
    Z jU                     r   S SK Jr  S SKJr  S SKrS SKJr  SSKJr  SSK	J
r
Jr  SSKJr  SSKJrJr  SS	KJr  SS
KJrJrJr  SSKJrJr  SSKJrJr  SSKJrJr  SSK J!r!  SSK"J#r#J$r$J%r%  SSK&J'r'J(r(  SSK)J*r*  SSK+J,r,   " S S\RZ                  5      r. " S S\RZ                  5      r/S\R`                  S\1S\R`                  4S jr2 S9S\RZ                  S\R`                  S\R`                  S\R`                  S \R`                  S-  S!\3S"\3S#\!\#   4S$ jjr4S% r5S:S& jr6\" \65       " S' S(\RZ                  5      5       r7\" S)5       " S* S+\RZ                  5      5       r8 " S, S-\5      r9\$ " S. S/\5      5       r:\$ " S0 S1\:5      5       r;\$ " S2 S3\:\5      5       r< " S4 S5\\:5      r= " S6 S7\\:5      r>/ S8Qr?g);    )Callable)OptionalN   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernelized_func)create_causal_mask) GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)maybe_autocastmerge_with_config_defaults)capture_outputs   )	GlmConfigc                   b   ^  \ rS rSrU 4S jrS\R                  S\R                  4S jrSrU =r	$ )GlmMLP/   c                    > [         TU ]  5         Xl        [        R                  " UR
                  SUR                  -  SS9U l        [        R                  " UR                  UR
                  SS9U l        [        UR                     U l        g )N   Fbias)super__init__confignnLinearhidden_sizeintermediate_sizegate_up_proj	down_projr   
hidden_actactivation_fnselfr(   	__class__s     u/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/glm/modeling_glm.pyr'   GlmMLP.__init__0   sn    IIf&8&8!f>V>V:V]bc6#;#;V=O=OV[\#F$5$56    hidden_statesreturnc                     U R                  U5      nUR                  SSS9u  p2X R                  U5      -  nU R                  U5      $ )Nr#   dim)r-   chunkr0   r.   )r2   r7   	up_statesgates       r4   forwardGlmMLP.forward8   sH    %%m4	#//!/4 2 24 88	~~i((r6   )r0   r(   r.   r-   )
__name__
__module____qualname____firstlineno__r'   torchFloatTensorr@   __static_attributes____classcell__r3   s   @r4   r    r    /   s,    7)U%6%6 )5;L;L ) )r6   r    c                      ^  \ rS rSr% \R
                  \S'   SS\4U 4S jjjr\	   SS\S-  S\
S   S\S-  S	\S
\4   4S jj5       r\R                  " 5       \S 5       5       rSrU =r$ )GlmRotaryEmbeddingA   inv_freqNr(   c                   > [         TU ]  5         UR                  U l        UR                  U l        Xl        U R
                  R                  S   U l        U R                  nU R                  S:w  a  [        U R                     nU" U R
                  U5      u  o@l
        U R                  SUSS9  U R                  SUR                  5       SS9  g )N	rope_typedefaultrN   F)
persistentoriginal_inv_freq)r&   r'   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr(   rope_parametersrP   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r2   r(   devicerope_init_fnrN   r3   s        r4   r'   GlmRotaryEmbedding.__init__D   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr6   r\   ztorch.deviceseq_lenr8   ztorch.Tensorc           	      j   U R                   S   nU R                   R                  SS5      n[        U SS5      =(       d    U R                  U R                  -  n[        XT-  5      nSnSU[        R                  " SUS[        R                  S9R                  U[        R                  S	9U-  -  -  nX4$ )
aH  
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   r#   dtype)r\   re   )rW   getgetattrr+   num_attention_headsintrF   arangeint64tofloat)	r(   r\   r_   baserb   rc   r<   attention_factorrN   s	            r4   rX   2GlmRotaryEmbedding.compute_default_rope_parametersT   s    & %%l3 & 6 6 : :;RTW X6:t4h8J8JfNhNh8h(23 U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r6   c                 L   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r:   r   mpscpuF)device_typeenabledr#   r;   rd   )rN   rm   expandshaperl   r\   
isinstancetypestrr   	transposerF   catcosrY   sinre   )
r2   xposition_idsinv_freq_expandedposition_ids_expandedrt   freqsembr}   r~   s
             r4   r@   GlmRotaryEmbedding.forwardt   sN    !MM$4-8>>@GGHZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E'E!((--[`J`ahhmmfkUC&,,.1F1L1L1NNYYZ[]^_E))UN3C'')d444C'')d444C	 D vvAGGv$cff177f&;;; DCs   BF
F#)rY   r(   rU   rV   rP   NNNN)rB   rC   rD   rE   rF   Tensor__annotations__r   r'   staticmethodr   ri   tuplerm   rX   no_gradr   r@   rH   rI   rJ   s   @r4   rL   rL   A   s    llVy V V  #'+/"*D *(* t* 
~u$	%	* *> ]]_<  <r6   rL   r7   n_repr8   c                     U R                   u  p#pEUS:X  a  U $ U SS2SS2SSS2SS24   R                  X#XU5      n U R                  X#U-  XE5      $ )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)rw   rv   reshape)r7   r   batchnum_key_value_headsslenrc   s         r4   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr6   modulequerykeyvalueattention_maskscalingdropoutkwargsc                    [        X R                  5      n[        X0R                  5      n	[        R                  " XR	                  SS5      5      U-  n
Ub  X-   n
[
        R                  R                  U
S[        R                  S9R                  UR                  5      n
[
        R                  R                  XU R                  S9n
[        R                  " X5      nUR	                  SS5      R                  5       nX4$ )Nr#   r   r:   )r<   re   )ptrainingr   )r   num_key_value_groupsrF   matmulr{   r)   
functionalsoftmaxfloat32rl   re   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r4   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r6   c                 x    U SSSS24   nU SSSS24   n[         R                  " U* U4SS9R                  S5      $ )	z*Rotates half the hidden dims of the input..r   Nr#   r   r:   r;   )rF   stackflatten)r   x1x2s      r4   rotate_halfr      sJ    	
319B	
319B;;Ryb)11"55r6   c                    UR                  U5      nUR                  U5      nUSSUR                  S   S-  24   R                  SSS9nUSSUR                  S   S-  24   R                  SSS9nUR                  S   nU SSU24   U SUS24   pvUSSU24   USUS24   pXb-  [        U5      U-  -   n
X-  [        U5      U-  -   n[        R
                  " X/SS9n
[        R
                  " X/SS9nX4$ )aI  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.
.Nr:   r#   r;   )	unsqueezerw   repeat_interleaver   rF   r|   )qkr}   r~   unsqueeze_dim
rotary_dimq_rotq_passk_rotk_passq_embedk_embeds               r4   apply_rotary_pos_embr      s6   $ --
&C
--
&C c'SYYr]a'''
(
:
:1"
:
EC
c'SYYr]a'''
(
:
:1"
:
EC 2Jc;J;&'3
+;)<6c;J;&'3
+;)<6 {{51C78G{{51C78G ii)r2Gii)r2Gr6   c                     ^  \ rS rSrSrSS\S\S-  4U 4S jjjr   SS\R                  S\
\R                  \R                  4   S-  S	\R                  S-  S
\S-  S\\   S\
\R                  \R                  4   4S jjrSrU =r$ )GlmAttention   z=Multi-headed attention from 'Attention Is All You Need' paperNr(   	layer_idxc                 <  > [         TU ]  5         Xl        X l        [	        USUR
                  UR                  -  5      U l        UR                  UR                  -  U l	        U R                  S-  U l
        UR                  U l        SU l        [        R                  " UR
                  UR                  U R                  -  UR                  S9U l        [        R                  " UR
                  UR                  U R                  -  UR                  S9U l        [        R                  " UR
                  UR                  U R                  -  UR                  S9U l        [        R                  " UR                  U R                  -  UR
                  SS9U l        g )Nrc   g      Tr$   F)r&   r'   r(   r   rg   r+   rh   rc   r   r   r   attention_dropout	is_causalr)   r*   attention_biasq_projk_projv_projo_projr2   r(   r   r3   s      r4   r'   GlmAttention.__init__   s@   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JFL^L^ejkr6   r7   position_embeddingsr   past_key_valuesr   r8   c                    UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
Uu  p[        XX5      u  pUb  UR                  XU R                  5      u  p[        R                  " U R                  R                  [        5      nU" U UU	U
U4U R                  (       d  SOU R                   U R"                  S.UD6u  pUR$                  " / UQSP76 R'                  5       nU R)                  U5      nX4$ )Nr:   r   r#           )r   r   )rw   rc   r   viewr{   r   r   r   updater   r   get_interfacer(   _attn_implementationr   r   r   r   r   r   r   )r2   r7   r   r   r   r   input_shapehidden_shapequery_statesr   r   r}   r~   attention_interfacer   r   s                   r4   r@   GlmAttention.forward   s~    $))#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&#7RU#[ &'6'='=jX\XfXf'g$J(?(M(MKK,,.E)
 %8	%
  $}}C$2H2HLL	%
 	%
! "));;;;FFHkk+.((r6   )r   r(   rc   r   r   r   r   r   r   r   r   r   r   )rB   rC   rD   rE   __doc__r   ri   r'   rF   r   r   r   r   r   r@   rH   rI   rJ   s   @r4   r   r      s    Gly lS4Z l l0 IM.2(,&)||&) #5<<#=>E&) t+	&)
 &) +,&) 
u||U\\)	*&) &)r6   r   RMSNormc                   x   ^  \ rS rSrS
S\SS4U 4S jjjrS\R                  S\R                  4S jrS r	S	r
U =r$ )
GlmRMSNormi  epsr8   Nc                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z)
GlmRMSNorm is equivalent to T5LayerNorm
N)r&   r'   r)   	ParameterrF   onesweightvariance_epsilon)r2   r+   r   r3   s      r4   r'   GlmRMSNorm.__init__  s/     	ll5::k#:; #r6   r7   c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )Nr#   r:   T)keepdim)	re   rl   rF   r   powmeanrsqrtr   r   )r2   r7   input_dtypevariances       r4   r@   GlmRMSNorm.forward$  sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r6   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)r   r   rw   r   )r2   s    r4   
extra_reprGlmRMSNorm.extra_repr+  s*    ))*+6$2G2G1HIIr6   )r   r   )gư>)rB   rC   rD   rE   rm   r'   rF   r   r@   r   rH   rI   rJ   s   @r4   r   r     sB    $ $$ $ $;U\\ ;ell ;J Jr6   r   c                     ^  \ rS rSrS\S\4U 4S jjr     SS\R                  S\R                  S-  S\R                  S-  S	\
S-  S
\S-  S\\R                  \R                  4   S-  S\\   S\R                  4S jjrSrU =r$ )GlmDecoderLayeri/  r(   r   c                   > [         TU ]  5         UR                  U l        [        XS9U l        [        U5      U l        [        UR                  UR                  S9U l	        [        UR                  UR                  S9U l
        g )N)r(   r   r   )r&   r'   r+   r   	self_attnr    mlpr   rms_norm_epsinput_layernormpost_attention_layernormr   s      r4   r'   GlmDecoderLayer.__init__0  si    !--%VI&>)&*<*<&BUBUV(263E3E6K^K^(_%r6   Nr7   r   r   r   	use_cacher   r   r8   c           
          UnU R                  U5      nU R                  " SUUUUUUS.UD6u  pX-   nUnU R                  U5      nU R                  U5      nX-   nU$ )N)r7   r   r   r   r   r    )r   r   r   r   )
r2   r7   r   r   r   r   r   r   residual_s
             r4   r@   GlmDecoderLayer.forward:  s     !,,];>> 
')%+ 3
 
 !0 !55mD/ 0r6   )r+   r   r   r   r   )NNNFN)rB   rC   rD   rE   r   ri   r'   rF   r   
LongTensorr   boolr   r   r   r@   rH   rI   rJ   s   @r4   r   r   /  s    `y `S ` /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r6   r   c                   R    \ rS rSr% \\S'   SrSrS/rS/r	Sr
SrSrSrSr\\S.rSrg	)
GlmPreTrainedModeliZ  r(   modelTr   r   )r7   
attentionsr   N)rB   rC   rD   rE   r   r   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsrH   r   r6   r4   r  r  Z  sQ    &*#*+#4"5N!"&("r6   r  c                     ^  \ rS rSrS\4U 4S jjr\\\      SS\	R                  S-  S\	R                  S-  S\	R                  S-  S\S-  S	\	R                  S-  S
\S-  S\\   S\4S jj5       5       5       rSrU =r$ )GlmModelim  r(   c           	        > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ s H  n[        X5      PM     sn5      U l        [        UR                  UR                  S9U l        [#        US9U l        SU l        U R)                  5         g s  snf )Nr   r(   F)r&   r'   pad_token_idpadding_idx
vocab_sizer)   	Embeddingr+   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normrL   
rotary_embgradient_checkpointing	post_initr   s      r4   r'   GlmModel.__init__o  s     !.. ++LL):):F<N<NPTP`P`ammAFvG_G_A`aA`I_V/A`a
 v11v7J7JK	,F;&+# 	 bs   C?N	input_idsr   r   r   inputs_embedsr   r   r8   c           
      >   US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc  [        U R                  S9nUcU  Ub  UR	                  5       OSn[
        R                  " UR                  S   UR                  S9U-   nUR                  S5      n[        U R                  UUUUS9n	Un
U R                  XS9nU R                  S U R                  R                    H  nU" U
4U	UUUUS.UD6n
M     U R                  U
5      n
[        U
US	9$ )
Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r   )r\   )r(   r#  r   r   r   )r   )r   r   r   r   r   )last_hidden_stater   )
ValueErrorr  r   r(   get_seq_lengthrF   rj   rw   r\   r   r   r  r  r  r  r   )r2   r"  r   r   r   r#  r   r   past_seen_tokenscausal_maskr7   r   decoder_layers                r4   r@   GlmModel.forward  sF    -t";<YZZ *.*;*;I*FM0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L(;;')+%
 &"oomoW![[)H4;;+H+HIM)*$7) /# M J 		-0&++
 	
r6   )r  r  r  r  r  r  r  )NNNNNN)rB   rC   rD   rE   r   r'   r   r   r   rF   r   r   r   rG   r  r   r   r   r@   rH   rI   rJ   s   @r4   r  r  m  s    y     .2.204(,26!%2
##d*2
 t+2
 &&-	2

 2
 ((4/2
 $;2
 +,2
 
!2
    2
r6   r  c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )GlmForCausalLMi  zlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr7   logitsc                    > [         TU ]  U5        [        U5      U l        UR                  U l        [
        R                  " UR                  UR                  SS9U l        U R                  5         g )NFr$   )
r&   r'   r  r  r  r)   r*   r+   r.  r   r1   s     r4   r'   GlmForCausalLM.__init__  sU     f%
 ++yy!3!3V5F5FUS 	r6   Nr"  r   r   r   r#  labelsr   logits_to_keepr   r8   c	           
      |   U R                   " SUUUUUUS.U	D6n
U
R                  n[        U[        5      (       a  [	        U* S5      OUnU R                  USS2USS24   5      nSnUb)  U R                  " SXU R                  R                  S.U	D6n[        UUU
R                  U
R                  U
R                  S9$ )ac  
Example:

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

>>> model = GlmForCausalLM.from_pretrained("meta-glm/Glm-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-glm/Glm-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"  r   r   r   r#  r   N)r0  r3  r  )lossr0  r   r7   r  r   )r  r%  rx   ri   slicer.  loss_functionr(   r  r   r   r7   r  )r2   r"  r   r   r   r#  r3  r   r4  r   outputsr7   slice_indicesr0  r6  s                  r4   r@   GlmForCausalLM.forward  s    > ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r6   )r.  r  r  )NNNNNNNr   )rB   rC   rD   rE   _tied_weights_keys_tp_plan_pp_planr'   r   r   rF   r   r   r   rG   r  ri   r   r   r   r@   rH   rI   rJ   s   @r4   r-  r-    s   *,GH23H_-z:;H  .2.204(,26*.!%-.6
##d*6
 t+6
 &&-	6

 6
 ((4/6
   4'6
 $;6
 ell*6
 +,6
 
 6
  6
r6   r-  c                       \ rS rSrSrg)GlmForSequenceClassificationi  r   NrB   rC   rD   rE   rH   r   r6   r4   r@  r@        r6   r@  c                       \ rS rSrSrg)GlmForTokenClassificationi  r   NrA  r   r6   r4   rD  rD    rB  r6   rD  )r  r  r-  r@  rD  )r   )r   )@collections.abcr   typingr   rF   torch.nnr)   activationsr   cache_utilsr   r   
generationr	   integrationsr
   r   masking_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r   utils.output_capturingr   configuration_glmr   Moduler    rL   r   ri   r   rm   r   r   r   r   r   r   r  r  r-  r@  rD  __all__r   r6   r4   <module>rX     s  * %    ! . ) L / 
 P K F & I I G 5 ()RYY )$@< @<F	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%26%P )*>)299 >) +>)B Y'J J (J((0 (V   $ F
! F
 F
R F
' F
 F
R	#CEW 		 =?Q 	r6   