"""Shared, deterministic model/data definitions. Copyright (c) 2026 sraivante."""
from __future__ import annotations
import hashlib
import json
import re
import unicodedata
from datetime import datetime, timezone
from pathlib import Path

SEED = 20261017
CONTEXT = 1024
VOCAB = 33069
SPECIALS = ["<|pad|>", "<|endoftext|>", "<|im_end|>", "<|im_start|>", "<think>", "</think>"]
MAX_SOURCE_BYTES = 20_000_000_000
MAX_FILE_BYTES = 7_000_000_000

def now():
    return datetime.now(timezone.utc).isoformat()

def sha(path):
    h = hashlib.sha256()
    with Path(path).open('rb') as f:
        for chunk in iter(lambda: f.read(8 * 1024 * 1024), b''):
            h.update(chunk)
    return h.hexdigest()

def dump(path, value):
    path = Path(path)
    path.parent.mkdir(parents=True, exist_ok=True)
    tmp = path.with_name(path.name + '.partial')
    tmp.write_text(json.dumps(value, indent=2, ensure_ascii=False) + '\n', encoding='utf-8')
    tmp.replace(path)

def prompt_key(prompt):
    # All identical normalized questions share a split, including different solutions.
    s = re.sub(r'\s+', ' ', unicodedata.normalize('NFKC', str(prompt))).strip().casefold()
    return hashlib.sha256(s.encode('utf-8')).digest()

def split_for(prompt):
    bucket = int.from_bytes(prompt_key(prompt)[:8], 'big') % 1000
    return 'validation' if bucket < 5 else ('test' if bucket < 10 else 'train')

def render_row(row):
    prompt = str(row.get('prompt') or '').strip()
    answer = str(row.get('answer') or '').strip()
    thought = str(row.get('think') or '').strip()
    if not prompt or not (answer or thought):
        return None
    # Preserve source Unicode, math, code indentation and newlines. No ASCII filtering.
    reasoning = '<think>\n' + thought + '\n</think>\n' if thought else ''
    return ('<|im_start|>user\n' + prompt + '<|im_end|>\n'
            + '<|im_start|>assistant\n' + reasoning + answer + '<|im_end|>\n<|endoftext|>')

def model_config():
    from transformers import Qwen3Config
    return Qwen3Config(
        vocab_size=VOCAB, hidden_size=512, intermediate_size=1536,
        num_hidden_layers=14, num_attention_heads=8, num_key_value_heads=4,
        head_dim=128, max_position_embeddings=CONTEXT,
        hidden_act='silu', rms_norm_eps=1e-6, rope_theta=1_000_000.0,
        attention_bias=False, attention_dropout=0.0, tie_word_embeddings=True,
        use_sliding_window=False, sliding_window=None, max_window_layers=14,
        pad_token_id=0, bos_token_id=1, eos_token_id=2, use_cache=False,
        architectures=['Qwen3ForCausalLM'],
    )