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from concurrent.futures import ThreadPoolExecutor
from typing import List, Any, Callable, Optional, Dict, Tuple, TypeVar, Iterator, Iterable
from itertools import product
def parallel_map(
objects: List[Any],
operation: Callable[[Any], Any],
max_workers: Optional[int] = None
) -> List[Any]:
"""
Execute operations on multiple objects in parallel and return the results.
Args:
objects: List of objects to process
operation: A callable (typically a lambda) that takes each object and returns a result
max_workers: Maximum number of threads to use for parallel execution
(None means use the default, which is min(32, os.cpu_count() + 4))
Returns:
List of results in the same order as the input objects
Example:
# For propositions p1, p2, p3
results = parallel_map([p1, p2, p3], lambda p: p.check())
# With arguments
results = parallel_map(
[p1, p2, p3],
lambda p: p.check(additional_context="Some context", return_full_response=True)
)
# Works with any operation
scores = parallel_map([p1, p2, p3], lambda p: p.score())
"""
with ThreadPoolExecutor(max_workers=max_workers) as executor:
results = list(executor.map(operation, objects))
return results
K = TypeVar('K') # Key type
V = TypeVar('V') # Value type
R = TypeVar('R') # Result type
def parallel_map_dict(
dictionary: Dict[K, V],
operation: Callable[[Tuple[K, V]], R],
max_workers: Optional[int] = None
) -> Dict[K, R]:
"""
Execute operations on dictionary items in parallel and return results as a dictionary.
Args:
dictionary: Dictionary whose items will be processed
operation: A callable that takes a (key, value) tuple and returns a result
max_workers: Maximum number of threads to use
Returns:
Dictionary mapping original keys to operation results
Example:
# For environment propositions
results = parallel_map_dict(
environment_propositions,
lambda item: item[1].score(world, return_full_response=True)
)
"""
with ThreadPoolExecutor(max_workers=max_workers) as executor:
# Create a list of (key, result) tuples
items = list(dictionary.items())
results = list(executor.map(operation, items))
# Combine original keys with results
return {item[0]: result for item, result in zip(items, results)}
def parallel_map_cross(
iterables: List[Iterable],
operation: Callable[..., R],
max_workers: Optional[int] = None
) -> List[R]:
"""
Apply operation to each combination of elements from the iterables in parallel.
This is similar to using nested loops.
Args:
iterables: List of iterables to generate combinations from
operation: A callable that takes elements from each iterable and returns a result
max_workers: Maximum number of threads to use
Returns:
List of results from applying operation to each combination
Example:
# For every agent and proposition
results = parallel_map_cross(
[agents, agent_propositions.items()],
lambda agent, prop_item: (prop_item[0], prop_item[1].score(agent))
)
"""
combinations = list(product(*iterables))
def apply_to_combination(combo):
return operation(*combo)
with ThreadPoolExecutor(max_workers=max_workers) as executor:
results = list(executor.map(apply_to_combination, combinations))
return results |