"""Release validation for KorByte-128K artifacts."""

from __future__ import annotations

import hashlib
import json
import random
from datetime import UTC, datetime
from pathlib import Path
from typing import Any

from tokenizers import Tokenizer
from transformers import PreTrainedTokenizerFast

from .challenges import CHALLENGE_TEXTS
from .config import CORE_VOCAB_SIZE, TOTAL_VOCAB_SIZE, Paths
from .special_tokens import special_token_strings

REQUIRED_RELEASE_FILES = (
    "README.md",
    "LICENSE",
    "NOTICE",
    "DATA_SOURCES.md",
    "tokenizer.json",
    "tokenizer_config.json",
    "special_tokens_map.json",
    "added_tokens.json",
    "vocab.json",
    "merges.txt",
    "provenance/corpus_manifest.json",
    "provenance/build_manifest.json",
    "reports/benchmark.json",
    "reports/benchmark.md",
    "reports/comparison.json",
    "reports/comparison.md",
    "reports/research.json",
    "reports/research.md",
)


def _sha256(path: Path) -> str:
    digest = hashlib.sha256()
    with path.open("rb") as handle:
        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
            digest.update(chunk)
    return digest.hexdigest()


def _random_challenges(*, count: int = 500, seed: int = 20260804) -> list[str]:
    rng = random.Random(seed)
    alphabets = (
        tuple(chr(codepoint) for codepoint in range(0xAC00, 0xD7A4, 173)),
        tuple(chr(codepoint) for codepoint in range(0x1100, 0x1200, 7)),
        tuple(chr(codepoint) for codepoint in range(0x4E00, 0x9FFF, 997)),
        tuple(chr(codepoint) for codepoint in range(0x1F300, 0x1FAFF, 47)),
        tuple(chr(codepoint) for codepoint in range(0x0300, 0x0370, 5)),
        tuple("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"),
        tuple(" .,!?;:'\"()[]{}<>+-=*/_@#%&|~`\t\n\r"),
    )
    strings: list[str] = []
    for _ in range(count):
        length = rng.randint(0, 160)
        value = "".join(rng.choice(rng.choice(alphabets)) for _ in range(length))
        strings.append(value)
    return strings


def _assert_round_trip(tokenizer: Tokenizer, texts: list[str]) -> int:
    tested = 0
    for text in texts:
        encoding = tokenizer.encode(text, add_special_tokens=False)
        decoded = tokenizer.decode(encoding.ids, skip_special_tokens=False)
        if decoded != text:
            raise AssertionError(
                f"Tokenizers round-trip failed at case {tested}: {text!r} != {decoded!r}"
            )
        if any(token_id < 0 or token_id >= TOTAL_VOCAB_SIZE for token_id in encoding.ids):
            raise AssertionError(f"Out-of-range token ID at case {tested}")
        tested += 1
    return tested


def validate_release(root: Path, *, require_benchmark_gate: bool = True) -> dict[str, Any]:
    """Validate packaging, ID allocation, round-trip behavior, and benchmark gate."""

    missing = [name for name in REQUIRED_RELEASE_FILES if not (root / name).is_file()]
    if missing:
        raise FileNotFoundError(f"Missing release files: {', '.join(missing)}")

    tokenizer_path = root / "tokenizer.json"
    tokenizer = Tokenizer.from_file(str(tokenizer_path))
    core_size = tokenizer.get_vocab_size(with_added_tokens=False)
    total_size = tokenizer.get_vocab_size(with_added_tokens=True)
    if core_size != CORE_VOCAB_SIZE:
        raise AssertionError(f"Core vocabulary is {core_size}, expected {CORE_VOCAB_SIZE}")
    if total_size != TOTAL_VOCAB_SIZE:
        raise AssertionError(f"Total vocabulary is {total_size}, expected {TOTAL_VOCAB_SIZE}")
    if tokenizer.normalizer is not None:
        raise AssertionError("A normalizer would prevent exact code-point preservation")

    vocab = tokenizer.get_vocab(with_added_tokens=True)
    special_ids = [vocab[token] for token in special_token_strings()]
    expected_special_ids = list(range(CORE_VOCAB_SIZE, TOTAL_VOCAB_SIZE))
    if special_ids != expected_special_ids:
        raise AssertionError("Special-token IDs are not stable and contiguous")

    fixed_texts = list(CHALLENGE_TEXTS)
    random_texts = _random_challenges()
    tokenizers_cases = _assert_round_trip(tokenizer, fixed_texts + random_texts)

    fast = PreTrainedTokenizerFast.from_pretrained(str(root), local_files_only=True)
    transformers_cases = 0
    for text in fixed_texts + random_texts[:100]:
        token_ids = fast.encode(text, add_special_tokens=False)
        decoded = fast.decode(
            token_ids,
            skip_special_tokens=False,
            clean_up_tokenization_spaces=False,
        )
        if decoded != text:
            raise AssertionError(
                f"Transformers round-trip failed at case {transformers_cases}: "
                f"{text!r} != {decoded!r}"
            )
        transformers_cases += 1

    paths = Paths(root)
    benchmark = json.loads(paths.benchmark_json.read_text(encoding="utf-8"))
    benchmark_passed = bool(benchmark.get("compression_gate_passed"))
    if require_benchmark_gate and not benchmark_passed:
        reduction = benchmark.get("korbyte_macro_reduction_vs_kanana_percent")
        raise AssertionError(f"Compression gate failed: macro reduction={reduction!r}%")

    comparison = json.loads(paths.comparison_json.read_text(encoding="utf-8"))
    comparison_passed = bool(comparison.get("first_place_gate_passed"))
    comparison_coverage_passed = bool(comparison.get("comparison_coverage_gate_passed"))
    if require_benchmark_gate and not (comparison_passed and comparison_coverage_passed):
        raise AssertionError("Pinned public first-place comparison gate failed")

    build_manifest = json.loads(paths.build_manifest.read_text(encoding="utf-8"))
    current_hash = _sha256(tokenizer_path)
    if build_manifest.get("tokenizer_sha256") != current_hash:
        raise AssertionError("tokenizer.json does not match the build manifest")

    result = {
        "schema_version": 1,
        "created_at": datetime.now(UTC).isoformat(),
        "passed": True,
        "core_vocab_size": core_size,
        "total_vocab_size": total_size,
        "special_token_ids_contiguous": True,
        "tokenizers_round_trip_cases": tokenizers_cases,
        "transformers_round_trip_cases": transformers_cases,
        "benchmark_gate_required": require_benchmark_gate,
        "benchmark_gate_passed": benchmark_passed,
        "comparison_gate_passed": comparison_passed,
        "comparison_coverage_gate_passed": comparison_coverage_passed,
        "tokenizer_sha256": current_hash,
        "required_release_files": list(REQUIRED_RELEASE_FILES),
    }
    paths.validation_json.parent.mkdir(parents=True, exist_ok=True)
    paths.validation_json.write_text(
        json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
    )
    return result