"""Синтаксические признаки-прокси.

Полноценный синтаксический анализ не используется: для коротких реплик он
нестабилен и требует тяжёлых моделей. Вместо этого считаются устойчивые
приближения по функциям слов, длине предложений и окончаниям.
"""

from __future__ import annotations

import re

import pandas as pd

from ..lexicons import COORDINATORS, MODALS, NEGATIONS, SUBORDINATORS
from .base import as_list, safe_div, sentence_word_counts, word_tokens

_IMPERATIVE_VERBS = {
    "go", "come", "look", "take", "get", "give", "tell", "let", "stop",
    "wait", "listen", "try", "keep", "put", "make", "do", "be", "have",
    "see", "say", "help", "leave", "stay", "bring", "show", "find",
}


def _sentence_first_words(row: pd.Series) -> list[str]:
    sentences = as_list(row, "sentences")
    first_words: list[str] = []
    for sentence in sentences:
        for token in sentence:
            if re.search(r"[A-Za-z]", token):
                first_words.append(token.lower())
                break
    return first_words


def extract(row: pd.Series) -> dict[str, float]:
    tokens = [str(token).lower() for token in as_list(row, "tokens")]
    words = word_tokens(tokens)
    n_words = max(len(words), 1)
    sentences = as_list(row, "sentences")
    n_sentences = max(len(sentences), 1)
    sentence_lengths = sentence_word_counts(row)
    first_words = _sentence_first_words(row)
    text = str(row.get("text", ""))

    coordinators = sum(1 for token in tokens if token in COORDINATORS)
    subordinators = sum(1 for token in tokens if token in SUBORDINATORS)
    clauses = coordinators + subordinators

    passive = len(
        re.findall(
            r"\b(?:be|is|are|was|were|been|being)\s+"
            r"(?:\w+ed|known|made|given|taken|seen|done|written|spoken)\b",
            text,
            flags=re.IGNORECASE,
        )
    )
    question_tags = len(
        re.findall(
            r"\b(?:right|isn't it|aren't you|don't you|didn't you|huh)\s*[?.!]?\s*$",
            text,
            flags=re.IGNORECASE,
        )
    )
    imperative = sum(1 for word in first_words if word in _IMPERATIVE_VERBS)
    sentence_initial_conjunction = sum(
        1 for word in first_words if word in COORDINATORS or word in SUBORDINATORS
    )

    return {
        "syn_mean_clauses_per_sentence": safe_div(clauses, n_sentences),
        "syn_coordinator_per_sentence": safe_div(coordinators, n_sentences),
        "syn_subordinator_per_sentence": safe_div(subordinators, n_sentences),
        "syn_sentence_initial_conjunction_rate": safe_div(
            sentence_initial_conjunction, n_sentences
        ),
        "syn_question_rate": safe_div(
            sum(
                1
                for sentence in sentences
                if sentence and " ".join(sentence).rstrip().endswith("?")
            ),
            n_sentences,
        ),
        "syn_exclamation_rate": safe_div(
            sum(
                1
                for sentence in sentences
                if sentence and " ".join(sentence).rstrip().endswith("!")
            ),
            n_sentences,
        ),
        "syn_modal_rate": safe_div(sum(1 for token in tokens if token in MODALS), n_words),
        "syn_negation_per_sentence": safe_div(
            sum(1 for token in tokens if token in NEGATIONS), n_sentences
        ),
        "syn_that_rate": safe_div(tokens.count("that"), n_words),
        "syn_to_rate": safe_div(tokens.count("to"), n_words),
        "syn_ing_rate": safe_div(
            sum(1 for token in words if token.endswith("ing")), n_words
        ),
        "syn_ed_rate": safe_div(
            sum(1 for token in words if token.endswith("ed")), n_words
        ),
        "syn_ly_rate": safe_div(
            sum(1 for token in words if token.endswith("ly")), n_words
        ),
        "syn_passive_proxy_per_sentence": safe_div(passive, n_sentences),
        "syn_question_tag_per_sentence": safe_div(question_tags, n_sentences),
        "syn_imperative_proxy_rate": safe_div(imperative, n_sentences),
        "syn_mean_sentence_words": safe_div(sum(sentence_lengths), n_sentences),
        "syn_max_sentence_words": float(max(sentence_lengths)) if sentence_lengths else 0.0,
    }