def _fingerprint()

in src/datasets/fingerprint.py [0:0]

• 41 lines of code
• 16 McCabe index (conditional complexity)

    def _fingerprint(func):
        if not inplace and not all(name in func.__code__.co_varnames for name in fingerprint_names):
            raise ValueError(f"function {func} is missing parameters {fingerprint_names} in signature")

        if randomized_function:  # randomized function have seed and generator parameters
            if "seed" not in func.__code__.co_varnames:
                raise ValueError(f"'seed' must be in {func}'s signature")
            if "generator" not in func.__code__.co_varnames:
                raise ValueError(f"'generator' must be in {func}'s signature")
        # this call has to be outside the wrapper or since __qualname__ changes in multiprocessing
        transform = format_transform_for_fingerprint(func, version=version)

        @wraps(func)
        def wrapper(*args, **kwargs):
            kwargs_for_fingerprint = format_kwargs_for_fingerprint(
                func,
                args,
                kwargs,
                use_kwargs=use_kwargs,
                ignore_kwargs=ignore_kwargs,
                randomized_function=randomized_function,
            )

            if args:
                dataset: Dataset = args[0]
                args = args[1:]
            else:
                dataset: Dataset = kwargs.pop(next(iter(inspect.signature(func).parameters)))

            # compute new_fingerprint and add it to the args of not in-place transforms
            if inplace:
                new_fingerprint = update_fingerprint(dataset._fingerprint, transform, kwargs_for_fingerprint)
            else:
                for fingerprint_name in fingerprint_names:  # transforms like `train_test_split` have several hashes
                    if kwargs.get(fingerprint_name) is None:
                        kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name
                        kwargs[fingerprint_name] = update_fingerprint(
                            dataset._fingerprint, transform, kwargs_for_fingerprint
                        )
                    else:
                        validate_fingerprint(kwargs[fingerprint_name])

            # Call actual function

            out = func(dataset, *args, **kwargs)

            # Update fingerprint of in-place transforms + update in-place history of transforms

            if inplace:  # update after calling func so that the fingerprint doesn't change if the function fails
                dataset._fingerprint = new_fingerprint

            return out

        wrapper._decorator_name_ = "fingerprint"
        return wrapper