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from abc import abstractmethod |
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from typing import Dict, Generator, List, Optional, Union |
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from .dataclass import NonPositionalField |
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from .operator import SourceOperator |
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from .random_utils import new_random_generator |
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from .stream import DynamicStream, MultiStream |
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from .type_utils import isoftype |
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class BaseFusion(SourceOperator): |
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"""BaseFusion operator that combines multiple multistreams into one. |
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Args: |
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subsets: a dict of named SourceOperator objects (each to yield a MultiStream) or a list thereof, |
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each is specified along with its input, so can generate a MultiStream |
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include_splits: List of splits to include from each input MultiStream. |
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If None, all splits are included. |
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""" |
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subsets: Union[List[SourceOperator], Dict[str, SourceOperator]] |
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include_splits: Optional[List[str]] = NonPositionalField(default=None) |
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@abstractmethod |
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def fusion_generator(self, split) -> Generator: |
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pass |
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def prepare(self): |
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assert isoftype(self.subsets, Dict[str, SourceOperator]) or isoftype( |
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self.subsets, List[SourceOperator] |
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) |
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self.named_subsets = ( |
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{i: self.subsets[i]() for i in range(len(self.subsets))} |
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if isinstance(self.subsets, list) |
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else {name: origin() for name, origin in self.subsets.items()} |
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) |
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def splits(self) -> List[str]: |
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splits = [] |
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for _, origin in self.named_subsets.items(): |
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for s in origin.keys(): |
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if s not in splits: |
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if self.include_splits is None or s in self.include_splits: |
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splits.append(s) |
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return splits |
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def process( |
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self, |
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) -> MultiStream: |
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result = {} |
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for split in self.splits(): |
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result[split] = DynamicStream( |
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self.fusion_generator, gen_kwargs={"split": split} |
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) |
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return MultiStream(result) |
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class FixedFusion(BaseFusion): |
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"""FixedFusion operator that combines multiple multistreams into one, limiting the number of instances taken from each split of each input multistream. |
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Args: |
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subsets: Dict of named SourceOperator objects (each to yield a MultiStream), or a list thereof |
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splits: List of splits (stream_names) to include, over all input multistreams. If None, all splits are included. |
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max_instances_per_subset: Number of instances to take from each input split of each input multistream. |
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If None, all instances of each split (that is specified in include_splits) are included in the result. |
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""" |
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max_instances_per_subset: Optional[int] = None |
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def prepare(self): |
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super().prepare() |
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def fusion_generator(self, split) -> Generator: |
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for origin_name, origin in self.named_subsets.items(): |
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if split not in origin: |
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continue |
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emitted_from_this_split = 0 |
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try: |
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for instance in origin[split]: |
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if ( |
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self.max_instances_per_subset is not None |
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and emitted_from_this_split >= self.max_instances_per_subset |
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): |
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break |
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if isinstance(origin_name, str): |
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if "subset" not in instance: |
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instance["subset"] = [] |
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instance["subset"].insert(0, origin_name) |
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emitted_from_this_split += 1 |
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yield instance |
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except Exception as e: |
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raise RuntimeError(f"Exception in subset: {origin_name}") from e |
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class WeightedFusion(BaseFusion): |
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"""Fusion operator that combines multiple MultiStream-s. |
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Args: |
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subsets: Dict of named MultiStream objects, or a list thereof |
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weights: Dict of named weights for each origin, or a list thereof |
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max_total_examples: Total number of instances to return per returned split. |
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If None, all instances are returned |
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""" |
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subsets: Union[Dict[str, SourceOperator], List[SourceOperator]] = None |
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weights: Union[Dict[str, Union[float, int]], List[Union[int, float]]] = None |
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max_total_samples: int = None |
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def verify(self): |
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super().verify() |
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assert self.subsets is not None, "subsets must be specified" |
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assert self.weights is not None, "weights must be specified" |
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assert len(self.subsets) == len( |
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self.weights |
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), "subsets and weights must have the same length" |
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assert isoftype(self.subsets, Dict[str, SourceOperator]) or isoftype( |
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self.subsets, List[SourceOperator] |
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) |
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assert isoftype(self.weights, Dict[str, Union[int, float]]) or isoftype( |
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self.weights, List[Union[int, float]] |
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) |
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assert isinstance(self.subsets, dict) == isinstance(self.weights, dict) |
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def prepare(self): |
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super().prepare() |
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self.named_weights = ( |
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{i: float(self.weights[i]) for i in range(len(self.weights))} |
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if isinstance(self.weights, list) |
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else {k: float(v) for (k, v) in self.weights.items()} |
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) |
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def fusion_generator(self, split) -> Generator: |
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iterators = { |
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named_origin: iter(origin[split]) |
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for named_origin, origin in self.named_subsets.items() |
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} |
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total_examples = 0 |
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random_generator = new_random_generator(sub_seed="weighted_fusion_" + split) |
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while ( |
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self.max_total_samples is None or total_examples < self.max_total_samples |
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) and len(iterators) > 0: |
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population = list(iterators.keys()) |
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origin_name = random_generator.choices( |
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population=population, |
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weights=[self.named_weights[name] for name in population], |
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)[0] |
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iterator = iterators[origin_name] |
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try: |
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instance = next(iterator) |
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if isinstance(origin_name, str): |
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if "subset" not in instance: |
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instance["subset"] = [] |
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instance["subset"].insert(0, origin_name) |
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total_examples += 1 |
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yield instance |
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except StopIteration: |
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iterators.pop(origin_name) |
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