
    hQ                     t   d dl mZmZ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 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$m%Z% ddl&m'Z'm(Z(m)Z) ddl*m+Z+ ddl,m-Z-  G d dej\                        Z/d Z0d<dZ1dejd                  de3dejd                  fdZ4	 d=dej\                  dejd                  dejd                  d ejd                  d!eejd                     d"e5d#e5d$e%e'   fd%Z6 G d& d'ej\                        Z7 ed(       G d) d*ej\                               Z8 G d+ d,e      Z9e( G d- d.e#             Z: G d/ d0ej\                        Z;e( G d1 d2e:             Z<e( G d3 d4e:e             Z= G d5 d6ee:      Z> G d7 d8ee:      Z? G d9 d:ee:      Z@g d;ZAy)>    )CallableOptionalUnionN)nn)check_model_inputs   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask!create_sliding_window_causal_mask)FlashAttentionKwargs)GenericForQuestionAnswering GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)deprecate_kwarg   )MistralConfigc                   $     e Zd Z fdZd Z xZS )
MistralMLPc                    t         |           || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _	        t        |j                     | _        y NFbias)super__init__confighidden_sizeintermediate_sizer   Linear	gate_projup_proj	down_projr	   
hidden_actact_fnselfr*   	__class__s     j/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/transformers/models/mistral/modeling_mistral.pyr)   zMistralMLP.__init__$   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../    c                     | j                  | j                  | j                  |            | j                  |      z        }|S N)r0   r2   r.   r/   )r4   xr0   s      r6   forwardzMistralMLP.forward.   s6    NN4;;t~~a/@#ADLLQRO#ST	r7   )__name__
__module____qualname__r)   r;   __classcell__r5   s   @r6   r#   r#   #   s    0r7   r#   c                     | 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..N   dim)shapetorchcat)r:   x1x2s      r6   rotate_halfrK   3   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r7   c                     |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.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        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.
    )	unsqueezerK   )qkcossinposition_idsunsqueeze_dimq_embedk_embeds           r6   apply_rotary_pos_embrV   :   sY    ( --
&C
--
&C3w;q>C/0G3w;q>C/0GGr7   hidden_statesn_repreturnc                     | j                   \  }}}}|dk(  r| S | dddddddddf   j                  |||||      } | j                  |||z  ||      S )z
    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)rF   expandreshape)rW   rX   batchnum_key_value_headsslenhead_dims         r6   	repeat_kvra   U   so    
 2?1D1D.Ehz!!Qa"23::5BUW\^bdlmM  (;e(CT8TTr7   modulequerykeyvalueattention_maskscalingdropoutkwargsc                 T   t        || j                        }t        || j                        }	t        j                  ||j	                  dd            |z  }
|#|d d d d d d d |j
                  d   f   }|
|z   }
t        j                  j                  |
dt        j                        j                  |j                        }
t        j                  j                  |
|| j                        }
t        j                  |
|	      }|j	                  dd      j                         }||
fS )NrC   r   rB   )rE   dtype)ptrainingr    )ra   num_key_value_groupsrG   matmul	transposerF   r   
functionalsoftmaxfloat32torl   rh   rn   
contiguous)rb   rc   rd   re   rf   rg   rh   ri   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r6   eager_attention_forwardr|   a   s    3 ; ;<JUF$?$?@L<<z';';Aq'ABWLL!$Q1.D
0@0@0D.D%DE#k1==((2U]](SVVW\WbWbcL==((6??([L,,|\:K''1-88:K$$r7   c                   0    e Zd ZdZdedef fdZ eddd      	 	 dd	ej                  d
e
ej                  ej                  f   deej                     dee   deej                     dee   de
ej                  eej                     f   fd       Z xZS )MistralAttentionz=Multi-headed attention from 'Attention Is All You Need' paperr*   	layer_idxc                    t         |           || _        || _        t	        |dd       xs |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      | _        y )Nr`   g      TFr&   )r(   r)   r*   r   getattrr+   num_attention_headsr`   r^   ro   rg   attention_dropout	is_causalr   r-   q_projk_projv_projo_projr4   r*   r   r5   s      r6   r)   zMistralAttention.__init__~   s2   "
D9mV=O=OSYSmSm=m$*$>$>&B\B\$\!}}d*!'!9!9ii 2 2F4N4NQUQ^Q^4^ejkii 2 2F4N4NQUQ^Q^4^ejkii 2 2F4N4NQUQ^Q^4^ejkii : :T]] JFL^L^ejkr7   past_key_valuepast_key_values4.58new_nameversionrW   position_embeddingsrf   cache_positionri   rY   c           
      `   |j                   d d }g |d| j                  }| j                  |      j                  |      j	                  dd      }	| j                  |      j                  |      j	                  dd      }
| j                  |      j                  |      j	                  dd      }|\  }}t        |	|
||      \  }	}
|'|||d}|j                  |
|| j                  |      \  }
}t        }| j                  j                  dk7  rt        | j                  j                     } || |	|
||f| j                  sdn| j                  | j                   t#        | j                  dd       d|\  }} |j$                  g |d j'                         }| j)                  |      }||fS )	NrB   r    rC   )rQ   rP   r   eager        sliding_window)rh   rg   r   )rF   r`   r   viewrq   r   r   rV   updater   r|   r*   _attn_implementationr   rn   r   rg   r   r\   rv   r   )r4   rW   r   rf   r   r   ri   input_shapehidden_shapequery_statesrw   rx   rP   rQ   cache_kwargsattention_interfacer{   ry   s                     r6   r;   zMistralAttention.forward   s    $))#2.88b8$--8{{=166|DNNqRST[[/44\BLLQPQR
{{=166|DNNqRST&S#7jRUWZ#[ j&#&snUL'6'='=j,X\XfXfht'u$J(?;;++w6"9$++:Z:Z"[$7
%
  $}}C$2H2HLL"4;;0@$G
%
 
%
!\ *k));;;;FFHkk+.L((r7   )NN)r<   r=   r>   __doc__r!   intr)   r   rG   Tensortupler   r
   
LongTensorr   r   r;   r?   r@   s   @r6   r~   r~   {   s    Gl} l l %0A6R ,059*)||*) #5<<#=>*) !.	*)
 "%*) !!1!12*) -.*) 
u||Xell33	4*) S*)r7   r~   RMSNormc                   ,     e Zd Zd fd	Zd Zd Z xZS )MistralRMSNormc                     t         |           t        j                  t	        j
                  |            | _        || _        y)z=
        MistralRMSNorm is equivalent to T5LayerNorm
        N)r(   r)   r   	ParameterrG   onesweightvariance_epsilon)r4   r+   epsr5   s      r6   r)   zMistralRMSNorm.__init__   s1     	ll5::k#:; #r7   c                 "   |j                   }|j                  t        j                        }|j	                  d      j                  dd      }|t        j                  || j                  z         z  }| j                  |j                  |      z  S )NrC   rB   T)keepdim)	rl   ru   rG   rt   powmeanrsqrtr   r   )r4   rW   input_dtypevariances       r6   r;   zMistralRMSNorm.forward   sy    #))%((7 $$Q',,R,>%Ht?T?T4T(UU{{]--k:::r7   c                 ^    t        | j                  j                         d| j                   S )Nz, eps=)r   r   rF   r   )r4   s    r6   
extra_reprzMistralRMSNorm.extra_repr   s*    ))*+6$2G2G1HIIr7   )gư>)r<   r=   r>   r)   r;   r   r?   r@   s   @r6   r   r      s    $;Jr7   r   c                   >    e Zd Zdedef fdZ eddd      	 	 	 	 	 	 ddej                  d	e	ej                     d
e	ej                     de	e   de	e   de	ej                     de	eej                  ej                  f      dee   dej                  fd       Z xZS )MistralDecoderLayerr*   r   c                     t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        t        |j                  |j                        | _
        y )N)r*   r   r   )r(   r)   r+   r~   	self_attnr#   mlpr   rms_norm_epsinput_layernormpost_attention_layernormr   s      r6   r)   zMistralDecoderLayer.__init__   sl    !--)9Mf%-f.@.@fFYFYZ(6v7I7IvObOb(c%r7   r   r   r   r   rW   rf   rR   	use_cacher   r   ri   rY   c                     |}	| j                  |      } | j                  d|||||||d|\  }}
|	|z   }|}	| j                  |      }| j                  |      }|	|z   }|S )N)rW   rf   rR   r   r   r   r    )r   r   r   r   )r4   rW   rf   rR   r   r   r   r   ri   residual_s              r6   r;   zMistralDecoderLayer.forward   s     !,,];)4>> 	
')%+) 3	
 	
q !=0 !55mD/ =0r7   )NNNFNN)r<   r=   r>   r!   r   r)   r   rG   r   r   r   r
   boolr   r   r   r;   r?   r@   s   @r6   r   r      s    d} d d %0A6R 2637+/$)59KO|| !. u//0	
 "% D> !!1!12 &eELL%,,,F&GH +, 
 Sr7   r   c                   J    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Zy)MistralPreTrainedModelr*   modelTr   r   )rW   
attentionsN)r<   r=   r>   r!   __annotations__base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r~   _can_record_outputsr   r7   r6   r   r      sQ    &*#./#4"5N!"&,&r7   r   c                   ~     e Zd ZU ej                  ed<   ddef fdZ ej                         e	d               Z
 xZS )MistralRotaryEmbeddinginv_freqr*   c                    t         |           t        |d      rUt        |j                  t
              r;|j                  j                  d|j                  j                  d            | _        nd| _        |j                  | _	        |j                  | _
        || _        t        | j                     | _        | j                  | j                  |      \  }| _        | j                  d|d       | j                   | _        y )Nrope_scaling	rope_typetypedefaultr   F)
persistent)r(   r)   hasattr
isinstancer   dictgetr   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr*   r   rope_init_fnattention_scalingregister_bufferr   original_inv_freq)r4   r*   devicer   r5   s       r6   r)   zMistralRotaryEmbedding.__init__  s    6>*z&:M:Mt/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q($(ZeD!%r7   c                 b   | 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        j                  |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   AxY w)Nr   rB   r    mpscpuF)device_typeenabledrC   rD   )rl   )r   floatr[   rF   ru   r   r   r   strrG   autocastrq   rH   rP   r   rQ   rl   )
r4   r:   rR   inv_freq_expandedposition_ids_expandedr   freqsembrP   rQ   s
             r6   r;   zMistralRotaryEmbedding.forward"  sV    !MM$4-8>>@GGHZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E!((--[`J`ahhmmfk^^UC 	5&,,.1F1L1L1NNYYZ[]^_E))UEN3C'')d444C'')d444C		5 vvAGGv$cff177f&;;;	5 	5s    BF%%F.r9   )r<   r=   r>   rG   r   r   r!   r)   no_gradr   r;   r?   r@   s   @r6   r   r     s=    ll/} /" U]]_<  <r7   r   c                       e Zd Zdef fdZee	 	 	 	 	 	 	 ddeej                     deej                     deej                     dee   deej                     dee   d	eej                     d
ee   defd              Z xZS )MistralModelr*   c           	         t         |   |       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                        | _        t        j                  t        |j                        D cg c]  }t        ||       c}      | _        t        |j                  |j                        | _        t#        |      | _        d| _        | j)                          y c c}w )Nr   r*   F)r(   r)   pad_token_idpadding_idx
vocab_sizer   	Embeddingr+   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normr   
rotary_embgradient_checkpointing	post_initr   s      r6   r)   zMistralModel.__init__4  s     !.. ++LL):):F<N<NPTP`P`ammEJ6KcKcEde	 3e
 #6#5#56;N;NO	0?&+# 	 fs   D	input_idsrf   rR   r   inputs_embedsr   r   ri   rY   c                    |d u |d uz  rt        d      || j                  |      }|r|t        | j                        }|F||j	                         nd}	t        j                  |	|	|j                  d   z   |j                        }||j                  d      }| j                  j                  t        nt        }
 |
| j                  |||||      }|}| j                  ||      }| j                  d | j                  j                   D ]  } ||f||||||d|} | j!                  |      }t#        ||r|      S d       S )	Nz:You must specify exactly one of input_ids or inputs_embedsr   r   r    )r   )r*   input_embedsrf   r   r   rR   )rf   rR   r   r   r   r   )last_hidden_stater   )
ValueErrorr   r   r*   get_seq_lengthrG   arangerF   r   rM   r   r   r   r  r  r   r  r   )r4   r  rf   rR   r   r  r   r   ri   past_seen_tokensmask_functionrz   rW   r   decoder_layers                  r6   r;   zMistralModel.forwardD  s    -t";<YZZ  --i8M0*$++>O!CRC^==?de"\\ "2]5H5H5K"KTaThThN )33A6L.2kk.H.H.P*Vw#;;&))+%
 &"oom\J![[)H4;;+H+HI 
	M)	*) /#-$7	 	M
	 		-0&+/8O
 	
>B
 	
r7   )NNNNNNN)r<   r=   r>   r!   r)   r   r   r   rG   r   r   r
   FloatTensorr   r   r   r   r;   r?   r@   s   @r6   r   r   2  s    }    151537+/59$(599
E,,-9
 !.9
 u//0	9

 "%9
   1 129
 D>9
 !!1!129
 +,9
 
!9
  9
r7   r   c                   d    e Zd ZdgZddiZddgdgfiZ fdZee	 	 	 	 	 	 	 	 	 dde	e
j                     de	e
j                     d	e	e
j                     d
e	e   de	e
j                     de	e
j                     de	e   de	e
j                     deee
j                  f   dee   defd              Z xZS )MistralForCausalLMzlm_head.weightlm_headcolwise_reprW   logitsc                     t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        | j                          y r%   )
r(   r)   r   r   r   r   r-   r+   r  r  r3   s     r6   r)   zMistralForCausalLM.__init__  sU     !&)
 ++yy!3!3V5F5FUS 	r7   r  rf   rR   r   r  labelsr   r   logits_to_keepri   rY   c
                 z    | j                   d|||||||d|
}|j                  }t        |	t              rt	        |	 d      n|	}| j                  |dd|ddf         }d}|* | j                  d||| j                  j                  d|
}t        |||j                  |j                  |j                        S )a  
        Example:

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

        >>> model = MistralForCausalLM.from_pretrained("meta-mistral/Mistral-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-mistral/Mistral-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  rf   rR   r   r  r   r   N)r  r  r   )lossr  r   rW   r   r   )r   r
  r   r   slicer  loss_functionr*   r   r   r   rW   r   )r4   r  rf   rR   r   r  r  r   r   r  ri   outputsrW   slice_indicesr  r  s                   r6   r;   zMistralForCausalLM.forward  s    @ ,64:: 	,
)%+')	,
 	,
  118B>SV8W~ot4]kmA}a,?@A%4%%pVFt{{OeOepiopD%#33!//))
 	
r7   )	NNNNNNNNr   )r<   r=   r>   _tied_weights_keys_tp_plan_pp_planr)   r   r   r   rG   r   r   r
   r  r   r   r   r   r   r   r;   r?   r@   s   @r6   r  r    s0   *+=)H_-z:;H  151537+/59-1$(59348
E,,-8
 !.8
 u//0	8

 "%8
   1 128
 ))*8
 D>8
 !!1!128
 c5<</08
 +,8
 
 8
  8
r7   r  c                       e Zd Zy)MistralForTokenClassificationNr<   r=   r>   r   r7   r6   r$  r$        r7   r$  c                       e Zd Zy) MistralForSequenceClassificationNr%  r   r7   r6   r(  r(    r&  r7   r(  c                       e Zd Zy)MistralForQuestionAnsweringNr%  r   r7   r6   r*  r*    s    r7   r*  )r  r*  r   r   r(  r$  )Nr    )r   )Btypingr   r   r   rG   r   transformers.utils.genericr   activationsr	   cache_utilsr
   r   
generationr   integrationsr   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.deprecationr   configuration_mistralr!   Moduler#   rK   rV   r   r   ra   r   r|   r~   r   r   r   r   r   r  r$  r(  r*  __all__r   r7   r6   <module>r=     s   - ,   9 ! . ) 7 R B  P K F & I I 0 0  (6	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%4<)ryy <)~ Y'JRYY J (J()4 )X _  $!<RYY !<H L
) L
 L
^ H
/ H
 H
V	$ACY 		'GI_ 	 \"=?U [r7   