Upload templates.py with huggingface_hub
Browse files- templates.py +38 -26
templates.py
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@@ -3,6 +3,7 @@ from dataclasses import field
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from typing import Any, Dict, List, Union
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from .artifact import Artifact
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from .instructions import Instruction, TextualInstruction
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from .operator import InstanceOperatorWithGlobalAccess, StreamInstanceOperator
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from .random_utils import random
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@@ -16,6 +17,9 @@ class Renderer(ABC):
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class Template(Artifact):
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@abstractmethod
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def process_inputs(self, inputs: Dict[str, object]) -> Dict[str, object]:
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pass
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@@ -45,19 +49,19 @@ class RenderFormatTemplate(Renderer, StreamInstanceOperator):
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outputs = instance.pop("outputs")
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source = self.template.process_inputs(inputs)
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if
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else:
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target = references[0] # what
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return {
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**instance,
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@@ -74,19 +78,13 @@ class RenderAutoFormatTemplate(RenderFormatTemplate):
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def prepare(self):
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if self.template is None:
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self.template = AutoInputOutputTemplate()
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elif isinstance(self.template, InputOutputTemplate):
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self.template = AutoInputOutputTemplate(
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input_format=self.template.input_format,
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output_format=self.template.output_format,
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)
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else:
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raise ValueError(
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f"Template must be an instance of InputOutputTemplate or AutoInputOutputTemplate, got {type(self.template)}"
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)
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def render(self, instance: Dict[str, object]) -> Dict[str, object]:
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self.template.
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inputs = {key: value for key, value in instance["inputs"].items()}
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@@ -185,11 +183,25 @@ class OutputQuantizingTemplate(InputOutputTemplate):
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return super().process_outputs(quantized_outputs)
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class
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spans_starts_field: str = "spans_starts"
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spans_ends_field: str = "spans_ends"
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text_field: str = "text"
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labels_field: str = "labels"
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span_label_format: str = "{span}: {label}"
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postprocessors = ["processors.to_span_label_pairs"]
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@@ -213,7 +225,7 @@ class SpanLabelingTemplate(InputOutputTemplate):
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for span, label in zip(text_spans, labels):
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targets.append(self.span_label_format.format(span=span, label=label))
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return super().process_outputs({"
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class AutoInputOutputTemplate(InputOutputTemplate):
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from typing import Any, Dict, List, Union
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from .artifact import Artifact
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from .dataclass import NonPositionalField
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from .instructions import Instruction, TextualInstruction
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from .operator import InstanceOperatorWithGlobalAccess, StreamInstanceOperator
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from .random_utils import random
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class Template(Artifact):
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is_multi_target: bool = NonPositionalField(default=False)
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is_multi_reference: bool = NonPositionalField(default=False)
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@abstractmethod
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def process_inputs(self, inputs: Dict[str, object]) -> Dict[str, object]:
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pass
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outputs = instance.pop("outputs")
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source = self.template.process_inputs(inputs)
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targets = self.template.process_outputs(outputs)
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if self.template.is_multi_reference:
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references = targets
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if self.random_reference:
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target = random.choice(references)
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else:
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if len(references) == 0:
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raise ValueError("No references found")
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target = references[0]
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else:
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references = [targets]
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target = targets
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return {
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**instance,
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def prepare(self):
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if self.template is None:
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self.template = AutoInputOutputTemplate()
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def render(self, instance: Dict[str, object]) -> Dict[str, object]:
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try:
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if not self.template.is_complete():
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self.template.infer_missing(instance["inputs"], instance["outputs"])
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except:
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pass
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inputs = {key: value for key, value in instance["inputs"].items()}
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return super().process_outputs(quantized_outputs)
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class MultiLabelTemplate(InputOutputTemplate):
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labels_field: str = "labels"
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labels_seprator: str = ", "
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postprocessors = ["processors.to_list"]
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output_format = "{labels}"
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empty_label = "None"
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def process_outputs(self, outputs: Dict[str, object]) -> Dict[str, object]:
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labels = outputs[self.labels_field]
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if len(labels) == 0:
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labels = [self.empty_label]
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labels_str = self.labels_seprator.join(labels)
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return super().process_outputs({"labels": labels_str})
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class SpanLabelingTemplate(MultiLabelTemplate):
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spans_starts_field: str = "spans_starts"
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spans_ends_field: str = "spans_ends"
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text_field: str = "text"
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span_label_format: str = "{span}: {label}"
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postprocessors = ["processors.to_span_label_pairs"]
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for span, label in zip(text_spans, labels):
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targets.append(self.span_label_format.format(span=span, label=label))
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return super().process_outputs({"labels": targets})
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class AutoInputOutputTemplate(InputOutputTemplate):
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