Ë
    ÜÓ^jýW  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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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$m%Z% ddl&m'Z'm(Z(m)Z) ddl*m+Z+m,Z, ddl-m.Z. ddl/m0Z0  G d„ dejb                  «      Z2d„ Z3 ed«      d>d„«       Z4dejj                  de6dejj                  fd„Z7	 d?dejb                  d ejj                  d!ejj                  d"ejj                  d#ejj                  dz  d$e8d%e8d&e%e'   fd'„Z9 ee4«       G d(„ d)ejb                  «      «       Z: ed*«       G d+„ d,ejb                  «      «       Z; G d-„ d.e«      Z<e( G d/„ d0e#«      «       Z= G d1„ d2ejb                  «      Z>e( G d3„ d4e=«      «       Z?e( G d5„ d6e=e«      «       Z@ G d7„ d8ee=«      ZA G d9„ d:ee=«      ZB G d;„ d<ee=«      ZCg d=¢ZDy)@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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MinistralConfigc                   ó$   ‡ — e Zd Zˆ fd„Zd„ Zˆ xZS )ÚMinistralMLPc                 óÊ  •— t         ‰| �  «        || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  d¬«      | _        t        j                  | j                  | j                  d¬«      | _        t        j                  | j                  | j                  d¬«      | _	        t        |j                     | _        y ©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     €ú{/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/ministral/modeling_ministral.pyr+   zMinistralMLP.__init__3   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�ó    c                 óˆ   — | j                  | j                  | j                  |«      «      | j                  |«      z  «      }|S ©N)r2   r4   r0   r1   )r6   Úxr2   s      r8   ÚforwardzMinistralMLP.forward=   s6   € Ø—N‘N 4§;¡;¨t¯~©~¸aÓ/@Ó#AÀDÇLÁLÐQRÃOÑ#SÓTˆ	ØÐr9   )Ú__name__Ú
__module__Ú__qualname__r+   r=   Ú__classcell__©r7   s   @r8   r%   r%   2   s   ø„ ô0ö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..Néÿÿÿÿé   ©Údim)ÚshapeÚtorchÚcat)r<   Úx1Úx2s      r8   Úrotate_halfrM   B   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	unsqueezerM   )ÚqÚkÚcosÚsinÚunsqueeze_dimÚq_embedÚk_embeds          r8   Úapply_rotary_pos_embrX   I   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_repÚreturnc                 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)rH   ÚexpandÚreshape)rY   rZ   ÚbatchÚnum_key_value_headsÚslenÚhead_dims         r8   Ú	repeat_kvrc   c   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 )NrE   r   rD   )rG   Údtype)ÚpÚtrainingr"   )rc   Únum_key_value_groupsrI   ÚmatmulÚ	transposer   Ú
functionalÚsoftmaxÚfloat32Útorm   rj   ro   Ú
contiguous)rd   re   rf   rg   rh   ri   rj   rk   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r8   Úeager_attention_forwardr|   o   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                   óð   ‡ — e Zd ZdZ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MinistralAttentionz=Multi-headed attention from 'Attention Is All You Need' paperÚ	layer_idxc                 ó¨  •— 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,                  | _        y d | _        y )NÚlayer_typesrb   g      à¿TFr(   Úsliding_attention)r*   r+   Úhasattrr�   Ú
layer_typer,   r   Úgetattrr-   Únum_attention_headsrb   r`   rp   ri   Úattention_dropoutÚ	is_causalr   r/   Úq_projÚk_projÚv_projÚo_projÚsliding_window©r6   r,   r   r7   s      €r8   r+   zMinistralAttention.__init__Œ   sl  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ä—i‘i × 2Ñ 2°F×4NÑ4NÐQU×Q^ÑQ^Ñ4^ÐejÔkˆŒÜ—i‘i × 2Ñ 2°F×4NÑ4NÐQU×Q^ÑQ^Ñ4^ÐejÔkˆŒÜ—i‘i × 2Ñ 2°F×4NÑ4NÐQU×Q^ÑQ^Ñ4^ÐejÔkˆŒÜ—i‘i × :Ñ :¸T¿]¹]Ñ JÈF×L^ÑL^ÐejÔkˆŒØ7;·±ÐJ]Ò7]˜f×3Ñ3ˆÕÐcgˆÕr9   NrY   Úposition_embeddingsrh   Úpast_key_valuesrk   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$                  dœ|¤Ž\  }} |j&                  g |¢d‘­Ž j)                  «       }| j+                  |«      }||fS )NrD   r"   rE   ç        )rj   ri   r�   )rH   rb   r‰   Úviewrr   rŠ   r‹   rX   Úupdater   r   Úget_interfacer,   Ú_attn_implementationr|   ro   r‡   ri   r�   r^   rw   rŒ   )r6   rY   r�   rh   r�   rk   Úinput_shapeÚhidden_shapeÚquery_statesrx   ry   rS   rT   Úattention_interfacer{   rz   s                   r8   r=   zMinistralAttention.forward�   sš  € ð $×)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   r;   )r>   r?   r@   Ú__doc__Úintr+   rI   ÚTensorÚtupler   r   r   r=   rA   rB   s   @r8   r~   r~   ˆ   s˜   ø„ áGðh¨#õ 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   r~   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MinistralRMSNormÚepsr[   Nc                 óŠ   •— t         ‰| �  «        t        j                  t	        j
                  |«      «      | _        || _        y)z?
        MinistralRMSNorm is equivalent to T5LayerNorm
        N)r*   r+   r   Ú	ParameterrI   ÚonesÚweightÚvariance_epsilon)r6   r-   r¢   r7   s      €r8   r+   zMinistralRMSNorm.__init__É   s1   ø€ ô 	‰ÑÔÜ—l‘l¤5§:¡:¨kÓ#:Ó;ˆŒØ #ˆÕr9   rY   c                 ó"  — |j                   }|j                  t        j                  «      }|j	                  d«      j                  dd¬«      }|t        j                  || j                  z   «      z  }| j                  |j                  |«      z  S )NrE   rD   T)Úkeepdim)	rm   rv   rI   ru   ÚpowÚmeanÚrsqrtr§   r¦   )r6   rY   Úinput_dtypeÚvariances       r8   r=   zMinistralRMSNorm.forwardÑ   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=)rž   r¦   rH   r§   )r6   s    r8   Ú
extra_reprzMinistralRMSNorm.extra_reprØ   s*   € Ü˜Ÿ™×)Ñ)Ó*Ð+¨6°$×2GÑ2GÐ1HÐIÐIr9   )g�íµ ÷Æ°>)
r>   r?   r@   Úfloatr+   rI   r�   r=   r°   rA   rB   s   @r8   r¡   r¡   Ç   s7   ø„ ñ$¨ð $¸$õ $ð; U§\¡\ð ;°e·l±ló ;öJr9   r¡   c                   ó  ‡ — e Zd Zdedefˆ fd„Z	 	 	 	 	 ddej                  dej                  dz  dej                  dz  de	dz  d	e
dz  d
eej                  ej                  f   dz  dee   dej                  fd„Zˆ xZS )ÚMinistralDecoderLayerr,   r   c                 ó   •— t         ‰| �  «        |j                  | _        t        ||¬«      | _        t        |«      | _        t        |j                  |j                  ¬«      | _	        t        |j                  |j                  ¬«      | _
        y )N)r,   r   ©r¢   )r*   r+   r-   r~   Ú	self_attnr%   Úmlpr¡   Úrms_norm_epsÚinput_layernormÚpost_attention_layernormrŽ   s      €r8   r+   zMinistralDecoderLayer.__init__Ý   sm   ø€ Ü‰ÑÔØ!×-Ñ-ˆÔä+°6ÀYÔOˆŒä Ó'ˆŒÜ/°×0BÑ0BÈ×H[ÑH[Ô\ˆÔÜ(8¸×9KÑ9KÐQW×QdÑQdÔ(eˆÕ%r9   NrY   rh   Úposition_idsr�   Ú	use_cacher�   rk   r[   c           
      óÀ   — |}| j                  |«      } | j                  d||||||dœ|¤Ž\  }}	||z   }|}| j                  |«      }| j                  |«      }||z   }|S )N)rY   rh   r»   r�   r¼   r�   © )r¹   r¶   rº   r·   )
r6   rY   rh   r»   r�   r¼   r�   rk   ÚresidualÚ_s
             r8   r=   zMinistralDecoderLayer.forwardç   s�   € ð !ˆØ×,Ñ,¨]Ó;ˆà)˜4Ÿ>™>ð 
Ø'Ø)Ø%Ø+ØØ 3ñ
ð ñ
Ñˆ�qð ! =Ñ0ˆð !ˆØ×5Ñ5°mÓDˆØŸ™ Ó/ˆØ  =Ñ0ˆØÐr9   )NNNFN)r>   r?   r@   r#   rœ   r+   rI   r�   Ú
LongTensorr   Úboolrž   r   r   r=   rA   rB   s   @r8   r³   r³   Ü   s¾   ø„ ðf˜ð f¸3õ fð /3Ø04Ø(,Ø!&ØHLñà—|‘|ðð Ÿ™ tÑ+ðð ×&Ñ&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5§<¡<°·±Ð#=Ñ>ÀÑEðð Ð+Ñ,ðð 
�‰÷r9   r³   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MinistralPreTrainedModelr,   ÚmodelTr³   r�   )rY   Ú
attentionsN)r>   r?   r@   r#   Ú__annotations__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_backendr³   r~   Ú_can_record_outputsr¾   r9   r8   rÄ   rÄ     sQ   … àÓØÐØ&*Ð#Ø0Ð1ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà.Ø(ñÑr9   rÄ   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MinistralRotaryEmbeddingÚinv_freqNr,   c                 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defaultrÔ   F)Ú
persistentÚoriginal_inv_freq)r*   r+   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenr,   Úrope_parametersrÖ   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)r6   r,   ÚdeviceÚrope_init_fnrÔ   r7   s        €r8   r+   z!MinistralRotaryEmbedding.__init__  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Ur9   râ   ztorch.deviceÚseq_lenr[   z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_thetarb   Ng      ð?r   rE   ©rm   )râ   rm   )	rÝ   r…   r-   r†   rI   ÚarangeÚint64rv   r±   )r,   râ   rä   ÚbaserG   Úattention_factorrÔ   s          r8   rÞ   z8MinistralRotaryEmbedding.compute_default_rope_parameters-  s�   € ð& ×%Ñ% lÑ3ˆÜ�f˜j¨$Ó/Òc°6×3EÑ3EÈ×IcÑIcÑ3cˆàÐð Ø”U—\‘\ ! S¨!´5·;±;Ô?×BÑBÈ&ÔX]×XcÑXcÐBÓdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r9   c                 óN  — | j                   d d d …d f   j                  «       j                  |j                  d   dd«      j	                  |j
                  «      }|d d …d d d …f   j                  «       }t        |j
                  j                  t        «      r/|j
                  j                  dk7  r|j
                  j                  nd}t        |d¬«      5  |j                  «       |j                  «       z  j                  dd«      }t        j                  ||fd¬	«      }|j                  «       | j                  z  }|j                  «       | j                  z  }	d d d «       j	                  |j                   ¬
«      	j	                  |j                   ¬
«      fS # 1 sw Y   ŒAxY w)Nr   rD   r"   ÚmpsÚcpuF)Údevice_typeÚenabledrE   rF   rç   )rÔ   r±   r]   rH   rv   râ   Ú
isinstanceÚtypeÚstrr   rr   rI   rJ   rS   rß   rT   rm   )
r6   r<   r»   Úinv_freq_expandedÚposition_ids_expandedrï   ÚfreqsÚembrS   rT   s
             r8   r=   z MinistralRotaryEmbedding.forwardK  sR  € ð !ŸM™M¨$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$r;   )NNN)r>   r?   r@   rI   r�   rÇ   r#   r+   Ústaticmethodr   rœ   rž   r±   rÞ   Úno_gradr   r=   rA   rB   s   @r8   rÓ   rÓ     s˜   ø… Ø�l‰lÓñV˜õ Vð  à)-Ø+/Ø"ñ*Ø $Ñ&ð*à˜Ñ(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ñ	%ò	*ó ð*ð: €U‡]�]ƒ_Øñ<ó ó ô<r9   rÓ   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MinistralModelr,   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 )Nrµ   ©r,   F)r*   r+   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	Embeddingr-   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersr³   Úlayersr¡   r¸   ÚnormrÓ   Ú
rotary_embÚgradient_checkpointingÚ	post_initrŽ   s      €r8   r+   zMinistralModel.__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‘mÜGLÈV×MeÑMeÓGfÖg¸)Ô" 6¨9Õ5Ògó
ˆŒô % V×%7Ñ%7¸V×=PÑ=PÔQˆŒ	Ü2¸&ÔAˆŒØ&+ˆÔ#ð 	�‰Õùò hs   ÂDNÚ	input_idsrh   r»   r�   Úinputs_embedsr¼   rk   r[   c           
      óÚ  — |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        «      s)| j                  ||||dœ}
t        d
i |
¤Žt        d
i |
¤Ždœ}	|}| j                  ||«      }t        | j                   d | j                  j"                   «      D ].  \  }} ||f|	| j                  j$                  |      ||||dœ|¤Ž}Œ0 | j'                  |«      }t)        ||r|¬	«      S d ¬	«      S )Nz:You must specify exactly one of input_ids or inputs_embedsrý   r   r"   )râ   )r,   r  rh   r�   r»   )Úfull_attentionr‚   )rh   r»   r�   r¼   r�   )Úlast_hidden_stater�   r¾   )Ú
ValueErrorr  r	   r,   Úget_seq_lengthrI   rè   rH   râ   rP   rñ   Údictr   r   r  Ú	enumerater  r  r�   r  r   )r6   r  rh   r»   r�   r  r¼   rk   Úpast_seen_tokensÚcausal_mask_mappingÚmask_kwargsrY   r�   ÚiÚdecoder_layers                  r8   r=   zMinistralModel.forwardm  s¨  € ð ˜Ð -°tÐ";Ò<ÜÐYÓZÐZàÐ Ø ×-Ñ-¨iÓ8ˆMá˜Ð0Ü*°$·+±+Ô>ˆOàÐØCRÐC^˜×=Ñ=Ô?ÐdeÐÜ Ÿ<™<¨×(;Ñ(;¸AÑ(>À}×G[ÑG[Ô\Ð_oÑoˆLØ'×1Ñ1°!Ó4ˆLô °Ð?Ð-ÄÔFð Ÿ+™+Ø!.Ø"0Ø#2Ø ,ñˆKô #5Ñ"C°{Ñ"CÜ%FÑ%UÈÑ%Uñ#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#Ø$7ñð ñ‰Mð		ð Ÿ	™	 -Ó0ˆÜ&Ø+Ù/8˜Oô
ð 	
à>Bô
ð 	
r9   )NNNNNN)r>   r?   r@   r#   r+   r    r!   r   rI   rÁ   r�   r   ÚFloatTensorrÂ   r   r   r   r=   rA   rB   s   @r8   rû   rû   [  sÍ   ø„ ð˜õ ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ñ:
à×#Ñ# dÑ*ð:
ð Ÿ™ tÑ+ð:
ð ×&Ñ&¨Ñ-ð	:
ð
  ™ð:
ð ×(Ñ(¨4Ñ/ð:
ð ˜$‘;ð:
ð Ð+Ñ,ð:
ð 
!ò:
ó ó ó  ô:
r9   rû   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MinistralForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputrY   Úlogitsc                 óè   •— t         ‰| �  |«       t        |«      | _        |j                  | _        t        j                  |j                  |j                  d¬«      | _        | j                  «        y r'   )
r*   r+   rû   rÅ   r   r   r/   r-   r  r
  r5   s     €r8   r+   zMinistralForCausalLM.__init__³  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  rh   r»   r�   r  Úlabelsr¼   Úlogits_to_keeprk   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 )aç  
        Example:

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

        >>> model = MinistralForCausalLM.from_pretrained("meta-ministral/Ministral-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-ministral/Ministral-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  rh   r»   r�   r  r¼   N)r  r   r   )Úlossr  r�   rY   rÆ   r¾   )rÅ   r  rñ   rœ   Úslicer  Úloss_functionr,   r   r   r�   rY   rÆ   )r6   r  rh   r»   r�   r  r   r¼   r!  rk   ÚoutputsrY   Úslice_indicesr  r#  s                  r8   r=   zMinistralForCausalLM.forward¼  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   )r>   r?   r@   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr+   r   r   rI   rÁ   r�   r   r  rÂ   rœ   r   r   r   r=   rA   rB   s   @r8   r  r  ­  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 ˜$‘;ð6
ð ˜eŸl™lÑ*ð6
ð Ð+Ñ,ð6
ð 
 ò6
ó ó ô6
r9   r  c                   ó   — e Zd Zy)Ú"MinistralForSequenceClassificationN©r>   r?   r@   r¾   r9   r8   r,  r,  ÷  ó   „ Ør9   r,  c                   ó   — e Zd Zy)ÚMinistralForTokenClassificationNr-  r¾   r9   r8   r0  r0  û  r.  r9   r0  c                   ó   — e Zd ZdZy)ÚMinistralForQuestionAnsweringÚtransformerN)r>   r?   r@   rÈ   r¾   r9   r8   r2  r2  ÿ  s   „ Ø%Ñr9   r2  )rÄ   rû   r  r,  r0  r2  )r"   )r’   )EÚcollections.abcr   Útypingr   rI   r   Úactivationsr   Úcache_utilsr   r	   Ú
generationr
   Úintegrationsr   r   r   Úmasking_utilsr   r   Úmodeling_flash_attention_utilsr   Úmodeling_layersr   r   r   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   r    Úutils.output_capturingr!   Úconfiguration_ministralr#   ÚModuler%   rM   rX   r�   rœ   rc   r±   r|   r~   r¡   r³   rÄ   rÓ   rû   r  r,  r0  r2  Ú__all__r¾   r9   r8   ú<module>rG     s  ðõ, %Ý ã Ý å !ß .Ý )ß fÑ fß RÝ B÷ó ÷ Pß Kß FÝ &ß IÑ Iß GÝ 5Ý 4ô�2—9‘9ô ò (ñ Ð*Ó+òó ,ðð2	U˜UŸ\™\ð 	U°#ð 	U¸%¿,¹,ó 	Uð& ñ%Ø�I‰Ið%à�<‰<ð%ð 
�‰ð%ð �<‰<ð	%ð
 —L‘L 4Ñ'ð%ð ð%ð ð%ð Ð'Ñ(ó%ñ2 Ð)Ó*ô;)˜Ÿ™ó ;)ó +ð;)ñ| ˜YÓ'ôJ�r—y‘yó Jó (ðJô((Ð6ô (ðV ô˜ó ó ðô$><˜rŸy™yô ><ðB ôN
Ð-ó N
ó ðN
ðb ôF
Ð3°_ó F
ó ðF
ôR	Ð)IÐKcô 	ô	Ð&CÐE]ô 	ô&Ð$?ÐAYô &ò�r9   