vhCddlmZddlmZddlmZddlmZmZddl Z ddl m Z ddl m Z ddl m Z dd l mZdd lmZdd lmZdd lmZdd lmZmZmZddlmZmZmZmZmZm Z m!Z!ddl"m#Z#ddl$Z%ee&cZ&Z'ee(cZ(Z)edZ*eejVdZ,d6dZ-d7dZ.d8dZ/d8dZ0d8dZ1d8dZ2d9dZ3d:dZ4d;dZ5eddZ6ed?dZ6ed@d Z6dAd!Z6dBd"Z7d#d$d%dCd&Z8dCd'Z9dCd(Z:e:Z;dCd)Zd6d,Z? dDd-Z@ee jd.d8d/ZBdE dFd0ZCe jdGd1ZDdDd2ZEe*dHd3ZFe*dId4ZGe*dEdJd5ZHy)K) annotations)Sequence)partial)AnyoverloadN)api)config)core)dtypes)lax) xla_client)SingleDeviceSharding)safe_zipsafe_map set_module)Array ArrayLikeDimSizeShape SupportsNdim SupportsShape SupportsSize)Shardingz jax.numpyT) canonicalizeczt|dkr#|Dcgc]}tj|c}S|Dcgc]}tj|c}t j jr;tj}t|Dcgc]\}}t||c}}StfdddDr#|Dcgc]}tj|c}SDchc]}|st|}}t|dkr#|Dcgc]}tj|c}St jjdk7r t|ttj}|Dcgc]}tj||c}Scc}wcc}wcc}}wcc}wcc}wcc}wcc}w)zHApply NumPy-style broadcasting, making args shape-compatible for lax.py.c3RK|]}tdt|k( ywrN)len.0sshapess N/opt/face_recognition/venv/lib/python3.12/site-packages/jax/_src/numpy/util.py z!promote_shapes..8s" :!S^s1v % :s$'Nallow)rr asarraynpshaper dynamic_shapesvaluebroadcast_shapeszip _broadcast_toallnumpy_rank_promotion _rank_promotion_warning_or_errorbroadcast_to_rank)fun_nameargsarg res_shapeshpnonscalar_ranks result_rankr#s @r$promote_shapesr;+sbY](, -CKK  --'+ ,bhhsm ,F ""&&/it|dr|jS|Sra)rerbrgs r$_check_jax_array_protocolrls%a9 @q@rGcyN)r4s r$ensure_arraylikerps58rGcyrnro)r4a1s r$rprps:=rGcyrnro)r4rra2s r$rprpsQTrGcyrnro)r4rrrta3s r$rprpsadrGcyrnro)r4rrrtrva4r5s r$rprpsmprGcvt|g|t|dk(rt|dStd|DS)z>Check that arguments are arraylike and convert them to arrays.r&rc32K|]}t|ywrnrjr!r6s r$r%z#ensure_arraylike..s73!#&7)check_arraylikerrjtupler4r5s r$rprps:("T"Y!^ d1g && 7$7 77rGc>t|g|td|DS)zCheck that argument elements are arraylike and convert to a tuple of arrays. 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Promotes non-inexact types to an inexact type.c32K|]}t|ywrnrr|s r$r%z'promote_args_inexact..rr})r~rrrr;rUrs r$promote_args_inexactrsQ("T" >> >$H$t$X%%  A#94#@ AArGcPt|tjr t|S|SrnrcxcDevicerdevices r$canonicalize_device_to_shardingrs  "  '' -rG)inlinec|Dcgc]}tj|}}|Dcgc]}|jc}rtfdDr#|Dcgc]}t j |c}St j }t j|}|Dcgc]}t|||c}Scc}wcc}wcc}wcc}w)z7Like Numpy's broadcast_arrays but doesn't return views.c3PK|]}tjd|ywr)r definitely_equal_shaper s r$r%z$_broadcast_arrays..s"QQt226!9a@Qs#&) r shaped_abstractifyr*r0r r(r-broadcast_shardingsr/)r5r6avalsa result_shaperesult_shardingr#s @r$_broadcast_arraysrs48 8C4 " "3 ' 8% 8" #AGG #& 3Q&QQ(, -CKK  --%%v.,++U3/GK L-\? ; LL 9 # - MsB6B;CCc td|}t|tr|ntj|}t|t st j|dk(r|f}tj|}t j|}tj||r|St|t|krtd|d|t|t|z }||d}tdt||D}|dks|sd}t|j!||tj"||t t%|t||S)N broadcast_torz;Cannot broadcast to shape with fewer dimensions: arr_shape=z shape=c3RK|]\}}tj|d|g!yw)r&N)r definitely_equal_one_of_dim)r!arr_dshape_ds r$r%z _broadcast_to..s/L'55ea\JLs%'z?Incompatible shapes for broadcasting: {} and requested shape {}) out_sharding)rprcrr r(rr)ndimr canonicalize_shaper*rrrEr0rrAbroadcast_in_dimrange)arrr*sharding arr_shapenlead shape_tail compatiblerFs r$r/r/ s3-##u%3;;s+;# E5 !bggen&9 HE  ! !% (%hhsm)   E2 J 5zC N" S T\V[U]^ __ JY 'EuvJL+3Iz+JLLJ qy Mc szz)U3 44   UE%s5z2J,K-5 77rGc(td|||\}}}||tdj||tjt |tj s)tj|tj|}t||\}}tj|dk(rt||\}}nt|||\}}} tj|j}|stj |||S|S#d}Y!xYw)NwherezeEither both or neither of the x and y arguments should be provided to jax.numpy.where, got {} and {}.rF)rprErAr)r_dtypebool_r ne_zerorQrrr is_empty_shaper*select) conditionrPyx_arry_arris_always_emptys r$_wherer)s$WiA>/)QY!) FfQl $$ vi("(( 3y#))I"67I 1 $!QWWY1$Q*LE5/ 1a@Iue))%++6O5DIue ,NNOs D DcPt|tjr t|S|Srnrrs r$normalize_device_to_shardingr?s  "  '' MrGct|dr |jStd|dt|dr|j}t j|S)aReturn the number of dimensions of an array. JAX implementation of :func:`numpy.ndim`. Unlike ``np.ndim``, this function raises a :class:`TypeError` if the input is a collection such as a list or tuple. Args: a: array-like object, or any object with an ``ndim`` attribute. Returns: An integer specifying the number of dimensions of ``a``. Examples: Number of dimensions for arrays: >>> x = jnp.arange(10) >>> jnp.ndim(x) 1 >>> y = jnp.ones((2, 3)) >>> jnp.ndim(y) 2 This also works for scalars: >>> jnp.ndim(3.14) 0 For arrays, this can also be accessed via the :attr:`jax.Array.ndim` property: >>> x.ndim 1 rTrrb)rerr~rbr)rs r$rrFsHD Q 66M&!$/ Q  A rGct|dr |jStd|dt|dr|j}t j|S)aReturn the shape an array. JAX implementation of :func:`numpy.shape`. Unlike ``np.shape``, this function raises a :class:`TypeError` if the input is a collection such as a list or tuple. Args: a: array-like object, or any object with a ``shape`` attribute. Returns: An tuple of integers representing the shape of ``a``. Examples: Shape for arrays: >>> x = jnp.arange(10) >>> jnp.shape(x) (10,) >>> y = jnp.ones((2, 3)) >>> jnp.shape(y) (2, 3) This also works for scalars: >>> jnp.shape(3.14) () For arrays, this can also be accessed via the :attr:`jax.Array.shape` property: >>> x.shape (10,) r*Trrb)rer*r~rbr)rs r$r*r*rsHD Q 77N'140 Q  A !rGc| t|ds|#t|drtj||Std|dt|dr|j }tj||S)aReturn number of elements along a given axis. JAX implementation of :func:`numpy.size`. Unlike ``np.size``, this function raises a :class:`TypeError` if the input is a collection such as a list or tuple. 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