&`ic% ddlmZddlmZmZmZddlZddlZddl m Z m Z ddl m Z e dededeeeffd Ze d eee ejjefd edeejjfd Ze d ejjdeed edeejjfdZe deedejjdejjfdZdS))deque)ListTupleUnionN)BatchedNdArraybatch) DeveloperAPI episode_lenTreturncg}g}t||}||tjdg|zdg||z zztj}||||z }|dkrrt||}||tjdg|zdg||z zztj}||||z}|dkr||fS)a7Creates loss mask and a seq_lens array, given an episode length and T. Args: episode_lens: A list of episode lengths to infer the loss mask and seq_lens array from. T: The maximum number of timesteps in each "row", also known as the maximum sequence length (max_seq_len). Episodes are split into chunks that are at most `T` long and remaining timesteps will be zero-padded (and masked out). Returns: Tuple consisting of a) list of the loss masks to use (masking out areas that are past the end of an episode (or rollout), but had to be zero-added due to the added extra time rank (of length T) and b) the list of sequence lengths resulting from splitting the given episodes into chunks of at most `T` timesteps. r)minappendnparraybool_)r r maskseq_lenslen_rowoverflow extra_rows /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/rllib/utils/postprocessing/zero_padding.pycreate_mask_and_seq_lensr s$ DH {A  D OOD (A3:q4x 00"( ; ;CKKQH Q,,8QHaS4Z1#T*::BHEE  IA  Q,, > item_list max_seq_lenctjd|d}g}g}d}t|}t|dkr]|}||z }tj|}g} g} d} |D]} t | tr`| | d|t| |krt| } W|} | | |dw| | d} || z }|tj || | r(| tj || ||kr(|t|dg}d}t|dk]|dkrF||kr@| |g||z z|t|d|S)aSplits the contents of `item_list` into a new list of ndarrays and returns it. In the returned list, each item is one ndarray of len (axis=0) `max_seq_len`. The last item in the returned list may be (right) zero-padded, if necessary, to reach `max_seq_len`. If `item_list` contains one or more `BatchedNdArray` (instead of individual items), these will be split accordingly along their axis=0 to yield the returned structure described above. .. testcode:: from ray.rllib.utils.postprocessing.zero_padding import ( BatchedNdArray, split_and_zero_pad, ) from ray.rllib.utils.test_utils import check # Simple case: `item_list` contains individual floats. check( split_and_zero_pad([0, 1, 2, 3, 4, 5, 6, 7], 5), [[0, 1, 2, 3, 4], [5, 6, 7, 0, 0]], ) # `item_list` contains BatchedNdArray (ndarrays that explicitly declare they # have a batch axis=0). check( split_and_zero_pad([ BatchedNdArray([0, 1]), BatchedNdArray([2, 3, 4, 5]), BatchedNdArray([6, 7, 8]), ], 5), [[0, 1, 2, 3, 4], [5, 6, 7, 8, 0]], ) Args: item_list: A list of individual items or BatchedNdArrays to be split into `max_seq_len` long pieces (the last of which may be zero-padded). max_seq_len: The maximum length of each item in the returned list. Returns: A list of np.ndarrays (all of length `max_seq_len`), which contains the same data as `item_list`, but split into sub-chunks of size `max_seq_len`. The last item in the returned list may be zero-padded, if necessary. cftjt|tr |dgn|S)Nr)r zeros_like isinstancer)ss rz$split_and_zero_pad..ds)"-*Q*G*G N1QOOrrNrauto)'individual_items_already_have_batch_dim) tree map_structurerlenpopleftflattenr"rr unflatten_as appendleftrextend) rr zero_elementretcurrent_time_row current_titemt item_flatitem_list_appendcurrent_time_row_flat_itemsadd_to_current_titms rsplit_and_zero_padr:1sKd%OO! L CIi  I i..1    "" ) #L&& &(# % %C#~.. %+223rr7;;;s88q=='*3xx$$'($$++CG4444,223777#$  %%  1$8S T TUUU  L  !249I!J!J K K K  # # JJ$( # ##r) collectionsrtypingrrrnumpyrr'"ray.rllib.utils.spaces.space_utilsrrray.util.annotationsr intr_typingNDArrayfloatr:r@rMrrrXs%%%%%%%%%% DDDDDDDD------"#"#"%d :K""""JrE."**