Weighted data batcher with deterministic resume
Build a sampler that draws weighted batches from a dataset registry and can resume deterministically from a saved offset, including the edge case where the batch size does not divide the data evenly.
class WeightedBatcher:
def __init__(self, datasets, weights, batch_size, seed=0, max_samples=None, drop_last=False, state=None): ...
def next_batch(self):
"""A list of (dataset name, item), or None when the run is over."""
def state(self):
"""Something JSON-friendly that resumes the exact same stream."""
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