+ NV-jup^RIt^RIt^RIHt]PR4t]PR4t]PR4t]PR4t]P!RR.R 7R 4t ]PR 4t ]PR 4t ]PR 4t ]PR4t ]PR4t]PR4t]P!RR.R 7R4t]P!RRRR..R"OR7R4t]P!RR.R 7R4t]P!RR.R 7R4t]P!RR.R 7R4t]P!RR.R 7R4t]P!RR.R 7R 4t]PR!4tR#)#N)Seriesc\h)z3A fixture providing the ExtensionDtype to validate.NotImplementedErrorp/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/pandas/tests/extension/conftest.pydtyper   rc\h)z| Length-10 array for this type. * data[0] and data[1] should both be non missing * data[0] and data[1] should not be equal rrrr datar   rcVP'g+VPR8Xg\P!V R24\h)z| Length-10 array in which all the elements are two. Call pytest.skip in your fixture if the dtype does not support divmod. mz is not a numeric dtype) _is_numerickindpytestskiprr s&r data_for_twosrs7    s!2  ug456 rc\h)zLength-2 array with [NA, Valid]rrrr data_missingr(r rr r)paramscRVPR8XdV#VPR8XdV#R#)z5Parametrized fixture giving 'data' and 'data_missing'r rNparam)requestr rs&&&r all_datar.s,}} . ( )rcaV3RlpV#)z Generate many datasets. Parameters ---------- data : fixture implementing `data` Returns ------- Callable[[int], Generator]: A callable that takes a `count` argument and returns a generator yielding `count` datasets. c3<<"\V4FpSxK R#5iN)range)count_r s& r gendata_repeated..genGsuAJsr)r r%sf r data_repeatedr'7s  Jrc\h)z Length-3 array with a known sort order. This should be three items [B, C, A] with A < B < C For boolean dtypes (for which there are only 2 values available), set B=C=True rrrr data_for_sortingr)Ns  rc\h)zk Length-3 array with a known sort order. This should be three items [B, NA, A] with A < B and NA missing. rrrr data_missing_for_sortingr+\rrc"\P#)z Binary operator for comparing NA values. Should return a function of two arguments that returns True if both arguments are (scalar) NA for your type. By default, uses ``operator.is_`` )operatoris_rrr na_cmpr/gs <<rcVP#)z The scalar missing value for this type. Default dtype.na_value. TODO: can be removed in 3.x (see https://github.com/pandas-dev/pandas/pull/54930) )na_valuers&r r1r1ts >>rc\h)z Data for factorization, grouping, and unique tests. Expected to be like [B, B, NA, NA, A, A, B, C] Where A < B < C and NA is missing. If a dtype has _is_boolean = True, i.e. only 2 unique non-NA entries, then set C=B. rrrr data_for_groupingr3~s  rTFcVP#)z#Whether to box the data in a Seriesrrs&r box_in_seriesr6s ==rc^#rxs&r r<!rc(^.\V4,#r8)lenr:s&r r<r<s1#A,rc:\^.\V4,4#r8)rr?r:s&r r<r<s&!s1v&rcV#r!rr:s&r r<r<r=r)ridscVP#)z$ Functions to test groupby.apply(). rr5s&r groupby_apply_oprDs ==rcVP#)zM Boolean fixture to support Series and Series.to_frame() comparison testing. rr5s&r as_framerF ==rcVP#)zD Boolean fixture to support arr and Series(arr) comparison testing. rr5s&r as_seriesrIrGrcVP#)zX Boolean fixture to support comparison testing of ExtensionDtype array and numpy array. rr5s&r use_numpyrK ==rffillbfillcVP#)z` Parametrized fixture giving method parameters 'ffill' and 'bfill' for Series. testing. rr5s&r fillna_methodrPrLrcVP#)zJ Boolean fixture to support ExtensionDtype _from_sequence method testing. rr5s&r as_arrayrRrGrc4\P\4#)z A scalar that *cannot* be held by this ExtensionArray. The default should work for most subclasses, but is not guaranteed. If the array can hold any item (i.e. object dtype), then use pytest.skip. )object__new__)r s&r invalid_scalarrVs >>& !!r)scalarlistseriesrT)r-rpandasrfixturer r rrrr'r)r+r/r1r3r6rDrFrIrKrPrRrVrrr r\s2     /01,      e}%& &   /e}%&e}%&e}%&)*+e}%&""r