Ë
    ÛÓ^jñZ  ã                   ó  — 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 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jR                  «      Z* G d„ dejR                  «      Z+ G d„ dejR                  «      Z,dejZ                  de.dejZ                  fd„Z/	 d5dejR                  d ejZ                  d!ejZ                  d"ejZ                  d#ejZ                  dz  d$e0d%e0d&ee   fd'„Z1d(„ Z2d6d)„Z3 G d*„ d+ejR                  «      Z4 G d,„ d-e«      Z5e  G d.„ d/e«      «       Z6e  G d0„ d1e6«      «       Z7e  G d2„ d3e6e«      «       Z8g d4¢Z9y)7é    )ÚCallable)ÚOptionalN)Únné   )ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úcreate_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CohereConfigc                   ó&   ‡ — e Zd Zdˆ fd„	Zd„ Zˆ xZS )ÚCohereLayerNormc                 óŠ   •— t         ‰| �  «        t        j                  t	        j
                  |«      «      | _        || _        y)zcThe hidden size can be a tuple or an int. The tuple is used for QKNorm to normalize across head_dimN)ÚsuperÚ__init__r   Ú	ParameterÚtorchÚonesÚweightÚvariance_epsilon)ÚselfÚhidden_sizeÚepsÚbiasÚ	__class__s       €úu/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/cohere/modeling_cohere.pyr!   zCohereLayerNorm.__init__4   s/   ø€ ä‰ÑÔÜ—l‘l¤5§:¡:¨kÓ#:Ó;ˆŒØ #ˆÕó    c                 ó’  — |j                   }|j                  t        j                  «      }|j	                  dd¬«      }||z
  j                  d«      j	                  dd¬«      }||z
  t        j                  || j                  z   «      z  }| j                  j                  t        j                  «      |z  }|j                  |«      S )NéÿÿÿÿT)Úkeepdimé   )	ÚdtypeÚtor#   Úfloat32ÚmeanÚpowÚrsqrtr&   r%   )r'   Úhidden_statesÚinput_dtyper5   Úvariances        r,   ÚforwardzCohereLayerNorm.forward:   sª   € Ø#×)Ñ)ˆØ%×(Ñ(¬¯©Ó7ˆØ×!Ñ! "¨dÐ!Ó3ˆØ! DÑ(×-Ñ-¨aÓ0×5Ñ5°bÀ$Ð5ÓGˆØ&¨Ñ-´·±¸XÈ×H]ÑH]Ñ=]Ó1^Ñ^ˆØŸ™Ÿ™¤u§}¡}Ó5¸ÑEˆØ×Ñ Ó,Ð,r-   )Ngñhãˆµøä>F©Ú__name__Ú
__module__Ú__qualname__r!   r;   Ú__classcell__©r+   s   @r,   r   r   3   s   ø„ õ$ö-r-   r   c                   óÄ   ‡ — e Zd ZU ej                  ed<   ddefˆ fd„Ze	 	 	 ddedz  de	d   de
dz  ded	ef   fd
„«       Z ej                  «       ed„ «       «       Zˆ xZS )ÚCohereRotaryEmbeddingÚinv_freqNÚconfigc                 ó®  •— t         ‰| �  «        |j                  | _        |j                  | _        || _        | j
                  j                  d   | _        | j                  }| j                  dk7  rt        | j                     } || j
                  |«      \  }| _
        | j                  d|d¬«       | j                  d|j                  «       d¬«       y )NÚ	rope_typeÚdefaultrD   F)Ú
persistentÚoriginal_inv_freq)r    r!   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenrE   Úrope_parametersrG   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)r'   rE   ÚdeviceÚrope_init_fnrD   r+   s        €r,   r!   zCohereRotaryEmbedding.__init__G   s¶   ø€ Ü‰ÑÔØ"(×"@Ñ"@ˆÔØ$*×$BÑ$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�$Ô(à×Ñ˜Z¨¸eÐÔDØ×ÑÐ0°(·.±.Ó2BÈuÐÕUr-   rS   ztorch.deviceÚseq_lenÚreturnztorch.Tensorc                 ó  — | j                   d   }t        | dd«      xs | j                  | j                  z  }d}d|t	        j
                  d|dt        j                  ¬«      j                  |t        j                  ¬«      |z  z  z  }||fS )	a¨  
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        Ú
rope_thetaÚhead_dimNg      ð?r   r1   ©r2   )rS   r2   )	rN   Úgetattrr(   Únum_attention_headsr#   ÚarangeÚint64r3   Úfloat)rE   rS   rU   ÚbaseÚdimÚattention_factorrD   s          r,   rO   z5CohereRotaryEmbedding.compute_default_rope_parametersW   s�   € ð& ×%Ñ% lÑ3ˆÜ�f˜j¨$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)r-   c                 ó  — | j                   d d d …d f   j                  «       j                  |j                  d   dd«      }|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                  |dd¬	«      }|j                  «       | j                  z  }|j                  «       | j                  z  }	d d d «       j                  |j                   ¬
«      	j                  |j                   ¬
«      fS # 1 sw Y   ŒAxY w)Nr   r/   r   ÚmpsÚcpuF)Údevice_typeÚenabledr1   ©ra   rZ   )rD   r_   ÚexpandÚshapeÚ
isinstancerS   ÚtypeÚstrr   Ú	transposer#   Úrepeat_interleaveÚcosrP   Úsinr3   r2   )
r'   ÚxÚposition_idsÚinv_freq_expandedÚposition_ids_expandedrf   ÚfreqsÚembrp   rq   s
             r,   r;   zCohereRotaryEmbedding.forwardu   s@  € ð !ŸM™M¨$²°4¨-Ñ8×>Ñ>Ó@×GÑGÈ×HZÑHZÐ[\ÑH]Ð_aÐcdÓeÐØ ,ªQ°²a¨ZÑ 8× >Ñ >Ó @Ðä'1°!·(±(·-±-ÄÔ'EÈ!Ï(É(Ï-É-Ð[`ÒJ`�a—h‘h—m’mÐfkˆÜ¨¸UÔCñ 	5Ø&×,Ñ,Ó.Ð1F×1LÑ1LÓ1NÑN×YÑYÐZ[Ð]^Ó_ˆEÜ×)Ñ)¨%°¸Ô;ˆCØ—'‘'“)˜d×4Ñ4Ñ4ˆCØ—'‘'“)˜d×4Ñ4Ñ4ˆC÷		5ð �v‰v˜AŸG™GˆvÓ$ c§f¡f°1·7±7 fÓ&;Ð;Ð;÷	5ð 	5ús   Â=BFÆF
©N)NNN)r=   r>   r?   r#   ÚTensorÚ__annotations__r   r!   Ústaticmethodr   ÚintÚtupler_   rO   Úno_gradr   r;   r@   rA   s   @r,   rC   rC   D   s˜   ø… Ø�l‰lÓñV˜|õ Vð  à&*Ø+/Ø"ñ*Ø˜tÑ#ð*à˜Ñ(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ñ	%ò	*ó ð*ð: €U‡]�]ƒ_Øñ<ó ó ô<r-   rC   c                   ó$   ‡ — e Zd Zˆ fd„Zd„ Zˆ xZS )Ú	CohereMLPc                 óÊ  •— t         ‰| �  «        || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  d¬«      | _        t        j                  | j                  | j                  d¬«      | _        t        j                  | j                  | j                  d¬«      | _	        t        |j                     | _        y ©NF©r*   )r    r!   rE   r(   Úintermediate_sizer   ÚLinearÚ	gate_projÚup_projÚ	down_projr   Ú
hidden_actÚact_fn©r'   rE   r+   s     €r,   r!   zCohereMLP.__init__†   s¦   ø€ Ü‰ÑÔØˆŒØ!×-Ñ-ˆÔØ!'×!9Ñ!9ˆÔÜŸ™ 4×#3Ñ#3°T×5KÑ5KÐRWÔXˆŒÜ—y‘y ×!1Ñ!1°4×3IÑ3IÐPUÔVˆŒÜŸ™ 4×#9Ñ#9¸4×;KÑ;KÐRWÔXˆŒÜ˜V×.Ñ.Ñ/ˆ�r-   c                 óˆ   — | j                  | j                  | j                  |«      «      | j                  |«      z  «      }|S rx   )rˆ   rŠ   r†   r‡   )r'   rr   rˆ   s      r,   r;   zCohereMLP.forward�   s6   € Ø—N‘N 4§;¡;¨t¯~©~¸aÓ/@Ó#AÀDÇLÁLÐQRÃOÑ#SÓTˆ	ØÐr-   r<   rA   s   @r,   r€   r€   …   s   ø„ ô0ör-   r€   r8   Ún_reprV   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)rj   ri   Úreshape)r8   r�   ÚbatchÚnum_key_value_headsÚslenrY   s         r,   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Tr-   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 )Nr1   r   r/   )ra   r2   )ÚpÚtrainingr   )r“   Únum_key_value_groupsr#   Úmatmulrn   r   Ú
functionalÚsoftmaxr4   r3   r2   rš   rž   Ú
contiguous)r”   r•   r–   r—   r˜   r™   rš   r›   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r,   Úeager_attention_forwardr¨   ¡   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$Ð$r-   c                 ó€   — | dd d d…f   }| ddd d…f   }t        j                  | |gd¬«      j                  d«      }|S )N.r1   r   r/   rh   éþÿÿÿ)r#   ÚstackÚflatten)rr   Úx1Úx2Úrot_xs       r,   Úrotate_halfr°   º   sL   € à	
ˆ3‘�!�ˆ8‰€BØ	
ˆ3���1�ˆ9‰€BÜ�K‰K˜"˜˜b˜	 rÔ*×2Ñ2°2Ó6€EØ€Lr-   c                 ó6  — | j                   }| j                  «       } |j                  «       }|j                  |«      }|j                  |«      }| |z  t        | «      |z  z   }||z  t        |«      |z  z   }|j	                  |¬«      |j	                  |¬«      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.
    rZ   )r2   r_   Ú	unsqueezer°   r3   )ÚqÚkrp   rq   Úunsqueeze_dimr2   Úq_embedÚk_embeds           r,   Úapply_rotary_pos_embr¸   Â   sŽ   € ð$ �G‰G€EØ	�‰‹	€AØ	�‰‹	€AØ
�-‰-˜Ó
&€CØ
�-‰-˜Ó
&€CØ�3‰wœ; q›>¨CÑ/Ñ0€GØ�3‰wœ; q›>¨CÑ/Ñ0€GØ�:‰:˜Eˆ:Ó" G§J¡J°U JÓ$;Ð;Ð;r-   c                   óü   ‡ — 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   de	ej                  ej                  dz  f   fd„Zˆ xZS )ÚCohereAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNrE   Ú	layer_idxc                 óh  •— 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(                  | _        | j(                  ret+        |j                  | j                  f|j,                  ¬«      | _        t+        |j                  | j                  f|j,                  ¬«      | _        y y )NrY   g      à¿Trƒ   ©r(   r)   )r    r!   rE   r»   r[   r(   r\   rY   r‘   rŸ   r™   Úattention_dropoutÚ	is_causalr   r…   Úattention_biasÚq_projÚk_projÚv_projÚo_projÚuse_qk_normr   Úlayer_norm_epsÚq_normÚk_norm©r'   rE   r»   r+   s      €r,   r!   zCohereAttention.__init__á   s¹  ø€ Ü‰ÑÔØˆŒØ"ˆŒÜ ¨
°F×4FÑ4FÈ&×JdÑJdÑ4dÓeˆŒØ$*×$>Ñ$>À&×B\ÑB\Ñ$\ˆÔ!Ø—}‘} dÑ*ˆŒØ!'×!9Ñ!9ˆÔØˆŒä—i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×&Ñ&¨¯©Ñ6¸×8JÑ8JÐQW×QfÑQfô
ˆŒð "×-Ñ-ˆÔØ×Òä)Ø#×7Ñ7¸¿¹ÐGÈV×MbÑMbôˆDŒKô *Ø#×7Ñ7¸¿¹ÐGÈV×MbÑMbôˆD�Kð r-   r8   Úposition_embeddingsr˜   Úpast_key_valuesr›   rV   c                 ór  — |j                   d d }g |¢d‘| j                  ‘­}| j                  |«      j                  |«      }| j	                  |«      j                  |«      }	| j                  |«      j                  |«      }
| j                  r"| j                  |«      }| j                  |	«      }	|j                  dd«      }|	j                  dd«      }	|
j                  dd«      }
|\  }}t        ||	||«      \  }}	|� |j                  |	|
| j                  «      \  }	}
t        j                  | j                  j                   t"        «      } || ||	|
|f| j$                  sdn| j&                  | j(                  dœ|¤Ž\  }} |j*                  g |¢d‘­Ž j-                  «       }| j/                  |«      }||fS )Nr/   r   r1   ç        )rš   r™   )rj   rY   rÁ   ÚviewrÂ   rÃ   rÅ   rÇ   rÈ   rn   r¸   Úupdater»   r   Úget_interfacerE   Ú_attn_implementationr¨   rž   r¾   r™   r�   r£   rÄ   )r'   r8   rÊ   r˜   rË   r›   Úinput_shapeÚhidden_shapeÚquery_statesr¤   r¥   rp   rq   Úattention_interfacer§   r¦   s                   r,   r;   zCohereAttention.forward  sÂ  € ð $×)Ñ)¨#¨2Ð.ˆØ8˜Ð8 bÐ8¨$¯-©-Ñ8ˆà—{‘{ =Ó1×6Ñ6°|ÓDˆØ—[‘[ Ó/×4Ñ4°\ÓBˆ
Ø—{‘{ =Ó1×6Ñ6°|ÓDˆà×ÒØŸ;™; |Ó4ˆLØŸ™ ZÓ0ˆJà#×-Ñ-¨a°Ó3ˆØ×)Ñ)¨!¨QÓ/ˆ
Ø#×-Ñ-¨a°Ó3ˆà&‰ˆˆSÜ#7¸ÀjÐRUÐWZÓ#[Ñ ˆ�jàÐ&Ø'6×'=Ñ'=¸jÈ,ÐX\×XfÑXfÓ'gÑ$ˆJ˜ä(?×(MÑ(MØ�K‰K×,Ñ,Ô.Eó)
Ðñ %8ØØØØØð	%
ð  $Ÿ}š}‘C°$×2HÑ2HØ—L‘Lñ	%
ð ñ	%
Ñ!ˆ�\ð *�k×)Ñ)Ð;¨;Ð;¸Ò;×FÑFÓHˆØ—k‘k +Ó.ˆØ˜LÐ(Ð(r-   rx   )r=   r>   r?   Ú__doc__r   r|   r!   r#   ry   r}   r   r   r   r;   r@   rA   s   @r,   rº   rº   Þ   s¢   ø„ ÙGñ˜|ð ¸¸d¹
õ ðJ )-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÷.)r-   rº   c                   óD  ‡ — 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ej                  eej                  ej                  f   dz  f   fd„Zˆ xZS )ÚCohereDecoderLayerrE   r»   c                 óÔ   •— t         ‰| �  «        |j                  | _        t        ||¬«      | _        t        |«      | _        t        |j                  |j                  ¬«      | _	        y )N)rE   r»   r½   )
r    r!   r(   rº   Ú	self_attnr€   Úmlpr   rÆ   Úinput_layernormrÉ   s      €r,   r!   zCohereDecoderLayer.__init__3  sR   ø€ Ü‰ÑÔØ!×-Ñ-ˆÔÜ(°À)ÔLˆŒÜ˜VÓ$ˆŒÜ.¸F×<NÑ<NÐU[×UjÑUjÔkˆÕr-   Nr8   r˜   rs   rË   Ú	use_cacherÊ   r›   rV   c           
      ó–   — |}| j                  |«      } | j                  d||||||dœ|¤Ž\  }	}
| j                  |«      }||	z   |z   }|S )a¼  
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*):
                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
                query_sequence_length, key_sequence_length)` if default attention is used.
            past_key_values (`Cache`, *optional*): cached past key and value projection states
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
                with `head_dim` being the embedding dimension of each attention head.
        )r8   r˜   rs   rË   rÝ   rÊ   © )rÜ   rÚ   rÛ   )r'   r8   r˜   rs   rË   rÝ   rÊ   r›   ÚresidualÚhidden_states_attentionÚ_Úhidden_states_mlps               r,   r;   zCohereDecoderLayer.forward:  sx   € ð6 !ˆØ×,Ñ,¨]Ó;ˆà%3 T§^¡^ð &
Ø'Ø)Ø%Ø+ØØ 3ñ&
ð ñ&
Ñ"Ð ð !ŸH™H ]Ó3ÐØ Ð#:Ñ:Ð=NÑNˆØÐr-   )NNNFN)r=   r>   r?   r   r|   r!   r#   ry   Ú
LongTensorr   Úboolr}   r   r   ÚFloatTensorr;   r@   rA   s   @r,   rØ   rØ   2  sæ   ø„ ðl˜|ð l¸õ l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 
ˆu× Ñ  %¨×(9Ñ(9¸5×;LÑ;LÐ(LÑ"MÐPTÑ"TÐTÑ	U÷*r-   rØ   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CoherePreTrainedModelrE   ÚmodelTrØ   rË   )r8   Ú
attentionsN)r=   r>   r?   r   rz   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_backendrØ   rº   Ú_can_record_outputsrß   r-   r,   rè   rè   g  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r-   rè   c                   óò   ‡ — e Zd Zdefˆ fd„Zeee	 	 	 	 	 	 ddej                  dz  dej                  dz  dej                  dz  dedz  dej                  dz  d	edz  d
ee   defd„«       «       «       Zˆ xZS )ÚCohereModelrE   c           	      ó  •— t         ‰| �  |«       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                  «      | _        t        j                  t        |j                  «      D �cg c]  }t        ||«      ‘Œ c}«      | _        t        |j                  |j                  ¬«      | _        t#        |¬«      | _        d| _        | j)                  «        y c c}w )Nr½   ©rE   F)r    r!   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	Embeddingr(   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersrØ   Úlayersr   rÆ   ÚnormrC   Ú
rotary_embÚgradient_checkpointingÚ	post_initrÉ   s      €r,   r!   zCohereModel.__init__|  sÆ   ø€ Ü‰Ñ˜Ô Ø!×.Ñ.ˆÔØ ×+Ñ+ˆŒäŸL™L¨×):Ñ):¸F×<NÑ<NÐPT×P`ÑP`ÓaˆÔÜ—m‘mÜDIÈ&×JbÑJbÓDcÖd°yÔ ¨	Õ2Òdó
ˆŒô $°×1CÑ1CÈ&×J_ÑJ_Ô`ˆŒ	Ü/°vÔ>ˆŒØ&+ˆÔ#ð 	�‰Õùò es   ÂDNÚ	input_idsr˜   rs   rË   Úinputs_embedsrÝ   r›   rV   c           
      ó<  — |d u |d uz  rt        d«      ‚|€| j                  |«      }|r|€t        | j                  ¬«      }|€V|�|j	                  «       nd}t        j                  |j                  d   |j                  ¬«      |z   }|j                  d«      }t        | j                  ||||¬«      }	|}
| j                  |
|¬«      }| j                  d | j                  j                   D ]  } ||
f|	||||dœ|¤Ž}
Œ | j                  |
«      }
t        |
|¬	«      S )
Nz:You must specify exactly one of input_ids or inputs_embedsrø   r   r   )rS   )rE   r  r˜   rË   rs   )rs   )r˜   rÊ   rs   rË   rÝ   )Úlast_hidden_staterË   )Ú
ValueErrorrý   r	   rE   Úget_seq_lengthr#   r]   rj   rS   r²   r   r  r  r   r  r   )r'   r  r˜   rs   rË   r  rÝ   r›   Úpast_seen_tokensÚcausal_maskr8   rÊ   Údecoder_layers                r,   r;   zCohereModel.forwardŒ  sL  € ð ˜Ð -°tÐ";Ò<ÜÐYÓZÐZàÐ Ø*.×*;Ñ*;¸IÓ*FˆMá˜Ð0Ü*°$·+±+Ô>ˆOàÐØCRÐC^˜×=Ñ=Ô?ÐdeÐÜ Ÿ<™<¨×(;Ñ(;¸AÑ(>À}×G[ÑG[Ô\Ð_oÑoˆLØ'×1Ñ1°!Ó4ˆLä(Ø—;‘;Ø'Ø)Ø+Ø%ô
ˆð &ˆØ"Ÿo™o¨mÈ,˜oÓWÐà!Ÿ[™[Ð)H¨4¯;©;×+HÑ+HÐIò 		ˆMÙ)Øðà*Ø$7Ø)Ø /Ø#ñð ñ‰Mð		ð Ÿ	™	 -Ó0ˆÜ&Ø+Ø+ô
ð 	
r-   )NNNNNN)r=   r>   r?   r   r!   r   r   r   r#   rä   ry   r   ræ   rå   r   r   r   r;   r@   rA   s   @r,   rö   rö   z  sÍ   ø„ ð˜|õ ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ñ2
à×#Ñ# dÑ*ð2
ð Ÿ™ tÑ+ð2
ð ×&Ñ&¨Ñ-ð	2
ð
  ™ð2
ð ×(Ñ(¨4Ñ/ð2
ð ˜$‘;ð2
ð Ð+Ñ,ð2
ð 
!ò2
ó ó ó  ô2
r-   rö   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CohereForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputr8   Úlogitsc                 ó,  •— t         ‰| �  |«       t        |«      | _        |j                  | _        t        j                  |j                  |j                  d¬«      | _        |j                  | _	        |j                  | _
        | j                  «        y r‚   )r    r!   rö   ré   rû   r   r…   r(   r  Úlogit_scaleÚtie_word_embeddingsr  r‹   s     €r,   r!   zCohereForCausalLM.__init__Ê  sq   ø€ Ü‰Ñ˜Ô Ü  Ó(ˆŒ
Ø ×+Ñ+ˆŒÜ—y‘y ×!3Ñ!3°V×5FÑ5FÈUÔSˆŒØ!×-Ñ-ˆÔØ#)×#=Ñ#=ˆÔ ð 	�‰Õr-   Nr  r˜   rs   rË   r  ÚlabelsrÝ   Úlogits_to_keepr›   rV   c	           
      ó–  —  | j                   d||||||dœ|	¤Ž}
|
j                  }t        |t        «      rt	        | d«      n|}| j                  |dd…|dd…f   «      }|| j                  z  }d}|�* | j                  d||| j                  j                  dœ|	¤Ž}t        |||
j                  |
j                  |
j                  ¬«      S )aÕ  
        Example:

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

        >> model = CohereForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01")
        >> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")

        >> 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˜   rs   rË   r  rÝ   N)r  r  rû   )Úlossr  rË   r8   rê   rß   )ré   r	  rk   r|   Úslicer  r  Úloss_functionrE   rû   r   rË   r8   rê   )r'   r  r˜   rs   rË   r  r  rÝ   r  r›   Úoutputsr8   Úslice_indicesr  r  s                  r,   r;   zCohereForCausalLM.forwardÕ  sé   € ð> ,6¨4¯:©:ð ,
ØØ)Ø%Ø+Ø'Øñ,
ð ñ,
ˆð  ×1Ñ1ˆÜ8BÀ>ÔSVÔ8Wœ˜~˜o¨tÔ4Ð]kˆØ—‘˜mªA¨}ºaÐ,?Ñ@ÓAˆØ˜$×*Ñ*Ñ*ˆàˆØÐØ%�4×%Ñ%Ðp¨V¸FÈtÏ{É{×OeÑOeÑpÐioÑpˆDä%ØØØ#×3Ñ3Ø!×/Ñ/Ø×)Ñ)ô
ð 	
r-   )NNNNNNNr   )r=   r>   r?   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr!   r   r   r#   rä   ry   r   ræ   rå   r|   r   r   r   r;   r@   rA   s   @r,   r  r  Ä  s  ø„ à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hô	ð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø-.ñ6
à×#Ñ# dÑ*ð6
ð Ÿ™ tÑ+ð6
ð ×&Ñ&¨Ñ-ð	6
ð
  ™ð6
ð ×(Ñ(¨4Ñ/ð6
ð × Ñ  4Ñ'ð6
ð ˜$‘;ð6
ð ˜eŸl™lÑ*ð6
ð Ð+Ñ,ð6
ð 
 ò6
ó ó ô6
r-   r  )r  rö   rè   )rÍ   )r   ):Úcollections.abcr   Útypingr   r#   r   Úactivationsr   Úcache_utilsr   r	   Ú
generationr
   Úmasking_utilsr   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_coherer   ÚModuler   rC   r€   ry   r|   r“   r_   r¨   r°   r¸   rº   rØ   rè   rö   r  Ú__all__rß   r-   r,   ú<module>r4     sœ  ðõ: %Ý ã Ý å !ß .Ý )Ý /Ý BÝ 9ß Oß Kß FÝ &ß IÑ Iß GÝ 5Ý .ô-�b—i‘iô -ô"><˜BŸI™Iô ><ôB�—	‘	ô ð 	U˜UŸ\™\ð 	U°#ð 	U¸%¿,¹,ó 	Uð& ñ%Ø�I‰Ið%à�<‰<ð%ð 
�‰ð%ð �<‰<ð	%ð
 —L‘L 4Ñ'ð%ð ð%ð ð%ð Ð'Ñ(ó%ò2ó<ô8Q)�b—i‘iô Q)ôh2Ð3ô 2ðj ô˜Oó ó ðð$ ôF
Ð'ó F
ó ðF
ðR ôH
Ð-¨ó H
ó ðH
òV H�r-   