import ast from collections import Counter from concurrent.futures import ThreadPoolExecutor from copy import deepcopy from dataclasses import dataclass import hashlib import json from pathlib import Path import random import re import shutil from huggingface_hub import hf_hub_download from omegaconf import OmegaConf import pyarrow.parquet as pq from rich import print from tqdm import tqdm from functional_rl import v2 from hinted_rl.teacher_programs import canonical from refined_v4.data import load_rows,write_rows,signature,atomic_json from . import ROOT,TASKS from .audit import authentic from .data import annotate,documents,gold_passes from .envs import Pool from .named_data import styles_for REPO='google-research-datasets/mbpp' REVISION='4bb6404fdc6cacfda99d4ac4205087b89d32030c' @dataclass class Config: source: str=str(ROOT/'variants/consistent-natural-names-pilot-v1') root: str=str(ROOT/'variants/full-mbpp-seeds-pilot-v2') tokens: int=2097152 benchmark_fraction: float=.5 seed: int=6719 workers: int=8 def native(raw: dict,task: str): question=raw['text'].strip() tree=ast.parse(raw['code']) functions=[n for n in tree.body if isinstance(n,ast.FunctionDef)] if len(functions)!=1 or any(not isinstance(n,(ast.Import,ast.ImportFrom,ast.FunctionDef)) for n in tree.body): raise ValueError('Require one standalone entry function and imports') setup=raw.get('test_setup_code') or '' setup_tree=ast.parse(setup) if any(not isinstance(n,(ast.Import,ast.ImportFrom)) for n in setup_tree.body): raise ValueError('Setup must contain imports only') tests=list(raw['test_list'])+list(raw.get('challenge_test_list') or []) if len(tests)<3 or not question:raise ValueError('Require instruction and three published tests') function=deepcopy(functions[0]) if ast.get_docstring(function) is not None:function.body.pop(0) example=ast.parse(tests[0]).body if not example or not isinstance(example[0],ast.Assert):raise ValueError('Require assertion examples') expression=example[0].test documentation=question+'\n\nExample:\n '+tests[0] if task.startswith('humaneval'): left,expected=ast.unparse(expression),'True' if isinstance(expression,ast.Compare) and len(expression.ops)==1 and isinstance(expression.ops[0],ast.Eq): try: expected=repr(ast.literal_eval(expression.comparators[0]));left=ast.unparse(expression.left) except (ValueError,TypeError):pass documentation=question+'\n\n>>> '+left+'\n'+expected if task.endswith('_docstring'):function.body.insert(0,ast.Expr(value=ast.Constant(value=documentation))) imports=[];seen=set() for node in setup_tree.body+[n for n in tree.body if isinstance(n,(ast.Import,ast.ImportFrom))]: key=ast.dump(node) if key not in seen:imports.append(deepcopy(node));seen.add(key) reference=ast.unparse(ast.fix_missing_locations(ast.Module(body=imports+[function],type_ignores=[]))) if task=='humaneval':reference=''.join('# '+line+'\n' if line else '#\n' for line in documentation.splitlines())+reference identity=str(raw['task_id']) row=annotate(dict(task=task,source_id='mbpp-extra-'+identity, group=hashlib.sha256(('full-mbpp/'+identity).encode()).hexdigest(),question=question, reference=reference,answer=reference,tests=tests,setup=setup, provenance=dict(source=REPO,revision=REVISION,upstream_config='full',upstream_split=raw['upstream_split'], upstream_task_id=raw['task_id'],license='cc-by-4.0',sft_origin='additional_original_mbpp'))) if len(v2.ENC.encode_ordinary(v2.render(row)))>850:raise ValueError('Too long for few-shot packing') return row def build(config: Config): source,root=Path(config.source),Path(config.root) if (root/'dataset.json').exists():raise ValueError('Full-source dataset already frozen') cache=ROOT/'inputs/mbpp-full';sanitized=set();raw=[] for split in tqdm(('train','validation','test','prompt'),desc='Read pinned original MBPP files',colour='cyan'): def download(flavor): return hf_hub_download(REPO,flavor+'/'+split+'-00000-of-00001.parquet',repo_type='dataset',revision=REVISION,cache_dir=cache) sanitized.update(pq.read_table(download('sanitized'),columns=['task_id'])['task_id'].to_pylist()) raw.extend(dict(row,upstream_split=split) for row in pq.read_table(download('full')).to_pylist()) blocked=set();questions=set();known_ids=set();stats=Counter() verified=ROOT/'variants/verified-reference-pilot-v1' practice=ROOT/'variants/clear-practice-rl-v1' for task in tqdm(TASKS,desc='Index existing training and held-out solutions',colour='cyan'): for split in ('sft','rl','calibration','dev','test'): rows=load_rows(verified,task,split) if split=='rl':rows+=load_rows(practice,task,'rl') for row in rows: match=re.match(r'mbpp-(\d+)(?:\D|$)',row['source_id']) if match:known_ids.add(int(match.group(1))) blocked.add(canonical(row['reference'])) if row['question']:questions.add(' '.join(row['question'].casefold().split())) for item in raw: if item['task_id'] not in sanitized|known_ids:continue questions.add(' '.join(item['text'].casefold().split())) try:blocked.add(canonical(item['code'])) except (SyntaxError,ValueError):pass try:blocked.add(canonical(native(item,'mbpp')['reference'])) except (SyntaxError,ValueError,TypeError,KeyError):pass candidates=[];seen=set(blocked) for item in tqdm(sorted(raw,key=lambda r:r['task_id']),desc='Exclude original IDs and known solutions',colour='cyan',mininterval=5): if item['task_id'] in sanitized:stats['original_sanitized_id']+=1;continue if item['task_id'] in known_ids:stats['existing_task_id']+=1;continue if ' '.join(item['text'].casefold().split()) in questions:stats['known_question']+=1;continue try: row=native(item,'mbpp');keys={canonical(item['code']),canonical(row['reference'])} if keys&seen:stats['known_solution']+=1;continue native_rows={task:native(item,task) for task in TASKS} except (ValueError,SyntaxError,TypeError,KeyError):stats['format_or_context']+=1;continue seen.update(keys);candidates.append((item,native_rows)) with ThreadPoolExecutor(max_workers=config.workers) as pool: passed=list(tqdm(pool.map(gold_passes,[rows['mbpp'] for item,rows in candidates]),total=len(candidates), desc='Check additional published MBPP tests once',colour='green',mininterval=5)) accepted=[item for item,ok in zip(candidates,passed,strict=True) if ok] stats['published_test_failure']=len(candidates)-len(accepted) if len(accepted)<100:raise ValueError('Insufficient additional original problems') is_rl=lambda item:int(hashlib.sha256(f'{config.seed}/{item["task_id"]}'.encode()).hexdigest()[:8],16)%5==0 report=json.loads((source/'dataset.json').read_text()) report.update(revision='full-mbpp-seeds-v2',source=str(source),sft_tokens=config.tokens,seed=config.seed, benchmark_draw_probability=config.benchmark_fraction,upstream=dict(repo=REPO,revision=REVISION,license='cc-by-4.0'), extra_source_filtering=dict(stats),extra_sft_ids=[item['task_id'] for item,rows in accepted if not is_rl(item)], extra_rl_ids=[item['task_id'] for item,rows in accepted if is_rl(item)], extra_source_policy='Additional original full-MBPP problems only. Exclude EVERY original sanitized task ID and every known existing upstream task ID. Also exclude original RAW upstream solutions/questions for those IDs, alongside current rewritten references. Deterministic80/20 SFT/RL split. Every admitted reference passes its published tests. No rewritten expected values.', sft_policy='Sample body band from ORIGINAL authentic SFT-training frequencies, then source origin at the declared benchmark fraction, then source group, then a natural local-name variant. Two to four complete native examples per document.', rl_policy='Uniform missing length1..16 first, then uniform original-benchmark/additional-MBPP origin, then eligible source. No synthetic practice contracts in this variant. Calibration/development/test stay byte-identical.') for task in TASKS: shutil.copytree(source/'data'/task,root/'data'/task,dirs_exist_ok=True) rows=load_rows(source,task,'sft') old_auth={r['group']:r for r in load_rows(verified,task,'sft') if authentic(r)} bands=Counter(r['provenance']['difficulty'] for r in old_auth.values()) weights={band:count/sum(bands.values()) for band,count in bands.items()} original=load_rows(verified,task,'rl') rl=[dict(row,sampling_origin='benchmark') for row in original] for item,native_rows in tqdm(accepted,desc=task+' original-source styles',colour='cyan',mininterval=5): row=native_rows[task] if is_rl(item): try:rl.append(annotate(dict(row,demonstrations=original[0]['demonstrations'],sampling_origin='full_mbpp'))) except ValueError:stats['rl_context_rejected']+=1 else: variants,filtered=styles_for(row,4,config.seed);rows.extend(variants) assert not {r['group'] for r in rows}&{r['group'] for r in rl} Pool(rl) write_rows(root/'data'/task/'sft.jsonl.gz',rows);write_rows(root/'data'/task/'rl.jsonl.gz',rl) corpus=documents(rows,config.tokens,random.Random(config.seed),config.benchmark_fraction,True,weights) target=root/'sft'/task/'train.jsonl.gz';write_rows(target,corpus) info=report['tasks'][task] info.update(rows=dict(info['rows'],sft=len(rows),rl=len(rl)),documents=len(corpus),sft_sha256=signature(target), source_groups=len({r['group'] for r in rows}),sft_body_band_target=weights, admitted_extra_rl_ids=[r['provenance']['upstream_task_id'] for r in rl if r['sampling_origin']=='full_mbpp'], unique_sft_programs_used=len({r['source_id'] for doc in corpus for r in doc['examples']}), document_example_counts=dict(Counter(len(d['examples']) for d in corpus))) for fold in ('sft','rl'): relative='data/'+task+'/'+fold+'.jsonl.gz';info['assets'][relative]=signature(root/relative) pool_rows=rows if fold=='sft' else rl info.setdefault('hint_coverage',{})[fold]={str(n):sum(n in r['hint_levels'] for r in pool_rows) for n in range(1,17)} for fold in ('calibration','dev','test'): assert signature(root/'data'/task/(fold+'.jsonl.gz'))==signature(source/'data'/task/(fold+'.jsonl.gz')) atomic_json(root/'build-progress.json',report) report['extra_source_filtering']=dict(stats) atomic_json(root/'dataset.json',report) print('[green]Additional original MBPP SFT/RL sources ready[/green]',len(report['extra_sft_ids']),len(report['extra_rl_ids']),flush=True) if __name__=='__main__': build(OmegaConf.to_object(OmegaConf.merge(OmegaConf.structured(Config),OmegaConf.from_cli())))