"""
Pure humanization helpers (no Streamlit).

Extracted from the original pages/humanize_text.py so the FastAPI backend and
any frontend can import these functions without pulling in Streamlit.
"""
import logging
import random
import re
import ssl
import warnings

import nltk
import spacy
from nltk.corpus import wordnet
from nltk.tokenize import sent_tokenize, word_tokenize

warnings.filterwarnings("ignore", category=FutureWarning)

logger = logging.getLogger(__name__)


def download_nltk_resources():
    try:
        _create_unverified_https_context = ssl._create_unverified_context
    except AttributeError:
        pass
    else:
        ssl._create_default_https_context = _create_unverified_https_context

    resources = [
        "punkt",
        "averaged_perceptron_tagger",
        "punkt_tab",
        "wordnet",
        "averaged_perceptron_tagger_eng",
    ]
    for r in resources:
        nltk.download(r, quiet=True)


download_nltk_resources()

try:
    nlp = spacy.load("en_core_web_sm")
except OSError:
    logger.warning(
        "spaCy en_core_web_sm model not found. Install with: python -m spacy download en_core_web_sm"
    )
    nlp = None


CITATION_REGEX = re.compile(
    r"\(\s*[A-Za-z&\-,\.\s]+(?:et al\.\s*)?,\s*\d{4}(?:,\s*(?:pp?\.\s*\d+(?:-\d+)?))?\s*\)"
)


def count_words(text):
    return len(word_tokenize(text))


def count_sentences(text):
    return len(sent_tokenize(text))


def extract_citations(text):
    refs = CITATION_REGEX.findall(text)
    placeholder_map = {}
    replaced_text = text
    for i, r in enumerate(refs, start=1):
        placeholder = f"[[REF_{i}]]"
        placeholder_map[placeholder] = r
        replaced_text = replaced_text.replace(r, placeholder, 1)
    return replaced_text, placeholder_map


PLACEHOLDER_REGEX = re.compile(r"\[\s*\[\s*REF_(\d+)\s*\]\s*\]")


def restore_citations(text, placeholder_map):
    def replace_placeholder(match):
        idx = match.group(1)
        key = f"[[REF_{idx}]]"
        return placeholder_map.get(key, match.group(0))

    return PLACEHOLDER_REGEX.sub(replace_placeholder, text)


WHOLE_CONTRACTIONS = {
    "can't": "cannot",
    "won't": "will not",
    "shan't": "shall not",
    "ain't": "is not",
    "i'm": "i am",
    "it's": "it is",
    "we're": "we are",
    "they're": "they are",
    "you're": "you are",
    "he's": "he is",
    "she's": "she is",
    "that's": "that is",
    "there's": "there is",
    "what's": "what is",
    "who's": "who is",
    "let's": "let us",
    "didn't": "did not",
    "doesn't": "does not",
    "don't": "do not",
    "couldn't": "could not",
    "shouldn't": "should not",
    "wouldn't": "would not",
    "isn't": "is not",
    "aren't": "are not",
    "weren't": "were not",
    "hasn't": "has not",
    "haven't": "have not",
    "hadn't": "had not",
}

SUFFIX_CONTRACTIONS = {
    "n't": " not",
    "'re": " are",
    "'s": " is",
    "'ll": " will",
    "'ve": " have",
    "'d": " would",
    "'m": " am",
}

ACADEMIC_TRANSITIONS = [
    "Moreover,",
    "Additionally,",
    "Furthermore,",
    "Hence,",
    "Therefore,",
    "Consequently,",
    "Nonetheless,",
    "Nevertheless,",
    "In contrast,",
    "On the other hand,",
    "In addition,",
    "As a result,",
]


def expand_contractions(sentence):
    alt = "|".join(re.escape(k) for k in WHOLE_CONTRACTIONS.keys())
    whole_pattern = rf"(?:(``)\s*)?(?P<word>(?:{alt}))(?:\s*(''))?"

    def _replace_whole_with_quotes(match):
        open_tok = match.group(1) or ""
        word = match.group("word")
        close_tok = match.group(3) or ""
        key = word.lower()
        repl = WHOLE_CONTRACTIONS.get(key, word)
        if word and word[0].isupper():
            repl = repl.capitalize()
        return f"{open_tok}{repl}{close_tok}"

    sentence = re.sub(
        whole_pattern, _replace_whole_with_quotes, sentence, flags=re.IGNORECASE
    )

    tokens = word_tokenize(sentence)
    out_tokens = []
    for t in tokens:
        lower_t = t.lower()
        replaced = False
        for contr, expansion in SUFFIX_CONTRACTIONS.items():
            if lower_t.endswith(contr):
                base = lower_t[: -len(contr)]
                new_t = base + expansion
                if t and t[0].isupper():
                    new_t = new_t.capitalize()
                out_tokens.append(new_t)
                replaced = True
                break
        if not replaced:
            out_tokens.append(t)
    return " ".join(out_tokens)


def get_synonyms(word, pos):
    wn_pos = None
    if pos.startswith("ADJ"):
        wn_pos = wordnet.ADJ
    elif pos.startswith("NOUN"):
        wn_pos = wordnet.NOUN
    elif pos.startswith("ADV"):
        wn_pos = wordnet.ADV
    elif pos.startswith("VERB"):
        wn_pos = wordnet.VERB

    synonyms = set()
    if wn_pos:
        for syn in wordnet.synsets(word, pos=wn_pos):
            for lemma in syn.lemmas():
                lemma_name = lemma.name().replace("_", " ")
                if lemma_name.lower() != word.lower():
                    synonyms.add(lemma_name)
    return list(synonyms)


def replace_synonyms(sentence, p_syn=0.2):
    if not nlp:
        return sentence

    doc = nlp(sentence)
    new_tokens = []
    for token in doc:
        if "[[REF_" in token.text:
            new_tokens.append(token.text)
            continue
        if token.pos_ in ["ADJ", "NOUN", "VERB", "ADV"] and wordnet.synsets(token.text):
            if random.random() < p_syn:
                synonyms = get_synonyms(token.text, token.pos_)
                if synonyms:
                    new_tokens.append(random.choice(synonyms))
                else:
                    new_tokens.append(token.text)
            else:
                new_tokens.append(token.text)
        else:
            new_tokens.append(token.text)
    return " ".join(new_tokens)


def add_academic_transition(sentence, p_transition=0.2):
    if random.random() < p_transition:
        transition = random.choice(ACADEMIC_TRANSITIONS)
        return f"{transition} {sentence}"
    return sentence


def minimal_humanize_line(line, p_syn=0.2, p_trans=0.2):
    line = expand_contractions(line)
    line = replace_synonyms(line, p_syn=p_syn)
    line = add_academic_transition(line, p_transition=p_trans)
    return line


def minimal_rewriting(text, p_syn=0.2, p_trans=0.2):
    lines = sent_tokenize(text)
    out_lines = [
        minimal_humanize_line(ln, p_syn=p_syn, p_trans=p_trans) for ln in lines
    ]
    return " ".join(out_lines)


def preserve_linebreaks_rewrite(text, p_syn=0.2, p_trans=0.2):
    """Rewrite text while preserving original line breaks."""
    lines = text.splitlines()
    out_lines = []
    for ln in lines:
        if not ln.strip():
            out_lines.append("")
        else:
            out_lines.append(
                minimal_rewriting(ln, p_syn=p_syn, p_trans=p_trans)
            )
    return "\n".join(out_lines)