src/modules/preprocess/preprocessor/label_funs.py

from const import (
    SUMMARY,
    EMOTIONS,
    EMOTION,
    UTTERANCE,
    ASPECTS,
    TARGET,
    VALUE,
    OPINION,
    SENTIMENT,
    CATEGORY,
    CHARACTERS,
    DIALOG,
    START,
    END,
    BELIEF_STATE,
    DOMAIN,
    INFORMED_SLOT_VALUE_TABLE,
    SLOT,
    VALUES,
    RELATION,
    SQL,
    SLOT_VALUE_TABLE,
    SLOTS_TO_FILL,
    ROLE_RELATIONS,
    REWRITTEN,
    ROLES_TO_SELECT,
    ACTIVE_INTENTS,
    TRAIN_SPLIT,
    OPTION_LABEL,
    CANDIDATES,
)
from typing import Dict
import re
import random
import copy
import json


def extract_summary(dial: Dict, **kwargs):
    """
    `dial` is the full dialog.
    """
    return dial[SUMMARY]


def extract_turn_emotion(turn: Dict, sep: str, **kwargs):
    if EMOTIONS not in turn:
        return None
    return sep.join(map(lambda x: x[EMOTION], turn[EMOTIONS]))


def extract_turn_emotion_wrapper(sep: str):
    def extract_turn_emotion_func(turn: Dict, **kwargs):
        return extract_turn_emotion(turn, sep)

    return extract_turn_emotion_func


def extract_turn_utterance(turn: Dict, **kwargs):
    return turn[UTTERANCE]


def extract_aspects(turn: Dict, ext_aspect_sep: str, int_aspect_sep: str):
    if not turn[ASPECTS]:
        return "None"

    aspects = turn[ASPECTS]

    tgt_seq = []
    for aspect in aspects:
        aspect_seq = []
        if TARGET in aspect:
            aspect_seq.append(aspect[TARGET][VALUE])
        if CATEGORY in aspect:
            aspect_seq.append(aspect[CATEGORY])

        if OPINION in aspect:
            aspect_seq.append(aspect[OPINION][VALUE])

        if SENTIMENT in aspect:
            aspect_seq.append(aspect[SENTIMENT])

        tgt_seq.append(int_aspect_sep.join(aspect_seq))

    return ext_aspect_sep.join(tgt_seq)


def extract_aspects_wrapper(ext_aspect_sep: str, int_aspect_sep: str):
    def extract_aspects_func(turn: Dict, **kwargs):
        return extract_aspects(turn, ext_aspect_sep, int_aspect_sep)

    return extract_aspects_func


def rebuild_utterance_with_characters(turn: Dict, split):
    if split == "train":
        utterance = turn[UTTERANCE]
        parts = []
        pre = 0

        for character in turn[CHARACTERS]:
            parts.append(utterance[pre : character[START]])
            parts.append(
                f"[{utterance[character[START]: character[END]]} | {character[VALUE]}]"
            )
            pre = character[END]

        parts.append(utterance[pre:])
        return "".join(parts)

    else:
        tuples = []
        for character in turn[CHARACTERS]:
            tuples.append(f"{character[VALUE]}, {character[START]}, {character[END]}")

        if not tuples:
            return "None"
        return " | ".join(tuples)


def extract_characters(example):
    for turn_id, turn in enumerate(example[DIALOG]):
        if CHARACTERS not in turn:
            continue

        for character in turn[CHARACTERS]:
            yield turn_id, character[VALUE], (character[END],)


def extract_belief_state(
    turn,
    value_sep,
    domain_sep,
    slot_sep,
    domain_prompt_op,
    ontology=None,
    do_train=True,
):
    domain_bs = dict()
    bs = turn[BELIEF_STATE]

    # spare_bs = {domain: {slot for slot in ontology[domain]} for domain in ontology}

    for state in bs:
        domain = state[DOMAIN]
        if domain not in domain_bs:
            domain_bs[domain] = dict()

        if INFORMED_SLOT_VALUE_TABLE not in state:
            continue

        for svp in state[INFORMED_SLOT_VALUE_TABLE]:
            slot = svp[SLOT]
            values = svp[VALUES]
            relation = svp[RELATION]

            if slot not in domain_bs[domain]:
                domain_bs[domain][slot] = {"relation": relation, "values": []}
            domain_bs[domain][slot]["values"] += list(map(lambda x: x[VALUE], values))

            # spare_bs[domain].remove(slot)

    domain_bs_list = []
    for domain in domain_bs:
        svp_list = []
        for slot in domain_bs[domain]:
            val_str = value_sep.join(domain_bs[domain][slot]["values"])
            svp_list.append(f"{slot} {domain_bs[domain][slot]['relation']} {val_str}")

        # control whether to add spare slots
        # for slot in sorted(spare_bs[domain]):
        #     svp_list.append(f"{slot} = None")
        if not svp_list:
            continue
        if do_train:
            # shuffle for training
            random.shuffle(svp_list)

        # append a slot separator at the end to alleviate the problem of end point prediction of T5
        svt_str = slot_sep.join(svp_list) + slot_sep

        domain_bs_list.append(f"{domain}{domain_prompt_op}{svt_str.strip()}")

    if not domain_bs_list:
        return "None"

    return domain_sep.join(domain_bs_list)


def extract_belief_state_wrapper(value_sep, domain_sep, slot_sep, domain_prompt_op):
    def extract_belief_state_func(turn, ontology, do_train=True, **kwargs):
        return extract_belief_state(
            turn,
            value_sep,
            domain_sep,
            slot_sep,
            domain_prompt_op,
            ontology,
            do_train=do_train,
        )

    return extract_belief_state_func


def normalize(query: str) -> str:
    def comma_fix(s):
        # Remove spaces in front of commas
        return s.replace(" , ", ", ")

    def white_space_fix(s):
        # Remove double and triple spaces
        return " ".join(s.split())

    def lower(s):
        # Convert everything except text between (single or double) quotation marks to lower case
        return re.sub(
            r"\b(?<!['\"])(\w+)(?!['\"])\b", lambda match: match.group(1).lower(), s
        )

    def space_fix(sql: str):
        def agg_fix(sql: str):
            return re.sub(
                r"(count|max|min|sum|avg)\s\(",
                lambda match: match.group(0).replace(" ", ""),
                sql,
            )

        def brackets_fix(sql: str):
            sql = re.sub(r"\(\s", lambda match: match.group(0)[:-1], sql)
            sql = re.sub(r"\s\)", lambda match: match.group(0)[1:], sql)

            return sql

        def double_chars_op_fix(sql: str):
            return re.sub(
                r"((>|<|!)\s=)",
                lambda match: match.group(0).replace(" ", ""),
                sql,
            )

        return double_chars_op_fix(brackets_fix(agg_fix(sql)))

    return space_fix(comma_fix(white_space_fix(lower(query))))


def extract_sql(turn, split):
    if SQL not in turn:
        return None
    _normalize = normalize if split == "train" else (lambda x: x)
    return _normalize(turn[SQL])


def extract_slots_without_intents(turn, value_sep, slot_sep):
    if SLOTS_TO_FILL not in turn or not turn[SLOTS_TO_FILL][SLOT_VALUE_TABLE]:
        return "None"
    slots = []
    for svp in turn[SLOTS_TO_FILL][SLOT_VALUE_TABLE]:
        slots.append(
            svp[SLOT]
            + " "
            + svp[RELATION]
            + " "
            + value_sep.join(map(lambda x: x[VALUE], svp[VALUES]))
        )

    return (slot_sep.join(slots) + slot_sep).strip()


def extract_slots_without_intents_wrapper(value_sep, slot_sep):
    def extract_slots_without_intents_func(turn, **kwargs):
        return extract_slots_without_intents(turn, value_sep, slot_sep)

    return extract_slots_without_intents_func


def extract_role_relation_without_turn(dialog, relation_sep):
    return relation_sep.join(map(lambda x: x[RELATION], dialog[ROLE_RELATIONS]))


def extract_role_relation_without_turn_wrapper(relation_sep):
    def extract_role_relation_without_turn_func(dialog, **kwargs):
        return extract_role_relation_without_turn(dialog, relation_sep)

    return extract_role_relation_without_turn_func


def extrac_rewritten(turn, **kwargs):
    if REWRITTEN not in turn:
        return None
    return turn[REWRITTEN]


def extract_options(turn, knowledge, split=None):
    if ROLES_TO_SELECT not in turn:
        return None
    if split == TRAIN_SPLIT:
        return knowledge[turn[ROLES_TO_SELECT][0]]
    else:
        return json.dumps(
            {OPTION_LABEL: turn[ROLES_TO_SELECT][0], CANDIDATES: knowledge}
        )


# def extract_roles_wrapper(role_sep):
#     def extract_roles_func(turn, knowledge, split=None):
#         return extract_options(turn, know)

#     return extract_roles_func


def extract_intents(turn, intent_sep):
    if not turn[ACTIVE_INTENTS]:
        return "None"
    return intent_sep.join(
        map(lambda intent: intent.replace("_", " "), turn[ACTIVE_INTENTS])
    )


def extract_intents_wrapper(intent_sep):
    def extract_intents_func(turn, **kwargs):
        return extract_intents(turn, intent_sep)

    return extract_intents_func