&`i!ddlZddlmZmZmZmZmZmZddlZ ddl m Z ddl m Z ddlmZerddlmZedGd d eZedd ed efd Zd ed efdZdeeee jefded efdZdZded efdZde jd efdZdee jdeefd e jfdZe ddeed e jfdZdS)N) TYPE_CHECKINGAnyListProtocolSequenceUnion)TENSOR_COLUMN_NAME) PublicAPI) DeveloperAPI) ABCSeriesbeta) stabilityceZdZdZdZdZdS) ArrayLikez:Protocol matching ndarray-like objects (like torch.Tensor)cdSNselfs x/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/air/util/tensor_extensions/utils.py __array__zArrayLike.__array__ cdSrrrs r__len__zArrayLike.__len__rrN)__name__ __module__ __qualname____doc__rrrrrrrs8DD        rrvaluereturncxt|do*t|dot|tj S)z]Checks whether objects are ndarray-like (for ex, torch.Tensor) but NOT and ndarray itselfrr)hasattr isinstancenpndarray)r s ris_ndarray_liker' s? {## . E9 % % .5"*-- -rcBddl}t||j|jfSNr)pyarrowr$Array ChunkedArray)r pas r_is_arrow_arrayr.+s' ebh8 9 99r column_values column_namect|dksJ|tkpRt|pCt|p4t |dt jpt|dSr))lenr _is_ndarray_tensor"_is_ndarray_like_not_pyarrow_arrayr$r%r&)r/r0s r_should_convert_to_tensorr51s }   ! ! ! ! )) @ m , , @ .m < < @ mA& 3 3 @ .mA.> ? ?rc@t|ot| Sr)r'r.)r/s rr4r4Ks = ) ) P/-2P2P.PPrtct|tjsdS|jdkrdS|jjtjur5t|dkr"t|dtjrdSdS)zReturn whether provided ndarray is a tensor (ie ndim > 1). NOTE: Tensor is defined as a NumPy array such that `len(arr.shape) > 1` FTr)r$r%r&ndimdtypetypeobject_r2)r7s rr3r3Osn a $ $u vzzt  # #A z!A$ 7S7S t 5rarrc2|jjtjurdSt |dkrdSt |dtjsdS|dj}|ddD]-}t |tjsdS|j|krdS.dS)zReturn whether the provided NumPy ndarray is comprised of variable-shaped tensors. NOTE: This is an O(rows) check. Frr9NT)r;r<r%r=r2r$r&shape)r>r@as r"_is_ndarray_variable_shaped_tensorrBcs  y~RZ''u 3xx1}}u c!fbj ) )u FLE W!RZ(( 55 7e  44  4rvaluesr c tj5tjtjdkrd}tjj}nd}tj}tjd|tj ||}|cdddS#1swxYwYdS#t$r3}t|}d|vsd|vrt|cYd}~S|dd}~wwxYw) a; Create a possibly ragged ndarray. Using the np.array() constructor will fail to construct a ragged ndarray that has a uniform first dimension (e.g. uniform channel dimension in imagery). This function catches this failure and tries a create-and-fill method to construct the ragged ndarray. z2.0.0NFignore)category)copyz*could not broadcast input array from shapez.The requested array has an inhomogeneous shape) warningscatch_warningsr%lib NumpyVersion __version__ exceptionsVisibleDeprecationWarning simplefilterarray ValueErrorstrcreate_ragged_ndarray)rCcopy_if_needed warning_typer>e error_strs r_create_possibly_ragged_ndarrayrXxsQ!  $ & &   v""2>22g==!%!}F !&!;  !(\ B B B B(6777C                   FF 8I E E?9LL)00 0 0 0 0 0 0 #sGB,A=B B,B##B,&B#'B,, C)6%C$C)!C$$C)alphac~tjt|t}t ||dd<|S)aCreate an array that contains arrays of different length If you're working with variable-length arrays like images, use this function to create ragged arrays instead of ``np.array``. .. note:: ``np.array`` fails to construct ragged arrays if the input arrays have a uniform first dimension: .. testsetup:: import numpy as np from ray.air.util.tensor_extensions.utils import create_ragged_ndarray .. doctest:: >>> values = [np.zeros((3, 1)), np.zeros((3, 2))] >>> np.array(values, dtype=object) Traceback (most recent call last): ... ValueError: could not broadcast input array from shape (3,1) into shape (3,) >>> create_ragged_ndarray(values) array([array([[0.], [0.], [0.]]), array([[0., 0.], [0., 0.], [0., 0.]])], dtype=object) Or if you're creating a ragged array from a single array: .. doctest:: >>> values = [np.zeros((3, 1))] >>> np.array(values, dtype=object)[0].dtype dtype('O') >>> create_ragged_ndarray(values)[0].dtype dtype('float64') ``create_ragged_ndarray`` avoids the limitations of ``np.array`` by creating an empty array and filling it with pointers to the variable-length arrays. )r;N)r%emptyr2objectlist)rCr>s rrSrSs7X (3v;;f - - -C &\\CF Jr)rHtypingrrrrrrnumpyr%ray.air.constantsr ray.utilr ray.util.annotationsr pandas.core.dtypes.genericr rboolr'r.r&rRr5r4r3rBrXrSrrrresy000000------5444444         9 :9::::: cBJ 9:IL 4QQQ#$(BJ4*+ "*k8C=8 9+Z++++\ W.(3-.BJ......r