import json from abc import abstractmethod from random import random from typing import Any, Dict, List, Optional, Tuple, Union from .artifact import Artifact from .collections import DictCollection, ListCollection from .dataclass import NonPositionalField from .dict_utils import dict_get, dict_set from .error_utils import Documentation, UnitxtError from .operator import InstanceOperator, Operator from .random_utils import new_random_generator from .serializers import ( ConversationSerializer, DialogSerializer, ImageSerializer, ListSerializer, MultiTypeSerializer, NumberQuantizingSerializer, Serializer, SQLDatabaseAsSchemaSerializer, TableSerializer, ToolCallSerializer, ToolsSerializer, VideoSerializer, ) from .settings_utils import get_constants from .type_utils import isoftype, to_type_string constants = get_constants() class TemplateFormatKeyError(UnitxtError): def __init__(self, template, data, data_type, format_str, format_name): keys = ", ".join(data.keys()) super().__init__( f"Available {data_type}s are [{keys}] " f"but {template.__class__.__name__}.{format_name} format requires a different ones: '{format_str}'", Documentation.ADDING_TEMPLATE, ) class Template(InstanceOperator): """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. Args: skip_rendered_instance (bool): if "source", "target", and "references" are already defined fields in the instance, skip its processing postprocessors: a list of strings being artifact names of text processors, to be applied on the model output instruction: a formatting string that yields an instruction with potential participation of values from the "input_fields" part of the instance target_prefix: a string to be used to format the prompt. Not a formatting string. """ skip_rendered_instance: bool = NonPositionalField(default=True) postprocessors: List[str] = NonPositionalField( default_factory=lambda: ["processors.to_string_stripped"] ) instruction: str = NonPositionalField(default="") target_prefix: str = NonPositionalField(default="") title_fields: List[str] = NonPositionalField(default_factory=list) serializer: Serializer = NonPositionalField( default_factory=lambda: MultiTypeSerializer( serializers=[ ImageSerializer(), VideoSerializer(), TableSerializer(), ToolCallSerializer(), ToolsSerializer(), DialogSerializer(), ConversationSerializer(), ListSerializer(), SQLDatabaseAsSchemaSerializer(), ] ) ) def verify(self): super().verify() assert isoftype( self.postprocessors, List[Union[Operator, str]] ), f"The template post processors field '{self.postprocessors}' is not a list of processors. Instead it is of type '{to_type_string(type(self.postprocessors))}'." def input_fields_to_instruction_and_target_prefix(self, input_fields, instruction): instruction = self.apply_formatting( input_fields, "input field", instruction, "instruction" ) target_prefix = self.apply_formatting( input_fields, "input field", self.target_prefix, "target_prefix", ) return instruction, target_prefix def preprocess_input_and_reference_fields( self, input_fields: Dict[str, Any], reference_fields: Dict[str, Any] ) -> Tuple[Dict[str, Any], Dict[str, Any]]: return input_fields, reference_fields def preprocess_input_fields(self, input_fields: Dict[str, Any]): return input_fields def preprocess_reference_fields(self, reference_fields: Dict[str, Any]): return reference_fields 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 input_fields = instance.get("input_fields") reference_fields = instance.get("reference_fields") if stream_name != constants.inference_stream: input_fields, reference_fields = self.preprocess_input_and_reference_fields( input_fields, reference_fields ) input_fields = self.preprocess_input_fields(input_fields) self.set_titles(input_fields) serialized_inputs = self.serialize(input_fields, instance) source = self.input_fields_to_source(serialized_inputs) instruction, target_prefix = self.input_fields_to_instruction_and_target_prefix( serialized_inputs, instance.get(constants.instruction_field, self.instruction), ) result = { **instance, "source": source, constants.instruction_field: instruction, "target_prefix": target_prefix, "postprocessors": self.postprocessors, } if stream_name == constants.inference_stream: return self.post_process_instance(result) if reference_fields is None: raise ValueError("Should have reference_fields") reference_fields = self.preprocess_reference_fields(reference_fields) serialized_references = self.serialize( reference_fields, instance ) # Dict[str, str] target, references = self.reference_fields_to_target_and_references( serialized_references ) result["target"] = target result["references"] = references return self.post_process_instance(result) def post_process_instance(self, instance): return instance def serialize( self, data: Dict[str, Any], instance: Dict[str, Any] ) -> Dict[str, str]: return {k: self.serializer.serialize(v, instance) for k, v in data.items()} @abstractmethod def input_fields_to_source(self, input_fields: Dict[str, object]) -> str: pass def set_titles(self, data): for field in self.title_fields: data[field] = data[field].title() @abstractmethod def reference_fields_to_target_and_references( self, reference_fields: Dict[str, object] ) -> Tuple[str, List[str]]: pass def apply_formatting( self, data: Dict[str, Any], data_type: str, format_str: str, format_name: str ) -> str: try: if format_str is None: raise UnitxtError( f"Required field '{format_name}' of class {self.__class__.__name__} not set in {self.__class__.__name__}", Documentation.ADDING_TEMPLATE, ) return format_str.format(**data) except KeyError as e: raise TemplateFormatKeyError( self, data, data_type, format_str, format_name ) from e class ApplyTemplate(InstanceOperator): demos_field: Optional[str] = None @abstractmethod def get_template(self, instance: Dict[str, Any]) -> Template: pass def apply( self, template: Template, instance: Dict[str, Any], stream_name: Optional[str] = None, ): return template.process_instance(instance, stream_name) def process( self, instance: Dict[str, Any], stream_name: Optional[str] = None ) -> Dict[str, Any]: template = self.get_template(instance) if self.demos_field is not None: if self.demos_field not in instance: raise ValueError("Demos field is missing.") instance[self.demos_field] = [ self.apply(template, demo_instance) for demo_instance in instance[self.demos_field] ] dict_set(instance, "recipe_metadata/template", template) return self.apply(template, instance, stream_name) class ApplySingleTemplate(ApplyTemplate): template: Template def get_template(self, instance: Dict[str, Any]) -> Template: return self.template class ApplyRandomTemplate(ApplyTemplate): templates: List[Template] def get_template(self, instance: Dict[str, Any]) -> Template: random_generator = new_random_generator( {**instance["input_fields"], **instance["reference_fields"]} ) return random_generator.choice(self.templates) class InputFormatTemplate(Template): input_format: str def input_fields_to_source(self, input_fields: Dict[str, object]) -> str: return self.apply_formatting( input_fields, "input field", self.input_format, "input_format", ) class OutputFormatTemplate(Template): output_format: str = None def reference_fields_to_target_and_references( self, reference_fields: Dict[str, object] ) -> str: target = self.apply_formatting( reference_fields, "reference field", self.output_format, "output_format", ) references = [target] return target, references class JsonOutputFormatTemplate(Template): output_fields: Dict[str, str] wrap_with_list_fields: List[str] def reference_fields_to_target_and_references( self, reference_fields: Dict[str, object] ) -> str: data = {} for field, target_field in self.output_fields.items(): value = reference_fields[field] if field in self.wrap_with_list_fields: value = [value] data[target_field] = value target = json.dumps(data, ensure_ascii=False) references = [target] return target, references class InputOutputTemplate(InputFormatTemplate, OutputFormatTemplate): """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 reference fields of the processed instance into one string ('source' and 'target'), and into a list of strings ('references'). """ pass class JsonOutputTemplate(InputFormatTemplate, JsonOutputFormatTemplate): """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 reference fields of the processed instance into one string ('source' and 'target'), and into a list of strings ('references'). """ pass class InputOutputTemplateWithCustomTarget(InputOutputTemplate): reference: str def reference_fields_to_target_and_references( self, reference_fields: Dict[str, object] ) -> str: target = self.apply_formatting( reference_fields, "reference field", self.output_format, "output_format", ) reference = self.apply_formatting( reference_fields, "reference field", self.reference, "reference", ) return target, [reference] class PairwiseChoiceTemplate(InputOutputTemplate): """PairwiseChoiceTemplate. Requirements: The answer field value should be of type Literal["choice_a", "choice_b", "tie"] Args: choice_a_field (str): The field which contains choice_a value choice_b_field (str): The field which contains choice_b value answer_field (str): The field which contains the answer value. Should be of type Literal["choice_1", "choice_2", "tie"] choice_a_label (str): The label of choice A answer as it is verbalized in the template. choice_b_label (str): The label of choice B answer as it is verbalized in the template. choice_tie_label (str): The label of a tie answer as it should be verbalized in the template. shuffle (bool): whether to shuffle the choices or not. This is done to take into account position bias. shuffle: 50% of the time: 1. The values of choice_a_field and choice_b_field will be swapped. 2. If the values of answer_field is choice_a_label, set it to choice_b_label. Else if the values of answer_field is choice_b_label, set it to choice_a_label. Else if the value of answer_field is choice_tie_label, do nothing. """ choice_a_field: str choice_b_field: str answer_field: str choice_a_label: str choice_b_label: str choice_tie_label: str shuffle: bool def verify(self): super().verify() def verbalize_answer_field(self, reference_fields: Dict[str, object]): answer = reference_fields[self.answer_field] assert answer in ["choice_a", "choice_b", "tie"] if answer == "choice_a": reference_fields[self.answer_field] = self.choice_a_label elif answer == "choice_b": reference_fields[self.answer_field] = self.choice_b_label else: reference_fields[self.answer_field] = self.choice_tie_label return reference_fields def shuffle_values( self, input_fields: Dict[str, object], reference_fields: Dict[str, object] ): if not self.shuffle: return input_fields, reference_fields outcome = random() # A float between 0 and 1 if outcome <= 0.5: choice_a_value = input_fields[self.choice_a_field] choice_b_value = input_fields[self.choice_b_field] input_fields[self.choice_a_field] = choice_b_value input_fields[self.choice_b_field] = choice_a_value answer = reference_fields[self.answer_field] assert answer in [ self.choice_a_label, self.choice_b_label, self.choice_tie_label, ] if answer == self.choice_a_label: reference_fields[self.answer_field] = self.choice_b_label elif answer == self.choice_b_label: reference_fields[self.answer_field] = self.choice_a_label return input_fields, reference_fields def preprocess_input_and_reference_fields( self, input_fields: Dict[str, Any], reference_fields: Dict[str, Any] ) -> Tuple[Dict[str, Any], Dict[str, Any]]: reference_fields = self.verbalize_answer_field(reference_fields) input_fields, reference_fields = self.shuffle_values( input_fields, reference_fields ) return input_fields, reference_fields class DialogFieldsData(Artifact): user_role_label: str assistant_role_label: str system_role_label: str dialog_field: str class DialogTemplate(InputOutputTemplate): dialog_fields: List[DialogFieldsData] turns_separator: str = "\n\n" label_separator: str = " " def process_dialog(self, input_fields: Dict[str, object]): for dialog_fields in self.dialog_fields: dialog = input_fields[dialog_fields.dialog_field] # TODO: update isoftype method to support Literal verification and check # it's List[Tuple[Literal["user", "assistant", "system"], str]] (Issue #799) assert isoftype(dialog, List[Tuple[str, str]]) user_role_label = dialog_fields.user_role_label assistant_role_label = dialog_fields.assistant_role_label system_role_label = dialog_fields.system_role_label dialog_str = "" for i, turn in enumerate(dialog): (turn_type, turn_text) = turn turns_separator = "" if i == 0 else self.turns_separator if turn_type == "user": dialog_str += f"{turns_separator}{user_role_label}{self.label_separator}{turn_text}" elif turn_type == "assistant": dialog_str += f"{turns_separator}{assistant_role_label}{self.label_separator}{turn_text}" elif turn_type == "system": dialog_str += f"{turns_separator}{system_role_label}{self.label_separator}{turn_text}" input_fields[dialog_fields.dialog_field] = dialog_str return input_fields def preprocess_input_fields(self, input_fields: Dict[str, Any]): return self.process_dialog(input_fields) class DialogPairwiseChoiceTemplate(DialogTemplate, PairwiseChoiceTemplate): pass class PairwiseComparativeRatingTemplate(InputOutputTemplate): """PairwiseChoiceTemplate. Args: choice_a_field (str): The field which contains choice_a value choice_b_field (str): The field which contains choice_b value answer_field (str): The field which contains the answer value. The value should be an int. Positive for preferring choice_a, and negative for preferring choice_b shuffle (bool): whether to shuffle the choices or not. This is done to take into account position bias. shuffle: 50% of the time: | 1) The values of choice_a_field and choice_b_field will be swapped. | 2) Replace the values of answer_field with its mapped value according to the reverse_preference_map Dict. """ choice_a_field: str choice_b_field: str choice_a_id_field: str choice_b_id_field: str answer_field: str shuffle: bool def shuffle_values( self, input_fields: Dict[str, object], reference_fields: Dict[str, object] ): if not self.shuffle: return input_fields, reference_fields outcome = random() # A float between 0 and 1 if outcome <= 0.5: choice_a_value = input_fields[self.choice_a_field] choice_b_value = input_fields[self.choice_b_field] input_fields[self.choice_a_field] = choice_b_value input_fields[self.choice_b_field] = choice_a_value choice_a_id_value = input_fields[self.choice_a_id_field] choice_b_id_value = input_fields[self.choice_b_id_field] input_fields[self.choice_a_id_field] = choice_b_id_value input_fields[self.choice_b_id_field] = choice_a_id_value assert isinstance(reference_fields[self.answer_field], int) reference_fields[self.answer_field] = ( int(reference_fields[self.answer_field]) * -1 ) return input_fields, reference_fields def preprocess_input_and_reference_fields( self, input_fields: Dict[str, Any], reference_fields: Dict[str, Any] ) -> Tuple[Dict[str, Any], Dict[str, Any]]: input_fields, reference_fields = self.shuffle_values( input_fields, reference_fields ) return input_fields, reference_fields class MultipleChoiceTemplate(InputFormatTemplate): """Formats the input that specifies a multiple-choice question, with a list of possible answers to choose from, and identifies the correct answer. Args: target_prefix (str): Optional prefix that can be added before the target label in generated prompts or outputs. choices_field (str): The key under which the multiple choices are stored in the input and reference dictionaries. target_field (str): The key under which the correct choice is stored in the reference dictionary (can be integer index or textual label). choices_separator (str): A string used to join formatted choices (e.g. ", "). source_choice_format (str): A Python format string used for displaying each choice in the input fields (e.g. "{choice_numeral}. {choice_text}"). target_choice_format (str): A Python format string used for displaying each choice in the target or final output (e.g. "{choice_numeral}"). enumerator (str): Determines how choice numerals are enumerated. Possible values include "capitals", "lowercase", "numbers", or "roman". shuffle_choices (bool): If True, shuffle the choices. The shuffling seed can be set with `shuffle_choices_seed`. shuffle_choices_seed (int, optional): If provided, the choices are shuffled with this fixed integer seed for reproducibility. sort_choices_by_length (bool): If True, sorts choices by their length (ascending). sort_choices_alphabetically (bool): If True, sorts choices in alphabetical order. reverse_choices (bool): If True, reverses the order of the choices after any sorting has been applied. Defaults to False to preserve backward compatibility. """ target_prefix: str = "" choices_field: str = "choices" target_field: str = "label" choices_separator: str = ", " source_choice_format: str = "{choice_numeral}. {choice_text}" target_choice_format: str = "{choice_numeral}" enumerator: str = "capitals" shuffle_choices: bool = False shuffle_choices_seed: int = None sort_choices_by_length: bool = False sort_choices_alphabetically: bool = False reverse_choices: bool = False # False by default for backward-compat place_correct_choice_position: int = None 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 verify(self): super().verify() if self.shuffle_choices and ( self.sort_choices_by_length or self.sort_choices_alphabetically or self.reverse_choices or self.place_correct_choice_position is not None ): raise UnitxtError( "You cannot combine shuffle_choices with sorting or reversing flags." ) if self.sort_choices_by_length and self.sort_choices_alphabetically: raise UnitxtError( "You cannot combine both sort_choices_by_length and sort_choices_alphabetically simultaneously." ) if self.place_correct_choice_position is not None and ( self.sort_choices_by_length or self.sort_choices_alphabetically or self.reverse_choices ): raise UnitxtError( "You cannot combine place_correct_choice_position with sorting or reversing flags." ) def inputs_to_choices(self, data: Dict[str, Any], 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_numerals(self, input_fields: Dict[str, Any]) -> Tuple[str, str]: return self.inputs_to_choices(input_fields, "{choice_numeral}") def prepare_multiple_choice_inputs( self, input_fields: Dict[str, Any] ) -> Dict[str, Any]: choices = self.inputs_to_choices(input_fields, self.source_choice_format) return { "numerals": self.inputs_to_numerals(input_fields), **input_fields, self.choices_field: self.choices_separator.join(choices), } def preprocess_input_fields(self, input_fields: Dict[str, Any]) -> Dict[str, Any]: return self.prepare_multiple_choice_inputs(input_fields) def outputs_to_target_index(self, reference_fields: Dict[str, object]) -> int: target = reference_fields[self.target_field] if not isinstance(target, int): try: return reference_fields[self.choices_field].index(target) except ValueError as e: raise UnitxtError( f"MultipleChoiceTemplate could not locate textual target '{target}' in choices list: {reference_fields[self.choices_field]}", Documentation.ADDING_TEMPLATE, ) from e return target def preprocess_reference_fields(self, reference_fields: Dict[str, Any]): target = reference_fields[self.target_field] if not isinstance(target, int): try: target = reference_fields[self.choices_field].index(target) except ValueError as e: raise UnitxtError( f"MultipleChoiceTemplate could not locate textual target '{target}' in choices list: {reference_fields[self.choices_field]}", Documentation.ADDING_TEMPLATE, ) from e choices = self.inputs_to_choices(reference_fields, self.target_choice_format) try: target = choices[target] except IndexError as e: raise UnitxtError( f"MultipleChoiceTemplate cannot find index number {target} in choices: {choices}", Documentation.ADDING_TEMPLATE, ) from e return {self.target_field: target} def reference_fields_to_target_and_references( self, reference_fields: Dict[str, object] ) -> str: target = reference_fields[self.target_field] return target, [target] def preprocess_input_and_reference_fields( self, input_fields: Dict[str, Any], reference_fields: Dict[str, Any] ) -> Tuple[Dict[str, Any], Dict[str, Any]]: if ( not self.shuffle_choices and not self.sort_choices_by_length and not self.sort_choices_alphabetically and not self.reverse_choices and self.place_correct_choice_position is None ): return input_fields, reference_fields choices = input_fields[self.choices_field] target_index = self.outputs_to_target_index(reference_fields) original_label_choice = reference_fields[self.choices_field][target_index] if self.sort_choices_by_length: choices.sort(key=len) if self.sort_choices_alphabetically: choices.sort() if self.reverse_choices: choices.reverse() if self.shuffle_choices: random_generator = new_random_generator( self.shuffle_choices_seed if self.shuffle_choices_seed is not None else {**input_fields} ) random_generator.shuffle(choices) if self.place_correct_choice_position is not None: fix_pos = self.place_correct_choice_position # Supporting negative indexes similar to Python lists # If fix_pos is negative, convert it to a valid positive index by adding len(choices). # For example, -1 becomes the last index, -2 becomes the one before last, etc. if fix_pos < 0: fix_pos += len(choices) self.place_correct_choice_position = fix_pos # Remove the original label choice from the list if not 0 <= self.place_correct_choice_position < len(choices): raise ValueError( f"fix_correct_choice_position={self.place_correct_choice_position} out of range (0..{len(choices) - 1})." ) choices.remove(original_label_choice) choices.insert(self.place_correct_choice_position, original_label_choice) # Update both input_fields and reference_fields once at the end input_fields[self.choices_field] = choices reference_fields[self.choices_field] = choices reference_fields[self.target_field] = choices.index(original_label_choice) return input_fields, reference_fields def post_process_instance(self, instance): instance["input_fields"]["options"] = self.inputs_to_choices( instance["input_fields"], self.target_choice_format ) return instance class YesNoTemplate(InputFormatTemplate): """A template for generating binary Yes/No questions asking whether an input text is of a specific class. Args: 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" def reference_fields_to_target_and_references( self, reference_fields: Dict[str, object] ) -> str: try: gold_class_names = reference_fields[self.label_field] except KeyError as e: raise UnitxtError( f"Available reference_fields are {list(reference_fields.keys())}, missing required label field: '{self.label_field}'." ) from e if not isinstance(gold_class_names, list): raise UnitxtError( f"Unexpected value for gold_class_names: '{gold_class_names}'. Expecting a list." ) try: queried_class_name = reference_fields[self.class_field] except KeyError as e: raise UnitxtError( f"Available reference_fields are {list(reference_fields.keys())}, missing required class field: '{self.class_field}'." ) from e if not queried_class_name or not isinstance(queried_class_name, str): raise UnitxtError( f"Unexpected value for queried_class_names: '{queried_class_name}'. Expected a string." ) if queried_class_name in gold_class_names: return self.yes_answer, [self.yes_answer] return self.no_answer, [self.no_answer] class NullTemplate(Template): """Templates that returns empty prompt and no references.""" postprocessors = [] def input_fields_to_source(self, input_fields: Dict[str, object]) -> str: return "" def reference_fields_to_target_and_references(self, reference_fields): return "", [] 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_separator: str = ", " key_val_separator: str = ": " use_keys_for_inputs: bool = True outputs_key_val_separator: str = ": " use_keys_for_outputs: bool = False def process_dict( self, data: Dict[str, object], key_val_sep, pairs_sep, use_keys ) -> str: pairs = [] for key, val in data.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 input_fields_to_source(self, input_fields: Dict[str, object]) -> str: return self.process_dict( input_fields, key_val_sep=self.key_val_separator, pairs_sep=self.pairs_separator, use_keys=self.use_keys_for_inputs, ) def reference_fields_to_target_and_references( self, reference_fields: Dict[str, object] ) -> str: target = self.process_dict( reference_fields, key_val_sep=self.key_val_separator, pairs_sep=self.pairs_separator, use_keys=self.use_keys_for_outputs, ) return target, [target] class OutputQuantizingTemplate(InputOutputTemplate): serializer: MultiTypeSerializer = NonPositionalField( default_factory=MultiTypeSerializer ) quantum: Union[float, int] = 0.1 def prepare(self): super().prepare() self.serializer.add_serializers( [NumberQuantizingSerializer(quantum=self.quantum)] ) class MultiLabelTemplate(InputOutputTemplate): labels_field: str = "labels" labels_separator: str = ", " postprocessors = ["processors.to_list_by_comma"] output_format: str = "{labels}" empty_label: str = "None" def preprocess_reference_fields( self, reference_fields: Dict[str, Any] ) -> Dict[str, Any]: labels = reference_fields[self.labels_field] if not isinstance(labels, list): raise UnitxtError( f"MultiLabelTemplate requires labels field '{self.labels_field}' to be a list. Got {self.labels_field}<{type(labels).__name__}>: {labels}", Documentation.ADDING_TEMPLATE, ) if len(labels) == 0: labels = [self.empty_label] labels_str = self.labels_separator.join(labels) return {self.labels_field: labels_str} class MultiReferenceTemplate(InputOutputTemplate): references_field: str = "references" random_reference: bool = False serializer: Serializer = NonPositionalField(default_factory=MultiTypeSerializer) def serialize( self, data: Dict[str, Any], instance: Dict[str, Any] ) -> Dict[str, str]: result = {} for k, v in data.items(): if k == self.references_field: v = [self.serializer.serialize(item, instance) for item in v] else: v = self.serializer.serialize(v, instance) result[k] = v return result def reference_fields_to_target_and_references( self, reference_fields: Dict[str, object] ) -> Tuple[str, List[str]]: references = reference_fields[self.references_field] if not isoftype(references, List[str]): raise UnitxtError( f"MultiReferenceTemplate requires references field '{self.references_field}' to be List[str]. Got {self.references_field}<{type(references).__name__}>: {references}", Documentation.ADDING_TEMPLATE, ) if len(references) == 0: return "", [] if self.random_reference: random_generator = new_random_generator(reference_fields) target = random_generator.choice(references) else: target = references[0] return target, references class MultiTurnTemplate(MultiReferenceTemplate): input_format = "" turns_field: str def post_process_instance(self, instance): turns = dict_get(instance["input_fields"], self.turns_field) instance["__turns__"] = turns return super().post_process_instance(instance) 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, reference_fields): spans_starts = reference_fields[self.spans_starts_field] spans_ends = reference_fields[self.spans_ends_field] text = reference_fields[self.text_field] labels = reference_fields[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 preprocess_reference_fields( self, reference_fields: Dict[str, Any] ) -> Dict[str, Any]: span_labels_pairs = self.extract_span_label_pairs(reference_fields) targets = self.span_label_pairs_to_targets(span_labels_pairs) return super().preprocess_reference_fields({"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(DictCollection): def verify(self): for template in self.items.values(): assert isinstance(template, Template)