
    iUX                        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$m%Z% ddl&m'Z'm(Z(m)Z) ddl*m+Z+m,Z, ddl-m.Z.  G d dej^                        Z0d Z1 ed      d=d       Z2dejf                  de4dejf                  fdZ5	 d>dej^                  dejf                  d ejf                  d!ejf                  d"ejf                  dz  d#e6d$e6d%e%e'   fd&Z7 ee2       G d' d(ej^                               Z8 ed)       G d* d+ej^                               Z9 G d, d-e      Z:e( G d. d/e#             Z; G d0 d1ej^                        Z<e( G d2 d3e;             Z=e( G d4 d5e;e             Z> G d6 d7ee;      Z? G d8 d9ee;      Z@ G d: d;ee;      ZAg d<ZBy)?    )Callable)OptionalN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernel_func_from_hubuse_kernelized_func)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)check_model_inputsmaybe_autocast   )MinistralConfigc                   $     e Zd Z fdZd Z xZS )MinistralMLPc                    t         |           || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _	        t        |j                     | _        y NFbias)super__init__confighidden_sizeintermediate_sizer   Linear	gate_projup_proj	down_projr   
hidden_actact_fnselfr+   	__class__s     z/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/transformers/models/ministral/modeling_ministral.pyr*   zMinistralMLP.__init__#   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../    c                     | j                  | j                  | j                  |            | j                  |      z        }|S N)r1   r3   r/   r0   )r5   xr1   s      r7   forwardzMinistralMLP.forward-   s6    NN4;;t~~a/@#ADLLQRO#ST	r8   )__name__
__module____qualname__r*   r<   __classcell__r6   s   @r7   r$   r$   "   s    0r8   r$   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      r7   rotate_halfrL   2   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r8   rotary_pos_embc                     |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.
    )	unsqueezerL   )qkcossinunsqueeze_dimq_embedk_embeds          r7   apply_rotary_pos_embrW   9   sY    & --
&C
--
&C3w;q>C/0G3w;q>C/0GGr8   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)rG   expandreshape)rX   rY   batchnum_key_value_headsslenhead_dims         r7   	repeat_kvrb   S   so    
 2?1D1D.Ehz!!Qa"23::5BUW\^bdlmM  (;e(CT8TTr8   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 )NrD   r   rC   )rF   dtype)ptrainingr!   )rb   num_key_value_groupsrH   matmul	transposerG   r   
functionalsoftmaxfloat32torm   ri   ro   
contiguous)rc   rd   re   rf   rg   rh   ri   rj   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r7   eager_attention_forwardr}   _   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$$r8   c                       e Zd ZdZdef 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j                  dz  d
ee   deej                  ej                  dz  f   fdZ xZS )MinistralAttentionz=Multi-headed attention from 'Attention Is All You Need' paper	layer_idxc                    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 )Nlayer_typesra   g      TFr'   sliding_attention)r)   r*   hasattrr   
layer_typer+   r   getattrr,   num_attention_headsra   r_   rp   rh   attention_dropout	is_causalr   r.   q_projk_projv_projo_projsliding_windowr5   r+   r   r6   s      r7   r*   zMinistralAttention.__init__}   sl   ;B6=;Y&,,Y7_c"
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii 2 2F4N4NQUQ^Q^4^ejkii 2 2F4N4NQUQ^Q^4^ejkii 2 2F4N4NQUQ^Q^4^ejkii : :T]] JFL^L^ejk7;J]7]f33cgr8   NrX   position_embeddingsrg   past_key_valuescache_positionrj   rZ   c                 .   |j                   d d }g |d| j                  }| j                  |      j                  |      j	                  dd      }	| j                  |      j                  |      j	                  dd      }
| j                  |      j                  |      j	                  dd      }|\  }}t        |	|
||      \  }	}
|'|||d}|j                  |
|| j                  |      \  }
}t        j                  | j                  j                  t              } || |	|
||f| j                  sdn| j                   | j"                  | j$                  d|\  }} |j&                  g |d j)                         }| j+                  |      }||fS )NrC   r!   rD   )rS   rR   r           )ri   rh   r   )rG   ra   r   viewrr   r   r   rW   updater   r   get_interfacer+   _attn_implementationr}   ro   r   rh   r   r]   rw   r   )r5   rX   r   rg   r   r   rj   input_shapehidden_shapequery_statesrx   ry   rR   rS   cache_kwargsattention_interfacer|   rz   s                     r7   r<   zMinistralAttention.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(?(M(MKK,,.E)
 %8
%
  $}}C$2H2HLL..
%
 
%
!\ *k));;;;FFHkk+.L((r8   )NN)r=   r>   r?   __doc__intr*   rH   Tensortupler   
LongTensorr   r   r<   r@   rA   s   @r7   r   r   y   s    Gh# h, )-26*)||*) #5<<#=>*) t+	*)
 *) ((4/*) -.*) 
u||U\\D00	1*)r8   r   RMSNormc                   h     e Zd Zddeddf fdZdej                  dej                  fdZd Z xZ	S )	MinistralRMSNormepsrZ   Nc                     t         |           t        j                  t	        j
                  |            | _        || _        y)z?
        MinistralRMSNorm is equivalent to T5LayerNorm
        N)r)   r*   r   	ParameterrH   onesweightvariance_epsilon)r5   r,   r   r6   s      r7   r*   zMinistralRMSNorm.__init__   s1     	ll5::k#:; #r8   rX   c                 "   |j                   }|j                  t        j                        }|j	                  d      j                  dd      }|t        j                  || j                  z         z  }| j                  |j                  |      z  S )NrD   rC   T)keepdim)	rm   rv   rH   ru   powmeanrsqrtr   r   )r5   rX   input_dtypevariances       r7   r<   zMinistralRMSNorm.forward   sy    #))%((7 $$Q',,R,>%Ht?T?T4T(UU{{]--k:::r8   c                 ^    t        | j                  j                         d| j                   S )Nz, eps=)r   r   rG   r   )r5   s    r7   
extra_reprzMinistralRMSNorm.extra_repr   s*    ))*+6$2G2G1HIIr8   )gư>)
r=   r>   r?   floatr*   rH   r   r<   r   r@   rA   s   @r7   r   r      s7    $ $$ $;U\\ ;ell ;Jr8   r   c                   "    e Zd Zdedef fdZ	 	 	 	 	 	 ddej                  dej                  dz  dej                  dz  de	dz  d	e
dz  d
ej                  dz  deej                  ej                  f   dz  dee   dej                  fdZ xZS )MinistralDecoderLayerr+   r   c                 H   t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        t        |j                  |j                        | _
        |j                  |   | _        y )N)r+   r   r   )r)   r*   r,   r   	self_attnr$   mlpr   rms_norm_epsinput_layernormpost_attention_layernormr   attention_typer   s      r7   r*   zMinistralDecoderLayer.__init__   s    !--+6YO'/0B0BH[H[\(89K9KQWQdQd(e%$00;r8   NrX   rg   position_idsr   	use_cacher   r   rj   rZ   c                     |}	| j                  |      } | j                  d|||||||d|\  }}
|	|z   }|}	| j                  |      }| j                  |      }|	|z   }|S )N)rX   rg   r   r   r   r   r    )r   r   r   r   )r5   rX   rg   r   r   r   r   r   rj   residual_s              r7   r<   zMinistralDecoderLayer.forward   s     !,,];)4>> 	
')%+) 3	
 	
q !=0 !55mD/ =0r8   )NNNFNN)r=   r>   r?   r"   r   r*   rH   r   r   r   boolr   r   r   r<   r@   rA   s   @r7   r   r      s    	< 	<3 	< /304(,!&26HL|| t+ &&-	
  $; ((4/ #5<<#=>E +, 
r8   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)MinistralPreTrainedModelr+   modelTr   r   )rX   
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   r8   r7   r   r      sQ    &*#01#4"5N!"&.(r8   r   c                        e Zd ZU ej                  ed<   ddef 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 xZS )MinistralRotaryEmbeddinginv_freqNr+   c                    t         |           |j                  | _        |j                  | _        || _        | j
                  j                  d   | _        | j                  }| j                  dk7  rt        | j                     } || j
                  |      \  }| _
        | j                  d|d       | j                  d|j                         d       y )N	rope_typedefaultr   F)
persistentoriginal_inv_freq)r)   r*   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr+   rope_parametersr   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r5   r+   devicerope_init_fnr   r6   s        r7   r*   z!MinistralRotaryEmbedding.__init__  s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L($(ZeD0(..2BuUr8   r   ztorch.deviceseq_lenrZ   ztorch.Tensorc                    | 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).
        
rope_thetara   Ng      ?r   rD   rm   )r   rm   )	r   r   r,   r   rH   arangeint64rv   r   )r+   r   r   baserF   attention_factorr   s          r7   r   z8MinistralRotaryEmbedding.compute_default_rope_parameters$  s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 )))r8   c                 N   | 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   AxY w)Nr   rC   r!   mpscpuF)device_typeenabledrD   rE   r   )r   r   r\   rG   rv   r   
isinstancetypestrr    rr   rH   rI   rR   r   rS   rm   )
r5   r;   r   inv_freq_expandedposition_ids_expandedr   freqsembrR   rS   s
             r7   r<   z MinistralRotaryEmbedding.forwardB  sR    !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$r:   )NNN)r=   r>   r?   rH   r   r   r"   r*   staticmethodr   r   r   r   r   no_gradr   r<   r@   rA   s   @r7   r   r     s    llV V  )-+/"*$&*(* t* 
~u$	%	* *: U]]_<  <r8   r   c                       e Zd Zdef 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dz  d
ej                  dz  dee   defd              Z xZS )MinistralModelr+   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      r7   r*   zMinistralModel.__init__T  s     !.. ++LL):):F<N<NPTP`P`ammGLVMeMeGfg)"695g
 %V%7%7V=P=PQ	2&A&+# 	 hs   DN	input_idsrg   r   r   inputs_embedsr   r   rj   rZ   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      }t        |x}
t              s*| j                  |||||d}t        d
i |t        d
i |d}
|}| j                  ||      }| j                  d | j                  j                    D ]  } ||f|
|j"                     |||||d|}! | j%                  |      }t'        ||r|	      S d 	      S )Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r!   )r   )r+   input_embedsrg   r   r   r   )full_attentionr   )rg   r   r   r   r   r   )last_hidden_stater   r   )
ValueErrorr  r	   r+   get_seq_lengthrH   r   rG   r   rO   r   dictr   r   r  r
  r	  r   r  r   )r5   r  rg   r   r   r  r   r   rj   past_seen_tokenscausal_mask_mappingmask_kwargsrX   r   decoder_layers                  r7   r<   zMinistralModel.forwardd  s    -t";<YZZ  --i8M0*$++>O!CRC^==?de"\\ "2]5H5H5K"KTaThThN )33A6L ?-F ++ -"0"0#2 ,K #5"C{"C%F%U%U#
 &"oom\J![[)H4;;+H+HI 
	M)	2=3O3OP) /#-$7	 	M
	 		-0&+/8O
 	
>B
 	
r8   )NNNNNNN)r=   r>   r?   r"   r*   r   r   rH   r   r   r   FloatTensorr   r   r   r   r<   r@   rA   s   @r7   r   r   R  s        .2.204(,26!%26A
##d*A
 t+A
 &&-	A

 A
 ((4/A
 $;A
 ((4/A
 +,A
 
!A
  A
r8   r   c                   b    e Zd ZddiZddiZddgdgfiZ 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	j                  dz  dee	j                  z  dee   defd              Z xZS )MinistralForCausalLMzlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputrX   logitsc                     t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        | j                          y r&   )
r)   r*   r   r   r  r   r.   r,   r  r  r4   s     r7   r*   zMinistralForCausalLM.__init__  sU     #F+
 ++yy!3!3V5F5FUS 	r8   Nr  rg   r   r   r  labelsr   r   logits_to_keeprj   rZ   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, 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  rg   r   r   r  r   r   N)r!  r#  r  )lossr!  r   rX   r   r   )r   r  r   r   slicer  loss_functionr+   r  r   r   rX   r   )r5   r  rg   r   r   r  r#  r   r   r$  rj   outputsrX   slice_indicesr!  r&  s                   r7   r<   zMinistralForCausalLM.forward  s    @ ,64:: 	,
)%+')	,
 	,
  118B>SV8W~ot4]kmA}a,?@A%4%%pVFt{{OeOepiopD%#33!//))
 	
r8   )	NNNNNNNNr   )r=   r>   r?   _tied_weights_keys_tp_plan_pp_planr*   r   r   rH   r   r   r   r  r   r   r   r   r   r<   r@   rA   s   @r7   r  r    s/   *,GH23H_-z:;H  .2.204(,26*.!%26-.8
##d*8
 t+8
 &&-	8

 8
 ((4/8
   4'8
 $;8
 ((4/8
 ell*8
 +,8
 
 8
  8
r8   r  c                       e Zd Zy)"MinistralForSequenceClassificationNr=   r>   r?   r   r8   r7   r/  r/        r8   r/  c                       e Zd Zy)MinistralForTokenClassificationNr0  r   r8   r7   r3  r3    r1  r8   r3  c                       e Zd ZdZy)MinistralForQuestionAnsweringtransformerN)r=   r>   r?   r   r   r8   r7   r5  r5    s    %r8   r5  )r   r   r  r/  r3  r5  )r!   )r   )Ccollections.abcr   typingr   rH   r   activationsr   cache_utilsr   r	   
generationr
   integrationsr   r   r   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r    configuration_ministralr"   Moduler$   rL   rW   r   r   rb   r   r}   r   r   r   r   r   r   r  r/  r3  r5  __all__r   r8   r7   <module>rI     s   %    ! . ) f f R B  P K F & I I ? 4299  ( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%4 )*>) >) +>)B Y'Jryy J (J(+6 +\   $><ryy ><B T
- T
 T
n H
3_ H
 H
V	)IKc 		&CE] 	&$?AY &r8   