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Upload schema.py with huggingface_hub

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  1. schema.py +56 -0
schema.py ADDED
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+ from datasets import Features, Sequence, Value
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+ from .operator import StreamInstanceOperatorValidator
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+
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+ from typing import Dict, Any, List
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+
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+ from dataclasses import field
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+
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+ UNITXT_DATASET_SCHEMA = Features(
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+ {
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+ "source": Value("string"),
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+ "target": Value("string"),
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+ "references": Sequence(Value("string")),
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+ "metrics": Sequence(Value("string")),
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+ "group": Value("string"),
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+ "postprocessors": Sequence(Value("string")),
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+ }
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+ )
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+
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+ # UNITXT_METRIC_SCHEMA = Features({
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+ # "predictions": Value("string", id="sequence"),
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+ # "target": Value("string", id="sequence"),
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+ # "references": Value("string", id="sequence"),
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+ # "metrics": Value("string", id="sequence"),
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+ # 'group': Value('string'),
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+ # 'postprocessors': Value("string", id="sequence"),
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+ # })
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+
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+
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+ class ToUnitxtGroup(StreamInstanceOperatorValidator):
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+ group: str
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+ metrics: List[str] = None
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+ postprocessors: List[str] = field(default_factory=lambda: ["to_string"])
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+ remove_unnecessary_fields: bool = True
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+
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+ def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
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+ if self.remove_unnecessary_fields:
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+ for key in instance.keys():
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+ if key not in UNITXT_DATASET_SCHEMA:
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+ del instance[key]
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+
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+ instance["group"] = self.group
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+ if self.metrics is not None:
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+ instance["metrics"] = self.metrics
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+ if self.postprocessors is not None:
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+ instance["postprocessors"] = self.postprocessors
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+
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+ return instance
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+
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+ def validate(self, instance: Dict[str, Any], stream_name: str = None):
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+ # verify the instance has the required schema
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+ assert instance is not None, f"Instance is None"
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+ assert isinstance(instance, dict), f"Instance should be a dict, got {type(instance)}"
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+ assert all(
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+ [key in instance for key in UNITXT_DATASET_SCHEMA]
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+ ), f"Instance should have the following keys: {UNITXT_DATASET_SCHEMA}"
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+ UNITXT_DATASET_SCHEMA.encode_example(instance)