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"""Independently audit raw PPL/MK reports and compare a fresh v1 GPU replay.
Only the Python standard library is required. Exit 1 means an identity,
structure, or arithmetic check failed. Exit 0 means both reports are internally
valid; inspect ``exact_replay`` and ``status`` for numerical/output differences.
No tokenizer decoding or model inference is performed by this offline audit.
"""
from __future__ import annotations
import argparse
from collections import Counter
import copy
import hashlib
import json
import math
from pathlib import Path
import re
import sys
import tempfile
ROOT = Path(__file__).resolve().parents[1]
PINS = {
'source_checkpoint_sha256': '47c2766f6aad89d73beafbeaecb334aab902d7370906d081764a90bb7a8bbbcb',
'tokenizer_sha256': '5862e2f71caf762bc9845662be5fec2867deb58d874568235a02a36c5111cd09',
'protocol_sha256': 'dee9457bfaca067b63837dcd3bc94bf9ef9a3d805b1ab04227db05a4b92c54c2',
'adapter_sha256': 'e8b2b4dfe69f8e85dc9e147c9aeaa3297cff14f4043e558bb1795ad476c1fca0',
}
PUBLISHED_METADATA_PINS = {
'data_manifest_sha256': '227737dd5fbb301d76e583457ff8ee29246047661b3e9a64ebd068a6f34f376c',
'dataset_fingerprint': '51d84d52d095f90d',
}
DATASET_PINS = {
'dataset': 'Salesforce/wikitext', 'configuration': 'wikitext-2-raw-v1',
'split': 'validation', 'revision_argument': 'b08601e04326c79dfdd32d625aee71d232d685c3',
'document_join': 'two newline characters',
'text_sha256': 'b44fb967f92b525731216a507d1fbc44b97446b7ef0a2b4d1a893f81f1bbc29e',
'token_stream_sha256_int64le': '5bbeae08ba8eb34a482f3b6e9d17b182e67229dd14b2853d87f89fc72e5ad027',
'total_tokens': 264765, 'tokenizer_sha256': PINS['tokenizer_sha256'],
'automatic_special_tokens': False,
}
MATCH = re.compile(r'(?<!\d)\d{6}(?!\d)')
SHA = re.compile(r'[0-9a-f]{64}')
IDENTITY_FIELDS = ('id', 'condition', 'N', 'template', 'expected',
'prompt_token_sha256_int64le', 'prompt_tokens', 'cache_bytes')
PPL_IDENTITY_FIELDS = ('start', 'input_tokens', 'target_tokens', 'token_sha256_int64le')
ARITHMETIC_REL_TOL = 1e-12
ARITHMETIC_ABS_TOL = 1e-9
def file_sha256(path):
digest = hashlib.sha256()
with Path(path).open('rb') as handle:
for block in iter(lambda: handle.read(1 << 20), b''):
digest.update(block)
return digest.hexdigest()
def load_json(path):
def reject_duplicates(pairs):
result = {}
for key, value in pairs:
if key in result:
raise ValueError('Duplicate JSON key: ' + key)
result[key] = value
return result
def reject_constant(value):
raise ValueError('Nonfinite JSON constant: ' + value)
return json.loads(Path(path).read_text(), object_pairs_hook=reject_duplicates,
parse_constant=reject_constant)
def write_new_audit(path, result):
"""Preserve existing receipts, including if a path appears during auditing."""
serialized = json.dumps(result, indent=2, allow_nan=False) + '\n'
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
with path.open('x') as handle:
handle.write(serialized)
def expected_mk_order():
return [(f'resurface-confirm-n{size}-t{template}-s{sample}' +
('-removed' if condition == 'target_removed' else ''),
condition, size, template)
for size in (16, 64) for template in range(3) for sample in range(64)
for condition in ('normal', 'target_removed')]
class Audit:
def __init__(self):
self.errors = []
self.decoded_outputs = {}
def check(self, condition, message):
if not condition:
self.errors.append(message)
def number(self, value, label, minimum=0):
valid = type(value) in (int, float) and math.isfinite(value) and value >= minimum
self.check(valid, label + ': expected finite number >= ' + str(minimum))
if not valid:
raise ValueError(label + ': invalid numeric value')
return value
def integer(self, value, label, minimum=0):
self.check(type(value) is int and value >= minimum, label + ': invalid integer')
def equal(self, actual, expected, label):
self.check(type(actual) is type(expected) and actual == expected,
label + ': inconsistent value')
def arithmetic(self, actual, expected, label):
actual = self.number(actual, label, minimum=-math.inf)
self.check(math.isclose(actual, expected, rel_tol=ARITHMETIC_REL_TOL,
abs_tol=ARITHMETIC_ABS_TOL),
f'{label}: stored={actual!r}, recomputed={expected!r}')
def ppl(self, block, label):
rows = block['windows']
self.equal(len(rows), 130, label + '.window_count')
self.equal(block['execution'], 'prefill', label + '.execution')
self.equal(block['cache_dtype'], 'SSD scan internal precision', label + '.cache_dtype')
self.equal(block['cache_bytes'], None, label + '.cache_bytes')
self.equal(block['logits_chunk_tokens'], 64, label + '.logits_chunk_tokens')
self.equal(block['score'], 'next-token cross entropy; no BOS/EOS added; each window starts from zero state',
label + '.score')
losses, targets = [], []
for index, row in enumerate(rows):
tag = f'{label}.windows[{index}]'
target_count = 2048 if index < 129 else 572
self.equal(row['start'], index * 2048, tag + '.start')
self.equal(row['target_tokens'], target_count, tag + '.target_tokens')
self.equal(row['input_tokens'], target_count, tag + '.input_tokens')
self.check(isinstance(row['token_sha256_int64le'], str) and
SHA.fullmatch(row['token_sha256_int64le']) is not None, tag + '.token_hash')
loss = self.number(row['nll'], tag + '.nll')
losses.append(loss)
targets.append(row['target_tokens'])
self.arithmetic(row['ppl'], math.exp(loss / target_count), tag + '.ppl')
target_total, nll = sum(targets), math.fsum(losses)
self.equal(target_total, 264764, label + '.target_count')
self.equal(block['target_tokens'], target_total, label + '.target_tokens')
self.arithmetic(block['nll'], nll, label + '.nll')
ppl = math.exp(nll / target_total)
self.arithmetic(block['ppl'], ppl, label + '.ppl')
return {'windows': len(rows), 'target_tokens': target_total, 'nll_recomputed': nll,
'ppl_recomputed': ppl, 'stored_nll_minus_recomputed': block['nll'] - nll,
'stored_ppl_minus_recomputed': block['ppl'] - ppl}
def mk(self, block, label, probe=False):
rows = block['rows']
order = expected_mk_order()[:8] if probe else expected_mk_order()
self.equal(len(rows), len(order), label + '.row_count')
self.equal(block['generation'], 'greedy max12, full256K, fresh FP16 cache', label + '.generation')
self.equal(block['matching'], 'first standalone six-digit integer', label + '.matching')
self.equal(len(set(row['id'] for row in rows)), len(rows), label + '.unique_ids')
self.equal(len(set(row['prompt_token_sha256_int64le'] for row in rows)), len(rows),
label + '.unique_prompt_hashes')
correct = []
for index, row in enumerate(rows):
tag = f'{label}.rows[{index}]'
if index < len(order):
self.equal(tuple(row[key] for key in ('id', 'condition', 'N', 'template')),
order[index], tag + '.ordered_case_identity')
self.check(isinstance(row['expected'], str) and
re.fullmatch(r'[0-9]{6}', row['expected']) is not None, tag + '.expected')
self.check(isinstance(row['output'], str), tag + '.output must be text')
self.check(isinstance(row['prompt_token_sha256_int64le'], str) and
SHA.fullmatch(row['prompt_token_sha256_int64le']) is not None, tag + '.prompt_hash')
self.integer(row['prompt_tokens'], tag + '.prompt_tokens', minimum=1)
self.equal(row['cache_bytes'], 122028032, tag + '.cache_bytes')
generated = row['generated_ids']
self.check(isinstance(generated, list) and 1 <= len(generated) <= 12,
tag + '.generated_ids count outside greedy max12')
self.check(all(type(token) is int and 0 <= token < 256000 for token in generated),
tag + '.generated_ids outside vocabulary')
if generated:
self.check(3 not in generated[:-1], tag + '.tokens after EOS')
self.check(len(generated) == 12 or generated[-1] == 3,
tag + '.short generation without EOS')
token_key = tuple(generated)
if token_key in self.decoded_outputs:
self.equal(row['output'], self.decoded_outputs[token_key], tag + '.same_ids_different_output')
else:
self.decoded_outputs[token_key] = row['output']
match = MATCH.search(row['output'])
prediction = match.group(0) if match else None
score = prediction == row['expected']
self.equal(row['prediction'], prediction, tag + '.prediction regex rescore')
self.equal(row['correct'], score, tag + '.correct regex rescore')
correct.append(score)
if index % 2:
self.equal(row['expected'], rows[index - 1]['expected'], tag + '.paired_expected')
summary = {}
for condition in ('normal', 'target_removed'):
selected = [i for i, row in enumerate(rows) if row['condition'] == condition]
count, successes = len(selected), sum(correct[i] for i in selected)
summary[condition] = {'correct': successes, 'count': count,
'accuracy': successes / count if count else None}
stored = block['summary'][condition]
self.equal(stored['correct'], successes, label + '.' + condition + '.correct')
self.equal(stored['count'], count, label + '.' + condition + '.count')
if count:
self.arithmetic(stored['accuracy'], successes / count, label + '.' + condition + '.accuracy')
per_n = {}
for size in (16, 64):
selected = [i for i, row in enumerate(rows) if row['condition'] == 'normal' and row['N'] == size]
if selected:
count, successes = len(selected), sum(correct[i] for i in selected)
per_n[str(size)] = {'correct': successes, 'count': count, 'accuracy': successes / count}
if not probe:
self.equal(sum(r['prompt_tokens'] for r in rows), 517120, label + '.total_prompt_tokens')
self.equal(max(r['prompt_tokens'] for r in rows), 1236, label + '.max_prompt_tokens')
return {'summary': summary, 'normal_by_N': per_n,
'generated_length_histogram': dict(sorted(Counter(len(r['generated_ids']) for r in rows).items()))}
def paired_mk(self, report, label):
before, after = report['baseline_mk']['rows'], report['adapter_mk']['rows']
self.equal(len(before), len(after), label + '.paired row count')
for index, (old, new) in enumerate(zip(before, after)):
self.equal(tuple(old[k] for k in IDENTITY_FIELDS), tuple(new[k] for k in IDENTITY_FIELDS),
f'{label}.paired.rows[{index}].identity')
results = {}
for condition in ('normal', 'target_removed'):
counts = {'both_correct': 0, 'both_wrong': 0, 'gained': 0, 'lost': 0}
for old, new in zip(before, after):
if old['condition'] != condition:
continue
old_match, new_match = MATCH.search(old['output']), MATCH.search(new['output'])
a = old_match is not None and old_match.group() == old['expected']
b = new_match is not None and new_match.group() == new['expected']
name = {(True, True): 'both_correct', (False, False): 'both_wrong',
(False, True): 'gained', (True, False): 'lost'}[(a, b)]
counts[name] += 1
counts.update(count=sum(counts.values()),
baseline_correct=counts['both_correct'] + counts['lost'],
adapter_correct=counts['both_correct'] + counts['gained'])
counts['delta_percentage_points'] = 100 * (counts['gained'] - counts['lost']) / counts['count']
for field, value in counts.items():
stored = report['mk_comparison'][condition][field]
if type(value) is float:
self.arithmetic(stored, value, label + '.mk_comparison.' + condition + '.' + field)
else:
self.equal(stored, value, label + '.mk_comparison.' + condition + '.' + field)
results[condition] = counts
return results
def report(self, report, label):
self.equal(report['format'], 'MAMBA2_SOURCE_RESURFACE_EVAL_V1', label + '.format')
self.equal(report['complete'], True, label + '.complete')
self.equal(report['smoke'], False, label + '.smoke')
self.equal(report['split'], 'confirm', label + '.split')
self.equal(report['ppl_windows'], 130, label + '.ppl_windows')
self.equal(report['mk_cases'], 768, label + '.mk_cases')
self.equal(report['model_precision'], 'source BF16 cast to native FP16', label + '.precision')
for key, expected in PINS.items():
self.equal(report[key], expected, label + '.' + key)
manifest_sha = report['data_manifest_sha256']
fingerprint = report['dataset']['dataset_fingerprint']
self.check(isinstance(manifest_sha, str) and SHA.fullmatch(manifest_sha) is not None,
label + '.data_manifest_sha256: invalid SHA256')
self.check(isinstance(fingerprint, str) and bool(fingerprint.strip()),
label + '.dataset.dataset_fingerprint: expected nonempty string')
if label == 'published':
self.equal(manifest_sha, PUBLISHED_METADATA_PINS['data_manifest_sha256'],
label + '.data_manifest_sha256')
self.equal(fingerprint, PUBLISHED_METADATA_PINS['dataset_fingerprint'],
label + '.dataset.dataset_fingerprint')
for key, expected in DATASET_PINS.items():
self.equal(report['dataset'][key], expected, label + '.dataset.' + key)
base = report['frozen_base_check']
self.equal(base['tensors'], 507, label + '.base.tensors')
self.equal(base['parameters'], 8236999680, label + '.base.parameters')
self.equal(base['identity_version_gradients_unchanged'], True, label + '.base.frozen')
result = {}
for arm in ('baseline', 'adapter'):
result[arm] = {'ppl': self.ppl(report[arm + '_ppl'], label + '.' + arm + '_ppl'),
'mk': self.mk(report[arm + '_mk'], label + '.' + arm + '_mk')}
for index, (a, b) in enumerate(zip(report['baseline_ppl']['windows'], report['adapter_ppl']['windows'])):
self.equal(tuple(a[k] for k in PPL_IDENTITY_FIELDS), tuple(b[k] for k in PPL_IDENTITY_FIELDS),
f'{label}.paired.ppl.windows[{index}].identity')
result['mk_comparison'] = self.paired_mk(report, label)
delta = 100 * (result['adapter']['ppl']['ppl_recomputed'] / result['baseline']['ppl']['ppl_recomputed'] - 1)
self.arithmetic(report['ppl_delta_percent'], delta, label + '.ppl_delta_percent')
result['ppl_delta_percent_recomputed'] = delta
result['restored_probe'] = self.mk(report['restored_probe'], label + '.restored_probe', probe=True)
self.equal(report['restored_probe']['rows'], report['baseline_mk']['rows'][:8], label + '.restored_probe_exact')
return result
def verify_reports(published, replay, actual_adapter_sha):
audit = Audit()
result = {
'format': 'MAMBA2_RESURFACE_REPLAY_AUDIT_V1',
'arithmetic_tolerance': {'relative': ARITHMETIC_REL_TOL, 'absolute': ARITHMETIC_ABS_TOL},
'actual_adapter_sha256': actual_adapter_sha, 'pins': PINS,
'published_metadata_pins': PUBLISHED_METADATA_PINS,
'scope': 'Offline raw-report arithmetic, content identity, regex rescore, and fresh-report comparison. exact_replay covers scientific inputs and outputs; environment-dependent metadata equality is reported separately.',
'limitations': [
'The verifier does not run the model or independently recompute logits.',
'Token hashes are compared; raw prompt/PPL token bytes are not embedded in these reports.',
'SentencePiece decoding is not rerun. Equal generated IDs must have equal output text across reports.',
'Original frozen-base receipt checks identities, version counters, and gradients, not full tensor contents.',
'CONFIRM templates/instances and WikiText validation were previously observed.',
],
}
audit.equal(actual_adapter_sha, PINS['adapter_sha256'], 'actual adapter SHA256')
for label, report in (('published', published), ('replay', replay)):
try:
result[label] = audit.report(report, label)
except (KeyError, TypeError, ValueError, ZeroDivisionError, OverflowError, IndexError) as error:
audit.errors.append(f'{label}: malformed report: {error}')
differences = {}
try:
for key in PINS:
audit.equal(published[key], replay[key], 'replay identity.' + key)
for key in DATASET_PINS:
audit.equal(published['dataset'][key], replay['dataset'][key], 'replay dataset identity.' + key)
result['metadata_comparison'] = {
'manifests_equal': published['data_manifest_sha256'] == replay['data_manifest_sha256'],
'published_data_manifest_sha256': published['data_manifest_sha256'],
'replay_data_manifest_sha256': replay['data_manifest_sha256'],
'dataset_fingerprints_equal': published['dataset']['dataset_fingerprint'] == replay['dataset']['dataset_fingerprint'],
'published_dataset_fingerprint': published['dataset']['dataset_fingerprint'],
'replay_dataset_fingerprint': replay['dataset']['dataset_fingerprint'],
'interpretation': 'The data manifest embeds Python version and dataset fingerprints depend on environment. These metadata may differ while all ordered prompt/token hashes, answers, dataset text/token digests, and revision remain identical.',
}
for arm in ('baseline', 'adapter'):
a, b = published[arm + '_ppl'], replay[arm + '_ppl']
for source_name, block in (('published', a), ('replay', b)):
for field in ('nll', 'ppl'):
audit.number(block[field], f'{source_name}.{arm}.comparison.{field}')
window_deltas = []
for index, (old, new) in enumerate(zip(a['windows'], b['windows'])):
audit.equal(tuple(old[k] for k in PPL_IDENTITY_FIELDS), tuple(new[k] for k in PPL_IDENTITY_FIELDS),
f'replay.{arm}.ppl.windows[{index}].identity')
for source_name, row in (('published', old), ('replay', new)):
for field in ('nll', 'ppl'):
audit.number(row[field], f'{source_name}.{arm}.comparison.windows[{index}].{field}')
delta = new['nll'] - old['nll']
window_deltas.append({'index': index, 'start': old['start'], 'nll_delta': delta,
'ppl_delta': new['ppl'] - old['ppl'], 'nll_exact': old['nll'] == new['nll']})
before, after = published[arm + '_mk']['rows'], replay[arm + '_mk']['rows']
mk_deltas = []
for index, (old, new) in enumerate(zip(before, after)):
audit.equal(tuple(old[k] for k in IDENTITY_FIELDS), tuple(new[k] for k in IDENTITY_FIELDS),
f'replay.{arm}.mk.rows[{index}].identity')
fields = [key for key in ('generated_ids', 'output', 'prediction', 'correct') if old[key] != new[key]]
if fields:
mk_deltas.append({'index': index, 'id': old['id'], 'changed_fields': fields,
'published_prediction': old['prediction'], 'replay_prediction': new['prediction'],
'published_correct': old['correct'], 'replay_correct': new['correct']})
differences[arm] = {
'ppl': {'aggregate_nll_exact': a['nll'] == b['nll'], 'aggregate_ppl_exact': a['ppl'] == b['ppl'],
'nll_delta': b['nll'] - a['nll'], 'ppl_delta': b['ppl'] - a['ppl'],
'max_abs_window_nll_error': max(abs(row['nll_delta']) for row in window_deltas),
'max_abs_window_ppl_error': max(abs(row['ppl_delta']) for row in window_deltas),
'exact_nll_windows': sum(row['nll_exact'] for row in window_deltas),
'windows': window_deltas},
'mk': {'rows': len(before), 'all_generated_ids_exact': all(a['generated_ids'] == b['generated_ids'] for a, b in zip(before, after)),
'all_output_text_exact': all(a['output'] == b['output'] for a, b in zip(before, after)),
'all_predictions_exact': all(a['prediction'] == b['prediction'] for a, b in zip(before, after)),
'all_correctness_exact': all(a['correct'] == b['correct'] for a, b in zip(before, after)),
'changed_rows': len(mk_deltas), 'differences': mk_deltas},
}
except (KeyError, TypeError, ValueError, ZeroDivisionError, OverflowError, IndexError) as error:
audit.errors.append('replay comparison: malformed report: ' + str(error))
result['comparison'] = differences
result['errors'] = audit.errors
result['integrity_passed'] = not audit.errors
result['exact_replay'] = not audit.errors and len(differences) == 2 and all(
row['ppl']['aggregate_nll_exact'] and row['ppl']['aggregate_ppl_exact']
and row['ppl']['exact_nll_windows'] == 130 and row['ppl']['max_abs_window_ppl_error'] == 0
and row['mk']['changed_rows'] == 0 for row in differences.values())
result['status'] = ('integrity_failure' if audit.errors else
'exact_replay' if result['exact_replay'] else 'valid_reports_with_replay_differences')
return result
def self_test(published, actual_sha):
checks = []
def expect(name, candidate, valid, exact=False):
result = verify_reports(published, candidate, actual_sha)
assert result['integrity_passed'] is valid, (name, result['errors'])
assert result['exact_replay'] is exact, name
checks.append(name)
expect('published versus itself', published, True, True)
for name, mutate in (
('source hash tamper', lambda r: r.update(source_checkpoint_sha256='0' * 64)),
('malformed replay manifest hash', lambda r: r.update(data_manifest_sha256='invalid')),
('empty replay dataset fingerprint', lambda r: r['dataset'].update(dataset_fingerprint='')),
('PPL arithmetic tamper', lambda r: r['adapter_ppl'].update(nll=r['adapter_ppl']['nll'] + 1)),
('PPL token identity tamper', lambda r: r['adapter_ppl']['windows'][0].update(token_sha256_int64le='0' * 64)),
('MK regex correctness tamper', lambda r: r['adapter_mk']['rows'][0].update(correct=False)),
('MK duplicate identity tamper', lambda r: r['adapter_mk']['rows'][1].update(id=r['adapter_mk']['rows'][0]['id'])),
('MK paired counts tamper', lambda r: r['mk_comparison']['normal'].update(gained=223)),
('restored probe tamper', lambda r: r['restored_probe']['rows'][0].update(output='000000')),
('same token IDs inconsistent decode', lambda r: r['adapter_mk']['rows'][0].update(output=r['adapter_mk']['rows'][0]['output'] + ' ')),
('nonfinite NLL tamper', lambda r: r['adapter_ppl']['windows'][0].update(nll=float('nan'))),
):
candidate = copy.deepcopy(published)
mutate(candidate)
expect(name, candidate, False)
candidate = copy.deepcopy(published)
block = candidate['adapter_ppl']
block['windows'][0]['nll'] += 1e-7
block['windows'][0]['ppl'] = math.exp(block['windows'][0]['nll'] / block['windows'][0]['target_tokens'])
block['nll'] = math.fsum(row['nll'] for row in block['windows'])
block['ppl'] = math.exp(block['nll'] / block['target_tokens'])
candidate['ppl_delta_percent'] = 100 * (block['ppl'] / candidate['baseline_ppl']['ppl'] - 1)
expect('consistent tiny NLL difference reported without integrity failure', candidate, True)
candidate = copy.deepcopy(published)
candidate['adapter_mk']['rows'][-1]['generated_ids'][-1] = 251555
candidate['adapter_mk']['rows'][-1]['output'] += '!'
expect('changed MK IDs and text reported without inventing decoder validation', candidate, True)
candidate = copy.deepcopy(published)
candidate['data_manifest_sha256'] = 'a' * 64
candidate['dataset']['dataset_fingerprint'] = 'same-data-different-environment'
expect('environment metadata differs but identical content and scores are accepted', candidate, True, True)
metadata = verify_reports(published, candidate, actual_sha)['metadata_comparison']
assert metadata['manifests_equal'] is False
assert metadata['dataset_fingerprints_equal'] is False
assert metadata['replay_data_manifest_sha256'] == 'a' * 64
assert metadata['replay_dataset_fingerprint'] == 'same-data-different-environment'
bad_sha = verify_reports(published, published, '0' * 64)
assert not bad_sha['integrity_passed']
checks.append('actual adapter hash tamper')
with tempfile.TemporaryDirectory(prefix='mamba2-audit-selftest-') as directory:
path = Path(directory) / 'receipt.json'
write_new_audit(path, {'receipt': 'original'})
original = path.read_bytes()
try:
write_new_audit(path, {'receipt': 'overwrite attempt'})
except FileExistsError:
pass
else:
raise AssertionError('Existing audit receipt was overwritten')
assert path.read_bytes() == original
checks.append('existing audit receipt preserved by exclusive creation')
return {'passed': len(checks), 'checks': checks}
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--published', type=Path)
parser.add_argument('--replay', type=Path)
parser.add_argument('--adapter', type=Path)
parser.add_argument('--output', type=Path)
parser.add_argument('--self-test', action='store_true', help='Audit bundled published report and exercise tamper cases')
args = parser.parse_args()
if args.self_test:
published = load_json(args.published or ROOT / 'reports/source_resurface_v1_confirm_full.json')
digest = file_sha256(args.adapter or ROOT / 'artifacts/source_resurface_v1/adapter_fp16.pt')
print(json.dumps(self_test(published, digest), indent=2, allow_nan=False))
return 0
if any(value is None for value in (args.published, args.replay, args.adapter, args.output)):
parser.error('--published, --replay, --adapter, and --output are required')
if args.output.exists():
print(f'Refusing to overwrite existing audit receipt: {args.output}', file=sys.stderr)
return 1
try:
result = verify_reports(load_json(args.published), load_json(args.replay), file_sha256(args.adapter))
result['files'] = {key: {'path': str(path.resolve()), 'sha256': file_sha256(path)}
for key, path in (('published', args.published), ('replay', args.replay), ('adapter', args.adapter))}
except (OSError, ValueError, TypeError) as error:
result = {'format': 'MAMBA2_RESURFACE_REPLAY_AUDIT_V1', 'integrity_passed': False,
'exact_replay': False, 'status': 'integrity_failure', 'errors': [str(error)]}
try:
write_new_audit(args.output, result)
except FileExistsError:
print(f'Refusing to overwrite existing audit receipt: {args.output}', file=sys.stderr)
return 1
except OSError as error:
print(f'Cannot write audit receipt {args.output}: {error}', file=sys.stderr)
return 1
print(json.dumps({key: result[key] for key in ('status', 'integrity_passed', 'exact_replay', 'errors')},
indent=2, allow_nan=False))
return 0 if result['integrity_passed'] else 1
if __name__ == '__main__':
sys.exit(main())
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