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#!/usr/bin/env python3
"""
Generate LLM solutions from problem README files.
Supports multiple providers: OpenAI, Google (Gemini), Anthropic (Claude), xAI (Grok), DeepSeek.
Usage:
python generate_solutions.py --model gpt-5
python generate_solutions.py research/flash_attn --model claude-sonnet-4-5
python generate_solutions.py --dryrun # Show what would be generated
"""
import sys
import os
import json
import time
import argparse
import re
import shutil
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass
from typing import Optional, Dict, Any, List, Tuple
from datetime import datetime
from importlib import metadata
from frontier_cs.models import get_model_prefix, is_reasoning_model
# Local modules
from gen_env import get_system_prompt_for_problem
from gen_api_keys import build_key_pools, get_fallback_api_key, APIKeyPool
from gen_llm import instantiate_llm_client, detect_provider
from gen_io import (
load_env_file,
load_solution_targets,
read_models_file,
read_variant_indices_file,
read_readme,
load_docker_config,
get_problem_name,
)
from gen_templates import create_solution
from gen_colors import (
bold, dim, red, green, yellow, blue, cyan, magenta,
success, error, warning, info, header, section,
model_name, problem_name, solution_name,
print_header, print_section, print_success, print_error, print_warning, print_info,
)
REQUIRED_NUMPY_VERSION = "2.3.4"
@dataclass
class GenerationTask:
"""Represents a single solution generation task."""
problem_path: Path
display_path: str
problem_name: str
readme: str
model: str
provider: str
reasoning_model: bool
variant_index: int # actual suffix index (0 -> no suffix)
variant_position: int # ordinal position in the configured variant list (0-based)
solution_name: str
total_variants: int = 1
def ensure_numpy_version(required: str) -> None:
"""Ensure the required NumPy version is installed."""
try:
installed = metadata.version("numpy")
except metadata.PackageNotFoundError as exc:
raise RuntimeError(
f"NumPy {required} is required but not installed; install it with "
f"`uv pip install --python .venv/bin/python numpy=={required}`."
) from exc
if installed != required:
raise RuntimeError(
f"NumPy {required} is required, but found {installed}; reinstall with "
f"`uv pip install --python .venv/bin/python numpy=={required}`."
)
def float_or_none(value: str) -> Optional[float]:
"""Parse a float or return None for 'none'/'null'."""
text = str(value).strip()
if text.lower() in {"none", "null"}:
return None
return float(text)
def int_or_none(value: str) -> Optional[int]:
"""Parse an int or return None for 'none'/'null'."""
text = str(value).strip()
if text.lower() in {"none", "null"}:
return None
return int(text)
def parse_extra_headers(entries: List[str]) -> Dict[str, str]:
"""Parse extra HTTP headers from command line."""
headers: Dict[str, str] = {}
for entry in entries:
if ":" not in entry:
raise ValueError(f"Invalid header format (expected Key: Value): {entry}")
key, value = entry.split(":", 1)
headers[key.strip()] = value.strip()
return headers
def load_json_args(payload_str: Optional[str]) -> Optional[Dict[str, Any]]:
"""Parse JSON payload from command line."""
if not payload_str:
return None
try:
data = json.loads(payload_str)
except json.JSONDecodeError as exc:
raise ValueError(f"Invalid JSON for --payload-extra: {exc}") from exc
if not isinstance(data, dict):
raise ValueError("--payload-extra must be a JSON object")
return data
def resolve_api_key(explicit_key: Optional[str], key_env: Optional[str]) -> Optional[str]:
"""Resolve API key from explicit value or environment variable."""
if explicit_key:
return explicit_key
if key_env:
return os.getenv(key_env)
return None
def is_reasoning(model: str, override: Optional[bool]) -> bool:
"""Check if a model is a reasoning model."""
return is_reasoning_model(model, override)
def generate_code(
readme: str,
*,
model: str,
api_key: Optional[str],
log_file: Path,
api_base: str,
endpoint: str,
temperature: Optional[float],
max_tokens: Optional[int],
max_reasoning_tokens: Optional[int],
is_reasoning_model: bool,
extra_headers: Dict[str, str],
timeout: float,
payload_overrides: Optional[Dict[str, Any]],
api_key_header: Optional[str],
api_key_prefix: Optional[str],
problem_name: str = "",
problem_path: Optional[Path] = None,
docker_config: Optional[Dict] = None,
) -> str:
"""Generate solution code using an LLM."""
base_url = (api_base or "").strip()
if base_url.lower() in {"none", ""}:
base_url = None
else:
base_url = base_url.rstrip("/")
endpoint_hint = (endpoint or "").strip() or "auto"
# Get environment-specific system prompt
system_prompt = get_system_prompt_for_problem(problem_name, problem_path, docker_config)
# Prepare prompts
user_prompt = f"Problem:\n\n{readme}\n\nGenerate solution code:"
combined_prompt = f"{system_prompt}\n\n{user_prompt}"
# Log request details
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
final_headers: Dict[str, str] = dict(extra_headers)
ignored_controls: Dict[str, Any] = {}
if final_headers:
ignored_controls["extra_headers"] = final_headers
if payload_overrides:
ignored_controls["payload_overrides"] = payload_overrides
if api_key_header:
ignored_controls["api_key_header"] = api_key_header
if api_key_prefix:
ignored_controls["api_key_prefix"] = api_key_prefix
if temperature is not None:
ignored_controls["temperature"] = temperature
if max_tokens is not None and not is_reasoning_model:
ignored_controls["max_tokens"] = max_tokens
if max_reasoning_tokens is not None and is_reasoning_model:
ignored_controls["max_reasoning_tokens"] = max_reasoning_tokens
llm_client, llm_config = instantiate_llm_client(
model,
is_reasoning_model=is_reasoning_model,
timeout=timeout,
base_url=base_url,
api_key=api_key,
)
with open(log_file, 'w', encoding='utf-8') as f:
f.write("=" * 80 + "\n")
f.write("GPT GENERATION LOG\n")
f.write(f"Timestamp: {timestamp}\n")
f.write("=" * 80 + "\n\n")
f.write(f"MODEL: {model}\n")
f.write(f"INTERFACE CLASS: {llm_client.__class__.__name__}\n")
for key, value in llm_config.items():
f.write(f"{key.upper()}: {value}\n")
f.write(f"ENDPOINT HINT: {endpoint_hint}\n")
f.write(f"TIMEOUT: {timeout}s\n")
f.write(f"REASONING MODEL: {is_reasoning_model}\n")
f.write(f"API KEY PROVIDED: {'yes' if bool(api_key) else 'no'}\n")
if ignored_controls:
f.write("IGNORED CONTROLS (not supported by llm_interface):\n")
f.write(json.dumps(ignored_controls, indent=2, ensure_ascii=False))
f.write("\n")
f.write("\n" + "=" * 80 + "\n")
f.write("SYSTEM PROMPT:\n")
f.write("=" * 80 + "\n")
f.write(system_prompt)
f.write("\n\n" + "=" * 80 + "\n")
f.write("USER PROMPT:\n")
f.write("=" * 80 + "\n")
f.write(user_prompt)
f.write("\n\n" + "=" * 80 + "\n")
f.write("CALLING test_scripts.llm_interface...\n")
f.write("=" * 80 + "\n\n")
print(f"Calling llm_interface (model: {model})...")
MAX_RETRIES = 5
RETRY_DELAY = 30
content: Optional[str] = None
meta: Any = None
for attempt in range(1, MAX_RETRIES + 1):
response_text, meta = llm_client.call_llm(combined_prompt)
content_ok = bool(response_text and not response_text.strip().lower().startswith("error:"))
if content_ok:
content = response_text
break
error_message = response_text or "Empty response"
with open(log_file, 'a', encoding='utf-8') as f:
f.write(f"ERROR calling llm_interface (attempt {attempt}/{MAX_RETRIES}): {error_message}\n")
if attempt < MAX_RETRIES:
sleep_time = RETRY_DELAY * attempt
with open(log_file, 'a', encoding='utf-8') as f:
f.write(f"Retrying after {sleep_time}s...\n")
time.sleep(sleep_time)
if content is None:
raise RuntimeError("llm_interface call failed after retries")
with open(log_file, 'a', encoding='utf-8') as f:
f.write("=" * 80 + "\n")
f.write("RAW OUTPUT:\n")
f.write("=" * 80 + "\n")
f.write(content)
f.write("\n\n")
f.write("=" * 80 + "\n")
f.write("LLM METADATA (stringified):\n")
f.write("=" * 80 + "\n")
f.write(str(meta))
f.write("\n\n")
code = content.strip()
# Try to extract code from markdown code blocks
code_block_pattern = r'```(?:python)?\s*\n(.*?)```'
matches = re.findall(code_block_pattern, code, re.DOTALL)
if matches:
code = max(matches, key=len).strip()
else:
if code.startswith("```python"):
code = code[9:].strip()
if code.startswith("```"):
code = code[3:].strip()
if code.endswith("```"):
code = code[:-3].strip()
# Log cleaned code
with open(log_file, 'a', encoding='utf-8') as f:
f.write("=" * 80 + "\n")
f.write("CLEANED CODE (after removing markdown):\n")
f.write("=" * 80 + "\n")
f.write(code)
f.write("\n\n")
f.write("=" * 80 + "\n")
f.write("END OF LOG\n")
f.write("=" * 80 + "\n")
return code
def build_tasks(
args,
repo_root: Path,
base_dir: Path,
models_list: List[str],
normalized_problems: List[Tuple[Path, str]],
prefix_to_model: Dict[str, str],
) -> Tuple[List[GenerationTask], List[str]]:
"""Build the list of generation tasks."""
tasks: List[GenerationTask] = []
skipped: List[str] = []
if args.solutions_file:
# Solutions file mode
solutions_path = Path(args.solutions_file)
if not solutions_path.is_absolute():
solutions_path = base_dir / solutions_path
solution_targets = load_solution_targets(solutions_path)
print(f"Loaded {len(solution_targets)} target solution(s) from {solutions_path}.")
problem_cache: Dict[str, Tuple[Path, str, str]] = {}
for solution_name, problem_entry in solution_targets:
model_prefix = solution_name.split("_", 1)[0]
model = prefix_to_model.get(model_prefix)
if not model:
print(f"WARNING: No model mapping found for solution prefix '{model_prefix}'; skipping {solution_name}.")
continue
provider = detect_provider(model)
if problem_entry.startswith("research/"):
relative_problem_str = problem_entry
else:
relative_problem_str = f"research/problems/{problem_entry.lstrip('./')}"
try:
problem_path_real = (repo_root / relative_problem_str).resolve()
except Exception:
print(f"WARNING: Invalid problem path '{problem_entry}' for {solution_name}; skipping.")
continue
if not problem_path_real.is_dir():
print(f"WARNING: Problem path {problem_path_real} not found; skipping {solution_name}.")
continue
cache_key = relative_problem_str
if cache_key not in problem_cache:
try:
readme_text = read_readme(problem_path_real)
except FileNotFoundError as exc:
print(f"WARNING: {exc}; skipping {solution_name}.")
continue
try:
rel_path_for_name = problem_path_real.relative_to(repo_root / "research")
except ValueError:
rel_path_for_name = Path(problem_path_real.name)
problem_name = get_problem_name(rel_path_for_name)
problem_cache[cache_key] = (problem_path_real, readme_text, problem_name)
problem_path_real, readme_text, inferred_problem_name = problem_cache[cache_key]
tail_parts = solution_name.rsplit("_", 1)
variant_index = 0
if len(tail_parts) == 2 and tail_parts[1].isdigit():
variant_index = int(tail_parts[1])
total_variants_for_task = max(variant_index + 1, 1)
sol_dir = repo_root / "solutions" / solution_name
if sol_dir.exists():
if args.force:
if not args.dryrun:
try:
shutil.rmtree(sol_dir)
except Exception as exc:
print(f"WARNING: Failed to remove {sol_dir}: {exc}; skipping")
skipped.append(solution_name)
continue
else:
skipped.append(solution_name)
continue
tasks.append(
GenerationTask(
problem_path=problem_path_real,
display_path=problem_entry,
problem_name=inferred_problem_name,
readme=readme_text,
model=model,
provider=provider,
reasoning_model=is_reasoning(model, args.reasoning_override),
variant_index=variant_index,
variant_position=variant_index,
solution_name=solution_name,
total_variants=total_variants_for_task,
)
)
else:
# Problem list mode
variants_file = base_dir / "num_solutions.txt"
if args.variants is None:
try:
variant_indices = read_variant_indices_file(variants_file)
except Exception as exc:
print(f"WARNING: Failed to read {variants_file}: {exc}; defaulting to [0]")
variant_indices = [0]
else:
variant_indices = list(range(args.variants))
for problem_path_real, display_path in normalized_problems:
if not problem_path_real.is_dir():
print(f"WARNING: Problem path {problem_path_real} not found; skipping")
continue
try:
readme = read_readme(problem_path_real)
except FileNotFoundError as exc:
print(f"WARNING: {exc}; skipping {problem_path_real}")
continue
relative_problem_path = problem_path_real
if problem_path_real.is_absolute():
try:
relative_problem_path = problem_path_real.relative_to(repo_root / "research")
except ValueError:
relative_problem_path = Path(problem_path_real.name)
problem_name = args.name or get_problem_name(relative_problem_path)
for model in models_list:
reasoning_model = is_reasoning(model, args.reasoning_override)
model_prefix = get_model_prefix(model)
provider = detect_provider(model)
for pos, variant_index in enumerate(variant_indices):
suffix = "" if variant_index == 0 else f"_{variant_index}"
solution_name = f"{model_prefix}_{problem_name}{suffix}"
sol_dir = repo_root / "solutions" / solution_name
if sol_dir.exists():
if args.force:
if not args.dryrun:
try:
shutil.rmtree(sol_dir)
except Exception as exc:
print(f"WARNING: Failed to remove {sol_dir}: {exc}; skipping")
skipped.append(solution_name)
continue
else:
skipped.append(solution_name)
continue
tasks.append(
GenerationTask(
problem_path=problem_path_real,
display_path=display_path,
problem_name=problem_name,
readme=readme,
model=model,
provider=provider,
reasoning_model=reasoning_model,
variant_index=variant_index,
variant_position=pos,
solution_name=solution_name,
total_variants=len(variant_indices),
)
)
return tasks, skipped
def main():
base_dir = Path(__file__).parent # research/scripts/
research_dir = base_dir.parent # research/
repo_root = research_dir.parent # Root of the repository
ensure_numpy_version(REQUIRED_NUMPY_VERSION)
load_env_file(base_dir / ".env")
parser = argparse.ArgumentParser(
description="Generate LLM solutions from problem README files",
epilog="""
Target selection (mutually exclusive):
Problem-based: problem_path, --problem, --problems-file (generate new solutions)
Solution-based: --solution, --solutions-file (regenerate existing solutions)
Examples:
%(prog)s --problem "cant_be_late*" --model gpt-4o --dryrun
%(prog)s --problems-file problems.txt
%(prog)s research/problems/vdb_pareto/balanced --model claude-sonnet-4-5
""",
formatter_class=argparse.RawDescriptionHelpFormatter,
)
# Target selection - Problem-based (generate new)
problem_group = parser.add_argument_group("Problem selection (generate new solutions)")
problem_group.add_argument("problem_path", nargs="?", help="Path to a single problem dir")
problem_group.add_argument("--problem", dest="problem_patterns", action="append", default=[],
help="Problem name pattern (wildcards supported), repeatable")
problem_group.add_argument("--problems-file", dest="problems_file",
help="File containing problem directories")
# Target selection - Solution-based (regenerate existing)
solution_group = parser.add_argument_group("Solution selection (regenerate existing)")
solution_group.add_argument("--solution", dest="solution_patterns", action="append", default=[],
help="Solution name pattern (wildcards supported), repeatable")
solution_group.add_argument("--solutions-file", dest="solutions_file",
help="File listing solution:problem entries")
# Model selection
model_group = parser.add_argument_group("Model selection")
model_exclusive = model_group.add_mutually_exclusive_group()
model_exclusive.add_argument("--model", dest="models", nargs="+",
help="Target model identifier(s), e.g. --model gpt-5 gpt-5.1")
model_exclusive.add_argument("--models-file", help="Newline-delimited model list")
# API configuration
api_group = parser.add_argument_group("API configuration")
api_group.add_argument("--api-key", help="API key value")
api_group.add_argument("--api-key-env", help="Environment variable for API key")
api_group.add_argument("--api-base", default=os.getenv("MODEL_API_BASE", "https://api.openai.com/v1"),
help="Base URL for the API")
api_group.add_argument("--timeout", type=float, default=float(os.getenv("MODEL_API_TIMEOUT", "180")),
help="Request timeout in seconds")
# Generation parameters
gen_group = parser.add_argument_group("Generation parameters")
gen_group.add_argument("--temperature", type=float_or_none, default=0.7,
help="Sampling temperature or 'none'")
gen_group.add_argument("--max-tokens", type=int_or_none, default=65536,
help="Max tokens for standard models")
gen_group.add_argument("--reasoning-model", dest="reasoning_override", action="store_const",
const=True, default=None, help="Force reasoning mode")
gen_group.add_argument("--no-reasoning-model", dest="reasoning_override", action="store_const",
const=False, help="Force standard mode")
# Execution control
exec_group = parser.add_argument_group("Execution control")
exec_group.add_argument("--force", action="store_true", help="Regenerate existing solutions")
exec_group.add_argument("--dryrun", action="store_true", help="Show what would be generated")
exec_group.add_argument("--variants", type=int, default=None,
help="Number of solutions per model")
exec_group.add_argument("--concurrency", type=int, default=max(1, min(8, os.cpu_count() or 4)),
help="Maximum parallel generations")
# Hidden/advanced options
parser.add_argument("--name", help=argparse.SUPPRESS) # Solution name override
parser.add_argument("--endpoint", default="chat/completions", help=argparse.SUPPRESS)
parser.add_argument("--api-key-header", default="Authorization", help=argparse.SUPPRESS)
parser.add_argument("--api-key-prefix", default="Bearer", help=argparse.SUPPRESS)
parser.add_argument("--header", dest="extra_headers", action="append", default=[], help=argparse.SUPPRESS)
parser.add_argument("--max-reasoning-tokens", type=int_or_none, default=65536, help=argparse.SUPPRESS)
parser.add_argument("--payload-extra", help=argparse.SUPPRESS)
args = parser.parse_args()
# Check for mutually exclusive target groups
has_problem_targets = args.problem_path or args.problem_patterns or args.problems_file
has_solution_targets = args.solution_patterns or args.solutions_file
if has_problem_targets and has_solution_targets:
print("ERROR: Cannot mix problem-based (--problem, --problems-file) and solution-based (--solution, --solutions-file) options")
sys.exit(1)
# Default to problems.txt if no targets provided
if not has_problem_targets and not has_solution_targets:
problems_default = base_dir / "problems.txt"
if problems_default.is_file():
args.problems_file = str(problems_default)
has_problem_targets = True
print(f"No targets provided; defaulting to {problems_default}.")
else:
print("ERROR: Provide --problem, --problems-file, --solution, or --solutions-file")
sys.exit(1)
# Parse extra headers
try:
extra_headers = parse_extra_headers(args.extra_headers)
except ValueError as exc:
print(f"ERROR: {exc}")
sys.exit(1)
# Parse payload overrides
try:
payload_overrides = load_json_args(args.payload_extra)
except ValueError as exc:
print(f"ERROR: {exc}")
sys.exit(1)
# Resolve API key
default_api_key = resolve_api_key(args.api_key, args.api_key_env)
# Normalize API key header/prefix
api_key_header = args.api_key_header
if api_key_header and api_key_header.lower() == "none":
api_key_header = None
api_key_prefix = args.api_key_prefix
if api_key_prefix and api_key_prefix.lower() == "none":
api_key_prefix = None
# Validate args
if args.variants is not None and args.variants < 1:
print("ERROR: --variants must be >= 1")
sys.exit(1)
if args.concurrency < 1:
print("ERROR: --concurrency must be >= 1")
sys.exit(1)
# Handle --solution patterns (expands to solutions-file format)
if args.solution_patterns:
import fnmatch
# Scan solutions directory and read config.yaml for problem mapping
solutions_dir = repo_root / "solutions"
solution_to_problem: Dict[str, str] = {}
if solutions_dir.is_dir():
for sol_dir in solutions_dir.iterdir():
if sol_dir.is_dir() and not sol_dir.name.startswith('.'):
config_file = sol_dir / "config.yaml"
if config_file.exists():
try:
content = config_file.read_text(encoding="utf-8")
for line in content.splitlines():
if line.strip().startswith("problem:"):
problem = line.split(":", 1)[1].strip()
solution_to_problem[sol_dir.name] = problem
break
except Exception:
pass
all_solutions = set(solution_to_problem.keys())
matched_solutions: List[Tuple[str, str]] = []
for pattern in args.solution_patterns:
matched = False
for sol_name in sorted(all_solutions):
if fnmatch.fnmatch(sol_name, pattern) or fnmatch.fnmatch(sol_name, f"*{pattern}*"):
prob_path = solution_to_problem.get(sol_name, "")
if prob_path:
matched_solutions.append((sol_name, prob_path))
matched = True
if not matched:
print(f"WARNING: No solutions matched pattern '{pattern}'")
if matched_solutions:
# Write to temp file and set as solutions_file
import tempfile
with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
for sol_name, prob_path in matched_solutions:
f.write(f"{sol_name}:{prob_path}\n")
args.solutions_file = f.name
print(f"Matched {len(matched_solutions)} solution(s) from pattern(s)")
# Build problem sources
problem_sources: List[Tuple[Path, str]] = []
# Handle --problem patterns (supports wildcards)
if args.problem_patterns:
import fnmatch
problems_dir = repo_root / "research" / "problems"
all_problems = []
if problems_dir.is_dir():
def find_problems_recursive(directory: Path, depth: int = 0, max_depth: int = 3) -> List[Path]:
"""Recursively find problem directories (those with readme files)."""
if depth > max_depth:
return []
excluded = {'common', 'resources', '__pycache__', 'data'}
# Check if this directory has a readme
has_readme = (directory / "readme").exists() or (directory / "README.md").exists()
# Find subdirectories (potential variants/categories)
subdirs = [sub for sub in directory.iterdir()
if sub.is_dir() and not sub.name.startswith('.')
and sub.name not in excluded]
# Recursively find problems in subdirs
sub_problems = []
for sub in subdirs:
sub_problems.extend(find_problems_recursive(sub, depth + 1, max_depth))
if sub_problems:
# Subdirectories have problems - use those (don't use parent readme)
return sub_problems
elif has_readme:
# This directory is a problem (leaf with readme)
return [directory]
elif subdirs:
# Has subdirs but no readme anywhere - error
print(f"ERROR: {directory.name}/ has subdirectories but no readme files found")
sys.exit(1)
else:
return []
for category in problems_dir.iterdir():
if category.is_dir() and not category.name.startswith('.'):
all_problems.extend(find_problems_recursive(category))
for pattern in args.problem_patterns:
matched = False
for prob_path in all_problems:
# Match against full path or just the problem name
rel_path = prob_path.relative_to(repo_root)
prob_name = "_".join(prob_path.relative_to(problems_dir).parts)
if fnmatch.fnmatch(str(rel_path), f"*{pattern}*") or \
fnmatch.fnmatch(prob_name, pattern) or \
fnmatch.fnmatch(prob_path.name, pattern):
display = str(rel_path)
problem_sources.append((prob_path, display))
matched = True
if not matched:
print(f"WARNING: No problems matched pattern '{pattern}'")
if args.problems_file:
list_path = Path(args.problems_file)
if not list_path.is_absolute():
list_path = base_dir / list_path
if not list_path.is_file():
print(f"ERROR: Problem list file {list_path} not found")
sys.exit(1)
for raw_line in list_path.read_text(encoding="utf-8").splitlines():
stripped = raw_line.strip()
if not stripped or stripped.startswith("#"):
continue
problem_sources.append((Path(stripped), stripped))
if args.problem_path:
problem_sources.append((Path(args.problem_path), args.problem_path))
# Normalize and deduplicate problems
seen_problems: set[str] = set()
normalized_problems: List[Tuple[Path, str]] = []
for path_obj, display in problem_sources:
key = display
if key in seen_problems:
continue
seen_problems.add(key)
if not path_obj.is_absolute():
resolved = repo_root / path_obj
else:
resolved = path_obj
normalized_problems.append((resolved, display))
if args.name and not args.solutions_file and len(normalized_problems) != 1:
print("ERROR: --name can only be used when generating a single problem")
sys.exit(1)
# Create logs directory
logs_dir = repo_root / "generation_logs"
if not args.dryrun:
logs_dir.mkdir(exist_ok=True)
# Resolve model selection
models_source_desc = ""
if args.models:
models_list = args.models
if len(models_list) == 1:
print(f"Using model from --model: {models_list[0]}")
models_source_desc = f"--model ({models_list[0]})"
else:
print(f"Using {len(models_list)} models from --model: {', '.join(models_list)}")
models_source_desc = f"--model ({len(models_list)} models)"
elif args.models_file:
# User explicitly specified --models-file, must exist
models_path = Path(args.models_file)
if not models_path.is_absolute():
models_path = base_dir / models_path
if not models_path.is_file():
print(f"ERROR: Models file not found: {models_path}")
sys.exit(1)
models_list = read_models_file(models_path)
if not models_list:
print(f"ERROR: Models file is empty: {models_path}")
sys.exit(1)
print(f"Detected {len(models_list)} models from {models_path}.")
models_source_desc = f"--models-file ({models_path})"
else:
# Default: use models.txt
models_path = base_dir / "models.txt"
if not models_path.is_file():
print(f"ERROR: No model specified and {models_path} not found.")
print("Use --model <model> or create models.txt")
sys.exit(1)
models_list = read_models_file(models_path)
if not models_list:
print(f"ERROR: Models file is empty: {models_path}")
sys.exit(1)
print(f"Detected {len(models_list)} models from {models_path}.")
models_source_desc = f"models.txt ({models_path})"
# Build key pools
provider_key_pools = build_key_pools(default_api_key)
if provider_key_pools:
for provider, pool in provider_key_pools.items():
print(f"Loaded {pool.size()} API key(s) for provider '{provider}'.")
# Build prefix to model mapping
prefix_to_model: Dict[str, str] = {}
for model in models_list:
prefix = get_model_prefix(model)
if prefix in prefix_to_model and prefix_to_model[prefix] != model:
print(f"WARNING: Multiple models map to prefix '{prefix}'. Using {prefix_to_model[prefix]}.")
continue
prefix_to_model[prefix] = model
# Load docker config for GPU detection
docker_config = load_docker_config(base_dir / "docker_images.txt")
# Build tasks
tasks, skipped = build_tasks(
args, repo_root, base_dir, models_list, normalized_problems, prefix_to_model
)
total_tasks = len(tasks)
# Print generation plan (both dryrun and normal mode)
def print_generation_plan(is_dryrun: bool) -> None:
line = "=" * 60
if is_dryrun:
print(f"\n{yellow(line)}")
print(yellow(bold("DRYRUN MODE - No changes will be made")))
print(f"{yellow(line)}\n")
else:
print(f"\n{cyan(line)}")
print(cyan(bold("GENERATION PLAN")))
print(f"{cyan(line)}\n")
print(f"{bold('Configuration:')}")
print(f" Models: {blue(models_source_desc)}")
print(f" Problems: {blue(str(len(normalized_problems)))}")
print(f" Concurrency: {blue(str(args.concurrency))}")
print(f" Force: {blue(str(args.force))}")
print()
if tasks:
action = "Would generate" if is_dryrun else "Will generate"
print(f"{green(action)} {green(bold(str(total_tasks)))} solution(s):\n")
# Group by problem
by_problem: Dict[str, List[GenerationTask]] = {}
for task in tasks:
key = task.display_path
if key not in by_problem:
by_problem[key] = []
by_problem[key].append(task)
for problem, problem_tasks in by_problem.items():
print(f" {problem_name(problem)}:")
for task in problem_tasks:
print(f" {dim('-')} {solution_name(task.solution_name)} "
f"({dim('model:')} {model_name(task.model)}, "
f"{dim('variant:')} {task.variant_index})")
print()
else:
print(dim("No new solutions to generate.\n"))
if skipped:
action = "Would skip" if is_dryrun else "Skipping"
print(f"{yellow(action)} {yellow(bold(str(len(skipped))))} existing solution(s):")
for name in skipped[:10]:
print(f" {dim('-')} {dim(name)}")
if len(skipped) > 10:
print(f" {dim(f'... and {len(skipped) - 10} more')}")
print()
color = yellow if is_dryrun else cyan
print(color(line))
if is_dryrun:
print(yellow("Run without --dryrun to execute"))
print(color(line) + "\n")
# Show plan
print_generation_plan(args.dryrun)
if args.dryrun:
return
# Execute tasks
if total_tasks == 0:
print("No new tasks to generate.")
return
generated: List[str] = []
failed: List[str] = []
def execute_task(task: GenerationTask) -> Tuple[str, str, Optional[str], str, Optional[int]]:
variant_label = f"{task.variant_position + 1}/{task.total_variants}"
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
log_file = logs_dir / f"{task.solution_name}_{timestamp}.log"
print(f"{cyan('â–¶')} Generating {solution_name(task.solution_name)} "
f"({dim('model:')} {model_name(task.model)}, {dim('variant')} {variant_label})...")
print(f" {dim('Log:')} {dim(str(log_file))}")
pool = provider_key_pools.get(task.provider)
api_key_for_task: Optional[str] = None
pool_token: Optional[int] = None
if pool:
api_key_for_task, pool_token = pool.acquire()
if api_key_for_task is None:
message = f"No available API key for provider {task.provider}; skipping."
print(f" {red('✗')} {red('ERROR:')} {message}")
return ("failed", task.solution_name, message, task.provider, None)
else:
api_key_for_task = get_fallback_api_key(task.provider, default_api_key)
try:
code = generate_code(
task.readme,
model=task.model,
api_key=api_key_for_task,
log_file=log_file,
api_base=args.api_base,
endpoint=args.endpoint,
temperature=args.temperature,
max_tokens=args.max_tokens,
max_reasoning_tokens=args.max_reasoning_tokens,
is_reasoning_model=task.reasoning_model,
extra_headers=extra_headers,
timeout=args.timeout,
payload_overrides=payload_overrides,
api_key_header=api_key_header,
api_key_prefix=api_key_prefix,
problem_name=task.problem_name,
problem_path=task.problem_path,
docker_config=docker_config,
)
# Extract problem path (remove "research/problems/" prefix if present)
problem_for_config = task.display_path
for prefix in ("research/problems/", "problems/"):
if problem_for_config.startswith(prefix):
problem_for_config = problem_for_config[len(prefix):]
break
sol_dir = create_solution(repo_root, task.solution_name, code, problem=problem_for_config)
print(f" {green('✓')} Created: {green(str(sol_dir))}")
print(f" {dim('Log saved:')} {dim(str(log_file))}")
return ("generated", task.solution_name, None, task.provider, pool_token)
except Exception as exc:
message = f"{exc} (log: {log_file})"
print(f" {red('✗')} {red('ERROR:')} {exc}")
return ("failed", task.solution_name, message, task.provider, pool_token)
if total_tasks:
max_workers = min(args.concurrency, total_tasks)
print(f"{cyan('â–¶')} Starting generation ({bold(str(total_tasks))} tasks, concurrency={max_workers})...\n")
with ThreadPoolExecutor(max_workers=max_workers) as executor:
future_to_task = {executor.submit(execute_task, task): task for task in tasks}
for future in as_completed(future_to_task):
status, sol_name, error_text, provider, pool_token = future.result()
pool = provider_key_pools.get(provider)
if pool:
if status == "generated":
pool.report_success(pool_token)
else:
pool.report_failure(pool_token, error_text)
if status == "generated":
generated.append(sol_name)
else:
failed.append(sol_name if error_text is None else f"{sol_name} ({error_text})")
# Print summary
print(f"\n{bold('Summary:')}")
line = "─" * 40
print(dim(line))
if generated:
print(f" {green('✓')} Generated: {green(bold(str(len(generated))))} solution(s)")
for name in generated[:5]:
print(f" {dim('•')} {solution_name(name)}")
if len(generated) > 5:
print(f" {dim(f'... and {len(generated) - 5} more')}")
else:
print(f" {dim('•')} No new solutions generated.")
if skipped:
print(f" {yellow('â—‹')} Skipped: {yellow(bold(str(len(skipped))))} existing (use {bold('--force')} to regenerate)")
if failed:
print(f" {red('✗')} Failed: {red(bold(str(len(failed))))} solution(s)")
for name in failed[:5]:
print(f" {dim('•')} {red(name)}")
if len(failed) > 5:
print(f" {dim(f'... and {len(failed) - 5} more')}")
print(dim(line))
if __name__ == "__main__":
main()
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