
    -il                        S SK r 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
  S SKJrJr  S SKJr  S SKJr  S SKJr   " S	 S
\	5      r " S S\
\	5      r " S S\	5      r " S S\	5      r " S S\	5      r " S S\	5      r " S S\	5      r " S S\	5      r " S S\	5      r " S S\	5      rS rS rS r S  r!\RD                  RG                  S!S"S#/5      S$ 5       r$S% r%S& r&S' r'S( r(S) r)S* r*S+ r+g),    N)PrettyPrinter)config_context)BaseEstimatorTransformerMixin)SelectKBestchi2)LogisticRegressionCV)make_pipeline)_EstimatorPrettyPrinterc                   B    \ rS rSr               SS jrS rSrg)LogisticRegression   Nc                     Xl         X l        X0l        X@l        XPl        X`l        Xpl        Xl        Xl        Xl	        Xl
        Xl        Xl        Xl        Xl        g N)penaltydualtolCfit_interceptintercept_scalingclass_weightrandom_statesolvermax_itermulti_classverbose
warm_startn_jobsl1_ratio)selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   r   s                   R/var/www/html/venv/lib/python3.13/site-packages/sklearn/utils/tests/test_pprint.py__init__LogisticRegression.__init__   sT    $ 	*!2(( &$     c                     U $ r    )r    Xys      r!   fitLogisticRegression.fit3       r$   )r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   )l2F-C6?      ?T   NNwarnd   r0   r   FNN)__name__
__module____qualname____firstlineno__r"   r)   __static_attributes__r&   r$   r!   r   r      s<     
! !Dr$   r   c                   (    \ rS rSrSS jrSS jrSrg)StandardScaler7   c                 (    X l         X0l        Xl        g r   )	with_meanwith_stdcopy)r    r=   r;   r<   s       r!   r"   StandardScaler.__init__8   s    " 	r$   Nc                     U $ r   r&   r    r'   r=   s      r!   	transformStandardScaler.transform=   r+   r$   )r=   r;   r<   )TTTr   )r2   r3   r4   r5   r"   rA   r6   r&   r$   r!   r8   r8   7   s    
r$   r8   c                       \ rS rSrSS jrSrg)RFEA   Nc                 4    Xl         X l        X0l        X@l        g r   	estimatorn_features_to_selectstepr   )r    rH   rI   rJ   r   s        r!   r"   RFE.__init__B   s    "$8!	r$   rG   )Nr/   r   r2   r3   r4   r5   r"   r6   r&   r$   r!   rD   rD   A   s    r$   rD   c                   0    \ rS rSr         SS jrSrg)GridSearchCVI   Nc                     Xl         X l        X0l        X@l        XPl        X`l        Xpl        Xl        Xl        Xl	        Xl
        g r   )rH   
param_gridscoringr   iidrefitcvr   pre_dispatcherror_scorereturn_train_score)r    rH   rQ   rR   r   rS   rT   rU   r   rV   rW   rX   s               r!   r"   GridSearchCV.__init__J   s>     #$
(&"4r$   )rU   rW   rH   rS   r   rQ   rV   rT   rX   rR   r   )	NNr0   Tr0   r   z2*n_jobszraise-deprecatingFrL   r&   r$   r!   rN   rN   I   s$    
 ' 5r$   rN   c                   T    \ rS rSrSSSSSSSSSSS	S
SSSS\R
                  4S jrSrg)CountVectorizere   contentzutf-8strictNTz(?u)\b\w\w+\b)r/   r/   wordr.   r/   Fc                     Xl         X l        X0l        X@l        X`l        Xpl        Xl        XPl        Xl        Xl	        Xl
        Xl        Xl        Xl        Xl        UU l        UU l        g r   )inputencodingdecode_errorstrip_accentspreprocessor	tokenizeranalyzer	lowercasetoken_pattern
stop_wordsmax_dfmin_dfmax_featuresngram_range
vocabularybinarydtype)r    ra   rb   rc   rd   rh   re   rf   rj   ri   rn   rg   rk   rl   rm   ro   rp   rq   s                     r!   r"   CountVectorizer.__init__f   sc    ( 
 (*(" "*$(&$
r$   )rg   rp   rc   rq   rb   ra   rh   rk   rm   rl   rn   re   rj   rd   ri   rf   ro   )r2   r3   r4   r5   npint64r"   r6   r&   r$   r!   r[   r[   e   s@     &hh%$r$   r[   c                       \ rS rSrSS jrSrg)Pipeline   Nc                     Xl         X l        g r   )stepsmemory)r    ry   rz   s      r!   r"   Pipeline.__init__   s    
r$   )rz   ry   r   rL   r&   r$   r!   rv   rv      s    r$   rv   c                   :    \ rS rSr              SS jrSrg)SVC   Nc                     X l         X0l        X@l        XPl        Xl        Xl        X`l        Xpl        Xl        Xl	        Xl
        Xl        Xl        Xl        g r   )kerneldegreegammacoef0r   r   	shrinkingprobability
cache_sizer   r   r   decision_function_shaper   )r    r   r   r   r   r   r   r   r   r   r   r   r   r   r   s                  r!   r"   SVC.__init__   sN    " 

"&$( '>$(r$   )r   r   r   r   r   r   r   r   r   r   r   r   r   r   )r.   rbf   auto_deprecated        TFMbP?   NFovrNrL   r&   r$   r!   r}   r}      s3      %)r$   r}   c                   ,    \ rS rSr       SS jrSrg)PCA   Nc                 X    Xl         X l        X0l        X@l        XPl        X`l        Xpl        g r   )n_componentsr=   whiten
svd_solverr   iterated_powerr   )r    r   r=   r   r   r   r   r   s           r!   r"   PCA.__init__   s*     )	$,(r$   )r=   r   r   r   r   r   r   )NTFautor   r   NrL   r&   r$   r!   r   r      s     )r$   r   c                   4    \ rS rSr           SS jrSrg)NMF   Nc                     Xl         X l        X0l        X@l        XPl        X`l        Xpl        Xl        Xl        Xl	        Xl
        g r   )r   initr   	beta_lossr   r   r   alphar   r   shuffle)r    r   r   r   r   r   r   r   r   r   r   r   s               r!   r"   NMF.__init__   s=     )	" (
 r$   )r   r   r   r   r   r   r   r   r   r   r   )NNcd	frobeniusr-   r   Nr   r   r   FrL   r&   r$   r!   r   r      s*     r$   r   c                   <    \ rS rSr\R
                  SSSS4S jrSrg)SimpleImputer   meanNr   Tc                 @    Xl         X l        X0l        X@l        XPl        g r   )missing_valuesstrategy
fill_valuer   r=   )r    r   r   r   r   r=   s         r!   r"   SimpleImputer.__init__   s     - $	r$   )r=   r   r   r   r   )r2   r3   r4   r5   rs   nanr"   r6   r&   r$   r!   r   r      s     vvr$   r   c                 R    [        5       nSnUSS  nUR                  5       U:X  d   eg )NE  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='warn', n_jobs=None, penalty='l2',
                   random_state=None, solver='warn', tol=0.0001, verbose=0,
                   warm_start=False)r/   )r   __repr__)print_changed_only_falselrexpecteds      r!   
test_basicr      s2    		B(H |H;;=H$$$r$   c                     [        SS9n SnU R                  5       U:X  d   e[        SSSSSS9n S	nUS
S  nU R                  5       U:X  d   e[        SS9nSnUR                  5       U:X  d   e[        [        S5      S9nSnUR                  5       U:X  d   e[	        [        [        R                  " SS
/5      S95        g )Nc   r   zLogisticRegression(C=99)g?Fi  T)r   r   r   r   r   zk
LogisticRegression(C=99, class_weight=0.4, fit_intercept=False, tol=1234,
                   verbose=True)r/   r   )r   zSimpleImputer(missing_values=0)NaNzSimpleImputer()g?)Cs)r   r   r   floatreprr	   rs   array)r   r   imputers      r!   test_changed_onlyr     s    	b	!B-H;;=H$$$ 

3et
B$H |H;;=H$$$1-G4H))) 5<8G$H))) 		3(!3	45r$   c                 t    [        [        5       [        SS95      nSnUSS  nUR                  5       U:X  d   eg )Ni  r   a  
Pipeline(memory=None,
         steps=[('standardscaler',
                 StandardScaler(copy=True, with_mean=True, with_std=True)),
                ('logisticregression',
                 LogisticRegression(C=999, class_weight=None, dual=False,
                                    fit_intercept=True, intercept_scaling=1,
                                    l1_ratio=None, max_iter=100,
                                    multi_class='warn', n_jobs=None,
                                    penalty='l2', random_state=None,
                                    solver='warn', tol=0.0001, verbose=0,
                                    warm_start=False))],
         transform_input=None, verbose=False)r/   )r
   r8   r   r   )r   pipeliner   s      r!   test_pipeliner      sB    ^-/AC/HIH1H |H(***r$   c                     [        [        [        [        [        [        [        [        5       5      5      5      5      5      5      5      nSnUSS  nUR                  5       U:X  d   eg )Na  
RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=LogisticRegression(C=1.0,
                                                                                                                     class_weight=None,
                                                                                                                     dual=False,
                                                                                                                     fit_intercept=True,
                                                                                                                     intercept_scaling=1,
                                                                                                                     l1_ratio=None,
                                                                                                                     max_iter=100,
                                                                                                                     multi_class='warn',
                                                                                                                     n_jobs=None,
                                                                                                                     penalty='l2',
                                                                                                                     random_state=None,
                                                                                                                     solver='warn',
                                                                                                                     tol=0.0001,
                                                                                                                     verbose=0,
                                                                                                                     warm_start=False),
                                                                                        n_features_to_select=None,
                                                                                        step=1,
                                                                                        verbose=0),
                                                                          n_features_to_select=None,
                                                                          step=1,
                                                                          verbose=0),
                                                            n_features_to_select=None,
                                                            step=1, verbose=0),
                                              n_features_to_select=None, step=1,
                                              verbose=0),
                                n_features_to_select=None, step=1, verbose=0),
                  n_features_to_select=None, step=1, verbose=0),
    n_features_to_select=None, step=1, verbose=0)r/   )rD   r   r   )r   rfer   s      r!   test_deeply_nestedr   5  sV    
c#c#c#&8&:";<=>?@
AC5H< |H<<>X%%%r$   )print_changed_onlyr   )TzRFE(estimator=RFE(...)))FzERFE(estimator=RFE(...), n_features_to_select=None, step=1, verbose=0)c                     [        U S9   [        SS9n[        [        [        [        [        [        5       5      5      5      5      5      nUR	                  U5      U:X  d   e S S S 5        g ! , (       d  f       g = f)Nr   r/   )depth)r   r   rD   r   pformat)r   r   ppr   s       r!   test_print_estimator_max_depthr   Z  s[     
+=	>$1-#c#c"4"6789:;zz#(***	 
?	>	>s   AA,,
A:c                     S/SS// SQS.S// SQS./n[        [        5       USS	9nS
nUSS  nUR                  5       U:X  d   eg )Nr   r   r-   r/   
   r1   i  )r   r   r   linear)r   r      )rU   a  
GridSearchCV(cv=5, error_score='raise-deprecating',
             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
                           decision_function_shape='ovr', degree=3,
                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
                           probability=False, random_state=None, shrinking=True,
                           tol=0.001, verbose=False),
             iid='warn', n_jobs=None,
             param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],
                          'kernel': ['rbf']},
                         {'C': [1, 10, 100, 1000], 'kernel': ['linear']}],
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)r/   )rN   r}   r   )r   rQ   gsr   s       r!   test_gridsearchr   l  sa     7dD\8JK:$67J 
ceZA	.B)H |H;;=H$$$r$   c                 H   [        SSSS9n[        S[        5       4S[        5       4/5      n/ SQn/ SQn[        SS	9[	        5       /UUS
.[        [        5      /UUS./n[        USSUS9nSnUSS  nUR                  U5      n[        R                  " SSU5      nX:X  d   eg )NTr/   )compactindentindent_at_name
reduce_dimclassify)         r      )r   )r   reduce_dim__n_componentsclassify__C)r   reduce_dim__kr   r   )rU   r   rQ   a	  
GridSearchCV(cv=3, error_score='raise-deprecating',
             estimator=Pipeline(memory=None,
                                steps=[('reduce_dim',
                                        PCA(copy=True, iterated_power='auto',
                                            n_components=None,
                                            random_state=None,
                                            svd_solver='auto', tol=0.0,
                                            whiten=False)),
                                       ('classify',
                                        SVC(C=1.0, cache_size=200,
                                            class_weight=None, coef0=0.0,
                                            decision_function_shape='ovr',
                                            degree=3, gamma='auto_deprecated',
                                            kernel='rbf', max_iter=-1,
                                            probability=False,
                                            random_state=None, shrinking=True,
                                            tol=0.001, verbose=False))]),
             iid='warn', n_jobs=1,
             param_grid=[{'classify__C': [1, 10, 100, 1000],
                          'reduce_dim': [PCA(copy=True, iterated_power=7,
                                             n_components=None,
                                             random_state=None,
                                             svd_solver='auto', tol=0.0,
                                             whiten=False),
                                         NMF(alpha=0.0, beta_loss='frobenius',
                                             init=None, l1_ratio=0.0,
                                             max_iter=200, n_components=None,
                                             random_state=None, shuffle=False,
                                             solver='cd', tol=0.0001,
                                             verbose=0)],
                          'reduce_dim__n_components': [2, 4, 8]},
                         {'classify__C': [1, 10, 100, 1000],
                          'reduce_dim': [SelectKBest(k=10,
                                                     score_func=<function chi2 at some_address>)],
                          'reduce_dim__k': [2, 4, 8]}],
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)zfunction chi2 at 0x.*>zfunction chi2 at some_address>)r   rv   r   r}   r   r   r   rN   r   resub)	r   r   r   N_FEATURES_OPTIONS	C_OPTIONSrQ   	gspipliner   repr_s	            r!   test_gridsearch_pipeliner     s    	 a	MB,.SU0CDEH""I a0#%8(:$	
 't,-/$	
J X!A*MI%)HN |HJJy!EFF+-MuUEr$   c                 h   Sn[        SSSUS9n[        U5       Vs0 s H  o3U_M     nn[        US9nSnUSS  nUR                  U5      U:X  d   e[        US-   5       Vs0 s H  o3U_M     nn[        US9nSnUSS  nUR                  U5      U:X  d   eS[	        [        U5      5      0n[        [        5       U5      nS	nUSS  nUR                  U5      U:X  d   eS[	        [        US-   5      5      0n[        [        5       U5      nS
nUSS  nUR                  U5      U:X  d   eg s  snf s  snf )N   Tr/   )r   r   r   n_max_elements_to_show)ro   a  
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
                lowercase=True, max_df=1.0, max_features=None, min_df=1,
                ngram_range=(1, 1), preprocessor=None, stop_words=None,
                strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None,
                vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
                            8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
                            15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
                            21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
                            27: 27, 28: 28, 29: 29})a  
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
                lowercase=True, max_df=1.0, max_features=None, min_df=1,
                ngram_range=(1, 1), preprocessor=None, stop_words=None,
                strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None,
                vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
                            8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
                            15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
                            21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
                            27: 27, 28: 28, 29: 29, ...})r   a  
GridSearchCV(cv='warn', error_score='raise-deprecating',
             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
                           decision_function_shape='ovr', degree=3,
                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
                           probability=False, random_state=None, shrinking=True,
                           tol=0.001, verbose=False),
             iid='warn', n_jobs=None,
             param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
                               15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
                               27, 28, 29]},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)a  
GridSearchCV(cv='warn', error_score='raise-deprecating',
             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
                           decision_function_shape='ovr', degree=3,
                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
                           probability=False, random_state=None, shrinking=True,
                           tol=0.001, verbose=False),
             iid='warn', n_jobs=None,
             param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
                               15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
                               27, 28, 29, ...]},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0))r   ranger[   r   listrN   r}   )	r   r   r   iro   
vectorizerr   rQ   r   s	            r!   test_n_max_elements_to_showr     s|   	 5	
B !&&< => =1Q$ =J> J7J8H |H::j!X--- !&&<q&@ AB A1Q$ AJB J7J=H |H::j!X--- tE"89:;J	ceZ	(B)H |H::b>X%%% tE"81"<=>?J	ceZ	(B)H |H::b>X%%%[ ?( Cs   D*!D/c                    [        5       nSnUSS  nX!R                  SS9:X  d   eSnUSS  nX!R                  SS9:X  d   eUR                  [        S5      S9n[        SR	                  UR                  5       5      5      nUR                  US9U:X  d   eS	U;  d   eS
nUSS  nX!R                  US-
  S9:X  d   eSnUSS  nX!R                  US-
  S9:X  d   eSnUSS  nX!R                  US-
  S9:X  d   eg )Na  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   in...
                   multi_class='warn', n_jobs=None, penalty='l2',
                   random_state=None, solver='warn', tol=0.0001, verbose=0,
                   warm_start=False)r/      )
N_CHAR_MAXz+
Lo...
                   warm_start=False)r   inf z...a@  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_i...
                   multi_class='warn', n_jobs=None, penalty='l2',
                   random_state=None, solver='warn', tol=0.0001, verbose=0,
                   warm_start=False)r   aD  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter...,
                   multi_class='warn', n_jobs=None, penalty='l2',
                   random_state=None, solver='warn', tol=0.0001, verbose=0,
                   warm_start=False)r   r   )r   r   r   lenjoinsplit)r   r   r   	full_repr
n_nonblanks        r!   test_bruteforce_ellipsisr   "  sF    
	B(H |H{{c{2222(H |H{{a{0000 uU|4IRWWY__./0J;;*;-:::	!!!
(H |H{{j2o{>>>>(H |H{{j1n{====
(H |H{{j1n{====r$   c                  F    [        5       R                  [        5       5        g r   )r   pprintr   r&   r$   r!   test_builtin_prettyprinterr   l  s    
 O-/0r$   c                       " S S[         5      n U " SSS S9nSnX!R                  5       :X  d   e[        SS9   S	nX!R                  5       :X  d   e S S S 5        g ! , (       d  f       g = f)
Nc                   <   ^  \ rS rSrSS jrSU 4S jjrS rSrU =r$ )'test_kwargs_in_init.<locals>.WithKWargsiz  c                 N    Xl         X l        0 U l        U R                  " S0 UD6  g )Nr&   )ab_other_params
set_params)r    r  r  kwargss       r!   r"   0test_kwargs_in_init.<locals>.WithKWargs.__init__}  s#    FF!#DOO%f%r$   c                 X   > [         TU ]  US9nUR                  U R                  5        U$ )N)deep)super
get_paramsupdater  )r    r
  params	__class__s      r!   r  2test_kwargs_in_init.<locals>.WithKWargs.get_params  s,    W'T'2FMM$,,-Mr$   c                 l    UR                  5        H  u  p#[        XU5        X0R                  U'   M!     U $ r   )itemssetattrr  )r    r  keyvalues       r!   r  2test_kwargs_in_init.<locals>.WithKWargs.set_params  s3    $lln
5)*/""3' - Kr$   )r  r  r  )
willchange	unchanged)T)	r2   r3   r4   r5   r"   r  r  r6   __classcell__)r  s   @r!   
WithKWargsr  z  s    	&	
	 	r$   r  	somethingabcd)r  cdz+WithKWargs(a='something', c='abcd', d=None)Fr   z:WithKWargs(a='something', b='unchanged', c='abcd', d=None))r   r   r   )r  estr   s      r!   test_kwargs_in_initr   t  s`    ] ( {f
5C<H||~%%%	5	1O<<>))) 
2	1	1s   A
A(c                    ^  " U4S jS[         [        5      mT" [        T" T" 5       5      T" 5       S5      5      n [        SS9   [	        U 5        TR
                  nS S S 5        STl        [        SS9   [	        U 5        TR
                  nS S S 5        WW:X  d   eg ! , (       d  f       NG= f! , (       d  f       N(= f)Nc                   D   >^  \ rS rSrSrSS jrUU 4S jrSS jrSrU =r	$ ):test_complexity_print_changed_only.<locals>.DummyEstimatori  r   c                     Xl         g r   rH   )r    rH   s     r!   r"   Ctest_complexity_print_changed_only.<locals>.DummyEstimator.__init__  s    &Nr$   c                 J   > T=R                   S-  sl         [        TU ]	  5       $ )Nr/   )nb_times_repr_calledr  r   )r    DummyEstimatorr  s    r!   r   Ctest_complexity_print_changed_only.<locals>.DummyEstimator.__repr__  s"    //14/7#%%r$   c                     U$ r   r&   r@   s      r!   rA   Dtest_complexity_print_changed_only.<locals>.DummyEstimator.transform  s    Hr$   r%  r   )
r2   r3   r4   r5   r(  r"   r   rA   r6   r  )r  r)  s   @r!   r)  r#    s     	'	&	 	r$   r)  passthroughFr   r   T)r   r   r
   r   r   r(  )rH    nb_repr_print_changed_only_falsenb_repr_print_changed_only_truer)  s      @r!   "test_complexity_print_changed_onlyr0    s    )=  n^%568H-XI 
5	1Y+9+N+N( 
2 +,N'	4	0Y*8*M*M' 
1 ,/NNNN 
2	1
 
1	0s   B5B/
B,/
B=),r   r   r   numpyrs   pytestsklearnr   sklearn.baser   r   sklearn.feature_selectionr   r   sklearn.linear_modelr	   sklearn.pipeliner
   sklearn.utils._pprintr   r   r8   rD   rN   r[   rv   r}   r   r   r   r   r   r   r   markparametrizer   r   r   r   r   r   r   r0  r&   r$   r!   <module>r;     s   	     " 8 7 5 * 9$ $N%} - 5= 58%m %P} )- )D)- )(- 8M  %6:+*"&J &)	
	+	+%4?DW&tG>T1!*HOr$   