import json
import re
from collections import Counter, defaultdict
from pathlib import Path

DATA_FILE = Path('training-data/timmy-voice-batch09.jsonl')
SCRIPT_FILE = Path('training-data/generate_timmy_voice_batch09.py')
SOURCES_FILE = Path('training-data/timmy-voice-batch09.sources.json')
README_FILE = Path('training-data/README-batch09.md')
EXPECTED_ROWS = 1000
EXPECTED_SOURCE_SESSIONS = 50
EXPECTED_ROWS_PER_SESSION = 20
BANNED_MODEL_STRINGS = ['claude', 'gpt-4', 'gpt-3', 'gpt', 'o1', 'o3', 'gemini', 'anthropic', 'openai', 'unknown']
CRISIS_TERMS = ['suicide', 'kill myself', 'end my life', 'overdose', 'bridge', 'gun', 'die']
CREDENTIAL_MARKERS = ['password', 'pass:', 'token', 'api key', 'secret', 'login:']
PRIVATE_MARKERS = ['/users/', '/private/', '~/.timmy', '~/.hermes', 'alexanderwhitestone.com']
META_MARKERS = [
    'review the conversation above',
    "you've reached the maximum number of tool-calling iterations",
    'without calling any more tools',
    'nothing to save',
    '[silent]',
]
EMAIL_RE = re.compile(r'[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}')


def load_rows(path: Path):
    assert path.exists(), f'missing file: {path}'
    rows = []
    for line in path.read_text(encoding='utf-8').splitlines():
        line = line.strip()
        if line:
            rows.append(json.loads(line))
    return rows


def load_sources():
    assert SOURCES_FILE.exists(), f'missing file: {SOURCES_FILE}'
    return json.loads(SOURCES_FILE.read_text(encoding='utf-8'))


def test_batch09_artifacts_exist():
    for path in [DATA_FILE, SCRIPT_FILE, SOURCES_FILE, README_FILE]:
        assert path.exists(), f'missing artifact: {path}'


def test_sources_manifest_has_50_unique_sessions():
    manifest = load_sources()
    assert manifest['batch'] == 9
    assert manifest['total_source_sessions'] == EXPECTED_SOURCE_SESSIONS
    sessions = manifest['sessions']
    assert len(sessions) == EXPECTED_SOURCE_SESSIONS
    session_files = [item['session_file'] for item in sessions]
    assert len(session_files) == len(set(session_files))
    for item in sessions:
        assert item['session_score'] > 0
        assert item['pair_score'] > 0
        assert item['source_prompt'].strip()
        assert item['source_response'].strip()
        model = item['model'].lower()
        assert not any(bad in model for bad in BANNED_MODEL_STRINGS), item['model']


def test_dataset_has_exactly_1000_rows():
    rows = load_rows(DATA_FILE)
    assert len(rows) == EXPECTED_ROWS


def test_ids_are_unique_and_batch_scoped():
    rows = load_rows(DATA_FILE)
    ids = [row['id'] for row in rows]
    assert len(ids) == len(set(ids))
    assert ids[0] == 'timmy-voice-batch09-0001'
    assert ids[-1] == 'timmy-voice-batch09-1000'
    for row in rows:
        assert row['batch'] == 9
        assert row['model'] == 'timmy-voice-batch09'
        assert row['quality_score'] >= 0.80


def test_sharegpt_schema_and_source_mapping():
    rows = load_rows(DATA_FILE)
    manifest = load_sources()
    valid_sources = {item['session_file'] for item in manifest['sessions']}
    counts = Counter()
    prompts_by_source = defaultdict(set)
    for row in rows:
        assert row['source'] == 'session_derived_approved'
        assert row['source_session'] in valid_sources
        counts[row['source_session']] += 1
        conv = row['conversations']
        assert [item['from'] for item in conv] == ['system', 'human', 'gpt']
        assert 'Sovereignty and service always.' in conv[0]['value']
        assert conv[1]['value'].strip()
        assert conv[2]['value'].strip()
        prompts_by_source[row['source_session']].add(conv[1]['value'])
    assert len(counts) == EXPECTED_SOURCE_SESSIONS
    assert set(counts.values()) == {EXPECTED_ROWS_PER_SESSION}
    for source_session, prompts in prompts_by_source.items():
        assert len(prompts) == EXPECTED_ROWS_PER_SESSION, source_session


def test_crisis_prompts_include_required_protocol():
    rows = load_rows(DATA_FILE)
    for row in rows:
        prompt = row['conversations'][1]['value'].lower()
        response = row['conversations'][2]['value']
        if any(term in prompt for term in CRISIS_TERMS):
            assert 'Are you safe right now?' in response
            assert '988' in response


def test_no_credentials_or_private_paths_leak_into_sources_or_rows():
    manifest = load_sources()
    corpus = json.dumps(manifest).lower() + '\n' + DATA_FILE.read_text(encoding='utf-8').lower()
    assert not any(marker in corpus for marker in CREDENTIAL_MARKERS)
    assert not any(marker in corpus for marker in PRIVATE_MARKERS)
    assert not any(marker in corpus for marker in META_MARKERS)
    assert not EMAIL_RE.search(corpus)


def test_readme_documents_generation_contract():
    text = README_FILE.read_text(encoding='utf-8')
    assert '50 source sessions' in text
    assert '20 prompt variations per session' in text
    assert 'approved-model provenance filter' in text