Ë àÌPhÌãó‚—UdZddlmZmZddlmZmZmZ gZ e e e d<ddœd„Zdd œd „Zdd œd „Zdd œd „Zdd „Zdd„Zy)aÜ Extra testing functions that forbid 0d-input, see #21044 While the xp_assert_* functions generally aim to follow the conventions of the underlying `xp` library, NumPy in particular is inconsistent in its handling of scalars vs. 0d-arrays, see https://github.com/numpy/numpy/issues/24897. For example, this means that the following operations (as of v2.0.1) currently return scalars, even though a 0d-array would often be more appropriate: import numpy as np np.array(0) * 2 # scalar, not 0d array - np.array(0) # scalar, not 0d-array np.sin(np.array(0)) # scalar, not 0d array np.mean([1, 2, 3]) # scalar, not 0d array Libraries like CuPy tend to return a 0d-array in scenarios like those above, and even `xp.asarray(0)[()]` remains a 0d-array there. To deal with the reality of the inconsistencies present in NumPy, as well as 20+ years of code on top, the `xp_assert_*` functions here enforce consistency in the only way that doesn't go against the tide, i.e. by forbidding 0d-arrays as the return type. However, when scalars are not generally the expected NumPy return type, it remains preferable to use the assert functions from the `scipy._lib._array_api` module, which have less surprising behaviour. é)Úarray_namespaceÚis_numpy)Úxp_assert_closeÚxp_assert_equalÚxp_assert_lessÚ__all__N)Úxpc ó®—d}|€ t|«}|j|«}t|«r|jdk(syd}|j |«sJ|«‚y)NT©aÂResult is a NumPy 0d-array. Many SciPy functions intend to follow the convention of many NumPy functions, returning a scalar when a 0d-array would be correct. The specialized `xp_assert_*` functions in the `scipy._lib._array_api_no_0d` module err on the side of caution and do not accept 0d-arrays by default. If the correct result may legitimately be a 0d-array, pass `check_0d=True`, or use the `xp_assert_*` functions from `scipy._lib._array_api`.)rÚasarrayrÚshapeÚisscalar)ÚactualÚdesiredr ÚkwargsÚ__tracebackhide__Ú_msgs úfC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\scipy/_lib/_array_api_no_0d.pyÚ _check_scalarr#s_€ØÐà €zÜ ˜VÓ $ˆðj‰j˜Ó!€Gô RŒL˜WŸ]™]¨bÒ0Øð O€Dð ;‰;vÔ Ð$ Ó$Ñ óF)Úcheck_0dc óB—d}|s t||fi|¤Žt||fd|i|¤ŽS©NTr)rÚxp_assert_equal_base©rrrrrs rrr;s5€ðÐñ Üf˜gÑ0¨Ò0Ü  ¨Ñ M¸(Ð MÀfÑ MÐMrc óB—d}|s t||fi|¤Žt||fd|i|¤ŽSr)rÚxp_assert_close_basers rrrFs1€àÐá Üf˜gÑ0¨Ò0Ü  ¨Ñ M¸(Ð MÀfÑ MÐMrc óB—d}|s t||fi|¤Žt||fd|i|¤ŽSr)rÚxp_assert_less_basers rrrOs1€àÐá Üf˜gÑ0¨Ò0Ü ˜v wÑ L¸Ð LÀVÑ LÐLrcóB—ddd| zz}}t||g|¢­||dddœ|¤ŽS©zPBackwards compatible replacement. In new code, use xp_assert_close instead. rgø?é F)ÚatolÚrtolÚ check_dtypeÚ check_shape©r©rrÚdecimalÚargsÚkwdsr$r#s rÚassert_array_almost_equalr,XóH€ðC˜˜g˜X™Ñ&ˆ$€DÜ ˜6 7ð *à ñ *Ø $¨4¸UÐPUñ *à$(ñ *ð*rcóB—ddd| zz}}t||g|¢­||dddœ|¤ŽSr!r'r(s rÚassert_almost_equalr/ar-r)é)é)Ú__doc__Úscipy._lib._array_apirrrrrrrrrÚlistÚstrÚ__annotations__rr,r/r rrÚr7s`ðò÷4<÷JñJð€ˆˆc‰Óð*.ô%ð027ôNð27ôNð16ôMó*ô*r