Upload schema.py with huggingface_hub
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schema.py
CHANGED
@@ -1,5 +1,5 @@
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from dataclasses import field
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from typing import Any, Dict, List
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from datasets import Features, Sequence, Value
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@@ -13,6 +13,9 @@ UNITXT_DATASET_SCHEMA = Features(
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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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@@ -32,7 +35,20 @@ class ToUnitxtGroup(StreamInstanceOperatorValidator):
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postprocessors: List[str] = field(default_factory=lambda: ["to_string_stripped"])
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remove_unnecessary_fields: bool = True
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def
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if self.remove_unnecessary_fields:
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keys_to_delete = []
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@@ -42,20 +58,20 @@ class ToUnitxtGroup(StreamInstanceOperatorValidator):
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for key in keys_to_delete:
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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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return instance
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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,
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assert isinstance(
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assert all(
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-
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), f"Instance should have the following keys: {UNITXT_DATASET_SCHEMA}"
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UNITXT_DATASET_SCHEMA.encode_example(instance)
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from dataclasses import field
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from typing import Any, Dict, List, Optional
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from datasets import Features, Sequence, Value
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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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"additional_inputs": Sequence(
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{"key": Value(dtype="string"), "value": Value("string")}
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),
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}
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)
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postprocessors: List[str] = field(default_factory=lambda: ["to_string_stripped"])
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remove_unnecessary_fields: bool = True
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def _to_lists_of_keys_and_values(self, dict: Dict[str, str]):
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return {
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"key": [key for key, _ in dict.items()],
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"value": [str(value) for _, value in dict.items()],
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}
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def process(
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self, instance: Dict[str, Any], stream_name: Optional[str] = None
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) -> Dict[str, Any]:
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additional_inputs = {**instance["inputs"], **instance["outputs"]}
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instance["additional_inputs"] = self._to_lists_of_keys_and_values(
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additional_inputs
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)
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if self.remove_unnecessary_fields:
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keys_to_delete = []
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for key in keys_to_delete:
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del instance[key]
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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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return instance
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def validate(self, instance: Dict[str, Any], stream_name: Optional[str] = None):
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# verify the instance has the required schema
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assert instance is not None, "Instance is None"
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assert isinstance(
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instance, dict
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), 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}. Instance is: {instance}"
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UNITXT_DATASET_SCHEMA.encode_example(instance)
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