Source code for gpflow.utilities.multipledispatch

# Copyright 2020 The GPflow Contributors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Any, Callable, Tuple, Type, TypeVar, Union

from multipledispatch import Dispatcher as GeneratorDispatcher
from multipledispatch.dispatcher import variadic_signature_matches
from multipledispatch.variadic import isvariadic

__all__ = ["Dispatcher"]


_C = TypeVar("_C", bound=Callable[..., Any])
Types = Union[Type[Any], Tuple[Type[Any], ...]]


[docs]class Dispatcher(GeneratorDispatcher): """ multipledispatch.Dispatcher uses a generator to yield the desired function implementation, which is problematic as TensorFlow's autograph is not able to compile code that passes through generators. This class overwrites the problematic method in the original Dispatcher and solely makes use of simple for-loops, which are compilable by AutoGraph. """
[docs] def register(self, *types: Types, **kwargs: Any) -> Callable[[_C], _C]: # Override to add type hints... return super().register(*types, **kwargs)
[docs] def dispatch(self, *types: Types) -> Callable[..., Any]: """ Returns matching function for `types`; if not existing returns None. """ if types in self.funcs: return self.funcs[types] return self.get_first_occurrence(*types)
[docs] def get_first_occurrence(self, *types: Types) -> Callable[..., Any]: """ Returns the first occurrence of a matching function Based on `multipledispatch.Dispatcher.dispatch_iter`, which returns an iterator of matching functions. This method uses the same logic to select functions, but simply returns the first element of the iterator. If no matching functions are found, `None` is returned. """ n = len(types) for signature in self.ordering: if len(signature) == n and all(map(issubclass, types, signature)): result = self.funcs[signature] return result elif len(signature) and isvariadic(signature[-1]): if variadic_signature_matches(types, signature): result = self.funcs[signature] return result return None