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import threading
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import time
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from contextlib import ContextDecorator
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class TimeBenchmark(ContextDecorator):
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"""
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Measures execution time using a context manager or decorator.
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This class supports both context manager and decorator usage, and is thread-safe for multithreaded
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environments.
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Args:
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print: If True, prints the elapsed time upon exiting the context or completing the function. Defaults
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to False.
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Examples:
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Using as a context manager:
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>>> benchmark = TimeBenchmark()
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>>> with benchmark:
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... time.sleep(1)
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>>> print(f"Block took {benchmark.result:.4f} seconds")
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Block took approximately 1.0000 seconds
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Using with multithreading:
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```python
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import threading
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benchmark = TimeBenchmark()
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def context_manager_example():
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with benchmark:
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time.sleep(0.01)
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print(f"Block took {benchmark.result_ms:.2f} milliseconds")
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threads = []
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for _ in range(3):
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t1 = threading.Thread(target=context_manager_example)
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threads.append(t1)
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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```
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Expected output:
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Block took approximately 10.00 milliseconds
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Block took approximately 10.00 milliseconds
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Block took approximately 10.00 milliseconds
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"""
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def __init__(self, print=False):
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self.local = threading.local()
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self.print_time = print
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def __enter__(self):
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self.local.start_time = time.perf_counter()
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return self
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def __exit__(self, *exc):
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self.local.end_time = time.perf_counter()
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self.local.elapsed_time = self.local.end_time - self.local.start_time
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if self.print_time:
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print(f"Elapsed time: {self.local.elapsed_time:.4f} seconds")
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return False
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@property
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def result(self):
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return getattr(self.local, "elapsed_time", None)
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@property
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def result_ms(self):
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return self.result * 1e3
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