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Upload renderers.py with huggingface_hub

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  1. renderers.py +107 -0
renderers.py ADDED
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+ from abc import ABC, abstractmethod
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+ from typing import Any, Dict, List
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+
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+ from .dataclass import InternalField
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+ from .formats import Format, ICLFormat
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+ from .instructions import Instruction
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+ from .operator import Operator, SequntialOperator, StreamInstanceOperator
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+ from .random_utils import random
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+ from .templates import Template
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+
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+
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+ class Renderer(ABC):
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+ pass
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+ # @abstractmethod
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+ # def get_postprocessors(self) -> List[str]:
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+ # pass
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+
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+
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+ class RenderTemplate(Renderer, StreamInstanceOperator):
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+ template: Template
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+ random_reference: bool = False
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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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+ inputs = instance.pop("inputs")
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+ outputs = instance.pop("outputs")
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+
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+ source = self.template.process_inputs(inputs)
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+ targets = self.template.process_outputs(outputs)
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+
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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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+
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+ instance.update(
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+ {
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+ "source": source,
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+ "target": target,
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+ "references": references,
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+ }
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+ )
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+
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+ return instance
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+
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+
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+ class RenderDemonstrations(RenderTemplate):
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+ demos_field: str
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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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+ demos = instance.get(self.demos_field, [])
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+
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+ processed_demos = []
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+ for demo_instance in demos:
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+ processed_demo = super().process(demo_instance)
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+ processed_demos.append(processed_demo)
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+
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+ instance[self.demos_field] = processed_demos
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+
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+ return instance
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+
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+
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+ class RenderInstruction(Renderer, StreamInstanceOperator):
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+ instruction: Instruction
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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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+ instance["instruction"] = self.instruction()
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+ return instance
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+
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+
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+ class RenderFormat(Renderer, StreamInstanceOperator):
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+ format: Format
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+ demos_field: str = None
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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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+ demos_instances = instance.pop(self.demos_field, None)
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+ if demos_instances is not None:
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+ instance["source"] = self.format.format(instance, demos_instances=demos_instances)
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+ else:
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+ instance["source"] = self.format.format(instance)
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+ return instance
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+
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+
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+ class StandardRenderer(Renderer, SequntialOperator):
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+ template: Template
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+ instruction: Instruction = None
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+ demos_field: str = None
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+ format: ICLFormat = None
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+
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+ steps: List[Operator] = InternalField(default_factory=list)
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+
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+ def prepare(self):
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+ self.steps = [
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+ RenderTemplate(template=self.template),
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+ RenderDemonstrations(template=self.template, demos_field=self.demos_field),
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+ RenderInstruction(instruction=self.instruction),
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+ RenderFormat(format=self.format, demos_field=self.demos_field),
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+ ]
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+
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+ def get_postprocessors(self):
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+ return self.template.get_postprocessors()