§
    ‚Štj0T  ã                   óx  — 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mZ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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/d„ Z0 ed¦  «        d;d„¦   «         Z1dej2        de3dej2        fd„Z4	 d<d ej.        d!ej2        d"ej2        d#ej2        d$ej2        dz  d%e5d&e5d'e"e$         fd(„Z6 ee1¦  «         G d)„ d*ej.        ¦  «        ¦   «         Z7 ed+¦  «         G d,„ d-ej.        ¦  «        ¦   «         Z8 G d.„ d/ej.        ¦  «        Z9 G d0„ d1e¦  «        Z:e% G d2„ d3e ¦  «        ¦   «         Z; G d4„ d5e¦  «        Z<e% G d6„ d7e;¦  «        ¦   «         Z=e% G d8„ d9e;e¦  «        ¦   «         Z>g d:¢Z?dS )=é    )ÚCallable)ÚOptionalN)Únné   )ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Ú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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	CwmConfigc                   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CwmRotaryEmbeddingÚinv_freqNÚconfigc                 ó²  •— t          ¦   «                              ¦   «          |j        | _        |j        | _        || _        | j        j        d         | _        | j        }| j        dk    rt          | j                 } || j        |¦  «        \  }| _
        |                      d|d¬¦  «         |                      d|                     ¦   «         d¬¦  «         d S )NÚ	rope_typeÚdefaultr#   F)Ú
persistentÚoriginal_inv_freq)ÚsuperÚ__init__Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenr$   Úrope_parametersr&   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)Úselfr$   ÚdeviceÚrope_init_fnr#   Ú	__class__s        €úb/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/cwm/modeling_cwm.pyr+   zCwmRotaryEmbedding.__init__0   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�$Ô(à×Ò˜Z¨¸eÐÑDÔDÐDØ×ÒÐ0°(·.².Ñ2BÔ2BÈuÐÑUÔUÐUÐUÐUó    r5   ztorch.deviceÚseq_lenÚreturnztorch.Tensorc                 óü   — | j         d         }t          | dd¦  «        p| j        | j        z  }d}d|t	          j        d|dt          j        ¬¦  «                             |t          j        ¬¦  «        |z  z  z  }||fS )	a¨  
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        Ú
rope_thetaÚhead_dimNg      ð?r   é   ©Údtype)r5   rA   )	r/   ÚgetattrÚhidden_sizeÚnum_attention_headsÚtorchÚarangeÚint64ÚtoÚfloat)r$   r5   r:   ÚbaseÚdimÚattention_factorr#   s          r8   r0   z2CwmRotaryEmbedding.compute_default_rope_parameters@   sŒ   € ð& Ô% lÔ3ˆÝ�f˜j¨$Ñ/Ô/Ðc°6Ô3EÈÔIcÑ3cˆàÐð Ø•U”\ ! S¨!µ5´;Ð?Ñ?Ô?×BÒBÈ&ÕX]ÔXcÐBÑdÔdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r9   c                 óN  — | j         d d d …d f                              ¦   «                              |j        d         dd¦  «                             |j        ¦  «        }|d d …d d d …f                              ¦   «         }t          |j        j        t          ¦  «        r|j        j        dk    r|j        j        nd}t          |d¬¦  «        5  |                     ¦   «         |                     ¦   «         z   
                    dd¦  «        }t          j        ||fd¬	¦  «        }|                     ¦   «         | j        z  }|                     ¦   «         | j        z  }	d d d ¦  «         n# 1 swxY w Y   |                     |j        ¬
¦  «        |	                     |j        ¬
¦  «        fS )Nr   éÿÿÿÿr   ÚmpsÚcpuF)Údevice_typeÚenabledr?   ©rK   r@   )r#   rI   ÚexpandÚshaperH   r5   Ú
isinstanceÚtypeÚstrr   Ú	transposerE   ÚcatÚcosr1   ÚsinrA   )
r4   ÚxÚposition_idsÚinv_freq_expandedÚposition_ids_expandedrQ   ÚfreqsÚembr[   r\   s
             r8   ÚforwardzCwmRotaryEmbedding.forward^   s·  € ð !œM¨$°°°°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)Ú__name__Ú
__module__Ú__qualname__rE   ÚTensorÚ__annotations__r    r+   Ústaticmethodr   ÚintÚtuplerI   r0   Úno_gradr   rc   Ú__classcell__©r7   s   @r8   r"   r"   -   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 <r9   r"   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..NrN   r?   rS   )rU   rE   rZ   )r]   Úx1Úx2s      r8   Úrotate_halfrs   n   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.
    )Ú	unsqueezers   )ÚqÚkr[   r\   Úunsqueeze_dimÚq_embedÚk_embeds          r8   Úapply_rotary_pos_embr|   u   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   Úhidden_statesÚ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)rU   rT   Úreshape)r}   r~   ÚbatchÚnum_key_value_headsÚslenr>   s         r8   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Tr9   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   rN   )rK   rA   )ÚpÚtrainingr   )r„   Únum_key_value_groupsrE   ÚmatmulrY   r   Ú
functionalÚsoftmaxÚfloat32rH   rA   rŒ   r�   Ú
contiguous)r†   r‡   rˆ   r‰   rŠ   r‹   rŒ   r�   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r8   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$Ð$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CwmAttentionz=Multi-headed attention from 'Attention Is All You Need' paperr$   Ú	layer_idxc                 óB  •— t          ¦   «                              ¦   «          t          |d¦  «        r|j        |         nd | _        || _        || _        t          |d|j        |j	        z  ¦  «        | _
        |j	        |j        z  | _        | j
        dz  | _        |j        | _        d| _        t           j                             |j        |j	        | j
        z  d¬¦  «        | _        t           j                             |j        |j        | j
        z  d¬¦  «        | _        t           j                             |j        |j        | j
        z  d¬¦  «        | _        t#          j        |j	        | j
        z  |j        d¬¦  «        | _        | j        dk    r|j        nd | _        d S )NÚlayer_typesr>   g      à¿TF©ÚbiasÚsliding_attention)r*   r+   Úhasattrr    Ú
layer_typer$   rž   rB   rC   rD   r>   r‚   r‘   r‹   Úattention_dropoutÚ	is_causalrE   r   ÚLinearÚq_projÚk_projÚv_projÚo_projÚsliding_window©r4   r$   rž   r7   s      €r8   r+   zCwmAttention.__init__¸   sg  ø€ Ý‰Œ×ÒÑÔÐÝ;BÀ6È=Ñ;YÔ;YÐc˜&Ô,¨YÔ7Ð7Ð_cˆŒØˆŒØ"ˆŒÝ ¨
°FÔ4FÈ&ÔJdÑ4dÑeÔeˆŒØ$*Ô$>À&ÔB\Ñ$\ˆÔ!Ø”} dÑ*ˆŒØ!'Ô!9ˆÔØˆŒÝ”h—o’o fÔ&8¸&Ô:TÐW[ÔWdÑ:dÐkp�oÑqÔqˆŒÝ”h—o’o fÔ&8¸&Ô:TÐW[ÔWdÑ:dÐkp�oÑqÔqˆŒÝ”h—o’o fÔ&8¸&Ô:TÐW[ÔWdÑ:dÐkp�oÑqÔqˆŒÝ”i Ô :¸T¼]Ñ JÈFÔL^ÐejÐkÑkÔkˆŒØ7;´ÐJ]Ò7]Ð7]˜fÔ3Ð3ÐcgˆÔÐÐr9   Nr}   Úposition_embeddingsrŠ   Úpast_key_valuesr�   r;   c                 ó.  — |j         d d…         }g |¢d‘| j        ‘R }|                      |¦  «                             |¦  «                             dd¦  «        }|                      |¦  «                             |¦  «                             dd¦  «        }	|                      |¦  «                             |¦  «                             dd¦  «        }
|\  }}t          ||	||¦  «        \  }}	|�|                     |	|
| j	        ¦  «        \  }	}
t          j        | j        j        t          ¦  «        } || ||	|
|f| j        sdn| j        | j        | j        dœ|¤Ž\  }} |j        g |¢d‘R Ž                      ¦   «         }|                      |¦  «        }||fS )NrN   r   r?   r…   )rŒ   r‹   r­   )rU   r>   r©   ÚviewrY   rª   r«   r|   Úupdaterž   r   Úget_interfacer$   Ú_attn_implementationr›   r�   r¦   r‹   r­   r€   r–   r¬   )r4   r}   r¯   rŠ   r°   r�   Úinput_shapeÚhidden_shapeÚquery_statesr—   r˜   r[   r\   Úattention_interfacerš   r™   s                   r8   rc   zCwmAttention.forwardÈ   sÆ  € ð $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”LØÔ.ð
%
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   rd   )re   rf   rg   Ú__doc__r    rk   r+   rE   rh   rl   r   r   r   rc   rn   ro   s   @r8   r�   r�   ´   så   ø€ € € € € àGÐGðh˜yð h°Sð hð hð hð hð hð h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�   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
CwmRMSNormç�íµ ÷Æ°>Úepsr;   Nc                 ó¬   •— t          ¦   «                              ¦   «          t          j        t	          j        |¦  «        ¦  «        | _        || _        dS )z9
        CwmRMSNorm is equivalent to T5LayerNorm
        N)r*   r+   r   Ú	ParameterrE   ÚonesÚweightÚvariance_epsilon)r4   rC   r¿   r7   s      €r8   r+   zCwmRMSNorm.__init__ô   sD   ø€ õ 	‰Œ×ÒÑÔÐÝ”l¥5¤:¨kÑ#:Ô#:Ñ;Ô;ˆŒØ #ˆÔÐÐr9   r}   c                 ó  — |j         }|                     t          j        ¦  «        }|                     d¦  «                             dd¬¦  «        }|t          j        || j        z   ¦  «        z  }| j        |                     |¦  «        z  S )Nr?   rN   T)Úkeepdim)	rA   rH   rE   r•   ÚpowÚmeanÚrsqrtrÄ   rÃ   )r4   r}   Úinput_dtypeÚvariances       r8   rc   zCwmRMSNorm.forwardü   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=)rl   rÃ   rU   rÄ   )r4   s    r8   Ú
extra_reprzCwmRMSNorm.extra_repr  s&   € Ý˜œÔ)Ñ*Ô*ÐIÐI°$Ô2GÐIÐIÐIr9   )r¾   )
re   rf   rg   rI   r+   rE   rh   rc   rÍ   rn   ro   s   @r8   r½   r½   ò   sŒ   ø€ € € € € ð$ð $¨ð $¸$ð $ð $ð $ð $ð $ð $ð; U¤\ð ;°e´lð ;ð ;ð ;ð ;ðJð Jð Jð Jð Jð Jð Jr9   r½   c                   ó$   ‡ — e Zd Zˆ fd„Zd„ Zˆ xZS )ÚCwmMLPc                 ó¶  •— t          ¦   «                              ¦   «          || _        |j        | _        |j        | _        t          j        | j        | j        |j        ¬¦  «        | _        t          j        | j        | j        |j        ¬¦  «        | _	        t          j        | j        | j        |j        ¬¦  «        | _
        t          |j                 | _        d S )Nr¡   )r*   r+   r$   rC   Úintermediate_sizer   r¨   Úmlp_biasÚ	gate_projÚup_projÚ	down_projr   Ú
hidden_actÚact_fn©r4   r$   r7   s     €r8   r+   zCwmMLP.__init__  s¯   ø€ Ý‰Œ×ÒÑÔÐØˆŒØ!Ô-ˆÔØ!'Ô!9ˆÔÝœ 4Ô#3°TÔ5KÐRXÔRaÐbÑbÔbˆŒÝ”y Ô!1°4Ô3IÐPVÔP_Ð`Ñ`Ô`ˆŒÝœ 4Ô#9¸4Ô;KÐRXÔRaÐbÑbÔbˆŒÝ˜VÔ.Ô/ˆŒˆˆr9   c                 ó¨   — |                       |                      |                      |¦  «        ¦  «        |                      |¦  «        z  ¦  «        }|S rd   )rÕ   r×   rÓ   rÔ   )r4   r]   rÕ   s      r8   rc   zCwmMLP.forward  sA   € Ø—N’N 4§;¢;¨t¯~ª~¸aÑ/@Ô/@Ñ#AÔ#AÀDÇLÂLÐQRÁOÄOÑ#SÑTÔTˆ	ØÐr9   )re   rf   rg   r+   rc   rn   ro   s   @r8   rÏ   rÏ     sG   ø€ € € € € ð0ð 0ð 0ð 0ð 0ðð ð ð ð ð ð r9   rÏ   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CwmDecoderLayerr$   rž   c                 ó4  •— t          ¦   «                              ¦   «          |j        | _        t          ||¬¦  «        | _        t          |¦  «        | _        t          |j        |j        ¬¦  «        | _	        t          |j        |j        ¬¦  «        | _
        d S )N)r$   rž   ©r¿   )r*   r+   rC   r�   Ú	self_attnrÏ   Úmlpr½   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormr®   s      €r8   r+   zCwmDecoderLayer.__init__  s�   ø€ Ý‰Œ×ÒÑÔÐØ!Ô-ˆÔÝ%¨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%r9   NFr}   rŠ   r^   r°   Ú	use_cacher¯   r�   r;   c           
      óÎ   — |}|                       |¦  «        } | j        d||||||dœ|¤Ž\  }}	||z   }|}|                      |¦  «        }|                      |¦  «        }||z   }|S )N)r}   rŠ   r^   r°   rã   r¯   © )rá   rÞ   râ   rß   )
r4   r}   rŠ   r^   r°   rã   r¯   r�   ÚresidualÚ_s
             r8   rc   zCwmDecoderLayer.forward!  s¡   € ð !ˆØ×,Ò,¨]Ñ;Ô;ˆà)˜4œ>ð 
Ø'Ø)Ø%Ø+ØØ 3ð
ð 
ð ð
ð 
Ñˆ�qð ! =Ñ0ˆð !ˆØ×5Ò5°mÑDÔDˆØŸš Ñ/Ô/ˆØ  =Ñ0ˆØÐr9   )NNNFN)re   rf   rg   r    rk   r+   rE   rh   Ú
LongTensorr   Úboolrl   r   r   rc   rn   ro   s   @r8   rÛ   rÛ     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 ˜$‘;ðð # 5¤<°´Ð#=Ô>ÀÑEðð Ð+Ô,ðð 
Œðð ð ð ð ð ð ð r9   rÛ   c                   óL   — e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedœZdS )ÚCwmPreTrainedModelr$   ÚmodelTrÛ   r°   )r}   Ú
attentionsN)re   rf   rg   r    ri   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_backendrÛ   r�   Ú_can_record_outputsrå   r9   r8   rë   rë   A  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r9   rë   c                   ó   — e Zd ZdS )ÚCwmModelOutputWithPastN)re   rf   rg   rå   r9   r8   rù   rù   T  s   € € € € € Ø€Dr9   rù   c                   óæ   ‡ — e Zd Ze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CwmModelr$   c                 óô  •‡— t          ¦   «                              ‰¦  «         ‰j        | _        ‰j        | _        t          j        ‰j        ‰j        | j        ¦  «        | _        t          j         
                    ˆfd„t          ‰j        ¦  «        D ¦   «         ¦  «        | _        t          ‰j        ‰j        ¬¦  «        | _        t#          ‰¬¦  «        | _        d| _        |                      ¦   «          d S )Nc                 ó0   •— g | ]}t          ‰|¦  «        ‘ŒS rå   )rÛ   )Ú.0rž   r$   s     €r8   ú
<listcomp>z%CwmModel.__init__.<locals>.<listcomp>c  s#   ø€ ÐaÐaÐa°I�_˜V YÑ/Ô/ÐaÐaÐar9   rÝ   ©r$   F)r*   r+   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	EmbeddingrC   Úembed_tokensrE   Ú
ModuleListÚrangeÚnum_hidden_layersÚlayersr½   rà   Únormr"   Ú
rotary_embÚgradient_checkpointingÚ	post_initrØ   s    `€r8   r+   zCwmModel.__init__\  sÚ   øø€ Ý‰Œ×Ò˜Ñ Ô Ð Ø!Ô.ˆÔØ Ô+ˆŒåœL¨Ô):¸FÔ<NÐPTÔP`ÑaÔaˆÔÝ”h×)Ò)ØaÐaÐaÐaÅÀvÔG_ÑA`ÔA`ÐaÑaÔañ
ô 
ˆŒõ ˜vÔ1°vÔ7JÐKÑKÔKˆŒ	Ý,°FÐ;Ñ;Ô;ˆŒØ&+ˆÔ#ð 	�ŠÑÔÐÐÐr9   NÚ	input_idsrŠ   r^   r°   Úinputs_embedsrã   r�   r;   c           	      ó   — |d u |d uz  rt          d¦  «        ‚|€|                      |¦  «        }|r|€t          | j        ¬¦  «        }|€V|�|                     ¦   «         nd}t          j        |j        d         |j        ¬¦  «        |z   }| 	                    d¦  «        }t          |x}	t          ¦  «        s:| j        ||||dœ}
|
                     ¦   «         }t          d
i |
¤Žt          d
i |¤Ždœ}	|}|                      ||¦  «        }t!          | j        d | j        j        …         ¦  «        D ])\  }} ||f|	| j        j        |                  |||dœ|¤Ž}Œ*|                      |¦  «        }t+          ||¬	¦  «        S )Nz:You must specify exactly one of input_ids or inputs_embedsr   r   r   )r5   )r$   r  rŠ   r°   r^   )Úfull_attentionr£   )rŠ   r^   r°   r¯   )Úlast_hidden_stater°   rå   )Ú
ValueErrorr  r	   r$   Úget_seq_lengthrE   rF   rU   r5   rv   rV   ÚdictÚcopyr   r   r  Ú	enumerater	  r  r    r
  rù   )r4   r  rŠ   r^   r°   r  rã   r�   Úpast_seen_tokensÚcausal_mask_mappingÚmask_kwargsÚsliding_mask_kwargsr}   r¯   ÚiÚdecoder_layers                   r8   rc   zCwmModel.forwardl  s÷  € ð ˜Ð -°tÐ";Ñ<ð 	[ÝÐYÑZÔZÐZàÐ Ø*.×*;Ò*;¸IÑ*FÔ*FˆMàð 	?˜Ð0Ý*°$´+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°Ð?Ð-ÅÑFÔFð 	àœ+Ø!.Ø"0Ø#2Ø ,ðð ˆKð #.×"2Ò"2Ñ"4Ô"4Ðõ #5Ð"CÐ"C°{Ð"CÐ"CÝ%FÐ%]Ð%]ÐI\Ð%]Ð%]ð#ð #Ðð
 &ˆØ"Ÿošo¨m¸\ÑJÔJÐå )¨$¬+Ð6U¸¼Ô8UÐ6UÔ*VÑ WÔ Wð 	ð 	ÑˆAˆ}Ø)˜MØðà2°4´;Ô3JÈ1Ô3MÔNØ)Ø /Ø$7ðð ð ðð ˆMˆMð Ÿ	š	 -Ñ0Ô0ˆÝ%Ø+Ø+ð
ñ 
ô 
ð 	
r9   )NNNNNN)re   rf   rg   r    Úconfig_classr+   r   r   r   rE   rè   rh   r   ÚFloatTensorré   r   r   rù   rc   rn   ro   s   @r8   rû   rû   X  s  ø€ € € € € à€Lð˜yð ð ð ð ð ð ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ð8
ð 8
àÔ# dÑ*ð8
ð œ tÑ+ð8
ð Ô&¨Ñ-ð	8
ð
  ™ð8
ð Ô(¨4Ñ/ð8
ð ˜$‘;ð8
ð Ð+Ô,ð8
ð 
 ð8
ð 8
ð 8
ñ „^ñ „_ñ  Ôð8
ð 8
ð 8
ð 8
ð 8
r9   rû   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CwmForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputr}   Úlogitsc                 óþ   •— t          ¦   «                              |¦  «         t          |¦  «        | _        |j        | _        t          j        |j        |j        d¬¦  «        | _        |  	                    ¦   «          d S )NFr¡   )
r*   r+   rû   rì   r  r   r¨   rC   r"  r  rØ   s     €r8   r+   zCwmForCausalLM.__init__°  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r9   Nr   r  rŠ   r^   r°   r  Úlabelsrã   Úlogits_to_keepr�   r;   c	           
      óP  —  | j         d||||||dœ|	¤Ž}
|
j        }t          |t          ¦  «        rt	          | d¦  «        n|}|                      |dd…|dd…f         ¦  «        }d}|� | j        d||| j        j        dœ|	¤Ž}t          |||
j
        |
j        |
j        ¬¦  «        S )aÃ  
        Example:

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

        >>> model = CwmForCausalLM.from_pretrained("meta-cwm/Cwm-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-cwm/Cwm-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  rŠ   r^   r°   r  rã   N)r$  r&  r  )Úlossr$  r°   r}   rí   rå   )rì   r  rV   rk   Úslicer"  Úloss_functionr$   r  r   r°   r}   rí   )r4   r  rŠ   r^   r°   r  r&  rã   r'  r�   Úoutputsr}   Úslice_indicesr$  r)  s                  r8   rc   zCwmForCausalLM.forward¹  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 	
r9   )NNNNNNNr   )re   rf   rg   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr+   r   r   rE   rè   rh   r   r  ré   rk   r   r   r   rc   rn   ro   s   @r8   r!  r!  ª  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 ˜$‘;ð6
ð ˜eœlÑ*ð6
ð Ð+Ô,ð6
ð 
 ð6
ð 6
ð 6
ñ „^ñ Ôð6
ð 6
ð 6
ð 6
ð 6
r9   r!  )rë   rû   r!  )r   )r…   )@Úcollections.abcr   Útypingr   rE   r   Úactivationsr   Úcache_utilsr   r	   Ú
generationr
   Úintegrationsr   r   r   Úmasking_utilsr   r   Úmodeling_flash_attention_utilsr   Úmodeling_layersr   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   r   Úutils.output_capturingr   Úconfiguration_cwmr    ÚModuler"   rs   r|   rh   rk   r„   rI   r›   r�   r½   rÏ   rÛ   rë   rù   rû   r!  Ú__all__rå   r9   r8   ú<module>rD     sŠ  ðð, %Ð $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 fÐ fÐ fÐ fÐ fÐ fÐ fÐ fÐ fÐ fØ RÐ RÐ RÐ RÐ RÐ RÐ RÐ RØ BÐ BÐ BÐ BÐ BÐ BØ 9Ð 9Ð 9Ð 9Ð 9Ð 9Ø OÐ 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 ><˜œñ ><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:)ð :)ð :)ð :)ð :)�2”9ñ :)ô :)ñ +Ô*ð:)ðz 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 ˆRŒYñ ô ð ð 'ð 'ð 'ð 'ð 'Ð0ñ 'ô 'ð 'ðT ðð ð ð ð ˜ñ ô ñ „ðð$	ð 	ð 	ð 	ð 	Ð4ñ 	ô 	ð 	ð ðN
ð N
ð N
ð N
ð N
Ð!ñ N
ô N
ñ „ðN
ðb ðF
ð F
ð F
ð F
ð F
Ð'¨ñ F
ô F
ñ „ðF
ðR ?Ð
>Ð
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