
    iӃ                     
   d Z ddlmZ ddlmZ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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%m&Z& ddl'm(Z(m)Z) ddl*m+Z+  e%       rddl,m-Z- ddl.m/Z/  e&j`                  e1      Z2 G d dejf                        Z4d Z5d4dZ6 G d dejf                        Z7	 d5dejf                  dejp                  dejp                  d ejp                  d!ejp                  dz  d"e9d#e9fd$Z: G d% d&ejf                        Z; G d' d(e      Z<e# G d) d*e             Z=e# G d+ d,e=             Z> G d- d.e=e      Z? G d/ d0ee=      Z@ G d1 d2ee=      ZAg d3ZBy)6zPyTorch Persimmon model.    )Callable)OptionalUnionN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)AttentionMaskConverter)FlashAttentionKwargs) GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)auto_docstringcan_return_tupleis_torch_flex_attn_availablelogging)is_flash_attention_requestedmaybe_autocast   )PersimmonConfig)	BlockMask)make_flex_block_causal_maskc                        e Zd ZU ej                  ed<   ddef fdZe	 	 	 ddedz  de	d   de
dz  ded	ef   fd
       Z ej                         ed               Z xZS )PersimmonRotaryEmbeddinginv_freqNconfigc                    t         |           |j                  | _        |j                  | _        || _        | j
                  j                  d   | _        | j                  }| j                  dk7  rt        | j                     } || j
                  |      \  }| _
        | j                  d|d       | j                  d|j                         d       y )N	rope_typedefaultr$   F)
persistentoriginal_inv_freq)super__init__max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr%   rope_parametersr'   compute_default_rope_parametersr   attention_scalingregister_bufferclone)selfr%   devicerope_init_fnr$   	__class__s        z/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/transformers/models/persimmon/modeling_persimmon.pyr,   z!PersimmonRotaryEmbedding.__init__A   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L($(ZeD0(..2BuU    r6   ztorch.deviceseq_lenreturnztorch.Tensorc                 n   | j                   d   }| j                   j                  dd      }t        | dd      xs | j                  | j                  z  }t        ||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_thetapartial_rotary_factorg      ?head_dimNr      dtype)r6   rC   )r0   getgetattrhidden_sizenum_attention_headsinttorcharangeint64tofloat)	r%   r6   r;   baser?   r@   dimattention_factorr$   s	            r9   r1   z8PersimmonRotaryEmbedding.compute_default_rope_parametersQ   s    ( %%l3 & 6 6 : :;RTW X6:t4h8J8JfNhNh8h(223 U\\!S!5;;?BB&X]XcXcBdgjjk
 )))r:   c                 N   | j                   d d d d f   j                         j                  |j                  d   dd      j	                  |j
                        }|d d d d d f   j                         }t        |j
                  j                  t              r/|j
                  j                  dk7  r|j
                  j                  nd}t        |d      5  |j                         |j                         z  j                  dd      }t        j                  ||fd	      }|j                         | j                  z  }|j                         | j                  z  }	d d d        j	                  |j                   
      	j	                  |j                   
      fS # 1 sw Y   AxY w)Nr   r   mpscpuF)device_typeenabledrA   rO   rB   )r$   rM   expandshaperL   r6   
isinstancetypestrr   	transposerI   catcosr2   sinrC   )
r5   xposition_idsinv_freq_expandedposition_ids_expandedrU   freqsembr_   r`   s
             r9   forwardz PersimmonRotaryEmbedding.forwardr   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$N)NNN)__name__
__module____qualname__rI   Tensor__annotations__r   r,   staticmethodr   rH   tuplerM   r1   no_gradr   rg   __classcell__r8   s   @r9   r#   r#   >   s    llV V   *.+/"*$&*(* t* 
~u$	%	* *> U]]_<  <r:   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..NrR   rA   rW   )rY   rI   r^   )ra   x1x2s      r9   rotate_halfrv      sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r:   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.
        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.
    )	unsqueezerv   )qkr_   r`   unsqueeze_dimq_embedk_embeds          r9   apply_rotary_pos_embr~      sY    $ --
&C
--
&C3w;q>C/0G3w;q>C/0GGr:   c                   $     e Zd Z fdZd Z xZS )PersimmonMLPc                    t         |           t        j                  |j                  |j
                        | _        t        j                  |j
                  |j                        | _        t        |j                     | _
        y rh   )r+   r,   r   LinearrF   intermediate_sizedense_h_to_4hdense_4h_to_hr   
hidden_actactr5   r%   r8   s     r9   r,   zPersimmonMLP.__init__   s^    YYv'9'96;S;STYYv'?'?ASAST&++,r:   c                 l    | j                  |      }| j                  |      }| j                  |      }|S rh   )r   r   r   )r5   hidden_statess     r9   rg   zPersimmonMLP.forward   s6    **=9/**=9r:   )ri   rj   rk   r,   rg   rq   rr   s   @r9   r   r      s    -r:   r   modulequerykeyvalueattention_maskscalingdropoutc                    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 )NrA   r   rR   )rO   rC   )ptrainingr   )rI   matmulr]   rY   r   
functionalsoftmaxfloat32rL   rC   r   r   
contiguous)r   r   r   r   r   r   r   kwargsattn_weightscausal_maskattn_outputs              r9   eager_attention_forwardr      s     <<s}}Q':;gEL!$Q1o		"o%=>#k1==((2U]](SVVW\WbWbcL==((6??([L,,|U3K''1-88:K$$r:   c                       e Zd ZdZddededz  f fdZdej                  de	ej                  ej                  ej                  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edej                  dz  de	ej                  ej                  f   dz  dee   de	ej                  ej                  dz  e	ej                     dz  f   fdZ xZS )PersimmonAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNr%   	layer_idxc                    t         |           || _        || _        |-t        j                  d| j                  j                   d       |j                  | _        |j                  | _
        | j                  | j                  z  | _        t        | j                  |j                  d   z        | _        d| _        | j                  | j                  z  | j                  k7  r&t!        d| j                   d| j                   d      t#        j$                  | j                  d| j                  z  d	      | _        t#        j$                  | j                  | j                  z  | j                  d	      | _        |j*                  | _        | j                  d
z  | _        | j*                  r|t#        j.                  |j                  | j                  z  |j0                  d      | _        t#        j.                  |j                  | j                  z  |j0                  d      | _        t#        j6                  |j8                        | _        y )NzInstantiating z without passing a `layer_idx` is not recommended and will lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` when creating this class.r?   Tz?hidden_size must be divisible by num_heads (got `hidden_size`: z and `num_heads`: z).r   biasg      )epselementwise_affine)r+   r,   r%   r   loggerwarning_oncer8   ri   rF   rG   	num_headsr@   rH   r0   rotary_ndims	is_causal
ValueErrorr   r   query_key_valuedenseqk_layernormr   	LayerNormlayer_norm_epsq_layernormk_layernormDropoutattention_dropoutr5   r%   r   r8   s      r9   r,   zPersimmonAttention.__init__   s   " !8!8 9 :, , "--33((DNN:0F0FG^0_ _`MMDNN*t/?/??QRVRbRbQc$T^^$4B8   "yy)9)91t?O?O;OVZ[YYt~~=t?O?OVZ[
"//}}d*!||""dnn4&:O:Odh D  "||""dnn4&:O:Odh D "$F,D,D!Er:   	fused_qkvr<   c                     |j                   \  }}}|j                  ||| j                  d| j                        }|ddddf   |ddddf   |ddddf   fS )a  
        Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory
        storage as `fused_qkv`

        Args:
            fused_qkv (`torch.tensor`): [batch_size, seq_length, num_heads * 3 * head_dim]

        Returns:
            query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
            value: [batch_size, seq_length, num_heads, head_dim]
        r   .r   Nr   rA   )rY   viewr   r@   )r5   r   
batch_size
seq_lengththree_times_hidden_sizes        r9   _split_headszPersimmonAttention._split_heads   sb     ;D//7
J 7NN:z4>>1dmm\	a#YsAqy%99S!QY;OOOr:   r   r   rb   past_key_valuesoutput_attentions	use_cachecache_positionposition_embeddingsr   c	                     |j                         \  }
}}| j                  |      }| j                  |      \  }}}| j                  r"| j	                  |      }| j                  |      }|j                  dd      }|j                  dd      }|j                  dd      }|\  }}|dd | j                  f   |d| j                  d f   }}|dd | j                  f   |d| j                  d f   }}t        ||||      \  }}t        j                  ||fd      }t        j                  ||fd      }|2||| j                  |d}|j                  ||| j                  |      \  }}t        j                  | j                  j                   t"              } || ||||f| j$                  sdn| j                  j&                  | j(                  d|	\  }}|j+                  |
|d      }| j-                  |      }|sd }||fS )	Nr   rA   .rR   rW   )r`   r_   partial_rotation_sizer           )r   r   )sizer   r   r   r   r   r]   r   r~   rI   r^   updater   r   get_interfacer%   _attn_implementationr   r   r   r   reshaper   )r5   r   r   rb   r   r   r   r   r   r   bszq_len_r   query_states
key_statesvalue_statesr_   r`   	query_rot
query_passkey_rotkey_passcache_kwargsattention_interfacer   r   s                              r9   rg   zPersimmonAttention.forward  sI    &**,UA ((7	 483D3DY3O0z<++L9L))*5J $--a3#--a3))!Q/
&S1 1 1112d//112 	
 s/d////0sD--//0 
 2)Wc3O	7 yy)Z!8bAYY2;
& )-):):"0	L (7'='=j,X\XfXfht'u$J(?(M(MKK,,.E)
 %8	%
  $}}C$++2O2OLL	%
 	%
!\ "))#ub9jj- LL((r:   rh   NNNFFNN)ri   rj   rk   __doc__r   rH   r,   rI   rl   ro   r   
LongTensorr	   boolr   r   rg   rq   rr   s   @r9   r   r      sJ   G#F #F3: #FJPell PuU\\5<<Y^YeYe=e7f P& /304(,"'26HLL)||L) t+L) &&-	L)
 L)  L) L) ((4/L) #5<<#=>EL) -.L) 
u||U\\D0%2E2LL	ML)r:   r   c                   p    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
dz  dej                  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 xZS )PersimmonDecoderLayerr%   r   c                    t         |           |j                  | _        t        ||      | _        t        |      | _        t        j                  |j                  |j                        | _
        t        j                  |j                  |j                        | _        t        j                  |j                        | _        y )N)r%   r   r   )r+   r,   rF   r   	self_attnr   mlpr   r   r   input_layernormpost_attention_layernormr   hidden_dropoutr   r   s      r9   r,   zPersimmonDecoderLayer.__init__R  s    !--+6YO'!||F,>,>FDYDYZ(*V5G5GVMbMb(c%zz&"7"78r:   Nr   r   rb   r   r   r   r   r   r   r<   c	                     |}
| j                  |      } | j                  d||||||||d|	\  }}|
|z   }|}
| j                  |      }| j                  |      }| j	                  |      }||
z   }|f}|r||fz  }|S )an  
        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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
                `[0, config.n_positions - 1]`.
                [What are position IDs?](../glossary#position-ids)
            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`).
            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
                Indices depicting the position of the input sequence tokens in the sequence
            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.
        )r   r   rb   r   r   r   r   r    )r   r   r   r   r   )r5   r   r   rb   r   r   r   r   r   r   residualself_attn_weightsoutputss                r9   rg   zPersimmonDecoderLayer.forward[  s    H !,,]; ,:4>> 
,
')%+/) 3
,
 
,
(( !=0 !55mD/]3%0 ")++Gr:   r   )ri   rj   rk   r   rH   r,   rI   rl   r   r	   r   ro   r   r   FloatTensorrg   rq   rr   s   @r9   r   r   Q  s   9 93 9 /304(,).!&26HLC||C t+C &&-	C
 C  $;C $;C ((4/C #5<<#=>EC -.C 
u  %(9(95;L;L(L"MPT"TT	UCr:   r   c                   :    e Zd ZU eed<   dZdZdgZdZdZ	dZ
dZdZy)PersimmonPreTrainedModelr%   modelTr   r   N)ri   rj   rk   r   rm   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_can_compile_fullgraph_supports_sdpa_supports_flash_attn_supports_attention_backendr   r:   r9   r   r     s:    &*#01"3!N"&r:   r   c                       e Zd 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dz  dedz  dej                  dz  dee   defd              Z	 ddeej                  df   dej                  dej                  dedef
dZedej                  dededej,                  dej                  defd       Z xZS )PersimmonModelz
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PersimmonDecoderLayer`]

    Args:
        config: PersimmonConfig
    r%   c           	      4   t         |   |       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                        | _        t        j                  t        |j                        D cg c]  }t        ||       c}      | _        t        j                  |j                  |j                        | _        t#        | j$                        | _        d| _        | j+                          y c c}w )Nr   r%   F)r+   r,   pad_token_idpadding_idx
vocab_sizer   	EmbeddingrF   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   final_layernormr#   r%   
rotary_embgradient_checkpointing	post_initr   s      r9   r,   zPersimmonModel.__init__  s     !.. ++LL):):F<N<NPTP`P`ammGLVMeMeGfg)"695g
  "||F,>,>FDYDYZ2$++F&+# hs   DN	input_idsr   rb   r   inputs_embedsr   r   output_hidden_statesr   r   r<   c
                 j   ||n| j                   j                  }||n| j                   j                  }||n| j                   j                  }|d u |d uz  rt	        d      | j
                  r%| j                  r|rt        j                  d       d}|r|t        | j                         }|| j                  |      }|	F||j                         nd}t        j                  |||j                  d   z   |j                        }	||	j!                  d      }| j#                  |||	||      }|}| j%                  ||      }|rd	nd }|rd	nd }| j&                  D ],  }|r||fz  } ||f||||||	|d
|
}|d   }|s$||d   fz  }. | j)                  |      }|r||fz  }t+        ||||      S )Nz:You must specify exactly one of input_ids or inputs_embedszZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...Fr   r   r   r6   )rb   r   )r   rb   r   r   r   r   r   )last_hidden_stater   r   
attentions)r%   r   r	  r   r   r  r   r   r   r
   r   get_seq_lengthrI   rJ   rY   r6   rx   _update_causal_maskr  r  r  r   )r5   r  r   rb   r   r  r   r   r	  r   r   past_seen_tokensr   r   r   all_hidden_statesall_self_attnsdecoder_layerlayer_outputss                      r9   rg   zPersimmonModel.forward  s'    2C1N-TXT_T_TqTq$8$D $++JjJj 	 "+!6IDKK<Q<Q	-t";<YZZ&&4==##p "	0*$++>O  --i8M!CRC^==?de"\\ "2]5H5H5K"KTaThThN )33A6L..M>?L]
 &"oom,oW #7BD0d![[ 	6M#!m%55!)
*) /"3#-$7
 
M *!,M =#3"55'	6* ,,];  -!11&+++%	
 	
r:   r    input_tensorc           	         t        | j                        r||dk(  j                         r|S y | j                  j                  dk(  r't	        |t
        j                        rt        |      }|S ||j                         nd}||j                  nd}| j                  j                  dk(  r(|s&|s$t        j                  |||| j                        ry |j                  }|j                  d   }	|r|j                         }
n1t	        |t
        j                        r|j                  d   n||	z   dz   }
| j!                  ||	|
|||j                  d   	      }| j                  j                  dk(  rQ|O|j"                  j$                  d
v r7|s5t        j&                  |      j(                  }t        j*                  ||      }|S )Nr   flex_attentionr   Fsdpa)r  past_key_values_lengthis_trainingr   rR   )sequence_lengthtarget_lengthrC   r   r   )cudaxpunpu)r   r%   anyr   rZ   rI   rl   r!   r  is_compileabler   _ignore_causal_mask_sdpar   rC   rY   get_max_cache_shape5_prepare_4d_causal_attention_mask_with_cache_positionr6   r[   finfomin_unmask_unattended)r5   r   r  r   r   r   r  using_compilable_cacherC   r  r  r   	min_dtypes                r9   r  z"PersimmonModel._update_causal_mask!  s    (4)~/D.I.I.K%%;;++/??.%,,7!<^!L!!
 @O?Z?99;`aCRC^!?!?di ;;++v5>T]n%>>*'7 MM	 ""&,,Q/!+??AM nell; $$R(%7!;  PP+')#))!, Q 
 KK,,6*%%**.DD%
 E*..I0CCKQZ[Kr:   r  r  rC   r   c                    | | j                         dk(  r| }|S t        j                  |      j                  }t        j                  ||f|||j
                        }|dk7  rt        j                  |d      }|t        j                  ||j
                        |j                  dd      kD  z  }|ddddddf   j                  |ddd      }| |j                         }| j                  d   }	|ddddddd|	f   | ddddddf   j                  |j
                        z   }
|
dk(  }
|ddddddd|	f   j                  |
|      |ddddddd|	f<   |S )	aM  
        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.

        Args:
            attention_mask (`torch.Tensor`):
                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
                `(batch_size, 1, query_length, key_value_length)`.
            sequence_length (`int`):
                The sequence length being processed.
            target_length (`int`):
                The target length: when generating with static cache, the mask should be as long as the static cache,
                to account for the 0 padding, the part of the cache that is not filled yet.
            dtype (`torch.dtype`):
                The dtype to use for the 4D attention mask.
            cache_position (`torch.Tensor`):
                Indices depicting the position of the input sequence tokens in the sequence.
            batch_size (`torch.Tensor`):
                Batch size.
        N   )
fill_valuerC   r6   r   )diagonalr  rR   r   )rO   rI   r%  r&  fullr6   triurJ   r   rX   r4   rY   rL   masked_fill)r   r  r  rC   r   r   r   r   r)  mask_lengthpadding_masks              r9   r$  zDPersimmonModel._prepare_4d_causal_attention_mask_with_cache_positione  s   > %.*<*<*>!*C(K* ' E*..I** -0Ye\j\q\qK !##jjqA5<<n>S>STWeWmWmnprsWtttK%dD!Q&67>>z1bRTUK))//1,2226*1aL[L+@ANSTVZ\`bcScDdDgDg&&E    ,q05@Aq,;,AV5W5c5c )6Aq!\k\12 r:   )	NNNNNNNNN)F)ri   rj   rk   r   r   r,   r   r   rI   r   rl   r	   r   r   r   r   r   rg   r   r  rn   rH   rC   r$  rq   rr   s   @r9   r   r     s      .2.204(,26!%)-,026V
##d*V
 t+V
 &&-	V

 V
 ((4/V
 $;V
  $;V
 #TkV
 ((4/V
 -.V
 
!V
  V
~ #(BellK78B llB 	B
 B  BH 444 4 {{	4
 4 4 4r:   r   c                   X    e Zd Zdd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dz  dedz  dej                  dz  deej                  z  defd              Z xZS )PersimmonForCausalLMzlm_head.weightzmodel.embed_tokens.weightc                     t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        | j                          y )NFr   )
r+   r,   r   r   r   r   r   rF   lm_headr  r   s     r9   r,   zPersimmonForCausalLM.__init__  sU     #F+
 ++yy!3!3V5F5FUS 	r:   Nr  r   rb   r   r  labelsr   r   r	  r   logits_to_keepr<   c                    ||n| j                   j                  }|	|	n| j                   j                  }	 | j                  d||||||||	|
d	|}|j                  }t        |t              rt        | d      n|}| j                  |dd|ddf         }d}|* | j                  ||fd| j                   j                  i|}t        |||j                  |j                  |j                        S )uk  
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Example:

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

        >>> model = PersimmonForCausalLM.from_pretrained("adept/persimmon-8b-base")
        >>> tokenizer = AutoTokenizer.from_pretrained("adept/persimmon-8b-base")

        >>> prompt = "human: Hey, what should I eat for dinner?"
        >>> 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]
        'human: Hey, what should I eat for dinner?\n\ncat: 🐱\n\nhuman: 😐\n\n'
        ```N)	r  r   rb   r   r  r   r   r	  r   r   )losslogitsr   r   r  r   )r%   r   r	  r   r  rZ   rH   slicer6  loss_functionr   r   r   r   r  )r5   r  r   rb   r   r  r7  r   r   r	  r   r8  r   r   r   slice_indicesr;  r:  s                     r9   rg   zPersimmonForCausalLM.forward  s*   P 2C1N-TXT_T_TqTq$8$D $++JjJj 	
 ,64:: ,
)%+'/!5),
 ,
  118B>SV8W~ot4]kmA}a,?@A%4%%  ;;11 	D &#33!//))
 	
r:   )NNNNNNNNNNr   )ri   rj   rk   _tied_weights_keysr,   r   r   rI   r   rl   r	   r   r   rH   r   rg   rq   rr   s   @r9   r4  r4    s0   *,GH  .2.204(,26*.!%)-,026-.M
##d*M
 t+M
 &&-	M

 M
 ((4/M
   4'M
 $;M
  $;M
 #TkM
 ((4/M
 ell*M
 
 M
  M
r:   r4  c                       e Zd Zy)"PersimmonForSequenceClassificationNri   rj   rk   r   r:   r9   rA  rA        r:   rA  c                       e Zd Zy)PersimmonForTokenClassificationNrB  r   r:   r9   rE  rE     rC  r:   rE  )r4  r   r   rA  rE  )r   )r   )Cr   collections.abcr   typingr   r   rI   r   activationsr   cache_utilsr	   r
   
generationr   modeling_attn_mask_utilsr   modeling_flash_attention_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   r   utils.genericr   r   configuration_persimmonr   !torch.nn.attention.flex_attentionr    integrations.flex_attentionr!   
get_loggerri   r   Moduler#   rv   r~   r   rl   rM   r   r   r   r   r   r4  rA  rE  __all__r   r:   r9   <module>rZ     s  &  $ "   ! . ) > B 
 G & \ \ I 4  !;J 
		H	%A<ryy A<J(4299 * %II%<<% 
% <<	%
 LL4'% % %.D) D)NM6 M` 	' 	' 	' l- l l^\
3_ \
~ j)IKc i d&CE] cr:   