+Ô,ð2
ð 
!ð2
ð 2
ð 2
ñ „^ñ „_ñ  Ôð2
ð 2
ð 2
ð 2
ð 2
r3   r   c                   ó  ‡ — e Zd ZddiZddiZddgdgfiZˆ fd„Zee	 	 	 	 	 	 	 	 dd
e	j
        dz  de	j        dz  de	j
        dz  dedz  de	j        dz  de	j
        dz  dedz  dee	j        z  dee         defd„¦   «         ¦   «         Zˆ xZS )ÚGlmForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputr4   Úlogitsc                 óþ   •— t          ¦   «                              |¦  «         t          |¦  «        | _        |j        | _        t          j        |j        |j        d¬¦  «        | _        |  	                    ¦   «          d S )NFr"   )
r$   r%   r   ró   r  r'   r(   r)   r  r  r/   s     €r2   r%   zGlmForCausalLM.__init__¼  sj   ø€ Ý‰Œ×Ò˜Ñ Ô Ð Ý˜fÑ%Ô%ˆŒ
Ø Ô+ˆŒÝ”y Ô!3°VÔ5FÈUÐSÑSÔSˆŒð 	�ŠÑÔÐÐÐr3   Nr   r  r�   rw   rÃ   r  Úlabelsrê   Úlogits_to_keepr’   r5   c	           
      óP  —  | j         d||||||dœ|	¤Ž}
|
j        }t          |t          ¦  «        rt	          | d¦  «        n|}|                      |dd…|dd…f         ¦  «        }d}|� | j        d||| j        j        dœ|	¤Ž}t          |||
j
        |
j        |
j        ¬¦  «        S )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  r�   rw   rÃ   r  rê   N)r   r"  r  )Úlossr   rÃ   r4   rô   rì   )ró   r  ro   ra   Úslicer  Úloss_functionr&   r  r   rÃ   r4   rô   )r0   r  r�   rw   rÃ   r  r"  rê   r#  r’   Úoutputsr4   Úslice_indicesr   r%  s                  r2   r=   zGlmForCausalLM.forwardÅ  sô   € ð> ,6¨4¬:ð ,
ØØ)Ø%Ø+Ø'Øð,
ð ,
ð ð,
ð ,
ˆð  Ô1ˆå8BÀ>ÕSVÑ8WÔ8WÐk�˜~˜o¨tÑ4Ô4Ð4Ð]kˆØ—’˜m¨A¨A¨A¨}¸a¸a¸aÐ,?Ô@ÑAÔAˆàˆØÐØ%�4Ô%Ðp¨V¸FÈtÌ{ÔOeÐpÐpÐioÐpÐpˆDå%ØØØ#Ô3Ø!Ô/ØÔ)ð
ñ 
ô 
ð 	
r3   )NNNNNNNr   )r>   r?   r@   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr%   r   r   rA   rï   r~   r   rB   rð   ra   r   r   r   r=   rC   rD   s   @r2   r  r  ¶  sK  ø€ € € € € à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hðð ð ð ð ð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø-.ð6
ð 6
àÔ# dÑ*ð6
ð œ tÑ+ð6
ð Ô&¨Ñ-ð	6
ð
  ™ð6
ð Ô(¨4Ñ/ð6
ð Ô  4Ñ'ð6
ð ˜$‘;ð6
ð ˜eœlÑ*ð6
ð Ð+Ô,ð6
ð 
 ð6
ð 6
ð 6
ñ „^ñ Ôð6
ð 6
ð 6
ð 6
ð 6
r3   r  c                   ó   — e Zd ZdS )ÚGlmForSequenceClassificationN©r>   r?   r@   rì   r3   r2   r.  r.     ó   € € € € € Ø€Dr3   r.  c                   ó   — e Zd ZdS )ÚGlmForTokenClassificationNr/  rì   r3   r2   r2  r2    r0  r3   r2  )rò   r   r  r.  r2  )rŠ   )r   )?Úcollections.abcr   Útypingr   rA   Útorch.nnr'   Úactivationsr   Úcache_utilsr   r   Ú
generationr	   Úintegrationsr
   Úmasking_utilsr   Úmodeling_layersr   r   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   r   Úutils.output_capturingr   Úconfiguration_glmr   ÚModuler   rF   r~   ra   r‰   re   r    r§   rµ   r·   rÐ   râ   rò   r   r  r.  r2  Ú__all__rì   r3   r2   ú<module>rF     s•  ðð* %Ð $Ð $Ð $Ð $Ð $Ø Ð Ð Ð Ð Ð à €€€Ø Ð Ð Ð Ð Ð à !Ð !Ð !Ð !Ð !Ð !Ø .Ð .Ð .Ð .Ð .Ð .Ð .Ð .Ø )Ð )Ð )Ð )Ð )Ð )Ø 7Ð 7Ð 7Ð 7Ð 7Ð 7Ø /Ð /Ð /Ð /Ð /Ð /ðð ð ð ð ð ð ð ð ð ð
 PÐ OÐ OÐ OÐ OÐ OÐ OÐ OØ KÐ KÐ KÐ KÐ KÐ KÐ KÐ KØ FÐ FÐ FÐ FÐ FÐ FÐ FÐ FØ &Ð &Ð &Ð &Ð &Ð &Ø IÐ IÐ IÐ IÐ IÐ IÐ IÐ IÐ IÐ IØ GÐ GÐ GÐ GÐ GÐ GÐ GÐ GØ 5Ð 5Ð 5Ð 5Ð 5Ð 5Ø (Ð (Ð (Ð (Ð (Ð (ð)ð )ð )ð )ð )ˆRŒYñ )ô )ð )ð$@<ð @<ð @<ð @<ð @<˜œñ @<ô @<ð @<ðF	U˜Uœ\ð 	U°#ð 	U¸%¼,ð 	Uð 	Uð 	Uð 	Uð& ð%ð %ØŒIð%àŒ<ð%ð 
Œð%ð Œ<ð	%ð
 ”L 4Ñ'ð%ð ð%ð ð%ð Ð'Ô(ð%ð %ð %ð %ð26ð 6ð 6ð%ð %ð %ð %ðP>)ð >)ð >)ð >)ð >)�2”9ñ >)ô >)ð >)ðB Ð˜YÑ'Ô'ðJð Jð Jð Jð J�”ñ Jô Jñ (Ô'ðJð((ð (ð (ð (ð (Ð0ñ (ô (ð (ðV ðð ð ð ð ˜ñ ô ñ „ðð$ ðF
ð F
ð F
ð F
ð F
Ð!ñ F
ô F
ñ „ðF
ðR ðF
ð F
ð F
ð F
ð F
Ð'¨ñ F
ô F
ñ „ðF
ðR	ð 	ð 	ð 	ð 	Ð#CÐEWñ 	ô 	ð 	ð	ð 	ð 	ð 	ð 	Ð =Ð?Qñ 	ô 	ð 	ðð ð €€€r3   