import json
from abc import abstractmethod
from dataclasses import field
from typing import Any, Dict, List, Optional, Tuple
from .collections import ListCollection
from .dataclass import NonPositionalField
from .operator import StreamInstanceOperator
from .random_utils import new_random_generator
from .type_utils import isoftype
class Template(StreamInstanceOperator):
"""The role of template is to take the fields of every instance and verbalize it.
Meaning the template is taking the instance and generating source, target and references.
"""
skip_rendered_instance: bool = NonPositionalField(default=True)
postprocessors: List[str] = NonPositionalField(
default_factory=lambda: ["processors.to_string_stripped"]
)
def process(
self, instance: Dict[str, Any], stream_name: Optional[str] = None
) -> Dict[str, Any]:
if self.skip_rendered_instance:
if (
"source" in instance
and "target" in instance
and "references" in instance
):
return instance
inputs = instance.get("inputs")
outputs = instance.get("outputs")
source = self.inputs_to_source(inputs)
target, references = self.outputs_to_target_and_references(outputs)
return {
**instance,
"source": source,
"target": target,
"references": references,
}
@abstractmethod
def inputs_to_source(self, inputs: Dict[str, object]) -> str:
pass
@abstractmethod
def outputs_to_target_and_references(
self, outputs: Dict[str, object]
) -> Tuple[str, List[str]]:
pass
def get_postprocessors(self) -> List[str]:
return self.postprocessors
class InputOutputTemplate(Template):
"""Generate field 'source' from fields designated as input, and fields 'target' and 'references' from fields designated as output, of the processed instance.
Args specify the formatting strings with which to glue together the input and output designated fields of the processed instance into one string ('source' and 'target'), and into a list of strings ('references').
"""
input_format: str = None
output_format: str = None
def process_template(self, template: str, data: Dict[str, object]) -> str:
data = {k: ", ".join(v) if isinstance(v, list) else v for k, v in data.items()}
return template.format(**data)
def inputs_to_source(self, inputs: Dict[str, object]) -> str:
try:
return self.process_template(self.input_format, inputs)
except KeyError as e:
raise KeyError(
f"Available inputs are {list(inputs.keys())} but input format requires a different ones: '{self.input_format}'"
) from e
def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
try:
target = self.process_template(self.output_format, outputs)
except KeyError as e:
raise KeyError(
f"Available outputs are {outputs.keys()} but output format requires a different one: {self.output_format}"
) from e
references = [target]
return target, references
class MultipleChoiceTemplate(Template):
"""Formats the input (that specifies the question), the multiple choices to select the answer from, and specifies the field with the correct answer."""
input_format: str
target_prefix: str = ""
choices_field: str = "choices"
target_field: str = "label"
choices_seperator: str = ", "
source_choice_format: str = "{choice_numeral}. {choice_text}"
target_choice_format: str = "{choice_numeral}"
add_numerals_as_field: str = None
enumerator: str = "capitals"
def prepare(self):
super().prepare()
if self.enumerator == "capitals":
self.enumerator = "ABCDEFGHIJKLMNOP"
if self.enumerator == "lowercase":
self.enumerator = "abcdefghijklmnop"
if self.enumerator == "numbers":
self.enumerator = [str(i + 1) for i in range(20)]
if self.enumerator == "roman":
self.enumerator = [
"I",
"II",
"III",
"IV",
"V",
"VI",
"VII",
"VIII",
"IX",
"X",
"XI",
"XII",
"XIII",
"XIV",
"XV",
"XVI",
"XVII",
"XVIII",
"XIX",
"XX",
]
def get_choices(self, data: Dict[str, object], choice_format: str) -> str:
choices = data[self.choices_field]
enumrated_choices = []
for i, choice in enumerate(choices):
enumrated_choices.append(
choice_format.format(
choice_text=choice,
choice_numeral=self.enumerator[i],
)
)
return enumrated_choices
def inputs_to_source(self, inputs: Dict[str, object]) -> str:
choices = self.get_choices(inputs, self.source_choice_format)
inputs = {
"numerals": ",".join(self.get_choices(inputs, "{choice_numeral}")),
**inputs,
self.choices_field: self.choices_seperator.join(choices),
}
try:
return self.input_format.format(**inputs)
except KeyError as e:
raise KeyError(
f"Available inputs are {inputs.keys()} but input format requires a different one: {self.input_format}"
) from e
def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
target = outputs[self.target_field]
if not isinstance(target, int):
try:
target = outputs[self.choices_field].index(target)
except ValueError as e:
raise ValueError(
f"MultipleChoiceTemplate could not locate textual target '{target}' in choices list: {outputs[self.choices_field]}"
) from e
choices = self.get_choices(outputs, self.target_choice_format)
try:
target = choices[target]
except IndexError as e:
raise IndexError(
f"MultipleChoiceTemplate cannot find index number {target} in choices: {choices}"
) from e
return target, [target]
def process(
self, instance: Dict[str, Any], stream_name: Optional[str] = None
) -> Dict[str, Any]:
result = super().process(instance, stream_name)
if "options" not in result["outputs"]:
result["outputs"]["options"] = self.get_choices(
instance["outputs"], self.target_choice_format
)
return result
class YesNoTemplate(Template):
"""A template for generating binary Yes/No questions asking whether an input text is of a specific class.
input_format:
Defines the format of the question.
class_field:
Defines the field that contains the name of the class that this template
asks of.
label_field:
Defines the field which contains the true label of the input text. If a gold label is equal to the
value in class_name, then the correct output is self.yes_answer (by default, "Yes").
Otherwise the correct output is self.no_answer (by default, "No").
yes_answer:
The output value for when the gold label equals self.class_name.
Defaults to "Yes".
no_answer:
The output value for when the gold label differs from self.class_name.
Defaults to "No".
"""
input_format: str = None
class_field: str = None
label_field: str = None
yes_answer: str = "Yes"
no_answer: str = "No"
postprocessors: List[str] = field(
default_factory=lambda: ["processors.to_string_stripped"]
)
def inputs_to_source(self, inputs: Dict[str, object]) -> str:
try:
data = {
k: ", ".join(v) if isinstance(v, list) else v for k, v in inputs.items()
}
return self.input_format.format(**data)
except KeyError as e:
raise RuntimeError(
f"Available inputs are {list(inputs.keys())} but input format requires a different one: {self.input_format}"
) from e
def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
try:
gold_class_names = outputs[self.label_field]
except KeyError as e:
raise RuntimeError(
f"Available outputs are {list(outputs.keys())}, missing required label field: '{self.label_field}'."
) from e
if not isinstance(gold_class_names, list) or not gold_class_names:
raise RuntimeError(
f"Unexpected value for gold_class_names: '{gold_class_names}'. Expected a non-empty list."
)
try:
queried_class_names = outputs[self.class_field]
except KeyError as e:
raise RuntimeError(
f"Available outputs are {list(outputs.keys())}, missing required class field: '{self.class_field}'."
) from e
if (
not queried_class_names
or not isinstance(queried_class_names, list)
or not len(queried_class_names) == 1
):
raise RuntimeError(
f"Unexpected value for queried_class_names: '{queried_class_names}'. Expected a list with one item."
)
queried_class_name = queried_class_names[0]
if queried_class_name in gold_class_names:
return self.yes_answer, [self.yes_answer]
return self.no_answer, [self.no_answer]
def get_postprocessors(self) -> List[str]:
return self.postprocessors
class KeyValTemplate(Template):
"""Generate field 'source' from fields designated as input, and fields 'target' and 'references' from fields designated as output, of the processed instance.
Args specify with what separators to glue together the input and output designated fields of the processed instance into one string ('source' and 'target'), and into a list of strings ('references').
"""
pairs_seperator: str = ", "
key_val_seperator: str = ": "
use_keys_for_inputs: bool = True
outputs_key_val_seperator: str = ": "
use_keys_for_outputs: bool = False
postprocessors: List[str] = field(
default_factory=lambda: ["processors.to_string_stripped"]
)
def process_dict(
self, dic: Dict[str, object], key_val_sep, pairs_sep, use_keys
) -> str:
dic = {
k: ", ".join([str(vi) for vi in v]) if isinstance(v, list) else v
for k, v in dic.items()
}
pairs = []
for key, val in dic.items():
key_val = [key, str(val)] if use_keys else [str(val)]
pairs.append(key_val_sep.join(key_val))
return pairs_sep.join(pairs)
def inputs_to_source(self, inputs: Dict[str, object]) -> str:
return self.process_dict(
inputs,
key_val_sep=self.key_val_seperator,
pairs_sep=self.pairs_seperator,
use_keys=self.use_keys_for_inputs,
)
def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
target = self.process_dict(
outputs,
key_val_sep=self.key_val_seperator,
pairs_sep=self.pairs_seperator,
use_keys=self.use_keys_for_outputs,
)
return target, [target]
def get_postprocessors(self) -> List[str]:
return self.postprocessors
class OutputQuantizingTemplate(InputOutputTemplate):
quantum: float = 0.1
def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
quantum_str = f"{self.quantum:.10f}".rstrip("0").rstrip(".")
quantized_outputs = {
key: f"{round(value / self.quantum) * self.quantum:{quantum_str}}"
for key, value in outputs.items()
}
return super().outputs_to_target_and_references(quantized_outputs)
class MultiLabelTemplate(InputOutputTemplate):
labels_field: str = "labels"
labels_seprator: str = ", "
postprocessors: List[str] = ["processors.to_list_by_comma"]
output_format: str = "{labels}"
empty_label: str = "None"
def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
labels = outputs[self.labels_field]
if not isinstance(labels, list):
raise ValueError(
f"MultiLabelTemplate requires labels field '{self.labels_field}' to be a list. Got {self.labels_field}<{type(labels).__name__}>: {labels}"
)
if len(labels) == 0:
labels = [self.empty_label]
labels_str = self.labels_seprator.join(labels)
return super().outputs_to_target_and_references({self.labels_field: labels_str})
class MultiReferenceTemplate(InputOutputTemplate):
references_field: str = "references"
random_reference: bool = False
def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> List[str]:
references = outputs[self.references_field]
if not isoftype(references, List[str]):
raise ValueError(
f"MultiReferenceTemplate requires references field '{self.references_field}' to be List[str]. Got {self.references_field}<{type(references).__name__}>: {references}"
)
if len(references) == 0:
raise ValueError(
"No references found. MultiReferenceTemplate requires at least one reference."
)
if self.random_reference:
random_generator = new_random_generator(outputs)
target = random_generator.choice(references)
else:
target = references[0]
return target, references
def escape_chars(s, chars_to_escape):
for char in chars_to_escape:
s = s.replace(char, f"\\{char}")
return s
class SpanLabelingBaseTemplate(MultiLabelTemplate):
spans_starts_field: str = "spans_starts"
spans_ends_field: str = "spans_ends"
text_field: str = "text"
labels_support: list = None
def extract_span_label_pairs(self, outputs):
spans_starts = outputs[self.spans_starts_field]
spans_ends = outputs[self.spans_ends_field]
text = outputs[self.text_field]
labels = outputs[self.labels_field]
spans = []
for span_start, span_end, label in zip(spans_starts, spans_ends, labels):
if self.labels_support is None or label in self.labels_support:
spans.append((span_start, span_end, text[span_start:span_end], label))
for span in sorted(spans):
if self.labels_support is None or span[3] in self.labels_support:
yield span[2], span[3]
def outputs_to_target_and_references(
self, outputs: Dict[str, object]
) -> Dict[str, object]:
span_lables_pairs = self.extract_span_label_pairs(outputs)
targets = self.span_label_pairs_to_targets(span_lables_pairs)
return super().outputs_to_target_and_references({"labels": targets})
@abstractmethod
def span_label_pairs_to_targets(self, pairs):
pass
class SpanLabelingTemplate(SpanLabelingBaseTemplate):
span_label_format: str = "{span}: {label}"
escape_characters: List[str] = [":", ","]
postprocessors: List[str] = ["processors.to_span_label_pairs"]
def span_label_pairs_to_targets(self, span_label_pairs):
targets = []
for span, label in span_label_pairs:
if self.escape_characters is not None:
span = escape_chars(span, self.escape_characters)
target = self.span_label_format.format(span=span, label=label)
targets.append(target)
return targets
class SpanLabelingJsonTemplate(SpanLabelingBaseTemplate):
postprocessors = [
"processors.load_json",
"processors.dict_of_lists_to_value_key_pairs",
]
def span_label_pairs_to_targets(self, span_label_pairs):
groups = {}
for span, label in span_label_pairs:
if label not in groups:
groups[label] = []
groups[label].append(span)
if len(groups) > 0:
targets = [json.dumps(groups, ensure_ascii=False)]
else:
targets = []
return targets
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)