§
    ‚Štjôe  ã                   óÌ  — d dl Z 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 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 ddlmZ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l0m1Z1  G d„ dej2        ¦  «        Z3 G d„ dej2        ¦  «        Z4d„ Z5 ed¦  «        d@d„¦   «         Z6dej7        de8dej7        fd „Z9	 dAd"ej2        d#ej7        d$ej7        d%ej7        d&ej7        dz  d'e:d(e:d)e&e(         fd*„Z;d+„ Z< ee6¦  «         G d,„ d-ej2        ¦  «        ¦   «         Z= ed.¦  «         G d/„ d0ej2        ¦  «        ¦   «         Z> G d1„ d2e¦  «        Z?e) G d3„ d4e$¦  «        ¦   «         Z@e) G d5„ d6e@¦  «        ¦   «         ZAe) G d7„ d8e@e¦  «        ¦   «         ZB G d9„ d:ee@¦  «        ZC G d;„ d<ee@¦  «        ZD G d=„ d>ee@¦  «        ZEg d?¢ZFdS )Bé    N)ÚCallable)ÚOptional)Únné   )Úinitialization)ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úuse_kernel_forward_from_hubÚuse_kernel_func_from_hubÚuse_kernelized_func)Úcreate_causal_mask)ÚGenericForQuestionAnsweringÚ 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DiffLlamaConfigc                   ó$   ‡ — e Zd Zˆ fd„Zd„ Zˆ xZS )ÚDiffLlamaMLPc                 ó˜  •— t          ¦   «                              ¦   «          || _        |j        | _        |j        | _        t          j        | j        | j        d¬¦  «        | _        t          j        | j        | j        d¬¦  «        | _        t          j        | j        | j        d¬¦  «        | _	        t          |j                 | _        d S ©NF©Úbias)ÚsuperÚ__init__ÚconfigÚhidden_sizeÚintermediate_sizer   ÚLinearÚ	gate_projÚup_projÚ	down_projr   Ú
hidden_actÚact_fn©Úselfr+   Ú	__class__s     €ún/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/diffllama/modeling_diffllama.pyr*   zDiffLlamaMLP.__init__5   s¦   ø€ Ý‰Œ×ÒÑÔÐØˆŒØ!Ô-ˆÔØ!'Ô!9ˆÔÝœ 4Ô#3°TÔ5KÐRWÐXÑXÔXˆŒÝ”y Ô!1°4Ô3IÐPUÐVÑVÔVˆŒÝœ 4Ô#9¸4Ô;KÐRWÐXÑXÔXˆŒÝ˜VÔ.Ô/ˆŒˆˆó    c                 ó¨   — |                       |                      |                      |¦  «        ¦  «        |                      |¦  «        z  ¦  «        }|S ©N)r1   r3   r/   r0   )r5   Úxr1   s      r7   ÚforwardzDiffLlamaMLP.forward?   sA   € Ø—N’N 4§;¢;¨t¯~ª~¸aÑ/@Ô/@Ñ#AÔ#AÀDÇLÂLÐQRÁOÄOÑ#SÑTÔTˆ	ØÐr8   )Ú__name__Ú
__module__Ú__qualname__r*   r<   Ú__classcell__©r6   s   @r7   r$   r$   4   sG   ø€ € € € € ð0ð 0ð 0ð 0ð 0ðð ð ð ð ð ð r8   r$   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DiffLlamaRotaryEmbeddingÚ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defaultrD   F)Ú
persistentÚoriginal_inv_freq)r)   r*   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenr+   Úrope_parametersrF   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)r5   r+   ÚdeviceÚrope_init_fnrD   r6   s        €r7   r*   z!DiffLlamaRotaryEmbedding.__init__G   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�$Ô(à×Ò˜Z¨¸eÐÑDÔDÐDØ×ÒÐ0°(·.².Ñ2BÔ2BÈuÐÑUÔUÐUÐUÐUr8   rR   ztorch.deviceÚseq_lenÚreturnztorch.Tensorc                 óü   — | j         d         }t          | dd¦  «        p| j        | j        z  }d}d|t	          j        d|dt          j        ¬¦  «                             |t          j        ¬¦  «        |z  z  z  }||fS )	a¨  
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        Ú
rope_thetaÚhead_dimNg      ð?r   é   ©Údtype)rR   r[   )	rM   Úgetattrr,   Únum_attention_headsÚtorchÚarangeÚint64ÚtoÚfloat)r+   rR   rT   ÚbaseÚdimÚattention_factorrD   s          r7   rN   z8DiffLlamaRotaryEmbedding.compute_default_rope_parametersW   sŒ   € ð& Ô% lÔ3ˆÝ�f˜j¨$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)r8   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enabledrY   ©rd   rZ   )rD   rb   ÚexpandÚshapera   rR   Ú
isinstanceÚtypeÚstrr   Ú	transposer^   ÚcatÚcosrO   Úsinr[   )
r5   r;   Úposition_idsÚinv_freq_expandedÚposition_ids_expandedrj   ÚfreqsÚembrt   ru   s
             r7   r<   z DiffLlamaRotaryEmbedding.forwardu   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*r:   )NNN)r=   r>   r?   r^   ÚTensorÚ__annotations__r"   r*   Ústaticmethodr   ÚintÚtuplerb   rN   Úno_gradr   r<   r@   rA   s   @r7   rC   rC   D   sù   ø€ € € € € € ØŒlÐÐÑðVð V˜ð Vð Vð Vð Vð Vð Vð  à)-Ø+/Ø"ð*ð *Ø $Ñ&ð*à˜Ô(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ô	%ð	*ð *ð *ñ „\ð*ð: €U„]�_„_Øð<ð <ñ Ôñ „_ð<ð <ð <ð <ð <r8   rC   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..Nrg   rY   rl   )rn   r^   rs   )r;   Úx1Úx2s      r7   Úrotate_halfr„   …   s]   € à	
ˆ3Ð"�!”'˜"”+ Ñ"Ð"Ð"Ô	#€BØ	
ˆ3�”˜”˜qÑ Ð"Ð"Ð"Ô	#€BÝŒ9�r�c˜2�Y BÐ'Ñ'Ô'Ð'r8   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	unsqueezer„   )ÚqÚkrt   ru   Úunsqueeze_dimÚq_embedÚk_embeds          r7   Úapply_rotary_pos_embr�   Œ   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r8   Úhidden_statesÚn_reprU   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)rŽ   r�   ÚbatchÚnum_key_value_headsÚslenrX   s         r7   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Tr8   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 )NrY   r   rg   ©rd   r[   )ÚpÚtrainingr!   )r•   Únum_key_value_groupsr^   Úmatmulrr   r   Ú
functionalÚsoftmaxÚfloat32ra   r[   r�   r¢   Ú
contiguous)r—   r˜   r™   rš   r›   rœ   r�   rž   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r7   Úeager_attention_forwardr­   ²   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$Ð$r8   c                 ó<   — ddt          j        d| z  ¦  «        z  z
  S )Ngš™™™™™é?g333333ã?g333333Ó¿)ÚmathÚexp)Ú	layer_idxs    r7   Úlambda_init_fnr²   Ë   s!   € Ø�•t”x  yÑ 0Ñ1Ô1Ñ1Ñ1Ð1r8   c                   ó¾   ‡ — e Zd ZdZddededz  fˆ fd„Z	 	 ddej        de	ej        ej        f         dej        dz  d	e
dz  d
e	ej        ej        f         f
d„Zˆ xZS )ÚDiffLlamaAttentionuÿ  Multi-headed differential attention (https://huggingface.co/papers/2410.05258).

    Computes ``(softmax(Q1 K1áµ€) - Î» Â· softmax(Q2 K2áµ€)) Â· V`` as two standard attention calls
    sharing Q and K over the two halves of V. The two-call structure is ~30% faster than the
    V-doubling shortcut at production shapes, since asymmetric V (``head_dim_v != head_dim_q``)
    forces SDPA off Flash/cuDNN onto the memory-efficient/math kernel; Flash Attention 2 also
    requires ``head_dim_v == head_dim_q``.
    Nr+   r±   c                 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        |j        ¬¦  «        | _        |j        dk    rt)          d¦  «        ‚|j        �|j        dz  dk    rt)          d	|j        › d
�¦  «        ‚t+          |¦  «        | _        t          j        t1          j        d|j        | j        f¬¦  «        ¦  «        | _        t          j        t1          j        d|j        | j        f¬¦  «        ¦  «        | _        t          j        t1          j        d|j        | j        f¬¦  «        ¦  «        | _        t          j        t1          j        d|j        | j        f¬¦  «        ¦  «        | _        t          j        d| j        z  |j         d¬¦  «        | _!        d S )NrX   g      à¿Tr'   r–   z€DiffLlama does not support `attention_dropout > 0`: the differential attention mechanism has no paper-defined dropout semantics.rY   r   z–DiffLlama requires `num_key_value_heads` to be even (and at least 2): the two-call differential attention splits the value tensor along KV heads, got ú.)ÚsizeF)ÚepsÚelementwise_affine)"r)   r*   r+   r±   r\   r,   r]   rX   r“   r£   rœ   Úattention_dropoutÚ	is_causalr   r.   Úattention_biasÚq_projÚk_projÚv_projÚo_projÚ
ValueErrorr²   Úlambda_initÚ	Parameterr^   ÚnormalÚlambda_std_devÚ	lambda_q1Ú	lambda_k1Ú	lambda_q2Ú	lambda_k2ÚRMSNormÚrms_norm_epsÚ	groupnorm©r5   r+   r±   r6   s      €r7   r*   zDiffLlamaAttention.__init__Ú   s�  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 ”iØÔ&¨¬Ñ6¸Ô8JÐQWÔQfð
ñ 
ô 
ˆŒð Ô# cÒ)Ð)ÝðDñô ð ð Ô%Ð-°Ô1KÈaÑ1OÐSTÒ1TÐ1TÝðtØV\ÔVpðtð tð tñô ð õ *¨)Ñ4Ô4ˆÔÝœ¥e¤l°1°fÔ6KÐSWÔS`ÐRbÐ&cÑ&cÔ&cÑdÔdˆŒÝœ¥e¤l°1°fÔ6KÐSWÔS`ÐRbÐ&cÑ&cÔ&cÑdÔdˆŒÝœ¥e¤l°1°fÔ6KÐSWÔS`ÐRbÐ&cÑ&cÔ&cÑdÔdˆŒÝœ¥e¤l°1°fÔ6KÐSWÔS`ÐRbÐ&cÑ&cÔ&cÑdÔdˆŒÝœ A¨¬Ñ$5¸6Ô;NÐchÐiÑiÔiˆŒˆˆr8   rŽ   Úposition_embeddingsr›   Úpast_key_valuesrU   c                 óˆ  — |j         d d…         }g |¢d‘| j        ‘R }|                      |¦  «                             |¦  «                             dd¦  «        }|                      |¦  «                             |¦  «                             dd¦  «        }	|                      |¦  «                             |¦  «                             dd¦  «        }
|\  }}t          ||	||¦  «        \  }}	|�|                     |	|
| j	        ¦  «        \  }	}
d„ t          j        |
dd¬¦  «        D ¦   «         \  }}t          j        | j        j        t           ¦  «        } || ||	||fd| j        dœ|¤Ž\  }} || ||	||fd| j        dœ|¤Ž\  }}t          j        ||gd¬¦  «        }t          j        |dd¬¦  «        \  }}t          j        t          j        | j        | j        z  dt          j        ¬¦  «        ¦  «                             |j        ¦  «        }t          j        t          j        | j        | j        z  dt          j        ¬¦  «        ¦  «                             |j        ¦  «        }||z
  | j        z   }|||z  z
  }d| j        z
  |                      |¦  «        z  } |j        g |¢d‘R Ž }|                      |¦  «        }||fS )	Nrg   r!   rY   c              3   óF   K  — | ]}|                      d dd d ¦  «        V — ŒdS )r!   rY   N)Úrepeat)Ú.0Úvs     r7   ú	<genexpr>z-DiffLlamaAttention.forward.<locals>.<genexpr>  s4   è è € Ð'jÐ'jÀ¨¯ª°°A°q¸!Ñ(<Ô(<Ð'jÐ'jÐ'jÐ'jÐ'jÐ'jr8   rl   r–   )r�   rœ   r    ) rn   rX   r½   Úviewrr   r¾   r¿   r�   Úupdater±   r^   Úchunkr   Úget_interfacer+   Ú_attn_implementationr­   rœ   rs   r°   ÚsumrÆ   rÇ   r§   ra   r[   rÈ   rÉ   rÂ   rÌ   r‘   rÀ   )r5   rŽ   rÎ   r›   rÏ   rž   Úinput_shapeÚhidden_shapeÚquery_statesr©   rª   rt   ru   Úvalue_states1Úvalue_states2Úattention_interfaceÚattn_output1r«   Úattn_output2Ú_r¬   Úlambda_1Úlambda_2Úlambda_fulls                           r7   r<   zDiffLlamaAttention.forward  s  € 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$ˆJ˜ð (kÐ'jÅeÄkÐR^Ð`aÐghÐFiÑFiÔFiÐ'jÑ'jÔ'jÑ$ˆ�}å(?Ô(MØŒKÔ,Õ.Eñ)
ô )
Ðð &9Ð%8ØØØØØð	&
ð Ø”Lð	&
ð 	&
ð ð	&
ð 	&
Ñ"ˆ�lð .Ð-ØØØØØð	
ð Ø”Lð	
ð 	
ð ð	
ð 	
‰ˆ�aõ ”i ¨|Ð <À"ÐEÑEÔEˆõ &+¤[°¸aÀQÐ%GÑ%GÔ%GÑ"ˆ�lÝ”9�UœY t¤~¸¼Ñ'FÈBÕV[ÔVcÐdÑdÔdÑeÔe×hÒhØÔñ
ô 
ˆõ ”9�UœY t¤~¸¼Ñ'FÈBÕV[ÔVcÐdÑdÔdÑeÔe×hÒhØÔñ
ô 
ˆð  Ñ)¨DÔ,<Ñ<ˆØ" [°<Ñ%?Ñ?ˆØ˜4Ô+Ñ+¨t¯~ª~¸kÑ/JÔ/JÑJˆØ)�kÔ)Ð;¨;Ð;¸Ð;Ð;Ð;ˆØ—k’k +Ñ.Ô.ˆØ˜LÐ(Ð(r8   r:   )NN)r=   r>   r?   Ú__doc__r"   r~   r*   r^   r{   r   r	   r<   r@   rA   s   @r7   r´   r´   Ï   sð   ø€ € € € € ðð ð+jð +j˜ð +j¸3À¹:ð +jð +jð +jð +jð +jð +jðb /3Ø(,ðC)ð C)à”|ðC)ð # 5¤<°´Ð#=Ô>ðC)ð œ tÑ+ð	C)ð
  ™ðC)ð 
ˆuŒ|˜Uœ\Ð)Ô	*ðC)ð C)ð C)ð C)ð C)ð C)ð C)ð C)r8   r´   rÊ   c                   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DiffLlamaRMSNormç�íµ ÷Æ°>r¸   rU   Nc                 ó¬   •— t          ¦   «                              ¦   «          t          j        t	          j        |¦  «        ¦  «        | _        || _        dS )z?
        DiffLlamaRMSNorm is equivalent to T5LayerNorm
        N)r)   r*   r   rÃ   r^   ÚonesÚweightÚvariance_epsilon)r5   r,   r¸   r6   s      €r7   r*   zDiffLlamaRMSNorm.__init__O  sD   ø€ õ 	‰Œ×ÒÑÔÐÝ”l¥5¤:¨kÑ#:Ô#:Ñ;Ô;ˆŒØ #ˆÔÐÐr8   rŽ   c                 ó  — |j         }|                     t          j        ¦  «        }|                     d¦  «                             dd¬¦  «        }|t          j        || j        z   ¦  «        z  }| j        |                     |¦  «        z  S )NrY   rg   T)Úkeepdim)	r[   ra   r^   r§   ÚpowÚmeanÚrsqrtrï   rî   )r5   rŽ   Úinput_dtypeÚvariances       r7   r<   zDiffLlamaRMSNorm.forwardW  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:r8   c                 óH   — t          | j        j        ¦  «        › d| j        › �S )Nz, eps=)r   rî   rn   rï   )r5   s    r7   Ú
extra_reprzDiffLlamaRMSNorm.extra_repr^  s&   € Ý˜œÔ)Ñ*Ô*ÐIÐI°$Ô2GÐIÐIÐIr8   )rë   )
r=   r>   r?   rb   r*   r^   r{   r<   rø   r@   rA   s   @r7   rê   rê   M  sŒ   ø€ € € € € ð$ð $¨ð $¸$ð $ð $ð $ð $ð $ð $ð; U¤\ð ;°e´lð ;ð ;ð ;ð ;ðJð Jð Jð Jð Jð Jð Jr8   rê   c                   óÒ   ‡ — e Zd Zdedefˆ fd„Z	 	 	 	 	 ddej        dej        dz  dej        dz  d	e	dz  d
e
dz  deej        ej        f         dz  dee         dej        fd„Zˆ xZS )ÚDiffLlamaDecoderLayerr+   r±   c                 ó4  •— t          ¦   «                              ¦   «          |j        | _        t          ||¬¦  «        | _        t          |¦  «        | _        t          |j        |j        ¬¦  «        | _	        t          |j        |j        ¬¦  «        | _
        d S )N)r+   r±   ©r¸   )r)   r*   r,   r´   Ú	self_attnr$   Úmlprê   rË   Úinput_layernormÚpost_attention_layernormrÍ   s      €r7   r*   zDiffLlamaDecoderLayer.__init__c  s„   ø€ Ý‰Œ×ÒÑÔÐØ!Ô-ˆÔå+°6ÀYÐOÑOÔOˆŒå Ñ'Ô'ˆŒÝ/°Ô0BÈÔH[Ð\Ñ\Ô\ˆÔÝ(8¸Ô9KÐQWÔQdÐ(eÑ(eÔ(eˆÔ%Ð%Ð%r8   NFrŽ   r›   rv   rÏ   Ú	use_cacherÎ   rž   rU   c           
      óÎ   — |}|                       |¦  «        } | j        d||||||dœ|¤Ž\  }}	||z   }|}|                      |¦  «        }|                      |¦  «        }||z   }|S )N)rŽ   r›   rv   rÏ   r  rÎ   © )rÿ   rý   r   rþ   )
r5   rŽ   r›   rv   rÏ   r  rÎ   rž   Úresidualrä   s
             r7   r<   zDiffLlamaDecoderLayer.forwardm  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r8   )NNNFN)r=   r>   r?   r"   r~   r*   r^   r{   Ú
LongTensorr	   Úboolr   r   r   r<   r@   rA   s   @r7   rú   rú   b  sÿ   ø€ € € € € ðf˜ð f¸3ð fð fð fð fð fð f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 r8   rú   c                   ó†   ‡ — 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 ej        ¦   «         ˆ fd„¦   «         Zˆ xZS )ÚDiffLlamaPreTrainedModelr+   ÚmodelTrú   rÏ   )rŽ   Ú
attentionsc                 ó   •— t          ¦   «                              |¦  «         t          |t          ¦  «        r–t	          j        |j        d| j        j        ¦  «         t	          j        |j	        d| j        j        ¦  «         t	          j        |j
        d| j        j        ¦  «         t	          j        |j        d| j        j        ¦  «         d S d S )Nr   )r)   Ú_init_weightsro   r´   ÚinitÚnormal_rÆ   r+   rÅ   rÇ   rÈ   rÉ   )r5   r—   r6   s     €r7   r  z&DiffLlamaPreTrainedModel._init_weightsŸ  s±   ø€ å‰Œ×Ò˜fÑ%Ô%Ð%Ý�fÕ0Ñ1Ô1ð 	JÝŒL˜Ô)¨1¨d¬kÔ.HÑIÔIÐIÝŒL˜Ô)¨1¨d¬kÔ.HÑIÔIÐIÝŒL˜Ô)¨1¨d¬kÔ.HÑIÔIÐIÝŒL˜Ô)¨1¨d¬kÔ.HÑIÔIÐIÐIÐIð		Jð 	Jr8   )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_backendrú   r´   Ú_can_record_outputsr^   r€   r  r@   rA   s   @r7   r  r  �  s±   ø€ € € € € € àÐÐÑØÐØ&*Ð#Ø0Ð1ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà.Ø(ðð Ðð
 €U„]�_„_ðJð Jð Jð Jñ „_ðJð Jð Jð Jð Jr8   r  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DiffLlamaModelr+   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 r  )rú   )rÓ   r±   r+   s     €r7   ú
<listcomp>z+DiffLlamaModel.__init__.<locals>.<listcomp>²  s$   ø€ ÐgÐgÐg¸)Õ" 6¨9Ñ5Ô5ÐgÐgÐgr8   rü   ©r+   F)r)   r*   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	Embeddingr,   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersÚlayersrê   rË   ÚnormrC   Ú
rotary_embÚgradient_checkpointingÚ	post_initr4   s    `€r7   r*   zDiffLlamaModel.__init__«  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gÐgÐgÐgÅuÈVÔMeÑGfÔGfÐgÑgÔgñ
ô 
ˆŒõ % VÔ%7¸VÔ=PÐQÑQÔQˆŒ	Ý2¸&ÐAÑAÔAˆŒØ&+ˆÔ#ð 	�ŠÑÔÐÐÐr8   NÚ	input_idsr›   rv   rÏ   Úinputs_embedsr  rž   rU   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!   )rR   )r+   r-  r›   rÏ   rv   )rv   )r›   rÎ   rv   rÏ   r  )Úlast_hidden_staterÏ   )rÁ   r#  r
   r+   Úget_seq_lengthr^   r_   rn   rR   r‡   r   r)  r'  r&  r(  r   )r5   r,  r›   rv   rÏ   r-  r  rž   Úpast_seen_tokensÚcausal_maskrŽ   rÎ   Údecoder_layers                r7   r<   zDiffLlamaModel.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$7Ø)Ø /Ø#ðð ð ðð ˆMˆMð Ÿ	š	 -Ñ0Ô0ˆÝ&Ø+Ø+ð
ñ 
ô 
ð 	
r8   )NNNNNN)r=   r>   r?   r"   r*   r   r    r   r^   r  r{   r	   ÚFloatTensorr  r   r   r   r<   r@   rA   s   @r7   r  r  ©  s  ø€ € € € € ð˜ð ð ð ð ð ð ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ð2
ð 2
àÔ# dÑ*ð2
ð œ tÑ+ð2
ð Ô&¨Ñ-ð	2
ð
  ™ð2
ð Ô(¨4Ñ/ð2
ð ˜$‘;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
r8   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DiffLlamaForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputrŽ   Úlogitsc                 óþ   •— t          ¦   «                              |¦  «         t          |¦  «        | _        |j        | _        t          j        |j        |j        d¬¦  «        | _        |  	                    ¦   «          d S r&   )
r)   r*   r  r	  r!  r   r.   r,   r7  r+  r4   s     €r7   r*   zDiffLlamaForCausalLM.__init__ù  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r8   Nr   r,  r›   rv   rÏ   r-  Úlabelsr  Úlogits_to_keeprž   rU   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 )aŒ  
        Example:

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

        >>> model = DiffLlamaForCausalLM.from_pretrained("google/diffllama-7b")
        >>> tokenizer = AutoTokenizer.from_pretrained("google/diffllama-7b")

        >>> prompt = "What is your favorite condiment?"
        >>> 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]
        "What is your favorite condiment?"
        ```)r,  r›   rv   rÏ   r-  r  N)r9  r;  r!  )Úlossr9  rÏ   rŽ   r
  r  )r	  r/  ro   r~   Úslicer7  Úloss_functionr+   r!  r   rÏ   rŽ   r
  )r5   r,  r›   rv   rÏ   r-  r;  r  r<  rž   ÚoutputsrŽ   Úslice_indicesr9  r>  s                  r7   r<   zDiffLlamaForCausalLM.forward  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 	
r8   )NNNNNNNr   )r=   r>   r?   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr*   r   r   r^   r  r{   r	   r4  r  r~   r   r   r   r<   r@   rA   s   @r7   r6  r6  ó  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 ˜$‘;ð6
ð ˜eœlÑ*ð6
ð Ð+Ô,ð6
ð 
 ð6
ð 6
ð 6
ñ „^ñ Ôð6
ð 6
ð 6
ð 6
ð 6
r8   r6  c                   ó   — e Zd ZdS )Ú"DiffLlamaForSequenceClassificationN©r=   r>   r?   r  r8   r7   rG  rG  =  ó   € € € € € Ø€Dr8   rG  c                   ó   — e Zd ZdZdS )ÚDiffLlamaForQuestionAnsweringÚtransformerN)r=   r>   r?   r  r  r8   r7   rK  rK  A  s   € € € € € Ø%ÐÐÐr8   rK  c                   ó   — e Zd ZdS )ÚDiffLlamaForTokenClassificationNrH  r  r8   r7   rN  rN  E  rI  r8   rN  )r  r  r6  rG  rK  rN  )r!   )r–   )Gr¯   Úcollections.abcr   Útypingr   r^   r   Ú r   r  Úactivationsr   Úcache_utilsr	   r
   Ú
generationr   Úintegrationsr   r   r   Úmasking_utilsr   Úmodeling_layersr   r   r   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   r   Úutils.output_capturingr    Úconfiguration_diffllamar"   ÚModuler$   rC   r„   r�   r{   r~   r•   rb   r­   r²   r´   rê   rú   r  r  r6  rG  rK  rN  Ú__all__r  r8   r7   ú<module>rb     s<  ðð. €€€Ø $Ð $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 !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 /Ð /Ð /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 4Ð 4Ð 4Ð 4Ð 4Ð 4ðð ð ð ð �2”9ñ ô ð ð ><ð ><ð ><ð ><ð ><˜rœyñ ><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2ð 2ð 2ð ÐÐ)Ñ*Ô*ðz)ð z)ð z)ð z)ð z)˜œñ z)ô z)ñ +Ô*ðz)ðz Ð˜YÑ'Ô'ðJð Jð Jð Jð J�r”yñ Jô Jñ (Ô'ðJð((ð (ð (ð (ð (Ð6ñ (ô (ð (ðV ðJð Jð Jð Jð J˜ñ Jô Jñ „ðJð6 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3°_ñ F
ô F
ñ „ðF
ðR	ð 	ð 	ð 	ð 	Ð)IÐKcñ 	ô 	ð 	ð&ð &ð &ð &ð &Ð$?ÐAYñ &ô &ð &ð	ð 	ð 	ð 	ð 	Ð&CÐE]ñ 	ô 	ð 	ðð ð €€€r8   