"""Byte hashes already proved the stored files are all distinct. This compares
their TEXT so we can say whether the stored resignation letter is simply an
older render of the same letter the user just attached.
"""
import io
import urllib.parse
import urllib.request
from pathlib import Path

from pypdf import PdfReader

JEI = Path(r"C:\Users\SJ\.minimax-agent\projects\JEI")
env = {}
for line in (JEI / ".env.local").read_text(encoding="utf-8").splitlines():
    line = line.strip()
    if line and not line.startswith("#") and "=" in line:
        k, v = line.split("=", 1)
        env[k.strip()] = v.strip().strip('"').strip("'")
PB = env.get("NEXT_PUBLIC_PB_URL") or env["PB_URL"]
TOK = env["PB_TOKEN"]

LOCAL = {
    "LOCAL: UFC - Shareholders Resolution Director Sune Resigned.pdf":
        r"C:\Users\SJ\.minimax\v2\assets\2026\10\12\13-41-56-012-asset_20261012-134156-012_a0299b3ae338_88b7c484-UFC - Shareholders Resolution Director Sune Resigned.pdf",
    "LOCAL: UFC - Sune Resignation Letter.pdf":
        r"C:\Users\SJ\.minimax\v2\assets\2026\10\12\13-41-56-017-asset_20261012-134156-017_9aec1b4380ba_261a3e6b-UFC - Sune Resignation Letter.pdf",
}

STORED = [
    ("STORED resolution.signed_letter", "pbc_266170881", "e0dbxprazb70rqx", "corrected_v2_avwpnq7o93.pdf"),
    ("STORED resignation.letter", "ysterbaard_director_resignations", "8cfie18uhgy0n0o",
     "resignation_letter_the_ultimate_fuel_company_1jk8t34nf3.pdf"),
    ("STORED resignation.signature", "ysterbaard_director_resignations", "8cfie18uhgy0n0o",
     "tbde1evt2m6_8mz8kutpki.pdf"),
]


def text_of(b: bytes) -> str:
    try:
        r = PdfReader(io.BytesIO(b))
        return "\n".join((p.extract_text() or "") for p in r.pages)
    except Exception as e:
        return f"<<unreadable: {e}>>"


def norm(s: str) -> str:
    return " ".join(s.split()).lower()


texts = {}
for label, path in LOCAL.items():
    b = Path(path).read_bytes()
    texts[label] = (norm(text_of(b)), len(b))
    print(f"{label}\n    {len(b):>8} B  chars={len(texts[label][0])}")

print()
for label, col, rid, fname in STORED:
    url = f"{PB}/api/files/{col}/{rid}/{urllib.parse.quote(fname)}"
    req = urllib.request.Request(url, headers={"Authorization": TOK})
    with urllib.request.urlopen(req, timeout=120) as r:
        b = r.read()
    texts[label] = (norm(text_of(b)), len(b))
    print(f"{label}\n    {len(b):>8} B  chars={len(texts[label][0])}")

print("\n=== TEXT SIMILARITY (fraction of the shorter text found in the longer) ===")
import difflib

labels = list(texts)
for i in range(len(labels)):
    for j in range(i + 1, len(labels)):
        a, b = texts[labels[i]][0], texts[labels[j]][0]
        if not a or not b:
            continue
        ratio = difflib.SequenceMatcher(None, a, b).ratio()
        print(f"  {ratio * 100:5.1f}%  {labels[i]}  <->  {labels[j]}")

print("\n=== FIRST 400 CHARS OF EACH ===")
for label in labels:
    t = texts[label][0]
    print(f"\n--- {label}\n{t[:400]}")