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from .text_utils import split_words
from .artifact import Artifact
from .operator import StreamInstanceOperator, InstanceOperatorWithGlobalAccess
from .instructions import Instruction
import random
from typing import Dict, Any, List
from abc import ABC, abstractmethod
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)
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