Ë
    ÛÓ^j0T  ã                   ór  — 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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'„Z6 ee1«       G d(„ d)ej\                  «      «       Z7 ed*«       G d+„ d,ej\                  «      «       Z8 G d-„ d.ej\                  «      Z9 G d/„ d0e«      Z:e% G d1„ d2e «      «       Z; G d3„ d4e«      Z<e% G d5„ d6e;«      «       Z=e% G d7„ d8e;e«      «       Z>g d9¢Z?y)<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                   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CwmRotaryEmbeddingÚinv_freqNÚconfigc                 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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        €úo/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/cwm/modeling_cwm.pyr+   zCwmRotaryEmbedding.__init__0   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Uó    r5   ztorch.deviceÚseq_lenÚreturnztorch.Tensorc                 ó  — | j                   d   }t        | dd«      xs | j                  | j                  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head_dimNg      ð?r   é   ©Údtype)r5   rA   )	r/   ÚgetattrÚhidden_sizeÚnum_attention_headsÚtorchÚarangeÚint64ÚtoÚfloat)r$   r5   r:   ÚbaseÚdimÚattention_factorr#   s          r8   r0   z2CwmRotaryEmbedding.compute_default_rope_parameters@   s�   € ð& ×%Ñ% lÑ3ˆÜ�f˜j¨$Ó/Òc°6×3EÑ3EÈ×IcÑIcÑ3cˆàÐð Ø”U—\‘\ ! S¨!´5·;±;Ô?×BÑBÈ&ÔX]×XcÑXcÐBÓdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r9   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   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^   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$©N)NNN)Ú__name__Ú
__module__Ú__qualname__rE   ÚTensorÚ__annotations__r    r+   Ústaticmethodr   ÚintÚtuplerI   r0   Úno_gradr   rc   Ú__classcell__©r7   s   @r8   r"   r"   -   s˜   ø… Ø�l‰lÓñV˜yõ Vð  à#'Ø+/Ø"ñ*Ø˜DÑ ð*à˜Ñ(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ñ	%ò	*ó ð*ð: €U‡]�]ƒ_Øñ<ó ó ô<r9   r"   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..NrN   r?   rS   )rU   rE   rZ   )r]   Úx1Úx2s      r8   Úrotate_halfrs   n   sZ   € à	
ˆ3Ð"�!—'‘'˜"‘+ Ñ"Ð"Ð"Ñ	#€BØ	
ˆ3�—‘˜‘˜qÑ Ñ"Ð"Ñ	#€BÜ�9‰9�r�c˜2�Y BÔ'Ð'r9   Úrotary_pos_embc                 óž   — |j                  |«      }|j                  |«      }| |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   sY   € ð& �-‰-˜Ó
&€CØ
�-‰-˜Ó
&€CØ�3‰wœ; q›>¨CÑ/Ñ0€GØ�3‰wœ; q›>¨CÑ/Ñ0€GØ�GÐÐr9   Úhidden_statesÚn_repr;   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)rU   rT   Úreshape)r}   r~   ÚbatchÚnum_key_value_headsÚslenr>   s         r8   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Tr9   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   rN   )rK   rA   )ÚpÚtrainingr   )r„   Únum_key_value_groupsrE   ÚmatmulrY   r   Ú
functionalÚsoftmaxÚfloat32rH   rA   r‹   r�   Ú
contiguous)r…   r†   r‡   rˆ   r‰   rŠ   r‹   rŒ   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r8   Úeager_attention_forwardrš   ›   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$Ð$r9   c                   óô   ‡ — 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$   Ú	layer_idxc                 óä  •— t         ‰| �  «        t        |d«      r|j                  |   nd | _        || _        || _        t        |d|j                  |j                  z  «      | _
        |j                  |j                  z  | _        | j                  dz  | _        |j                  | _        d| _        t         j"                  j%                  |j                  |j                  | j                  z  d¬«      | _        t         j"                  j%                  |j                  |j                  | j                  z  d¬«      | _        t         j"                  j%                  |j                  |j                  | j                  z  d¬«      | _        t#        j$                  |j                  | j                  z  |j                  d¬«      | _        | j                  dk(  r|j.                  | _        y d | _        y )NÚlayer_typesr>   g      à¿TF©ÚbiasÚsliding_attention)r*   r+   ÚhasattrrŸ   Ú
layer_typer$   r�   rB   rC   rD   r>   r‚   r�   rŠ   Úattention_dropoutÚ	is_causalrE   r   ÚLinearÚq_projÚk_projÚv_projÚo_projÚsliding_window©r4   r$   r�   r7   s      €r8   r+   zCwmAttention.__init__¸   s~  ø€ Ü‰ÑÔÜ;BÀ6È=Ô;Y˜&×,Ñ,¨YÒ7Ð_cˆŒ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Ü—h‘h—o‘o f×&8Ñ&8¸&×:TÑ:TÐW[×WdÑWdÑ:dÐkp�oÓqˆŒÜ—h‘h—o‘o f×&8Ñ&8¸&×:TÑ:TÐW[×WdÑWdÑ:dÐkp�oÓqˆŒÜ—h‘h—o‘o f×&8Ñ&8¸&×:TÑ:TÐW[×WdÑWdÑ:dÐkp�oÓqˆŒÜ—i‘i × :Ñ :¸T¿]¹]Ñ JÈF×L^ÑL^ÐejÔkˆŒØ7;·±ÐJ]Ò7]˜f×3Ñ3ˆÕÐcgˆÕr9   Nr}   Úposition_embeddingsr‰   Úpast_key_valuesrŒ   r;   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"                  | j$                  dœ|¤Ž\  }} |j&                  g |¢d‘­Ž j)                  «       }| j+                  |«      }||fS )NrN   r   r?   ç        )r‹   rŠ   r¬   )rU   r>   r¨   ÚviewrY   r©   rª   r|   Úupdater�   r   Úget_interfacer$   Ú_attn_implementationrš   r�   r¥   rŠ   r¬   r€   r•   r«   )r4   r}   r®   r‰   r¯   rŒ   Úinput_shapeÚhidden_shapeÚquery_statesr–   r—   r[   r\   Úattention_interfacer™   r˜   s                   r8   rc   zCwmAttention.forwardÈ   sš  € 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‘LØ×.Ñ.ñ
%
ð ñ
%
Ñ!ˆ�\ð *�k×)Ñ)Ð;¨;Ð;¸Ò;×FÑFÓHˆØ—k‘k +Ó.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   rœ   rœ   ´   s    ø„ áGðh˜yð h°Sõ hð* )-ñ')à—|‘|ð')ð # 5§<¡<°·±Ð#=Ñ>ð')ð Ÿ™ tÑ+ð	')ð
  ™ð')ð Ð-Ñ.ð')ð 
ˆu�|‰|˜UŸ\™\¨DÑ0Ð0Ñ	1÷')r9   rœ   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
CwmRMSNormÚepsr;   Nc                 óŠ   •— t         ‰| �  «        t        j                  t	        j
                  |«      «      | _        || _        y)z9
        CwmRMSNorm is equivalent to T5LayerNorm
        N)r*   r+   r   Ú	ParameterrE   ÚonesÚweightÚvariance_epsilon)r4   rC   r¾   r7   s      €r8   r+   zCwmRMSNorm.__init__ô   s1   ø€ ô 	‰ÑÔÜ—l‘l¤5§:¡:¨kÓ#:Ó;ˆŒØ #ˆÕr9   r}   c                 ó"  — |j                   }|j                  t        j                  «      }|j	                  d«      j                  dd¬«      }|t        j                  || j                  z   «      z  }| j                  |j                  |«      z  S )Nr?   rN   T)Úkeepdim)	rA   rH   rE   r”   ÚpowÚmeanÚrsqrtrÃ   rÂ   )r4   r}   Úinput_dtypeÚvariances       r8   rc   zCwmRMSNorm.forwardü   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:r9   c                 ó^   — t        | j                  j                  «      › d| j                  › �S )Nz, eps=)rl   rÂ   rU   rÃ   )r4   s    r8   Ú
extra_reprzCwmRMSNorm.extra_repr  s*   € Ü˜Ÿ™×)Ñ)Ó*Ð+¨6°$×2GÑ2GÐ1HÐIÐIr9   )g�íµ ÷Æ°>)
re   rf   rg   rI   r+   rE   rh   rc   rÌ   rn   ro   s   @r8   r½   r½   ò   s7   ø„ ñ$¨ð $¸$õ $ð; U§\¡\ð ;°e·l±ló ;öJr9   r½   c                   ó$   ‡ — 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                     | _        y )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€ Ü‰ÑÔØˆŒØ!×-Ñ-ˆÔØ!'×!9Ñ!9ˆÔÜŸ™ 4×#3Ñ#3°T×5KÑ5KÐRX×RaÑRaÔbˆŒÜ—y‘y ×!1Ñ!1°4×3IÑ3IÐPV×P_ÑP_Ô`ˆŒÜŸ™ 4×#9Ñ#9¸4×;KÑ;KÐRX×RaÑRaÔbˆŒÜ˜V×.Ñ.Ñ/ˆ�r9   c                 óˆ   — | j                  | j                  | j                  |«      «      | j                  |«      z  «      }|S rd   )rÔ   rÖ   rÒ   rÓ   )r4   r]   rÔ   s      r8   rc   zCwmMLP.forward  s6   € Ø—N‘N 4§;¡;¨t¯~©~¸aÓ/@Ó#AÀDÇLÁLÐQRÃOÑ#SÓTˆ	ØÐr9   )re   rf   rg   r+   rc   rn   ro   s   @r8   rÎ   rÎ     s   ø„ ô0ör9   rÎ   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CwmDecoderLayerr$   r�   c                 ó   •— t         ‰| �  «        |j                  | _        t        ||¬«      | _        t        |«      | _        t        |j                  |j                  ¬«      | _	        t        |j                  |j                  ¬«      | _
        y )N)r$   r�   ©r¾   )r*   r+   rC   rœ   Ú	self_attnrÎ   Úmlpr½   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormr­   s      €r8   r+   zCwmDecoderLayer.__init__  sk   ø€ Ü‰ÑÔØ!×-Ñ-ˆÔÜ%¨V¸yÔIˆŒä˜&“>ˆŒÜ)¨&×*<Ñ*<À&×BUÑBUÔVˆÔÜ(2°6×3EÑ3EÈ6×K^ÑK^Ô(_ˆÕ%r9   Nr}   r‰   r^   r¯   Ú	use_cacher®   rŒ   r;   c           
      óÀ   — |}| j                  |«      } | j                  d||||||dœ|¤Ž\  }}	||z   }|}| j                  |«      }| j                  |«      }||z   }|S )N)r}   r‰   r^   r¯   râ   r®   © )rà   rÝ   rá   rÞ   )
r4   r}   r‰   r^   r¯   râ   r®   rŒ   ÚresidualÚ_s
             r8   rc   zCwmDecoderLayer.forward!  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r9   )NNNFN)re   rf   rg   r    rk   r+   rE   rh   Ú
LongTensorr   Úboolrl   r   r   rc   rn   ro   s   @r8   rÚ   rÚ     s¾   ø„ ð`˜yð `°Sõ `ð /3Ø04Ø(,Ø!&ØHLñà—|‘|ðð Ÿ™ tÑ+ðð ×&Ñ&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5§<¡<°·±Ð#=Ñ>ÀÑEðð Ð+Ñ,ðð 
�‰÷r9   rÚ   c                   óJ   — e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedœZy)ÚCwmPreTrainedModelr$   ÚmodelTrÚ   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_backendrÚ   rœ   Ú_can_record_outputsrä   r9   r8   rê   rê   A  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r9   rê   c                   ó   — e Zd Zy)ÚCwmModelOutputWithPastN)re   rf   rg   rä   r9   r8   rø   rø   T  s   „ Ør9   rø   c                   óö   ‡ — 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$   c           	      ó   •— t         ‰| �  |«       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                  «      | _        t        j
                  j                  t        |j                  «      D �cg c]  }t        ||«      ‘Œ c}«      | _        t        |j                  |j                   ¬«      | _        t%        |¬«      | _        d| _        | j+                  «        y c c}w )NrÜ   ©r$   F)r*   r+   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	EmbeddingrC   Úembed_tokensrE   Ú
ModuleListÚrangeÚnum_hidden_layersrÚ   Úlayersr½   rß   Únormr"   Ú
rotary_embÚgradient_checkpointingÚ	post_initr­   s      €r8   r+   zCwmModel.__init__\  sË   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—h‘h×)Ñ)ÜAFÀv×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_idsr‰   r^   r¯   Úinputs_embedsrâ   rŒ   r;   c           	      óæ  — |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        |x}	t        «      s9| j                  ||||dœ}
|
j                  «       }t        d
i |
¤Žt        d
i |¤Ždœ}	|}| j                  ||«      }t!        | j"                  d | j                  j$                   «      D ]-  \  }} ||f|	| j                  j&                  |      |||dœ|¤Ž}Œ/ | j)                  |«      }t+        ||¬	«      S )Nz:You must specify exactly one of input_ids or inputs_embedsrü   r   r   )r5   )r$   r  r‰   r¯   r^   )Úfull_attentionr¢   )r‰   r^   r¯   r®   )Úlast_hidden_stater¯   rä   )Ú
ValueErrorr  r	   r$   Úget_seq_lengthrE   rF   rU   r5   rv   rV   ÚdictÚcopyr   r   r  Ú	enumerater  r  rŸ   r  rø   )r4   r
  r‰   r^   r¯   r  râ   rŒ   Úpast_seen_tokensÚcausal_mask_mappingÚmask_kwargsÚsliding_mask_kwargsr}   r®   ÚiÚdecoder_layers                   r8   rc   zCwmModel.forwardl  s¤  € ð ˜Ð -°tÐ";Ò<ÜÐYÓZÐZàÐ Ø*.×*;Ñ*;¸IÓ*FˆMá˜Ð0Ü*°$·+±+Ô>ˆOàÐØCRÐC^˜×=Ñ=Ô?ÐdeÐÜ Ÿ<™<¨×(;Ñ(;¸AÑ(>À}×G[ÑG[Ô\Ð_oÑoˆLØ'×1Ñ1°!Ó4ˆLä°Ð?Ð-ÄÔFàŸ+™+Ø!.Ø"0Ø#2Ø ,ñˆKð #.×"2Ñ"2Ó"4Ðô #5Ñ"C°{Ñ"CÜ%FÑ%]ÐI\Ñ%]ñ#Ðð
 &ˆØ"Ÿo™o¨m¸\ÓJÐä )¨$¯+©+Ð6U¸¿¹×8UÑ8UÐ*VÓ Wò 	ÑˆAˆ}Ù)Øðà2°4·;±;×3JÑ3JÈ1Ñ3MÑNØ)Ø /Ø$7ñð ñ‰Mð	ð Ÿ	™	 -Ó0ˆÜ%Ø+Ø+ô
ð 	
r9   )NNNNNN)re   rf   rg   r    Úconfig_classr+   r   r   r   rE   rç   rh   r   ÚFloatTensorrè   r   r   rø   rc   rn   ro   s   @r8   rú   rú   X  sÒ   ø„ à€Lð˜yõ ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ñ8
à×#Ñ# dÑ*ð8
ð Ÿ™ tÑ+ð8
ð ×&Ñ&¨Ñ-ð	8
ð
  ™ð8
ð ×(Ñ(¨4Ñ/ð8
ð ˜$‘;ð8
ð Ð+Ñ,ð8
ð 
 ò8
ó ó ó  ô8
r9   rú   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CwmForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputr}   Úlogitsc                 óè   •— t         ‰| �  |«       t        |«      | _        |j                  | _        t        j                  |j                  |j                  d¬«      | _        | j                  «        y )NFr    )
r*   r+   rú   rë   rÿ   r   r§   rC   r  r	  r×   s     €r8   r+   zCwmForCausalLM.__init__°  sU   ø€ Ü‰Ñ˜Ô Ü˜fÓ%ˆŒ
Ø ×+Ñ+ˆŒÜ—y‘y ×!3Ñ!3°V×5FÑ5FÈUÔSˆŒð 	�‰Õr9   Nr
  r‰   r^   r¯   r  Úlabelsrâ   Úlogits_to_keeprŒ   r;   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, 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
  r‰   r^   r¯   r  râ   N)r   r"  rÿ   )Úlossr   r¯   r}   rì   rä   )rë   r  rV   rk   Úslicer  Úloss_functionr$   rÿ   r   r¯   r}   rì   )r4   r
  r‰   r^   r¯   r  r"  râ   r#  rŒ   Úoutputsr}   Úslice_indicesr   r%  s                  r8   rc   zCwmForCausalLM.forward¹  sÙ   € ð> ,6¨4¯:©:ð ,
ØØ)Ø%Ø+Ø'Øñ,
ð ñ,
ˆð  ×1Ñ1ˆä8BÀ>ÔSVÔ8Wœ˜~˜o¨tÔ4Ð]kˆØ—‘˜mªA¨}ºaÐ,?Ñ@ÓAˆàˆØÐØ%�4×%Ñ%Ðp¨V¸FÈtÏ{É{×OeÑOeÑpÐioÑpˆDä%ØØØ#×3Ñ3Ø!×/Ñ/Ø×)Ñ)ô
ð 	
r9   )NNNNNNNr   )re   rf   rg   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr+   r   r   rE   rç   rh   r   r  rè   rk   r   r   r   rc   rn   ro   s   @r8   r  r  ª  s  ø„ à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hôð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø-.ñ6
à×#Ñ# dÑ*ð6
ð Ÿ™ tÑ+ð6
ð ×&Ñ&¨Ñ-ð	6
ð
  ™ð6
ð ×(Ñ(¨4Ñ/ð6
ð × Ñ  4Ñ'ð6
ð ˜$‘;ð6
ð ˜eŸl™lÑ*ð6
ð Ð+Ñ,ð6
ð 
 ò6
ó ó ô6
r9   r  )rê   rú   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   rš   rœ   r½   rÎ   rÚ   rê   rø   rú   r  Ú__all__rä   r9   r8   ú<module>r@     sí  ðõ, %Ý ã Ý å !ß .Ý )ß fÑ fß RÝ BÝ 9ß Oß Kß FÝ &ß IÑ Iß GÝ 5Ý (ô><˜Ÿ™ô ><òB(ñ Ð*Ó+òó ,ðð2	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%ñ2 Ð)Ó*ô:)�2—9‘9ó :)ó +ð:)ñz ˜YÓ'ôJ�—‘ó Jó (ðJô(ˆR�Y‰Yô ô 'Ð0ô 'ðT ô˜ó ó ðô$	Ð4ô 	ð ôN
Ð!ó N
ó ðN
ðb ôF
Ð'¨ó F
ó ðF
òR ?�r9   