#!/usr/bin/env python3
"""
Phase 2 verification: corrected URL patterns for eDiAna and deeper checks.
"""

import json
import os
import random
import time
import sys
import io
from pathlib import Path

sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8', errors='replace')

import requests
import urllib3
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
from urllib.parse import quote

AUDIT_DIR = Path(r"C:\Users\alvin\hf-ancient-scripts\data\training\audit_trails")

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
                  "(KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
}
REQUEST_DELAY = 1.5
random.seed(42)


def load_audit_trail(filepath):
    entries = []
    with open(filepath, "r", encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if line:
                try:
                    entries.append(json.loads(line))
                except json.JSONDecodeError:
                    pass
    return entries


def check_url(url, timeout=15):
    try:
        resp = requests.get(url, headers=HEADERS, timeout=timeout, allow_redirects=True, verify=False)
        return resp.status_code, resp.ok, resp.text[:5000] if resp.ok else ""
    except Exception as e:
        return None, False, str(e)


# ============================================================
# 1. eDiAna with CORRECT URL pattern
# ============================================================
def verify_ediana_corrected():
    print("=" * 70)
    print("eDiAna VERIFICATION (Corrected URL: /dictionary.php?lemma=ID)")
    print("=" * 70)

    audit_files = sorted(AUDIT_DIR.glob("ediana_*.jsonl"))

    for af in audit_files:
        lang = af.stem.replace("ediana_", "")
        entries = load_audit_trail(af)
        if not entries:
            print(f"\n  --- {lang}: EMPTY ---")
            continue

        print(f"\n  --- Language: {lang} ({len(entries)} entries) ---")
        samples = random.sample(entries, min(5, len(entries)))

        for entry in samples:
            word = entry.get("word", "?")
            gloss = entry.get("gloss", "?")
            entry_id = entry.get("entry_id", "")

            if entry_id:
                url = f"https://ediana.gwi.uni-muenchen.de/dictionary.php?lemma={entry_id}"
                status, ok, text = check_url(url, timeout=15)

                # Check if the word appears on the page
                word_found = False
                if ok and text:
                    # Clean word for matching (remove special chars)
                    clean_word = word.replace("*", "").replace("-", "").replace("(", "").replace(")", "").strip()
                    if clean_word and len(clean_word) >= 2:
                        word_found = clean_word.lower() in text.lower()

                status_str = f"HTTP {status}"
                if ok:
                    status_str += f" | word_on_page={'YES' if word_found else 'NO'}"
                print(f"    id={entry_id:5s}  word={word[:25]:25s}  gloss={gloss[:30]:30s}  -> {status_str}")
                time.sleep(REQUEST_DELAY)


# ============================================================
# 2. Palaeolexicon deeper verification (content match)
# ============================================================
def verify_palaeolexicon_deep():
    print("\n" + "=" * 70)
    print("PALAEOLEXICON DEEP VERIFICATION (gloss + word match)")
    print("=" * 70)

    # Pick Etruscan (ett) for deep check
    entries = load_audit_trail(AUDIT_DIR / "palaeolexicon_ett.jsonl")
    print(f"\n  Etruscan: {len(entries)} entries")

    # Pick well-known Etruscan words
    known_words = ["ais", "clan", "sec", "huth", "avil", "tur", "lautni"]
    matches = [e for e in entries if e.get("word") in known_words]

    if not matches:
        # Just sample some
        matches = random.sample(entries, min(5, len(entries)))

    for entry in matches[:7]:
        word = entry.get("word", "?")
        gloss = entry.get("gloss", "?")
        word_id = entry.get("word_id")

        if word_id:
            url = f"https://www.palaeolexicon.com/Word/Show/{word_id}"
            status, ok, text = check_url(url, timeout=15)

            word_found = ok and word.lower() in text.lower()
            gloss_parts = [g.strip() for g in gloss.split(",")]
            gloss_found = ok and any(g.lower() in text.lower() for g in gloss_parts if len(g) > 2)

            print(f"    word={word:15s}  gloss={gloss:30s}  word_id={word_id}")
            print(f"      URL: {url}")
            print(f"      word_match={word_found}  gloss_match={gloss_found}  HTTP={status}")
            time.sleep(REQUEST_DELAY)


# ============================================================
# 3. TIR Raetica with corrected URL (tir.univie.ac.at)
# ============================================================
def verify_tir_corrected():
    print("\n" + "=" * 70)
    print("TIR RAETICA (Corrected URL: tir.univie.ac.at)")
    print("=" * 70)

    entries = load_audit_trail(AUDIT_DIR / "tir_raetica_xrr.jsonl")
    print(f"  {len(entries)} entries")

    # Check if wiki has a Word category
    cat_url = "https://tir.univie.ac.at/wiki/Category:Word"
    status, ok, text = check_url(cat_url)
    print(f"\n  Category:Word -> HTTP {status}")

    # Try some word pages
    samples = random.sample(entries, min(5, len(entries)))
    for entry in samples:
        word = entry.get("word", "?")
        gloss = entry.get("gloss", "?")

        # TIR wiki words might be under specific naming conventions
        # Try the raw word
        url = f"https://tir.univie.ac.at/wiki/{quote(word)}"
        status, ok, text = check_url(url, timeout=15)

        word_found = ok and word.lower() in text.lower() if ok else False
        status_str = f"HTTP {status}"
        if ok:
            # Check if the page is a real content page or just a search redirect
            is_search = "There were no results" in text or "Search results" in text
            status_str += f" | content={'SEARCH_REDIRECT' if is_search else 'CONTENT_PAGE'}"

        print(f"    word={word:20s}  gloss={gloss[:25]:25s}  -> {status_str}")
        time.sleep(REQUEST_DELAY)


# ============================================================
# 4. ORACC/eCUT glossary verification
# ============================================================
def verify_oracc_deep():
    print("\n" + "=" * 70)
    print("ORACC/eCUT DEEP VERIFICATION")
    print("=" * 70)

    entries = load_audit_trail(AUDIT_DIR / "oracc_ecut_xur.jsonl")
    print(f"  {len(entries)} entries")

    # Try various ORACC URL patterns for eCUT glossaries
    urls_to_try = [
        "https://oracc.museum.upenn.edu/ecut/",
        "https://oracc.museum.upenn.edu/ecut/corpus",
        "https://oracc.museum.upenn.edu/ecut/pager",
        # Try the Urartian-specific glossary
        "https://oracc.museum.upenn.edu/ecut/xur/index.html",
        "https://oracc.museum.upenn.edu/ecut/glossary/xur",
    ]

    for url in urls_to_try:
        status, ok, text = check_url(url, timeout=15)
        has_content = len(text) > 500 if ok else False
        print(f"  {url}")
        print(f"    -> HTTP {status} | content={'YES' if has_content else 'NO/SMALL'}")
        time.sleep(REQUEST_DELAY)

    # Also try the main ORACC search
    # ORACC has a JSON API: /ecut/cbd/xxx/xxx.json
    json_urls = [
        "https://oracc.museum.upenn.edu/ecut/signlist",
    ]
    for url in json_urls:
        status, ok, text = check_url(url, timeout=15)
        print(f"  {url}")
        print(f"    -> HTTP {status}")
        time.sleep(REQUEST_DELAY)

    print(f"\n  Sample Urartian words in audit trail:")
    samples = random.sample(entries, min(10, len(entries)))
    for entry in samples:
        word = entry.get("word", "?")
        gloss = entry.get("gloss", "?")
        print(f"    word={word:20s}  gloss={gloss}")


# ============================================================
# 5. Avesta.org data quality deep check
# ============================================================
def verify_avesta_deep():
    print("\n" + "=" * 70)
    print("AVESTA.ORG DEEP VERIFICATION")
    print("=" * 70)

    entries = load_audit_trail(AUDIT_DIR / "avesta_org_ave.jsonl")

    # Check the dictionary page for known Avestan words
    dict_url = "https://avesta.org/avdict/avdict.htm"
    status, ok, text = check_url(dict_url, timeout=15)
    print(f"  Dictionary page: HTTP {status} | length={len(text) if ok else 0} chars")

    # Check if audit trail words appear in the dictionary
    real_entries = [e for e in entries if e.get("word") not in ("NOTE", "Example", "swift", "AVESTA")]
    print(f"\n  Checking {len(real_entries)} entries against dictionary page:")

    found_count = 0
    for entry in real_entries:
        word = entry.get("word", "")
        gloss = entry.get("gloss", "")
        if ok and word and len(word) >= 2:
            in_dict = word.lower() in text.lower()
            if in_dict:
                found_count += 1
            print(f"    word={word:20s}  gloss={gloss:20s}  in_dictionary={'YES' if in_dict else 'NO'}")

    print(f"\n  Found {found_count}/{len(real_entries)} words in dictionary page")

    # Critical check: the entries look like they were scraped as word-pairs
    # e.g., word="aiwi" gloss="druxt" -- this looks like the scraper grabbed
    # consecutive words from a text, not word-definition pairs
    print(f"\n  DATA QUALITY ASSESSMENT:")
    print(f"  The avesta.org audit trail entries have a suspicious pattern:")
    print(f"  - 'word' and 'gloss' fields often look like adjacent Avestan words,")
    print(f"    not word-definition pairs.")
    print(f"  - Examples: word='aiwi' gloss='druxt' (both are Avestan words)")
    print(f"  - Examples: word='aurvat' gloss='aspa' (both are Avestan words)")
    print(f"  - The 'gloss' field appears to contain the NEXT word in the text,")
    print(f"    not an English translation.")
    print(f"  - NOTE: avesta.org data is NOT used in the ave.tsv lexicon (which uses wiktionary)")
    print(f"  - The audit trail exists but was apparently not imported into the lexicon")


# ============================================================
# 6. Wiktionary expansion deep verification
# ============================================================
def verify_wiktionary_deep():
    print("\n" + "=" * 70)
    print("WIKTIONARY EXPANSION DEEP VERIFICATION")
    print("=" * 70)

    # Check PIE reconstructions
    entries = load_audit_trail(AUDIT_DIR / "wiktionary_expansion_ine-pro.jsonl")
    print(f"\n  PIE (ine-pro): {len(entries)} entries")

    samples = random.sample(entries, min(5, len(entries)))
    for entry in samples:
        word = entry.get("word", "?")
        page_title = entry.get("page_title", "")
        if page_title:
            url = f"https://en.wiktionary.org/wiki/{quote(page_title)}"
            status, ok, text = check_url(url, timeout=15)
            word_found = ok and word in text
            print(f"    word={word:20s}  page={page_title[:40]:40s}  HTTP={status}  found={word_found}")
            time.sleep(REQUEST_DELAY)

    # Check Ugaritic
    entries = load_audit_trail(AUDIT_DIR / "wiktionary_expansion_uga.jsonl")
    print(f"\n  Ugaritic (uga): {len(entries)} entries")

    samples = random.sample(entries, min(5, len(entries)))
    for entry in samples:
        word = entry.get("word", "?")
        page_title = entry.get("page_title", "")
        if page_title:
            url = f"https://en.wiktionary.org/wiki/{quote(page_title)}"
            status, ok, text = check_url(url, timeout=15)
            print(f"    word={word:20s}  page={page_title[:40]:40s}  HTTP={status}")
            time.sleep(REQUEST_DELAY)


def main():
    print("PHASE 2: DEEP SOURCE VERIFICATION")
    print("=" * 70)
    print(f"Date: {time.strftime('%Y-%m-%d %H:%M:%S')}\n")

    verify_ediana_corrected()
    verify_palaeolexicon_deep()
    verify_tir_corrected()
    verify_oracc_deep()
    verify_avesta_deep()
    verify_wiktionary_deep()

    print("\n" + "=" * 70)
    print("PHASE 2 COMPLETE")
    print("=" * 70)


if __name__ == "__main__":
    main()