
    hA                     F   S SK Jr  S SKrS SKrS SKJr  S SKJrJrJ	r	J
r
  \R                  " / SQS9S 5       r\R                  " SS	/S9S
 5       r\R                  " / SQS9S 5       r\R                  " / SQS9S 5       r\R                  S 5       r\R                  S 5       r\R                  S 5       r\R                  S 5       r\R                  S 5       rS rS!S jrS rS rS rS rS r " S S\5      r\R<                  R>                  S 5       r S r!S r"S  r#g)"    )partialN)assert_allclose)LinearNumpyOpsReluchain)      	   )paramsc                     U R                   $ Nparamrequests    ^/home/james-whalen/.local/lib/python3.13/site-packages/thinc/tests/layers/test_feed_forward.pynBr   
       ==    r	      c                     U R                   $ r   r   r   s    r   nIr      r   r   )r	         c                     U R                   $ r   r   r   s    r   nHr      r   r   )r	   r
      r   c                     U R                   $ r   r   r   s    r   nOr       r   r   c                 8    [        X5      R                  5       nU$ r   )r   
initialize)r   r   models      r   model1r$      s    L##%ELr   c                 8    [        X5      R                  5       nU$ r   )r   r"   )r    r   r#   s      r   model2r&   $   s    2N%%'ELr   c                 4    [         R                  " X4SS9S-   $ Nfdtype      ?)numpyones)r   r   s     r   
input_datar/   *   s    ::rhc*S00r   c                 4    [         R                  " X4SS9S-
  $ r(   )r-   zeros)r   r    s     r   gradient_datar2   /   s    ;;xs+c11r   c                 4    [        X5      R                  5       $ r   )r   r"   )r$   r&   s     r   r#   r#   4   s     ++--r   c                     [        5       nU n[        [        X5      5       HB  u  nu  pgUR                  U5      nUS:  a  XDS:  -  n[        R
                  " XFS/S//S9U-   nMD     U$ )Nr   r	   )axes)r   	enumeratezipasarrayr-   	tensordot)r/   Wsbs	numpy_opsXiWbs           r   get_expected_predictrA   9   sm    
IAs2{+	6Aa q5QJAOOAaSz2Q6	 ,
 Hr   c                 >    U " X-   5      nU " X-
  5      nX4-
  SU-  -  $ )Nr
    )predictweightsepsilonout1out2s        r   numeric_gradientrI   D   s-    7$%D7$%DKAK((r   c                    U R                  S5      R                  X24:X  d   eU R                  S5      R                  U4:X  d   eUR                  S5      R                  XC4:X  d   eUR                  S5      R                  U4:X  d   eg )Nr?   r@   )	get_paramshape)r$   r&   r   r   r    s        r   test_models_have_shaperM   J   s    C &&2(222C &&2%///C &&2(222C &&2%///r   c                     U R                  S5      UR                  S5      :X  d   eU R                  S5      UR                  S5      :X  d   eg )Nr   r    )get_dim)r#   r$   r&   r   r   r    s         r   test_model_shaperP   Q   sB    ==&.."6666==&.."6666r   c                  P   [        SS9n U R                  R                  SS5      nU R                  R                  SS5      nU R                  S5      b   eU R                  S5      b   eU R	                  XS9  U R                  S5      S:X  d   eU R                  S5      S:X  d   eg )	N皙?)dropout   r   r
   r   r    )r=   Y)r   opsalloc2fhas_dimr"   rO   )r#   r=   rU   s      r   test_infer_output_shaperY   V   s    E		!QA		!QA==&&&==&&&	q==!###==!###r   c                 (   [        X5      n U R                  U5      nU R                  U5      u  pV[        XE5        [	        UUR                  S5      UR                  S5      /UR                  S5      UR                  S5      /5      n[        XWSSS9  g )Nr?   r@   g{Gz?-C6?atolrtol)r   rD   begin_updater   rA   rK   )r#   r$   r&   r/   via_predict
via_update_expecteds           r   #test_predict_and_begin_update_matchrd   a   s    &!E--
+K&&z2MJK,#			#	 0 0 56			#	 0 0 56H
 Jt$?r   c                  f  ^ 0 mSU4S jjn [        S5      n[        S5      n[        S5      n[        U S5      Ul        [        U S5      Ul        [        U S5      Ul        [        U[        X#5      5      nT(       a   eUR	                  5         TS   (       d   eTS   (       d   eTS   (       d   eg )Nc                    > STU '   g )NTrC   )namer#   r=   rU   init_was_calleds       r   register_init5test_init_functions_are_called.<locals>.register_initq   s     $r   r   onetwothree)NN)r   r   initr   r"   )ri   layer1layer2layer3r#   rh   s        @r   test_init_functions_are_calledrr   n   s    O% AYFAYFAYF-/FK-/FK-1FK&%/0E	5!!!5!!!7###r   c                        \ rS rSrS rS rSrg)GradientSpy   c                      S U l         S U l        g r   rE   	d_weights)selfs    r   __init__GradientSpy.__init__   s    r   c                     Xl         X l        g r   rw   )ry   rE   grads      r   __call__GradientSpy.__call__   s    r   )rx   rE   N)__name__
__module____qualname____firstlineno__rz   r~   __static_attributes__rC   r   r   rt   rt      s    r   rt   c            	         [         R                  " [        [        4SS9n SU S'   [        R                  [        5      u  pU" X-
  5        [        R                   Hf  nUR                   HS  nUR                  U5      R                  5       n[        X4[        5      n[        XeR                  U 5      n[        XWSSS9  MU     Mh     g )Nfloat32r*   r,   r   rR   r\   )r-   r1   r   r    r#   r_   r/   layersparam_namesget_gradravelget_predictget_numeric_gradientsizer   )truthguessbackproplayerrg   agradrD   ngrads           r   test_gradientr      s     KKR	2EE!H((4OEU]%%DNN4(..0E!%z:G(**eDEEs=	 & r   c                    ^ ^^ UU U4S jnU$ )zHelper for gradient check. To do the numeric gradient check, we have
to be able to wiggle one value in a parameter, and check the prediction
before and after. So we need to get a callback that gives an output
given a change to one weight.
c                 V  > TR                  T5      nUR                  nUR                  5       nX ==   U-  ss'   TR                  TUR	                  U5      5        TR                  T5      nX ==   U-  ss'   TR                  TUR	                  U5      5        UR	                  U5      $ r   )rK   rL   r   	set_paramreshaperD   )r>   rF   r   rL   outputsinputsr   
param_names        r   rD   get_predict.<locals>.predict   s    
+G
EMM%$89--'G
EMM%$89u%%r   rC   )r   r   r   rD   s   ``` r   r   r      s    	& Nr   c                     [         R                  " U5      n[        U5       HA  nU " US5      nU " US5      n[        XR5      n[        Xb5      nXx-
  S-  X4'   [	        SXGU5        MC     U$ )Nr[   g-C6g-C6*?NGrad)r-   r1   range	_get_lossprint)	rD   ntargetgradientr>   rG   rH   err1err2s	            r   r   r      sj    {{1~H1Xq$q% &&{x0gq%  Or   c           	          [         R                  " [         R                  " S[         R                  " X-
  5      -  S5      5      $ )Ng      ?r	   )r-   sumsquare)r   r   s     r   r   r      s,    99UYYsU\\%-%@@!DEEr   )r[   )$	functoolsr   r-   pytestnumpy.testingr   	thinc.apir   r   r   r   fixturer   r   r   r    r$   r&   r/   r2   r#   rA   rI   rM   rP   rY   rd   rr   objectrt   markskipr   r   r   r   rC   r   r   <module>r      se      ) 3 3 y! " 1v  y! " |$ %  
  
 1 1 2 2 . .)07
$
@$*&  > >*
Fr   