import random from abc import ABC, abstractmethod from typing import Any, Dict, List from .artifact import Artifact from .instructions import Instruction from .operator import InstanceOperatorWithGlobalAccess, StreamInstanceOperator from .text_utils import split_words class Renderer(ABC): @abstractmethod def get_postprocessors(self) -> List[str]: pass class Template(Artifact): @abstractmethod def process_inputs(self, inputs: Dict[str, object]) -> Dict[str, object]: pass @abstractmethod def process_outputs(self, outputs: Dict[str, object]) -> Dict[str, object]: pass @abstractmethod def get_postprocessors(self) -> List[str]: pass class RenderFormatTemplate(Renderer, StreamInstanceOperator): template: Template = None random_reference: bool = False def verify(self): assert isinstance(self.template, Template), "Template must be an instance of Template" assert self.template is not None, "Template must be specified" def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]: return self.render(instance) def render(self, instance: Dict[str, Any]) -> Dict[str, Any]: inputs = instance.pop("inputs") outputs = instance.pop("outputs") source = self.template.process_inputs(inputs) key, targets = next(iter(outputs.items())) if not isinstance(targets, list): targets = [targets] references = [self.template.process_outputs({key: target}) for target in targets] if self.random_reference: target = random.choice(references) else: if len(references) == 0: raise ValueError("No references found") target = references[0] # what return { **instance, "source": source, "target": target, "references": references, } def get_postprocessors(self) -> List[str]: return self.template.get_postprocessors() class RenderAutoFormatTemplate(RenderFormatTemplate): def prepare(self): if self.template is None: self.template = AutoInputOutputTemplate() elif isinstance(self.template, InputOutputTemplate): self.template = AutoInputOutputTemplate( input_format=self.template.input_format, output_format=self.template.output_format, ) else: raise ValueError( f"Template must be an instance of InputOutputTemplate or AutoInputOutputTemplate, got {type(self.template)}" ) def render(self, instance: Dict[str, object]) -> Dict[str, object]: if not self.template.is_complete(): self.template.infer_missing(instance["inputs"], instance["outputs"]) inputs = {key: value for key, value in instance["inputs"].items()} return super().render({**instance, "inputs": inputs}) class CharacterSizeLimiter(Artifact): limit: int = 1000 def check(self, text: str) -> bool: return len(text) <= self.limit class RenderTemplatedICL(RenderAutoFormatTemplate): instruction: Instruction = None input_prefix: str = "Input: " output_prefix: str = "Output: " instruction_prefix: str = "" demos_field: str = None size_limiter: Artifact = None input_output_separator: str = "\n" demo_separator: str = "\n\n" demos_cache = None def verify(self): assert self.demos_cache is None def render(self, instance: Dict[str, object]) -> Dict[str, object]: if self.demos_cache is None: self.demos_cache = instance.pop(self.demos_field, []) else: instance.pop(self.demos_field, None) source = "" example = super().render(instance) input_str = self.input_prefix + example["source"] + self.input_output_separator + self.output_prefix if self.instruction is not None: source += self.instruction_prefix + self.instruction() + self.demo_separator for demo_instance in self.demos_cache: demo_example = super().render(demo_instance) demo_str = ( self.input_prefix + demo_example["source"] + self.input_output_separator + self.output_prefix + demo_example["target"] + self.demo_separator ) if self.size_limiter is not None: if not self.size_limiter.check(source + demo_str + input_str + example["target"]): continue source += demo_str source += input_str return { **example, "source": source, } class InputOutputTemplate(Template): input_format: str = None output_format: str = None def process_inputs(self, inputs: Dict[str, object]) -> Dict[str, object]: return self.input_format.format(**inputs) def process_outputs(self, outputs: Dict[str, object]) -> Dict[str, object]: return self.output_format.format(**outputs) def get_postprocessors(self) -> List[str]: return ["to_string"] class AutoInputOutputTemplate(InputOutputTemplate): def infer_input_format(self, inputs): input_format = "" for key in inputs.keys(): name = " ".join(word.lower().capitalize() for word in split_words(key) if word != " ") input_format += name + ": " + "{" + key + "}" + "\n" self.input_format = input_format def infer_output_format(self, outputs): self.output_format = "{" + next(iter(outputs.keys())) + "}" def infer_missing(self, inputs, outputs): if self.input_format is None: self.infer_input_format(inputs) if self.output_format is None: self.infer_output_format(outputs) def is_complete(self): return self.input_format is not None and self.output_format is not None from .collections import ListCollection class TemplatesList(ListCollection): def verify(self): for template in self.items: assert isinstance(template, Template) class TemplatesDict(Dict): def verify(self): for key, template in self.items(): assert isinstance(template, Template)